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:   The calling sequence is
586: .vb
587:    MatGetRow(matrix,row,ncols,cols,values,ierr)
588:          Mat         matrix (input)
589:          PetscInt    row    (input)
590:          PetscInt    ncols  (output)
591:          PetscInt    cols(maxcols) (output)
592:          PetscScalar values(maxcols) output
593: .ve
594:   where maxcols >= maximum nonzeros in any row of the matrix.

596: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
597: @*/
598: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
599: {
600:   PetscInt incols;

602:   PetscFunctionBegin;
605:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
606:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
607:   MatCheckPreallocated(mat, 1);
608:   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);
609:   PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
610:   PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
611:   if (ncols) *ncols = incols;
612:   PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
613:   PetscFunctionReturn(PETSC_SUCCESS);
614: }

616: /*@
617:   MatConjugate - replaces the matrix values with their complex conjugates

619:   Logically Collective

621:   Input Parameter:
622: . mat - the matrix

624:   Level: advanced

626: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
627: @*/
628: PetscErrorCode MatConjugate(Mat mat)
629: {
630:   PetscFunctionBegin;
632:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
633:   if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
634:     PetscUseTypeMethod(mat, conjugate);
635:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
636:   }
637:   PetscFunctionReturn(PETSC_SUCCESS);
638: }

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

643:   Not Collective

645:   Input Parameters:
646: + mat   - the matrix
647: . row   - the row to get
648: . ncols - the number of nonzeros
649: . cols  - the columns of the nonzeros
650: - vals  - if nonzero the column values

652:   Level: advanced

654:   Notes:
655:   This routine should be called after you have finished examining the entries.

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

661:   Fortran Note:
662:   `MatRestoreRow()` MUST be called after `MatGetRow()`
663:   before another call to `MatGetRow()` can be made.

665: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
666: @*/
667: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
668: {
669:   PetscFunctionBegin;
671:   if (ncols) PetscAssertPointer(ncols, 3);
672:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
673:   PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
674:   if (ncols) *ncols = 0;
675:   if (cols) *cols = NULL;
676:   if (vals) *vals = NULL;
677:   PetscFunctionReturn(PETSC_SUCCESS);
678: }

680: /*@
681:   MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
682:   You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.

684:   Not Collective

686:   Input Parameter:
687: . mat - the matrix

689:   Level: advanced

691:   Note:
692:   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.

694: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
695: @*/
696: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
697: {
698:   PetscFunctionBegin;
701:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
702:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
703:   MatCheckPreallocated(mat, 1);
704:   PetscTryTypeMethod(mat, getrowuppertriangular);
705:   PetscFunctionReturn(PETSC_SUCCESS);
706: }

708: /*@
709:   MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.

711:   Not Collective

713:   Input Parameter:
714: . mat - the matrix

716:   Level: advanced

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

721: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
722: @*/
723: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
724: {
725:   PetscFunctionBegin;
728:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
729:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
730:   MatCheckPreallocated(mat, 1);
731:   PetscTryTypeMethod(mat, restorerowuppertriangular);
732:   PetscFunctionReturn(PETSC_SUCCESS);
733: }

735: /*@
736:   MatSetOptionsPrefix - Sets the prefix used for searching for all
737:   `Mat` options in the database.

739:   Logically Collective

741:   Input Parameters:
742: + A      - the matrix
743: - prefix - the prefix to prepend to all option names

745:   Level: advanced

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

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

755: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
756: @*/
757: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
758: {
759:   PetscFunctionBegin;
761:   PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
762:   PetscFunctionReturn(PETSC_SUCCESS);
763: }

765: /*@
766:   MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
767:   for matrices created with `MatGetFactor()`

769:   Logically Collective

771:   Input Parameters:
772: + A      - the matrix
773: - prefix - the prefix to prepend to all option names for the factored matrix

775:   Level: developer

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

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

784: .seealso: [](ch_matrices), `Mat`,   [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
785: @*/
786: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
787: {
788:   PetscFunctionBegin;
790:   if (prefix) {
791:     PetscAssertPointer(prefix, 2);
792:     PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
793:     if (prefix != A->factorprefix) {
794:       PetscCall(PetscFree(A->factorprefix));
795:       PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
796:     }
797:   } else PetscCall(PetscFree(A->factorprefix));
798:   PetscFunctionReturn(PETSC_SUCCESS);
799: }

801: /*@
802:   MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
803:   for matrices created with `MatGetFactor()`

805:   Logically Collective

807:   Input Parameters:
808: + A      - the matrix
809: - prefix - the prefix to prepend to all option names for the factored matrix

811:   Level: developer

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

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

820: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
821:           `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
822:           `MatSetOptionsPrefix()`
823: @*/
824: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
825: {
826:   size_t len1, len2, new_len;

828:   PetscFunctionBegin;
830:   if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
831:   if (!A->factorprefix) {
832:     PetscCall(MatSetOptionsPrefixFactor(A, prefix));
833:     PetscFunctionReturn(PETSC_SUCCESS);
834:   }
835:   PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");

837:   PetscCall(PetscStrlen(A->factorprefix, &len1));
838:   PetscCall(PetscStrlen(prefix, &len2));
839:   new_len = len1 + len2 + 1;
840:   PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
841:   PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
842:   PetscFunctionReturn(PETSC_SUCCESS);
843: }

845: /*@
846:   MatAppendOptionsPrefix - Appends to the prefix used for searching for all
847:   matrix options in the database.

849:   Logically Collective

851:   Input Parameters:
852: + A      - the matrix
853: - prefix - the prefix to prepend to all option names

855:   Level: advanced

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

861: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
862: @*/
863: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
864: {
865:   PetscFunctionBegin;
867:   PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
868:   PetscFunctionReturn(PETSC_SUCCESS);
869: }

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

875:   Not Collective

877:   Input Parameter:
878: . A - the matrix

880:   Output Parameter:
881: . prefix - pointer to the prefix string used

883:   Level: advanced

885:   Fortran Note:
886:   The user should pass in a string `prefix` of
887:   sufficient length to hold the prefix.

889: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
890: @*/
891: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
892: {
893:   PetscFunctionBegin;
895:   PetscAssertPointer(prefix, 2);
896:   PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
897:   PetscFunctionReturn(PETSC_SUCCESS);
898: }

900: /*@
901:   MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`

903:   Not Collective

905:   Input Parameter:
906: . A - the matrix

908:   Output Parameter:
909: . state - the object state

911:   Level: advanced

913:   Note:
914:   Object state is an integer which gets increased every time
915:   the object is changed. By saving and later querying the object state
916:   one can determine whether information about the object is still current.

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

920: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
921: @*/
922: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
923: {
924:   PetscFunctionBegin;
926:   PetscAssertPointer(state, 2);
927:   PetscCall(PetscObjectStateGet((PetscObject)A, state));
928:   PetscFunctionReturn(PETSC_SUCCESS);
929: }

931: /*@
932:   MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`

934:   Collective

936:   Input Parameter:
937: . A - the matrix

939:   Level: beginner

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

946:   Current values in the matrix are lost in this call.

948:   Currently only supported for  `MATAIJ` matrices.

950: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
951: @*/
952: PetscErrorCode MatResetPreallocation(Mat A)
953: {
954:   PetscFunctionBegin;
957:   PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
958:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
959:   PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
960:   PetscFunctionReturn(PETSC_SUCCESS);
961: }

963: /*@
964:   MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.

966:   Collective

968:   Input Parameter:
969: . A - the matrix

971:   Level: intermediate

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

976:   Currently only supported for `MATAIJ` matrices.

978: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
979: @*/
980: PetscErrorCode MatResetHash(Mat A)
981: {
982:   PetscFunctionBegin;
985:   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()");
986:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
987:   PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
988:   /* These flags are used to determine whether certain setups occur */
989:   A->was_assembled = PETSC_FALSE;
990:   A->assembled     = PETSC_FALSE;
991:   /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
992:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
993:   PetscFunctionReturn(PETSC_SUCCESS);
994: }

996: /*@
997:   MatSetUp - Sets up the internal matrix data structures for later use by the matrix

999:   Collective

1001:   Input Parameter:
1002: . A - the matrix

1004:   Level: advanced

1006:   Notes:
1007:   If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
1008:   setting values in the matrix.

1010:   This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users

1012: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1013: @*/
1014: PetscErrorCode MatSetUp(Mat A)
1015: {
1016:   PetscFunctionBegin;
1018:   if (!((PetscObject)A)->type_name) {
1019:     PetscMPIInt size;

1021:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1022:     PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1023:   }
1024:   if (!A->preallocated) PetscTryTypeMethod(A, setup);
1025:   PetscCall(PetscLayoutSetUp(A->rmap));
1026:   PetscCall(PetscLayoutSetUp(A->cmap));
1027:   A->preallocated = PETSC_TRUE;
1028:   PetscFunctionReturn(PETSC_SUCCESS);
1029: }

1031: #if defined(PETSC_HAVE_SAWS)
1032: #include <petscviewersaws.h>
1033: #endif

1035: /*
1036:    If threadsafety is on extraneous matrices may be printed

1038:    This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1039: */
1040: #if !defined(PETSC_HAVE_THREADSAFETY)
1041: static PetscInt insidematview = 0;
1042: #endif

1044: /*@
1045:   MatViewFromOptions - View properties of the matrix based on options set in the options database

1047:   Collective

1049:   Input Parameters:
1050: + A    - the matrix
1051: . obj  - optional additional object that provides the options prefix to use
1052: - name - command line option

1054:   Options Database Key:
1055: . -mat_view [viewertype]:... - the viewer and its options

1057:   Level: intermediate

1059:   Note:
1060: .vb
1061:     If no value is provided ascii:stdout is used
1062:        ascii[:[filename][:[format][:append]]]    defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
1063:                                                   for example ascii::ascii_info prints just the information about the object not all details
1064:                                                   unless :append is given filename opens in write mode, overwriting what was already there
1065:        binary[:[filename][:[format][:append]]]   defaults to the file binaryoutput
1066:        draw[:drawtype[:filename]]                for example, draw:tikz, draw:tikz:figure.tex  or draw:x
1067:        socket[:port]                             defaults to the standard output port
1068:        saws[:communicatorname]                    publishes object to the Scientific Application Webserver (SAWs)
1069: .ve

1071: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1072: @*/
1073: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1074: {
1075:   PetscFunctionBegin;
1077: #if !defined(PETSC_HAVE_THREADSAFETY)
1078:   if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1079: #endif
1080:   PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1081:   PetscFunctionReturn(PETSC_SUCCESS);
1082: }

1084: /*@
1085:   MatView - display information about a matrix in a variety ways

1087:   Collective on viewer

1089:   Input Parameters:
1090: + mat    - the matrix
1091: - viewer - visualization context

1093:   Options Database Keys:
1094: + -mat_view ::ascii_info           - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1095: . -mat_view ::ascii_info_detail    - Prints more detailed info
1096: . -mat_view                        - Prints matrix in ASCII format
1097: . -mat_view ::ascii_matlab         - Prints matrix in MATLAB format
1098: . -mat_view draw                   - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1099: . -display <name>                  - Sets display name (default is host)
1100: . -draw_pause <sec>                - Sets number of seconds to pause after display
1101: . -mat_view socket                 - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1102: . -viewer_socket_machine <machine> - -
1103: . -viewer_socket_port <port>       - -
1104: . -mat_view binary                 - save matrix to file in binary format
1105: - -viewer_binary_filename <name>   - -

1107:   Level: beginner

1109:   Notes:
1110:   The available visualization contexts include
1111: +    `PETSC_VIEWER_STDOUT_SELF`   - for sequential matrices
1112: .    `PETSC_VIEWER_STDOUT_WORLD`  - for parallel matrices created on `PETSC_COMM_WORLD`
1113: .    `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1114: -     `PETSC_VIEWER_DRAW_WORLD`   - graphical display of nonzero structure

1116:   The user can open alternative visualization contexts with
1117: +    `PetscViewerASCIIOpen()`  - Outputs matrix to a specified file
1118: .    `PetscViewerBinaryOpen()` - Outputs matrix in binary to a  specified file; corresponding input uses `MatLoad()`
1119: .    `PetscViewerDrawOpen()`   - Outputs nonzero matrix nonzero structure to an X window display
1120: -    `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.

1122:   The user can call `PetscViewerPushFormat()` to specify the output
1123:   format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1124:   `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`).  Available formats include
1125: +    `PETSC_VIEWER_DEFAULT`           - default, prints matrix contents
1126: .    `PETSC_VIEWER_ASCII_MATLAB`      - prints matrix contents in MATLAB format
1127: .    `PETSC_VIEWER_ASCII_DENSE`       - prints entire matrix including zeros
1128: .    `PETSC_VIEWER_ASCII_COMMON`      - prints matrix contents, using a sparse  format common among all matrix types
1129: .    `PETSC_VIEWER_ASCII_IMPL`        - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1130: .    `PETSC_VIEWER_ASCII_INFO`        - prints basic information about the matrix size and structure (not the matrix entries)
1131: -    `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)

1133:   The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1134:   the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

1136:   In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).

1138:   See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1139:   viewer is used.

1141:   See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1142:   viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.

1144:   One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1145:   and then use the following mouse functions.
1146: .vb
1147:   left mouse: zoom in
1148:   middle mouse: zoom out
1149:   right mouse: continue with the simulation
1150: .ve

1152: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1153:           `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1154: @*/
1155: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1156: {
1157:   PetscInt          rows, cols, rbs, cbs;
1158:   PetscBool         isascii, isstring, issaws;
1159:   PetscViewerFormat format;
1160:   PetscMPIInt       size;

1162:   PetscFunctionBegin;
1165:   if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));

1168:   PetscCall(PetscViewerGetFormat(viewer, &format));
1169:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1170:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);

1172: #if !defined(PETSC_HAVE_THREADSAFETY)
1173:   insidematview++;
1174: #endif
1175:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1176:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1177:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1178:   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");

1180:   PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1181:   if (isascii) {
1182:     if (!mat->preallocated) {
1183:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated 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:     if (!mat->assembled) {
1191:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1192: #if !defined(PETSC_HAVE_THREADSAFETY)
1193:       insidematview--;
1194: #endif
1195:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1196:       PetscFunctionReturn(PETSC_SUCCESS);
1197:     }
1198:     PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1199:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1200:       MatNullSpace nullsp, transnullsp;

1202:       PetscCall(PetscViewerASCIIPushTab(viewer));
1203:       PetscCall(MatGetSize(mat, &rows, &cols));
1204:       PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1205:       if (rbs != 1 || cbs != 1) {
1206:         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" : ""));
1207:         else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1208:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1209:       if (mat->factortype) {
1210:         MatSolverType solver;
1211:         PetscCall(MatFactorGetSolverType(mat, &solver));
1212:         PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1213:       }
1214:       if (mat->ops->getinfo) {
1215:         MatInfo info;
1216:         PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1217:         PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1218:         if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1219:       }
1220:       PetscCall(MatGetNullSpace(mat, &nullsp));
1221:       PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1222:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached null space\n"));
1223:       if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached transposed null space\n"));
1224:       PetscCall(MatGetNearNullSpace(mat, &nullsp));
1225:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached near null space\n"));
1226:       PetscCall(PetscViewerASCIIPushTab(viewer));
1227:       PetscCall(MatProductView(mat, viewer));
1228:       PetscCall(PetscViewerASCIIPopTab(viewer));
1229:       if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1230:         IS tmp;

1232:         PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1233:         PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1234:         PetscCall(PetscViewerASCIIPushTab(viewer));
1235:         PetscCall(ISView(tmp, viewer));
1236:         PetscCall(PetscViewerASCIIPopTab(viewer));
1237:         PetscCall(ISDestroy(&tmp));
1238:       }
1239:     }
1240:   } else if (issaws) {
1241: #if defined(PETSC_HAVE_SAWS)
1242:     PetscMPIInt rank;

1244:     PetscCall(PetscObjectName((PetscObject)mat));
1245:     PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1246:     if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1247: #endif
1248:   } else if (isstring) {
1249:     const char *type;
1250:     PetscCall(MatGetType(mat, &type));
1251:     PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1252:     PetscTryTypeMethod(mat, view, viewer);
1253:   }
1254:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1255:     PetscCall(PetscViewerASCIIPushTab(viewer));
1256:     PetscUseTypeMethod(mat, viewnative, viewer);
1257:     PetscCall(PetscViewerASCIIPopTab(viewer));
1258:   } else if (mat->ops->view) {
1259:     PetscCall(PetscViewerASCIIPushTab(viewer));
1260:     PetscUseTypeMethod(mat, view, viewer);
1261:     PetscCall(PetscViewerASCIIPopTab(viewer));
1262:   }
1263:   if (isascii) {
1264:     PetscCall(PetscViewerGetFormat(viewer, &format));
1265:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1266:   }
1267:   PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1268: #if !defined(PETSC_HAVE_THREADSAFETY)
1269:   insidematview--;
1270: #endif
1271:   PetscFunctionReturn(PETSC_SUCCESS);
1272: }

1274: #if defined(PETSC_USE_DEBUG)
1275: #include <../src/sys/totalview/tv_data_display.h>
1276: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1277: {
1278:   TV_add_row("Local rows", "int", &mat->rmap->n);
1279:   TV_add_row("Local columns", "int", &mat->cmap->n);
1280:   TV_add_row("Global rows", "int", &mat->rmap->N);
1281:   TV_add_row("Global columns", "int", &mat->cmap->N);
1282:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1283:   return TV_format_OK;
1284: }
1285: #endif

1287: /*@
1288:   MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1289:   with `MatView()`.  The matrix format is determined from the options database.
1290:   Generates a parallel MPI matrix if the communicator has more than one
1291:   processor.  The default matrix type is `MATAIJ`.

1293:   Collective

1295:   Input Parameters:
1296: + mat    - the newly loaded matrix, this needs to have been created with `MatCreate()`
1297:             or some related function before a call to `MatLoad()`
1298: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer

1300:   Options Database Key:
1301: . -matload_block_size <bs> - set block size

1303:   Level: beginner

1305:   Notes:
1306:   If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1307:   `Mat` before calling this routine if you wish to set it from the options database.

1309:   `MatLoad()` automatically loads into the options database any options
1310:   given in the file filename.info where filename is the name of the file
1311:   that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1312:   file will be ignored if you use the -viewer_binary_skip_info option.

1314:   If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1315:   sets the default matrix type AIJ and sets the local and global sizes.
1316:   If type and/or size is already set, then the same are used.

1318:   In parallel, each processor can load a subset of rows (or the
1319:   entire matrix).  This routine is especially useful when a large
1320:   matrix is stored on disk and only part of it is desired on each
1321:   processor.  For example, a parallel solver may access only some of
1322:   the rows from each processor.  The algorithm used here reads
1323:   relatively small blocks of data rather than reading the entire
1324:   matrix and then subsetting it.

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

1340:   See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1341:   and src/mat/tutorials/ex10.c with the second approach.

1343:   In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1344:   is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1345:   Multiple objects, both matrices and vectors, can be stored within the same file.
1346:   Their `PetscObject` name is ignored; they are loaded in the order of their storage.

1348:   Most users should not need to know the details of the binary storage
1349:   format, since `MatLoad()` and `MatView()` completely hide these details.
1350:   But for anyone who is interested, the standard binary matrix storage
1351:   format is

1353: .vb
1354:     PetscInt    MAT_FILE_CLASSID
1355:     PetscInt    number of rows
1356:     PetscInt    number of columns
1357:     PetscInt    total number of nonzeros
1358:     PetscInt    *number nonzeros in each row
1359:     PetscInt    *column indices of all nonzeros (starting index is zero)
1360:     PetscScalar *values of all nonzeros
1361: .ve
1362:   If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1363:   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
1364:   case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.

1366:   PETSc automatically does the byte swapping for
1367:   machines that store the bytes reversed. Thus if you write your own binary
1368:   read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1369:   and `PetscBinaryWrite()` to see how this may be done.

1371:   In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1372:   Each processor's chunk is loaded independently by its owning MPI process.
1373:   Multiple objects, both matrices and vectors, can be stored within the same file.
1374:   They are looked up by their PetscObject name.

1376:   As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1377:   by default the same structure and naming of the AIJ arrays and column count
1378:   within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1379: $    save example.mat A b -v7.3
1380:   can be directly read by this routine (see Reference 1 for details).

1382:   Depending on your MATLAB version, this format might be a default,
1383:   otherwise you can set it as default in Preferences.

1385:   Unless -nocompression flag is used to save the file in MATLAB,
1386:   PETSc must be configured with ZLIB package.

1388:   See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

1390:   This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`

1392:   Corresponding `MatView()` is not yet implemented.

1394:   The loaded matrix is actually a transpose of the original one in MATLAB,
1395:   unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1396:   With this format, matrix is automatically transposed by PETSc,
1397:   unless the matrix is marked as SPD or symmetric
1398:   (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).

1400:   See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>

1402: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1403:  @*/
1404: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1405: {
1406:   PetscBool flg;

1408:   PetscFunctionBegin;

1412:   if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));

1414:   flg = PETSC_FALSE;
1415:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1416:   if (flg) {
1417:     PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1418:     PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1419:   }
1420:   flg = PETSC_FALSE;
1421:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1422:   if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));

1424:   PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1425:   PetscUseTypeMethod(mat, load, viewer);
1426:   PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1427:   PetscFunctionReturn(PETSC_SUCCESS);
1428: }

1430: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1431: {
1432:   Mat_Redundant *redund = *redundant;

1434:   PetscFunctionBegin;
1435:   if (redund) {
1436:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1437:       PetscCall(ISDestroy(&redund->isrow));
1438:       PetscCall(ISDestroy(&redund->iscol));
1439:       PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1440:     } else {
1441:       PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1442:       PetscCall(PetscFree(redund->sbuf_j));
1443:       PetscCall(PetscFree(redund->sbuf_a));
1444:       for (PetscInt i = 0; i < redund->nrecvs; i++) {
1445:         PetscCall(PetscFree(redund->rbuf_j[i]));
1446:         PetscCall(PetscFree(redund->rbuf_a[i]));
1447:       }
1448:       PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1449:     }

1451:     if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1452:     PetscCall(PetscFree(redund));
1453:   }
1454:   PetscFunctionReturn(PETSC_SUCCESS);
1455: }

1457: /*@
1458:   MatDestroy - Frees space taken by a matrix.

1460:   Collective

1462:   Input Parameter:
1463: . A - the matrix

1465:   Level: beginner

1467:   Developer Note:
1468:   Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1469:   `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1470:   `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1471:   if changes are needed here.

1473: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1474: @*/
1475: PetscErrorCode MatDestroy(Mat *A)
1476: {
1477:   PetscFunctionBegin;
1478:   if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1480:   if (--((PetscObject)*A)->refct > 0) {
1481:     *A = NULL;
1482:     PetscFunctionReturn(PETSC_SUCCESS);
1483:   }

1485:   /* if memory was published with SAWs then destroy it */
1486:   PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1487:   PetscTryTypeMethod(*A, destroy);

1489:   PetscCall(PetscFree((*A)->factorprefix));
1490:   PetscCall(PetscFree((*A)->defaultvectype));
1491:   PetscCall(PetscFree((*A)->defaultrandtype));
1492:   PetscCall(PetscFree((*A)->bsizes));
1493:   PetscCall(PetscFree((*A)->solvertype));
1494:   for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1495:   if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1496:   PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1497:   PetscCall(MatProductClear(*A));
1498:   PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1499:   PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1500:   PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1501:   PetscCall(MatDestroy(&(*A)->schur));
1502:   PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1503:   PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1504:   PetscCall(PetscHeaderDestroy(A));
1505:   PetscFunctionReturn(PETSC_SUCCESS);
1506: }

1508: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1509: /*@
1510:   MatSetValues - Inserts or adds a block of values into a matrix.
1511:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1512:   MUST be called after all calls to `MatSetValues()` have been completed.

1514:   Not Collective

1516:   Input Parameters:
1517: + mat  - the matrix
1518: . v    - a logically two-dimensional array of values
1519: . m    - the number of rows
1520: . idxm - the global indices of the rows
1521: . n    - the number of columns
1522: . idxn - the global indices of the columns
1523: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1525:   Level: beginner

1527:   Notes:
1528:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1530:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1531:   options cannot be mixed without intervening calls to the assembly
1532:   routines.

1534:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1535:   as well as in C.

1537:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1538:   simply ignored. This allows easily inserting element stiffness matrices
1539:   with homogeneous Dirichlet boundary conditions that you don't want represented
1540:   in the matrix.

1542:   Efficiency Alert:
1543:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1544:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1546:   Fortran Notes:
1547:   If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1548: .vb
1549:   MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES)
1550: .ve

1552:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1554:   Developer Note:
1555:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1556:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1558: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1559:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1560: @*/
1561: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1562: {
1563:   PetscFunctionBeginHot;
1566:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1567:   PetscAssertPointer(idxm, 3);
1568:   PetscAssertPointer(idxn, 5);
1569:   MatCheckPreallocated(mat, 1);

1571:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1572:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

1574:   if (PetscDefined(USE_DEBUG)) {
1575:     PetscInt i, j;

1577:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1578:     if (v) {
1579:       for (i = 0; i < m; i++) {
1580:         for (j = 0; j < n; j++) {
1581:           if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1582: #if defined(PETSC_USE_COMPLEX)
1583:             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]);
1584: #else
1585:             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]);
1586: #endif
1587:         }
1588:       }
1589:     }
1590:     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);
1591:     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);
1592:   }

1594:   if (mat->assembled) {
1595:     mat->was_assembled = PETSC_TRUE;
1596:     mat->assembled     = PETSC_FALSE;
1597:   }
1598:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1599:   PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1600:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1601:   PetscFunctionReturn(PETSC_SUCCESS);
1602: }

1604: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1605: /*@
1606:   MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1607:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1608:   MUST be called after all calls to `MatSetValues()` have been completed.

1610:   Not Collective

1612:   Input Parameters:
1613: + mat  - the matrix
1614: . v    - a logically two-dimensional array of values
1615: . ism  - the rows to provide
1616: . isn  - the columns to provide
1617: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1619:   Level: beginner

1621:   Notes:
1622:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1624:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1625:   options cannot be mixed without intervening calls to the assembly
1626:   routines.

1628:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1629:   as well as in C.

1631:   Negative indices may be passed in `ism` and `isn`, these rows and columns are
1632:   simply ignored. This allows easily inserting element stiffness matrices
1633:   with homogeneous Dirichlet boundary conditions that you don't want represented
1634:   in the matrix.

1636:   Efficiency Alert:
1637:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1638:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1640:   This is currently not optimized for any particular `ISType`

1642:   Developer Note:
1643:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1644:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1646: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1647:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1648: @*/
1649: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1650: {
1651:   PetscInt        m, n;
1652:   const PetscInt *rows, *cols;

1654:   PetscFunctionBeginHot;
1656:   PetscCall(ISGetIndices(ism, &rows));
1657:   PetscCall(ISGetIndices(isn, &cols));
1658:   PetscCall(ISGetLocalSize(ism, &m));
1659:   PetscCall(ISGetLocalSize(isn, &n));
1660:   PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1661:   PetscCall(ISRestoreIndices(ism, &rows));
1662:   PetscCall(ISRestoreIndices(isn, &cols));
1663:   PetscFunctionReturn(PETSC_SUCCESS);
1664: }

1666: /*@
1667:   MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1668:   values into a matrix

1670:   Not Collective

1672:   Input Parameters:
1673: + mat - the matrix
1674: . row - the (block) row to set
1675: - v   - a logically two-dimensional array of values

1677:   Level: intermediate

1679:   Notes:
1680:   The values, `v`, are column-oriented (for the block version) and sorted

1682:   All the nonzero values in `row` must be provided

1684:   The matrix must have previously had its column indices set, likely by having been assembled.

1686:   `row` must belong to this MPI process

1688: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1689:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1690: @*/
1691: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1692: {
1693:   PetscInt globalrow;

1695:   PetscFunctionBegin;
1698:   PetscAssertPointer(v, 3);
1699:   PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1700:   PetscCall(MatSetValuesRow(mat, globalrow, v));
1701:   PetscFunctionReturn(PETSC_SUCCESS);
1702: }

1704: /*@
1705:   MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1706:   values into a matrix

1708:   Not Collective

1710:   Input Parameters:
1711: + mat - the matrix
1712: . row - the (block) row to set
1713: - 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

1715:   Level: advanced

1717:   Notes:
1718:   The values, `v`, are column-oriented for the block version.

1720:   All the nonzeros in `row` must be provided

1722:   THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

1724:   `row` must belong to this process

1726: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1727:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1728: @*/
1729: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1730: {
1731:   PetscFunctionBeginHot;
1734:   MatCheckPreallocated(mat, 1);
1735:   PetscAssertPointer(v, 3);
1736:   PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1737:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1738:   mat->insertmode = INSERT_VALUES;

1740:   if (mat->assembled) {
1741:     mat->was_assembled = PETSC_TRUE;
1742:     mat->assembled     = PETSC_FALSE;
1743:   }
1744:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1745:   PetscUseTypeMethod(mat, setvaluesrow, row, v);
1746:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1747:   PetscFunctionReturn(PETSC_SUCCESS);
1748: }

1750: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1751: /*@
1752:   MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1753:   Using structured grid indexing

1755:   Not Collective

1757:   Input Parameters:
1758: + mat  - the matrix
1759: . m    - number of rows being entered
1760: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1761: . n    - number of columns being entered
1762: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1763: . v    - a logically two-dimensional array of values
1764: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

1766:   Level: beginner

1768:   Notes:
1769:   By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

1771:   Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1772:   options cannot be mixed without intervening calls to the assembly
1773:   routines.

1775:   The grid coordinates are across the entire grid, not just the local portion

1777:   `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1778:   as well as in C.

1780:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1782:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1783:   or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1785:   The columns and rows in the stencil passed in MUST be contained within the
1786:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1787:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1788:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1789:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1791:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1792:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1793:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1794:   `DM_BOUNDARY_PERIODIC` boundary type.

1796:   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
1797:   a single value per point) you can skip filling those indices.

1799:   Inspired by the structured grid interface to the HYPRE package
1800:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1802:   Efficiency Alert:
1803:   The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1804:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1806:   Fortran Note:
1807:   `idxm` and `idxn` should be declared as
1808: $     MatStencil idxm(4,m),idxn(4,n)
1809:   and the values inserted using
1810: .vb
1811:     idxm(MatStencil_i,1) = i
1812:     idxm(MatStencil_j,1) = j
1813:     idxm(MatStencil_k,1) = k
1814:     idxm(MatStencil_c,1) = c
1815:     etc
1816: .ve

1818: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1819:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1820: @*/
1821: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1822: {
1823:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1824:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1825:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1827:   PetscFunctionBegin;
1828:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1831:   PetscAssertPointer(idxm, 3);
1832:   PetscAssertPointer(idxn, 5);

1834:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1835:     jdxm = buf;
1836:     jdxn = buf + m;
1837:   } else {
1838:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1839:     jdxm = bufm;
1840:     jdxn = bufn;
1841:   }
1842:   for (i = 0; i < m; i++) {
1843:     for (j = 0; j < 3 - sdim; j++) dxm++;
1844:     tmp = *dxm++ - starts[0];
1845:     for (j = 0; j < dim - 1; j++) {
1846:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1847:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1848:     }
1849:     if (mat->stencil.noc) dxm++;
1850:     jdxm[i] = tmp;
1851:   }
1852:   for (i = 0; i < n; i++) {
1853:     for (j = 0; j < 3 - sdim; j++) dxn++;
1854:     tmp = *dxn++ - starts[0];
1855:     for (j = 0; j < dim - 1; j++) {
1856:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1857:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1858:     }
1859:     if (mat->stencil.noc) dxn++;
1860:     jdxn[i] = tmp;
1861:   }
1862:   PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1863:   PetscCall(PetscFree2(bufm, bufn));
1864:   PetscFunctionReturn(PETSC_SUCCESS);
1865: }

1867: /*@
1868:   MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1869:   Using structured grid indexing

1871:   Not Collective

1873:   Input Parameters:
1874: + mat  - the matrix
1875: . m    - number of rows being entered
1876: . idxm - grid coordinates for matrix rows being entered
1877: . n    - number of columns being entered
1878: . idxn - grid coordinates for matrix columns being entered
1879: . v    - a logically two-dimensional array of values
1880: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

1882:   Level: beginner

1884:   Notes:
1885:   By default the values, `v`, are row-oriented and unsorted.
1886:   See `MatSetOption()` for other options.

1888:   Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1889:   options cannot be mixed without intervening calls to the assembly
1890:   routines.

1892:   The grid coordinates are across the entire grid, not just the local portion

1894:   `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1895:   as well as in C.

1897:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1899:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1900:   or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1902:   The columns and rows in the stencil passed in MUST be contained within the
1903:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1904:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1905:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1906:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1908:   Negative indices may be passed in idxm and idxn, these rows and columns are
1909:   simply ignored. This allows easily inserting element stiffness matrices
1910:   with homogeneous Dirichlet boundary conditions that you don't want represented
1911:   in the matrix.

1913:   Inspired by the structured grid interface to the HYPRE package
1914:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

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

1927: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1928:           `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1929:           `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1930: @*/
1931: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1932: {
1933:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1934:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1935:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1937:   PetscFunctionBegin;
1938:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1941:   PetscAssertPointer(idxm, 3);
1942:   PetscAssertPointer(idxn, 5);
1943:   PetscAssertPointer(v, 6);

1945:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1946:     jdxm = buf;
1947:     jdxn = buf + m;
1948:   } else {
1949:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1950:     jdxm = bufm;
1951:     jdxn = bufn;
1952:   }
1953:   for (i = 0; i < m; i++) {
1954:     for (j = 0; j < 3 - sdim; j++) dxm++;
1955:     tmp = *dxm++ - starts[0];
1956:     for (j = 0; j < sdim - 1; j++) {
1957:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1958:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1959:     }
1960:     dxm++;
1961:     jdxm[i] = tmp;
1962:   }
1963:   for (i = 0; i < n; i++) {
1964:     for (j = 0; j < 3 - sdim; j++) dxn++;
1965:     tmp = *dxn++ - starts[0];
1966:     for (j = 0; j < sdim - 1; j++) {
1967:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1968:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1969:     }
1970:     dxn++;
1971:     jdxn[i] = tmp;
1972:   }
1973:   PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1974:   PetscCall(PetscFree2(bufm, bufn));
1975:   PetscFunctionReturn(PETSC_SUCCESS);
1976: }

1978: /*@
1979:   MatSetStencil - Sets the grid information for setting values into a matrix via
1980:   `MatSetValuesStencil()`

1982:   Not Collective

1984:   Input Parameters:
1985: + mat    - the matrix
1986: . dim    - dimension of the grid 1, 2, or 3
1987: . dims   - number of grid points in x, y, and z direction, including ghost points on your processor
1988: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1989: - dof    - number of degrees of freedom per node

1991:   Level: beginner

1993:   Notes:
1994:   Inspired by the structured grid interface to the HYPRE package
1995:   (www.llnl.gov/CASC/hyper)

1997:   For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1998:   user.

2000: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
2001:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
2002: @*/
2003: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
2004: {
2005:   PetscFunctionBegin;
2007:   PetscAssertPointer(dims, 3);
2008:   PetscAssertPointer(starts, 4);

2010:   mat->stencil.dim = dim + (dof > 1);
2011:   for (PetscInt i = 0; i < dim; i++) {
2012:     mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
2013:     mat->stencil.starts[i] = starts[dim - i - 1];
2014:   }
2015:   mat->stencil.dims[dim]   = dof;
2016:   mat->stencil.starts[dim] = 0;
2017:   mat->stencil.noc         = (PetscBool)(dof == 1);
2018:   PetscFunctionReturn(PETSC_SUCCESS);
2019: }

2021: /*@
2022:   MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

2024:   Not Collective

2026:   Input Parameters:
2027: + mat  - the matrix
2028: . v    - a logically two-dimensional array of values
2029: . m    - the number of block rows
2030: . idxm - the global block indices
2031: . n    - the number of block columns
2032: . idxn - the global block indices
2033: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

2035:   Level: intermediate

2037:   Notes:
2038:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2039:   MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

2041:   The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2042:   NOT the total number of rows/columns; for example, if the block size is 2 and
2043:   you are passing in values for rows 2,3,4,5  then `m` would be 2 (not 4).
2044:   The values in `idxm` would be 1 2; that is the first index for each block divided by
2045:   the block size.

2047:   You must call `MatSetBlockSize()` when constructing this matrix (before
2048:   preallocating it).

2050:   By default the values, `v`, are row-oriented, so the layout of
2051:   `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.

2053:   Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2054:   options cannot be mixed without intervening calls to the assembly
2055:   routines.

2057:   `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2058:   as well as in C.

2060:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2061:   simply ignored. This allows easily inserting element stiffness matrices
2062:   with homogeneous Dirichlet boundary conditions that you don't want represented
2063:   in the matrix.

2065:   Each time an entry is set within a sparse matrix via `MatSetValues()`,
2066:   internal searching must be done to determine where to place the
2067:   data in the matrix storage space.  By instead inserting blocks of
2068:   entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2069:   reduced.

2071:   Example:
2072: .vb
2073:    Suppose m=n=2 and block size(bs) = 2 The array is

2075:    1  2  | 3  4
2076:    5  6  | 7  8
2077:    - - - | - - -
2078:    9  10 | 11 12
2079:    13 14 | 15 16

2081:    v[] should be passed in like
2082:    v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

2084:   If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2085:    v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2086: .ve

2088:   Fortran Notes:
2089:   If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2090: .vb
2091:   MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES)
2092: .ve

2094:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2096: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2097: @*/
2098: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2099: {
2100:   PetscFunctionBeginHot;
2103:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2104:   PetscAssertPointer(idxm, 3);
2105:   PetscAssertPointer(idxn, 5);
2106:   MatCheckPreallocated(mat, 1);
2107:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2108:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2109:   if (PetscDefined(USE_DEBUG)) {
2110:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2111:     PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2112:   }
2113:   if (PetscDefined(USE_DEBUG)) {
2114:     PetscInt rbs, cbs, M, N, i;
2115:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2116:     PetscCall(MatGetSize(mat, &M, &N));
2117:     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);
2118:     for (i = 0; i < n; i++)
2119:       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);
2120:   }
2121:   if (mat->assembled) {
2122:     mat->was_assembled = PETSC_TRUE;
2123:     mat->assembled     = PETSC_FALSE;
2124:   }
2125:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2126:   if (mat->ops->setvaluesblocked) {
2127:     PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2128:   } else {
2129:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2130:     PetscInt i, j, bs, cbs;

2132:     PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2133:     if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2134:       iidxm = buf;
2135:       iidxn = buf + m * bs;
2136:     } else {
2137:       PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2138:       iidxm = bufr;
2139:       iidxn = bufc;
2140:     }
2141:     for (i = 0; i < m; i++) {
2142:       for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2143:     }
2144:     if (m != n || bs != cbs || idxm != idxn) {
2145:       for (i = 0; i < n; i++) {
2146:         for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2147:       }
2148:     } else iidxn = iidxm;
2149:     PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2150:     PetscCall(PetscFree2(bufr, bufc));
2151:   }
2152:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2153:   PetscFunctionReturn(PETSC_SUCCESS);
2154: }

2156: /*@
2157:   MatGetValues - Gets a block of local values from a matrix.

2159:   Not Collective; can only return values that are owned by the give process

2161:   Input Parameters:
2162: + mat  - the matrix
2163: . v    - a logically two-dimensional array for storing the values
2164: . m    - the number of rows
2165: . idxm - the  global indices of the rows
2166: . n    - the number of columns
2167: - idxn - the global indices of the columns

2169:   Level: advanced

2171:   Notes:
2172:   The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2173:   The values, `v`, are then returned in a row-oriented format,
2174:   analogous to that used by default in `MatSetValues()`.

2176:   `MatGetValues()` uses 0-based row and column numbers in
2177:   Fortran as well as in C.

2179:   `MatGetValues()` requires that the matrix has been assembled
2180:   with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
2181:   `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2182:   without intermediate matrix assembly.

2184:   Negative row or column indices will be ignored and those locations in `v` will be
2185:   left unchanged.

2187:   For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2188:   That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2189:   from `MatGetOwnershipRange`(mat,&rstart,&rend).

2191: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2192: @*/
2193: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2194: {
2195:   PetscFunctionBegin;
2198:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2199:   PetscAssertPointer(idxm, 3);
2200:   PetscAssertPointer(idxn, 5);
2201:   PetscAssertPointer(v, 6);
2202:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2203:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2204:   MatCheckPreallocated(mat, 1);

2206:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2207:   PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2208:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2209:   PetscFunctionReturn(PETSC_SUCCESS);
2210: }

2212: /*@
2213:   MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2214:   defined previously by `MatSetLocalToGlobalMapping()`

2216:   Not Collective

2218:   Input Parameters:
2219: + mat  - the matrix
2220: . nrow - number of rows
2221: . irow - the row local indices
2222: . ncol - number of columns
2223: - icol - the column local indices

2225:   Output Parameter:
2226: . y - a logically two-dimensional array of values

2228:   Level: advanced

2230:   Notes:
2231:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

2233:   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,
2234:   are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2235:   determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2236:   with `MatSetLocalToGlobalMapping()`.

2238:   Developer Note:
2239:   This is labelled with C so does not automatically generate Fortran stubs and interfaces
2240:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

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 logically two-dimensional array of values
2460: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2462:   Level: intermediate

2464:   Notes:
2465:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

2467:   Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2468:   options cannot be mixed without intervening calls to the assembly
2469:   routines.

2471:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2472:   MUST be called after all calls to `MatSetValuesLocal()` have been completed.

2474:   Fortran Notes:
2475:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2476: .vb
2477:   MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES)
2478: .ve

2480:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2482:   Developer Note:
2483:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2484:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2486: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2487:           `MatGetValuesLocal()`
2488: @*/
2489: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2490: {
2491:   PetscFunctionBeginHot;
2494:   MatCheckPreallocated(mat, 1);
2495:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2496:   PetscAssertPointer(irow, 3);
2497:   PetscAssertPointer(icol, 5);
2498:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2499:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2500:   if (PetscDefined(USE_DEBUG)) {
2501:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2502:     PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2503:   }

2505:   if (mat->assembled) {
2506:     mat->was_assembled = PETSC_TRUE;
2507:     mat->assembled     = PETSC_FALSE;
2508:   }
2509:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2510:   if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2511:   else {
2512:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2513:     const PetscInt *irowm, *icolm;

2515:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2516:       bufr  = buf;
2517:       bufc  = buf + nrow;
2518:       irowm = bufr;
2519:       icolm = bufc;
2520:     } else {
2521:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2522:       irowm = bufr;
2523:       icolm = bufc;
2524:     }
2525:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2526:     else irowm = irow;
2527:     if (mat->cmap->mapping) {
2528:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2529:         PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2530:       } else icolm = irowm;
2531:     } else icolm = icol;
2532:     PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2533:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2534:   }
2535:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2536:   PetscFunctionReturn(PETSC_SUCCESS);
2537: }

2539: /*@
2540:   MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2541:   using a local ordering of the nodes a block at a time.

2543:   Not Collective

2545:   Input Parameters:
2546: + mat  - the matrix
2547: . nrow - number of rows
2548: . irow - the row local indices
2549: . ncol - number of columns
2550: . icol - the column local indices
2551: . y    - a logically two-dimensional array of values
2552: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2554:   Level: intermediate

2556:   Notes:
2557:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2558:   before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

2560:   Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2561:   options cannot be mixed without intervening calls to the assembly
2562:   routines.

2564:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2565:   MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

2567:   Fortran Notes:
2568:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2569: .vb
2570:   MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES)
2571: .ve

2573:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2575:   Developer Note:
2576:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2577:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2579: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2580:           `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2581: @*/
2582: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2583: {
2584:   PetscFunctionBeginHot;
2587:   MatCheckPreallocated(mat, 1);
2588:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2589:   PetscAssertPointer(irow, 3);
2590:   PetscAssertPointer(icol, 5);
2591:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2592:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2593:   if (PetscDefined(USE_DEBUG)) {
2594:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2595:     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);
2596:   }

2598:   if (mat->assembled) {
2599:     mat->was_assembled = PETSC_TRUE;
2600:     mat->assembled     = PETSC_FALSE;
2601:   }
2602:   if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2603:     PetscInt irbs, rbs;
2604:     PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2605:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2606:     PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2607:   }
2608:   if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2609:     PetscInt icbs, cbs;
2610:     PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2611:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2612:     PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2613:   }
2614:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2615:   if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2616:   else {
2617:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2618:     const PetscInt *irowm, *icolm;

2620:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2621:       bufr  = buf;
2622:       bufc  = buf + nrow;
2623:       irowm = bufr;
2624:       icolm = bufc;
2625:     } else {
2626:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2627:       irowm = bufr;
2628:       icolm = bufc;
2629:     }
2630:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2631:     else irowm = irow;
2632:     if (mat->cmap->mapping) {
2633:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2634:         PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2635:       } else icolm = irowm;
2636:     } else icolm = icol;
2637:     PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2638:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2639:   }
2640:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2641:   PetscFunctionReturn(PETSC_SUCCESS);
2642: }

2644: /*@
2645:   MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal

2647:   Collective

2649:   Input Parameters:
2650: + mat - the matrix
2651: - x   - the vector to be multiplied

2653:   Output Parameter:
2654: . y - the result

2656:   Level: developer

2658:   Note:
2659:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2660:   call `MatMultDiagonalBlock`(A,y,y).

2662: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2663: @*/
2664: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2665: {
2666:   PetscFunctionBegin;

2672:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2673:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2674:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2675:   MatCheckPreallocated(mat, 1);

2677:   PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2678:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2679:   PetscFunctionReturn(PETSC_SUCCESS);
2680: }

2682: /*@
2683:   MatMult - Computes the matrix-vector product, $y = Ax$.

2685:   Neighbor-wise Collective

2687:   Input Parameters:
2688: + mat - the matrix
2689: - x   - the vector to be multiplied

2691:   Output Parameter:
2692: . y - the result

2694:   Level: beginner

2696:   Note:
2697:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2698:   call `MatMult`(A,y,y).

2700: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2701: @*/
2702: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2703: {
2704:   PetscFunctionBegin;
2708:   VecCheckAssembled(x);
2710:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2711:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2712:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2713:   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);
2714:   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);
2715:   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);
2716:   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);
2717:   PetscCall(VecSetErrorIfLocked(y, 3));
2718:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2719:   MatCheckPreallocated(mat, 1);

2721:   PetscCall(VecLockReadPush(x));
2722:   PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2723:   PetscUseTypeMethod(mat, mult, x, y);
2724:   PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2725:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2726:   PetscCall(VecLockReadPop(x));
2727:   PetscFunctionReturn(PETSC_SUCCESS);
2728: }

2730: /*@
2731:   MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.

2733:   Neighbor-wise Collective

2735:   Input Parameters:
2736: + mat - the matrix
2737: - x   - the vector to be multiplied

2739:   Output Parameter:
2740: . y - the result

2742:   Level: beginner

2744:   Notes:
2745:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2746:   call `MatMultTranspose`(A,y,y).

2748:   For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2749:   use `MatMultHermitianTranspose()`

2751: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2752: @*/
2753: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2754: {
2755:   PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;

2757:   PetscFunctionBegin;
2761:   VecCheckAssembled(x);

2764:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2765:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2766:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2767:   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);
2768:   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);
2769:   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);
2770:   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);
2771:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2772:   MatCheckPreallocated(mat, 1);

2774:   if (!mat->ops->multtranspose) {
2775:     if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2776:     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);
2777:   } else op = mat->ops->multtranspose;
2778:   PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2779:   PetscCall(VecLockReadPush(x));
2780:   PetscCall((*op)(mat, x, y));
2781:   PetscCall(VecLockReadPop(x));
2782:   PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2783:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2784:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2785:   PetscFunctionReturn(PETSC_SUCCESS);
2786: }

2788: /*@
2789:   MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.

2791:   Neighbor-wise Collective

2793:   Input Parameters:
2794: + mat - the matrix
2795: - x   - the vector to be multiplied

2797:   Output Parameter:
2798: . y - the result

2800:   Level: beginner

2802:   Notes:
2803:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2804:   call `MatMultHermitianTranspose`(A,y,y).

2806:   Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2808:   For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

2810: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2811: @*/
2812: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2813: {
2814:   PetscFunctionBegin;

2820:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2821:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2822:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2823:   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);
2824:   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);
2825:   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);
2826:   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);
2827:   MatCheckPreallocated(mat, 1);

2829:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2830: #if defined(PETSC_USE_COMPLEX)
2831:   if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2832:     PetscCall(VecLockReadPush(x));
2833:     if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2834:     else PetscUseTypeMethod(mat, mult, x, y);
2835:     PetscCall(VecLockReadPop(x));
2836:   } else {
2837:     Vec w;
2838:     PetscCall(VecDuplicate(x, &w));
2839:     PetscCall(VecCopy(x, w));
2840:     PetscCall(VecConjugate(w));
2841:     PetscCall(MatMultTranspose(mat, w, y));
2842:     PetscCall(VecDestroy(&w));
2843:     PetscCall(VecConjugate(y));
2844:   }
2845:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2846: #else
2847:   PetscCall(MatMultTranspose(mat, x, y));
2848: #endif
2849:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2850:   PetscFunctionReturn(PETSC_SUCCESS);
2851: }

2853: /*@
2854:   MatMultAdd -  Computes $v3 = v2 + A * v1$.

2856:   Neighbor-wise Collective

2858:   Input Parameters:
2859: + mat - the matrix
2860: . v1  - the vector to be multiplied by `mat`
2861: - v2  - the vector to be added to the result

2863:   Output Parameter:
2864: . v3 - the result

2866:   Level: beginner

2868:   Note:
2869:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2870:   call `MatMultAdd`(A,v1,v2,v1).

2872: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2873: @*/
2874: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2875: {
2876:   PetscFunctionBegin;

2883:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2884:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2885:   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);
2886:   /* 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);
2887:      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); */
2888:   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);
2889:   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);
2890:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2891:   MatCheckPreallocated(mat, 1);

2893:   PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2894:   PetscCall(VecLockReadPush(v1));
2895:   PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2896:   PetscCall(VecLockReadPop(v1));
2897:   PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2898:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2899:   PetscFunctionReturn(PETSC_SUCCESS);
2900: }

2902: /*@
2903:   MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.

2905:   Neighbor-wise Collective

2907:   Input Parameters:
2908: + mat - the matrix
2909: . v1  - the vector to be multiplied by the transpose of the matrix
2910: - v2  - the vector to be added to the result

2912:   Output Parameter:
2913: . v3 - the result

2915:   Level: beginner

2917:   Note:
2918:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2919:   call `MatMultTransposeAdd`(A,v1,v2,v1).

2921: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2922: @*/
2923: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2924: {
2925:   PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

2927:   PetscFunctionBegin;

2934:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2935:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2936:   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);
2937:   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);
2938:   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);
2939:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2940:   PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2941:   MatCheckPreallocated(mat, 1);

2943:   PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2944:   PetscCall(VecLockReadPush(v1));
2945:   PetscCall((*op)(mat, v1, v2, v3));
2946:   PetscCall(VecLockReadPop(v1));
2947:   PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2948:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2949:   PetscFunctionReturn(PETSC_SUCCESS);
2950: }

2952: /*@
2953:   MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.

2955:   Neighbor-wise Collective

2957:   Input Parameters:
2958: + mat - the matrix
2959: . v1  - the vector to be multiplied by the Hermitian transpose
2960: - v2  - the vector to be added to the result

2962:   Output Parameter:
2963: . v3 - the result

2965:   Level: beginner

2967:   Note:
2968:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2969:   call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

2971: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2972: @*/
2973: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2974: {
2975:   PetscFunctionBegin;

2982:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2983:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2984:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2985:   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);
2986:   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);
2987:   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);
2988:   MatCheckPreallocated(mat, 1);

2990:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2991:   PetscCall(VecLockReadPush(v1));
2992:   if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2993:   else {
2994:     Vec w, z;
2995:     PetscCall(VecDuplicate(v1, &w));
2996:     PetscCall(VecCopy(v1, w));
2997:     PetscCall(VecConjugate(w));
2998:     PetscCall(VecDuplicate(v3, &z));
2999:     PetscCall(MatMultTranspose(mat, w, z));
3000:     PetscCall(VecDestroy(&w));
3001:     PetscCall(VecConjugate(z));
3002:     if (v2 != v3) {
3003:       PetscCall(VecWAXPY(v3, 1.0, v2, z));
3004:     } else {
3005:       PetscCall(VecAXPY(v3, 1.0, z));
3006:     }
3007:     PetscCall(VecDestroy(&z));
3008:   }
3009:   PetscCall(VecLockReadPop(v1));
3010:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
3011:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
3012:   PetscFunctionReturn(PETSC_SUCCESS);
3013: }

3015: /*@
3016:   MatGetFactorType - gets the type of factorization a matrix is

3018:   Not Collective

3020:   Input Parameter:
3021: . mat - the matrix

3023:   Output Parameter:
3024: . 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`

3026:   Level: intermediate

3028: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3029:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3030: @*/
3031: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3032: {
3033:   PetscFunctionBegin;
3036:   PetscAssertPointer(t, 2);
3037:   *t = mat->factortype;
3038:   PetscFunctionReturn(PETSC_SUCCESS);
3039: }

3041: /*@
3042:   MatSetFactorType - sets the type of factorization a matrix is

3044:   Logically Collective

3046:   Input Parameters:
3047: + mat - the matrix
3048: - 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`

3050:   Level: intermediate

3052: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3053:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3054: @*/
3055: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3056: {
3057:   PetscFunctionBegin;
3060:   mat->factortype = t;
3061:   PetscFunctionReturn(PETSC_SUCCESS);
3062: }

3064: /*@
3065:   MatGetInfo - Returns information about matrix storage (number of
3066:   nonzeros, memory, etc.).

3068:   Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

3070:   Input Parameters:
3071: + mat  - the matrix
3072: - 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)

3074:   Output Parameter:
3075: . info - matrix information context

3077:   Options Database Key:
3078: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

3080:   Level: intermediate

3082:   Notes:
3083:   The `MatInfo` context contains a variety of matrix data, including
3084:   number of nonzeros allocated and used, number of mallocs during
3085:   matrix assembly, etc.  Additional information for factored matrices
3086:   is provided (such as the fill ratio, number of mallocs during
3087:   factorization, etc.).

3089:   Example:
3090:   See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3091:   data within the `MatInfo` context.  For example,
3092: .vb
3093:       MatInfo info;
3094:       Mat     A;
3095:       double  mal, nz_a, nz_u;

3097:       MatGetInfo(A, MAT_LOCAL, &info);
3098:       mal  = info.mallocs;
3099:       nz_a = info.nz_allocated;
3100: .ve

3102:   Fortran Note:
3103:   Declare info as a `MatInfo` array of dimension `MAT_INFO_SIZE`, and then extract the parameters
3104:   of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
3105:   a complete list of parameter names.
3106: .vb
3107:       MatInfo info(MAT_INFO_SIZE)
3108:       double  precision mal, nz_a
3109:       Mat     A
3110:       integer ierr

3112:       call MatGetInfo(A, MAT_LOCAL, info, ierr)
3113:       mal = info(MAT_INFO_MALLOCS)
3114:       nz_a = info(MAT_INFO_NZ_ALLOCATED)
3115: .ve

3117: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3118: @*/
3119: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3120: {
3121:   PetscFunctionBegin;
3124:   PetscAssertPointer(info, 3);
3125:   MatCheckPreallocated(mat, 1);
3126:   PetscUseTypeMethod(mat, getinfo, flag, info);
3127:   PetscFunctionReturn(PETSC_SUCCESS);
3128: }

3130: /*
3131:    This is used by external packages where it is not easy to get the info from the actual
3132:    matrix factorization.
3133: */
3134: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3135: {
3136:   PetscFunctionBegin;
3137:   PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3138:   PetscFunctionReturn(PETSC_SUCCESS);
3139: }

3141: /*@
3142:   MatLUFactor - Performs in-place LU factorization of matrix.

3144:   Collective

3146:   Input Parameters:
3147: + mat  - the matrix
3148: . row  - row permutation
3149: . col  - column permutation
3150: - info - options for factorization, includes
3151: .vb
3152:           fill - expected fill as ratio of original fill.
3153:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3154:                    Run with the option -info to determine an optimal value to use
3155: .ve

3157:   Level: developer

3159:   Notes:
3160:   Most users should employ the `KSP` interface for linear solvers
3161:   instead of working directly with matrix algebra routines such as this.
3162:   See, e.g., `KSPCreate()`.

3164:   This changes the state of the matrix to a factored matrix; it cannot be used
3165:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3167:   This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3168:   when not using `KSP`.

3170:   Developer Note:
3171:   The Fortran interface is not autogenerated as the
3172:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3174: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3175:           `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3176: @*/
3177: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3178: {
3179:   MatFactorInfo tinfo;

3181:   PetscFunctionBegin;
3185:   if (info) PetscAssertPointer(info, 4);
3187:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3188:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3189:   MatCheckPreallocated(mat, 1);
3190:   if (!info) {
3191:     PetscCall(MatFactorInfoInitialize(&tinfo));
3192:     info = &tinfo;
3193:   }

3195:   PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3196:   PetscUseTypeMethod(mat, lufactor, row, col, info);
3197:   PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3198:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3199:   PetscFunctionReturn(PETSC_SUCCESS);
3200: }

3202: /*@
3203:   MatILUFactor - Performs in-place ILU factorization of matrix.

3205:   Collective

3207:   Input Parameters:
3208: + mat  - the matrix
3209: . row  - row permutation
3210: . col  - column permutation
3211: - info - structure containing
3212: .vb
3213:       levels - number of levels of fill.
3214:       expected fill - as ratio of original fill.
3215:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3216:                 missing diagonal entries)
3217: .ve

3219:   Level: developer

3221:   Notes:
3222:   Most users should employ the `KSP` interface for linear solvers
3223:   instead of working directly with matrix algebra routines such as this.
3224:   See, e.g., `KSPCreate()`.

3226:   Probably really in-place only when level of fill is zero, otherwise allocates
3227:   new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3228:   when not using `KSP`.

3230:   Developer Note:
3231:   The Fortran interface is not autogenerated as the
3232:   interface definition cannot be generated correctly [due to MatFactorInfo]

3234: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3235: @*/
3236: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3237: {
3238:   PetscFunctionBegin;
3242:   PetscAssertPointer(info, 4);
3244:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3245:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3246:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3247:   MatCheckPreallocated(mat, 1);

3249:   PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3250:   PetscUseTypeMethod(mat, ilufactor, row, col, info);
3251:   PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3252:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3253:   PetscFunctionReturn(PETSC_SUCCESS);
3254: }

3256: /*@
3257:   MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3258:   Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

3260:   Collective

3262:   Input Parameters:
3263: + fact - the factor matrix obtained with `MatGetFactor()`
3264: . mat  - the matrix
3265: . row  - the row permutation
3266: . col  - the column permutation
3267: - info - options for factorization, includes
3268: .vb
3269:           fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3270:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3271: .ve

3273:   Level: developer

3275:   Notes:
3276:   See [Matrix Factorization](sec_matfactor) for additional information about factorizations

3278:   Most users should employ the simplified `KSP` interface for linear solvers
3279:   instead of working directly with matrix algebra routines such as this.
3280:   See, e.g., `KSPCreate()`.

3282:   Developer Note:
3283:   The Fortran interface is not autogenerated as the
3284:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3286: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3287: @*/
3288: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3289: {
3290:   MatFactorInfo tinfo;

3292:   PetscFunctionBegin;
3297:   if (info) PetscAssertPointer(info, 5);
3300:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3301:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3302:   MatCheckPreallocated(mat, 2);
3303:   if (!info) {
3304:     PetscCall(MatFactorInfoInitialize(&tinfo));
3305:     info = &tinfo;
3306:   }

3308:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3309:   PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3310:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3311:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3312:   PetscFunctionReturn(PETSC_SUCCESS);
3313: }

3315: /*@
3316:   MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3317:   Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

3319:   Collective

3321:   Input Parameters:
3322: + fact - the factor matrix obtained with `MatGetFactor()`
3323: . mat  - the matrix
3324: - info - options for factorization

3326:   Level: developer

3328:   Notes:
3329:   See `MatLUFactor()` for in-place factorization.  See
3330:   `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

3332:   Most users should employ the `KSP` interface for linear solvers
3333:   instead of working directly with matrix algebra routines such as this.
3334:   See, e.g., `KSPCreate()`.

3336:   Developer Note:
3337:   The Fortran interface is not autogenerated as the
3338:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3340: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3341: @*/
3342: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3343: {
3344:   MatFactorInfo tinfo;

3346:   PetscFunctionBegin;
3351:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3352:   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,
3353:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3355:   MatCheckPreallocated(mat, 2);
3356:   if (!info) {
3357:     PetscCall(MatFactorInfoInitialize(&tinfo));
3358:     info = &tinfo;
3359:   }

3361:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3362:   else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3363:   PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3364:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3365:   else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3366:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3367:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3368:   PetscFunctionReturn(PETSC_SUCCESS);
3369: }

3371: /*@
3372:   MatCholeskyFactor - Performs in-place Cholesky factorization of a
3373:   symmetric matrix.

3375:   Collective

3377:   Input Parameters:
3378: + mat  - the matrix
3379: . perm - row and column permutations
3380: - info - expected fill as ratio of original fill

3382:   Level: developer

3384:   Notes:
3385:   See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
3386:   `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

3388:   Most users should employ the `KSP` interface for linear solvers
3389:   instead of working directly with matrix algebra routines such as this.
3390:   See, e.g., `KSPCreate()`.

3392:   Developer Note:
3393:   The Fortran interface is not autogenerated as the
3394:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3396: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3397:           `MatGetOrdering()`
3398: @*/
3399: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3400: {
3401:   MatFactorInfo tinfo;

3403:   PetscFunctionBegin;
3406:   if (info) PetscAssertPointer(info, 3);
3408:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3409:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3410:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3411:   MatCheckPreallocated(mat, 1);
3412:   if (!info) {
3413:     PetscCall(MatFactorInfoInitialize(&tinfo));
3414:     info = &tinfo;
3415:   }

3417:   PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3418:   PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3419:   PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3420:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3421:   PetscFunctionReturn(PETSC_SUCCESS);
3422: }

3424: /*@
3425:   MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3426:   of a symmetric matrix.

3428:   Collective

3430:   Input Parameters:
3431: + fact - the factor matrix obtained with `MatGetFactor()`
3432: . mat  - the matrix
3433: . perm - row and column permutations
3434: - info - options for factorization, includes
3435: .vb
3436:           fill - expected fill as ratio of original fill.
3437:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3438:                    Run with the option -info to determine an optimal value to use
3439: .ve

3441:   Level: developer

3443:   Notes:
3444:   See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
3445:   `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

3447:   Most users should employ the `KSP` interface for linear solvers
3448:   instead of working directly with matrix algebra routines such as this.
3449:   See, e.g., `KSPCreate()`.

3451:   Developer Note:
3452:   The Fortran interface is not autogenerated as the
3453:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3455: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3456:           `MatGetOrdering()`
3457: @*/
3458: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3459: {
3460:   MatFactorInfo tinfo;

3462:   PetscFunctionBegin;
3466:   if (info) PetscAssertPointer(info, 4);
3469:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3470:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3471:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3472:   MatCheckPreallocated(mat, 2);
3473:   if (!info) {
3474:     PetscCall(MatFactorInfoInitialize(&tinfo));
3475:     info = &tinfo;
3476:   }

3478:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3479:   PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3480:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3481:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3482:   PetscFunctionReturn(PETSC_SUCCESS);
3483: }

3485: /*@
3486:   MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3487:   of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3488:   `MatCholeskyFactorSymbolic()`.

3490:   Collective

3492:   Input Parameters:
3493: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3494: . mat  - the initial matrix that is to be factored
3495: - info - options for factorization

3497:   Level: developer

3499:   Note:
3500:   Most users should employ the `KSP` interface for linear solvers
3501:   instead of working directly with matrix algebra routines such as this.
3502:   See, e.g., `KSPCreate()`.

3504:   Developer Note:
3505:   The Fortran interface is not autogenerated as the
3506:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3508: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3509: @*/
3510: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3511: {
3512:   MatFactorInfo tinfo;

3514:   PetscFunctionBegin;
3519:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3520:   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,
3521:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3522:   MatCheckPreallocated(mat, 2);
3523:   if (!info) {
3524:     PetscCall(MatFactorInfoInitialize(&tinfo));
3525:     info = &tinfo;
3526:   }

3528:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3529:   else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3530:   PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3531:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3532:   else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3533:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3534:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3535:   PetscFunctionReturn(PETSC_SUCCESS);
3536: }

3538: /*@
3539:   MatQRFactor - Performs in-place QR factorization of matrix.

3541:   Collective

3543:   Input Parameters:
3544: + mat  - the matrix
3545: . col  - column permutation
3546: - info - options for factorization, includes
3547: .vb
3548:           fill - expected fill as ratio of original fill.
3549:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3550:                    Run with the option -info to determine an optimal value to use
3551: .ve

3553:   Level: developer

3555:   Notes:
3556:   Most users should employ the `KSP` interface for linear solvers
3557:   instead of working directly with matrix algebra routines such as this.
3558:   See, e.g., `KSPCreate()`.

3560:   This changes the state of the matrix to a factored matrix; it cannot be used
3561:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3563:   Developer Note:
3564:   The Fortran interface is not autogenerated as the
3565:   interface definition cannot be generated correctly [due to MatFactorInfo]

3567: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3568:           `MatSetUnfactored()`
3569: @*/
3570: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3571: {
3572:   PetscFunctionBegin;
3575:   if (info) PetscAssertPointer(info, 3);
3577:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3578:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3579:   MatCheckPreallocated(mat, 1);
3580:   PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3581:   PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3582:   PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3583:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3584:   PetscFunctionReturn(PETSC_SUCCESS);
3585: }

3587: /*@
3588:   MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3589:   Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

3591:   Collective

3593:   Input Parameters:
3594: + fact - the factor matrix obtained with `MatGetFactor()`
3595: . mat  - the matrix
3596: . col  - column permutation
3597: - info - options for factorization, includes
3598: .vb
3599:           fill - expected fill as ratio of original fill.
3600:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3601:                    Run with the option -info to determine an optimal value to use
3602: .ve

3604:   Level: developer

3606:   Note:
3607:   Most users should employ the `KSP` interface for linear solvers
3608:   instead of working directly with matrix algebra routines such as this.
3609:   See, e.g., `KSPCreate()`.

3611:   Developer Note:
3612:   The Fortran interface is not autogenerated as the
3613:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3615: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3616: @*/
3617: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3618: {
3619:   MatFactorInfo tinfo;

3621:   PetscFunctionBegin;
3625:   if (info) PetscAssertPointer(info, 4);
3628:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3629:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3630:   MatCheckPreallocated(mat, 2);
3631:   if (!info) {
3632:     PetscCall(MatFactorInfoInitialize(&tinfo));
3633:     info = &tinfo;
3634:   }

3636:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3637:   PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3638:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3639:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3640:   PetscFunctionReturn(PETSC_SUCCESS);
3641: }

3643: /*@
3644:   MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3645:   Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

3647:   Collective

3649:   Input Parameters:
3650: + fact - the factor matrix obtained with `MatGetFactor()`
3651: . mat  - the matrix
3652: - info - options for factorization

3654:   Level: developer

3656:   Notes:
3657:   See `MatQRFactor()` for in-place factorization.

3659:   Most users should employ the `KSP` interface for linear solvers
3660:   instead of working directly with matrix algebra routines such as this.
3661:   See, e.g., `KSPCreate()`.

3663:   Developer Note:
3664:   The Fortran interface is not autogenerated as the
3665:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3667: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3668: @*/
3669: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3670: {
3671:   MatFactorInfo tinfo;

3673:   PetscFunctionBegin;
3678:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3679:   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,
3680:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3682:   MatCheckPreallocated(mat, 2);
3683:   if (!info) {
3684:     PetscCall(MatFactorInfoInitialize(&tinfo));
3685:     info = &tinfo;
3686:   }

3688:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3689:   else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3690:   PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3691:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3692:   else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3693:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3694:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3695:   PetscFunctionReturn(PETSC_SUCCESS);
3696: }

3698: /*@
3699:   MatSolve - Solves $A x = b$, given a factored matrix.

3701:   Neighbor-wise Collective

3703:   Input Parameters:
3704: + mat - the factored matrix
3705: - b   - the right-hand-side vector

3707:   Output Parameter:
3708: . x - the result vector

3710:   Level: developer

3712:   Notes:
3713:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3714:   call `MatSolve`(A,x,x).

3716:   Most users should employ the `KSP` interface for linear solvers
3717:   instead of working directly with matrix algebra routines such as this.
3718:   See, e.g., `KSPCreate()`.

3720: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3721: @*/
3722: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3723: {
3724:   PetscFunctionBegin;
3729:   PetscCheckSameComm(mat, 1, b, 2);
3730:   PetscCheckSameComm(mat, 1, x, 3);
3731:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3732:   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);
3733:   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);
3734:   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);
3735:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3736:   MatCheckPreallocated(mat, 1);

3738:   PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3739:   PetscCall(VecFlag(x, mat->factorerrortype));
3740:   if (mat->factorerrortype) {
3741:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3742:   } else PetscUseTypeMethod(mat, solve, b, x);
3743:   PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3744:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3745:   PetscFunctionReturn(PETSC_SUCCESS);
3746: }

3748: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3749: {
3750:   Vec      b, x;
3751:   PetscInt N, i;
3752:   PetscErrorCode (*f)(Mat, Vec, Vec);
3753:   PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

3755:   PetscFunctionBegin;
3756:   if (A->factorerrortype) {
3757:     PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3758:     PetscCall(MatSetInf(X));
3759:     PetscFunctionReturn(PETSC_SUCCESS);
3760:   }
3761:   f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3762:   PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3763:   PetscCall(MatBoundToCPU(A, &Abound));
3764:   if (!Abound) {
3765:     PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3766:     PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3767:   }
3768: #if PetscDefined(HAVE_CUDA)
3769:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3770:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3771: #elif PetscDefined(HAVE_HIP)
3772:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3773:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3774: #endif
3775:   PetscCall(MatGetSize(B, NULL, &N));
3776:   for (i = 0; i < N; i++) {
3777:     PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3778:     PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3779:     PetscCall((*f)(A, b, x));
3780:     PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3781:     PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3782:   }
3783:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3784:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3785:   PetscFunctionReturn(PETSC_SUCCESS);
3786: }

3788: /*@
3789:   MatMatSolve - Solves $A X = B$, given a factored matrix.

3791:   Neighbor-wise Collective

3793:   Input Parameters:
3794: + A - the factored matrix
3795: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

3797:   Output Parameter:
3798: . X - the result matrix (dense matrix)

3800:   Level: developer

3802:   Note:
3803:   If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3804:   otherwise, `B` and `X` cannot be the same.

3806: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3807: @*/
3808: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3809: {
3810:   PetscFunctionBegin;
3815:   PetscCheckSameComm(A, 1, B, 2);
3816:   PetscCheckSameComm(A, 1, X, 3);
3817:   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);
3818:   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);
3819:   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");
3820:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3821:   MatCheckPreallocated(A, 1);

3823:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3824:   if (!A->ops->matsolve) {
3825:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3826:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3827:   } else PetscUseTypeMethod(A, matsolve, B, X);
3828:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3829:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3830:   PetscFunctionReturn(PETSC_SUCCESS);
3831: }

3833: /*@
3834:   MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.

3836:   Neighbor-wise Collective

3838:   Input Parameters:
3839: + A - the factored matrix
3840: - B - the right-hand-side matrix  (`MATDENSE` matrix)

3842:   Output Parameter:
3843: . X - the result matrix (dense matrix)

3845:   Level: developer

3847:   Note:
3848:   The matrices `B` and `X` cannot be the same.  I.e., one cannot
3849:   call `MatMatSolveTranspose`(A,X,X).

3851: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3852: @*/
3853: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3854: {
3855:   PetscFunctionBegin;
3860:   PetscCheckSameComm(A, 1, B, 2);
3861:   PetscCheckSameComm(A, 1, X, 3);
3862:   PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3863:   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);
3864:   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);
3865:   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);
3866:   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");
3867:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3868:   MatCheckPreallocated(A, 1);

3870:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3871:   if (!A->ops->matsolvetranspose) {
3872:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3873:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3874:   } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3875:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3876:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3877:   PetscFunctionReturn(PETSC_SUCCESS);
3878: }

3880: /*@
3881:   MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.

3883:   Neighbor-wise Collective

3885:   Input Parameters:
3886: + A  - the factored matrix
3887: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`

3889:   Output Parameter:
3890: . X - the result matrix (dense matrix)

3892:   Level: developer

3894:   Note:
3895:   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
3896:   format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

3898: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3899: @*/
3900: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3901: {
3902:   PetscFunctionBegin;
3907:   PetscCheckSameComm(A, 1, Bt, 2);
3908:   PetscCheckSameComm(A, 1, X, 3);

3910:   PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3911:   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);
3912:   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);
3913:   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");
3914:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3915:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3916:   MatCheckPreallocated(A, 1);

3918:   PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3919:   PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3920:   PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3921:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3922:   PetscFunctionReturn(PETSC_SUCCESS);
3923: }

3925: /*@
3926:   MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3927:   $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3929:   Neighbor-wise Collective

3931:   Input Parameters:
3932: + mat - the factored matrix
3933: - b   - the right-hand-side vector

3935:   Output Parameter:
3936: . x - the result vector

3938:   Level: developer

3940:   Notes:
3941:   `MatSolve()` should be used for most applications, as it performs
3942:   a forward solve followed by a backward solve.

3944:   The vectors `b` and `x` cannot be the same,  i.e., one cannot
3945:   call `MatForwardSolve`(A,x,x).

3947:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3948:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3949:   `MatForwardSolve()` solves $U^T*D y = b$, and
3950:   `MatBackwardSolve()` solves $U x = y$.
3951:   Thus they do not provide a symmetric preconditioner.

3953: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3954: @*/
3955: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3956: {
3957:   PetscFunctionBegin;
3962:   PetscCheckSameComm(mat, 1, b, 2);
3963:   PetscCheckSameComm(mat, 1, x, 3);
3964:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3965:   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);
3966:   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);
3967:   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);
3968:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3969:   MatCheckPreallocated(mat, 1);

3971:   PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3972:   PetscUseTypeMethod(mat, forwardsolve, b, x);
3973:   PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3974:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3975:   PetscFunctionReturn(PETSC_SUCCESS);
3976: }

3978: /*@
3979:   MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3980:   $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3982:   Neighbor-wise Collective

3984:   Input Parameters:
3985: + mat - the factored matrix
3986: - b   - the right-hand-side vector

3988:   Output Parameter:
3989: . x - the result vector

3991:   Level: developer

3993:   Notes:
3994:   `MatSolve()` should be used for most applications, as it performs
3995:   a forward solve followed by a backward solve.

3997:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3998:   call `MatBackwardSolve`(A,x,x).

4000:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
4001:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4002:   `MatForwardSolve()` solves $U^T*D y = b$, and
4003:   `MatBackwardSolve()` solves $U x = y$.
4004:   Thus they do not provide a symmetric preconditioner.

4006: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
4007: @*/
4008: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
4009: {
4010:   PetscFunctionBegin;
4015:   PetscCheckSameComm(mat, 1, b, 2);
4016:   PetscCheckSameComm(mat, 1, x, 3);
4017:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4018:   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);
4019:   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);
4020:   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);
4021:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4022:   MatCheckPreallocated(mat, 1);

4024:   PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
4025:   PetscUseTypeMethod(mat, backwardsolve, b, x);
4026:   PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
4027:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4028:   PetscFunctionReturn(PETSC_SUCCESS);
4029: }

4031: /*@
4032:   MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.

4034:   Neighbor-wise Collective

4036:   Input Parameters:
4037: + mat - the factored matrix
4038: . b   - the right-hand-side vector
4039: - y   - the vector to be added to

4041:   Output Parameter:
4042: . x - the result vector

4044:   Level: developer

4046:   Note:
4047:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4048:   call `MatSolveAdd`(A,x,y,x).

4050: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4051: @*/
4052: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4053: {
4054:   PetscScalar one = 1.0;
4055:   Vec         tmp;

4057:   PetscFunctionBegin;
4063:   PetscCheckSameComm(mat, 1, b, 2);
4064:   PetscCheckSameComm(mat, 1, y, 3);
4065:   PetscCheckSameComm(mat, 1, x, 4);
4066:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4067:   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);
4068:   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);
4069:   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);
4070:   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);
4071:   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);
4072:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4073:   MatCheckPreallocated(mat, 1);

4075:   PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4076:   PetscCall(VecFlag(x, mat->factorerrortype));
4077:   if (mat->factorerrortype) {
4078:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4079:   } else if (mat->ops->solveadd) {
4080:     PetscUseTypeMethod(mat, solveadd, b, y, x);
4081:   } else {
4082:     /* do the solve then the add manually */
4083:     if (x != y) {
4084:       PetscCall(MatSolve(mat, b, x));
4085:       PetscCall(VecAXPY(x, one, y));
4086:     } else {
4087:       PetscCall(VecDuplicate(x, &tmp));
4088:       PetscCall(VecCopy(x, tmp));
4089:       PetscCall(MatSolve(mat, b, x));
4090:       PetscCall(VecAXPY(x, one, tmp));
4091:       PetscCall(VecDestroy(&tmp));
4092:     }
4093:   }
4094:   PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4095:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4096:   PetscFunctionReturn(PETSC_SUCCESS);
4097: }

4099: /*@
4100:   MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.

4102:   Neighbor-wise Collective

4104:   Input Parameters:
4105: + mat - the factored matrix
4106: - b   - the right-hand-side vector

4108:   Output Parameter:
4109: . x - the result vector

4111:   Level: developer

4113:   Notes:
4114:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4115:   call `MatSolveTranspose`(A,x,x).

4117:   Most users should employ the `KSP` interface for linear solvers
4118:   instead of working directly with matrix algebra routines such as this.
4119:   See, e.g., `KSPCreate()`.

4121: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4122: @*/
4123: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4124: {
4125:   PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

4127:   PetscFunctionBegin;
4132:   PetscCheckSameComm(mat, 1, b, 2);
4133:   PetscCheckSameComm(mat, 1, x, 3);
4134:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4135:   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);
4136:   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);
4137:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4138:   MatCheckPreallocated(mat, 1);
4139:   PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4140:   PetscCall(VecFlag(x, mat->factorerrortype));
4141:   if (mat->factorerrortype) {
4142:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4143:   } else {
4144:     PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4145:     PetscCall((*f)(mat, b, x));
4146:   }
4147:   PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4148:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4149:   PetscFunctionReturn(PETSC_SUCCESS);
4150: }

4152: /*@
4153:   MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4154:   factored matrix.

4156:   Neighbor-wise Collective

4158:   Input Parameters:
4159: + mat - the factored matrix
4160: . b   - the right-hand-side vector
4161: - y   - the vector to be added to

4163:   Output Parameter:
4164: . x - the result vector

4166:   Level: developer

4168:   Note:
4169:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4170:   call `MatSolveTransposeAdd`(A,x,y,x).

4172: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4173: @*/
4174: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4175: {
4176:   PetscScalar one = 1.0;
4177:   Vec         tmp;
4178:   PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

4180:   PetscFunctionBegin;
4186:   PetscCheckSameComm(mat, 1, b, 2);
4187:   PetscCheckSameComm(mat, 1, y, 3);
4188:   PetscCheckSameComm(mat, 1, x, 4);
4189:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4190:   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);
4191:   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);
4192:   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);
4193:   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);
4194:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4195:   MatCheckPreallocated(mat, 1);

4197:   PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4198:   PetscCall(VecFlag(x, mat->factorerrortype));
4199:   if (mat->factorerrortype) {
4200:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4201:   } else if (f) {
4202:     PetscCall((*f)(mat, b, y, x));
4203:   } else {
4204:     /* do the solve then the add manually */
4205:     if (x != y) {
4206:       PetscCall(MatSolveTranspose(mat, b, x));
4207:       PetscCall(VecAXPY(x, one, y));
4208:     } else {
4209:       PetscCall(VecDuplicate(x, &tmp));
4210:       PetscCall(VecCopy(x, tmp));
4211:       PetscCall(MatSolveTranspose(mat, b, x));
4212:       PetscCall(VecAXPY(x, one, tmp));
4213:       PetscCall(VecDestroy(&tmp));
4214:     }
4215:   }
4216:   PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4217:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4218:   PetscFunctionReturn(PETSC_SUCCESS);
4219: }

4221: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4222: /*@
4223:   MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

4225:   Neighbor-wise Collective

4227:   Input Parameters:
4228: + mat   - the matrix
4229: . b     - the right-hand side
4230: . omega - the relaxation factor
4231: . flag  - flag indicating the type of SOR (see below)
4232: . shift - diagonal shift
4233: . its   - the number of iterations
4234: - lits  - the number of local iterations

4236:   Output Parameter:
4237: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

4239:   SOR Flags:
4240: +     `SOR_FORWARD_SWEEP` - forward SOR
4241: .     `SOR_BACKWARD_SWEEP` - backward SOR
4242: .     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4243: .     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4244: .     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4245: .     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4246: .     `SOR_EISENSTAT` - SOR with Eisenstat trick
4247: .     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4248:   upper/lower triangular part of matrix to
4249:   vector (with omega)
4250: -     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

4252:   Level: developer

4254:   Notes:
4255:   `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4256:   `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4257:   on each processor.

4259:   Application programmers will not generally use `MatSOR()` directly,
4260:   but instead will employ the `KSP`/`PC` interface.

4262:   For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

4264:   Most users should employ the `KSP` interface for linear solvers
4265:   instead of working directly with matrix algebra routines such as this.
4266:   See, e.g., `KSPCreate()`.

4268:   Vectors `x` and `b` CANNOT be the same

4270:   The flags are implemented as bitwise inclusive or operations.
4271:   For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4272:   to specify a zero initial guess for SSOR.

4274:   Developer Note:
4275:   We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

4277: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4278: @*/
4279: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4280: {
4281:   PetscFunctionBegin;
4286:   PetscCheckSameComm(mat, 1, b, 2);
4287:   PetscCheckSameComm(mat, 1, x, 8);
4288:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4289:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4290:   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);
4291:   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);
4292:   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);
4293:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4294:   PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4295:   PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

4297:   MatCheckPreallocated(mat, 1);
4298:   PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4299:   PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4300:   PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4301:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4302:   PetscFunctionReturn(PETSC_SUCCESS);
4303: }

4305: /*
4306:       Default matrix copy routine.
4307: */
4308: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4309: {
4310:   PetscInt           i, rstart = 0, rend = 0, nz;
4311:   const PetscInt    *cwork;
4312:   const PetscScalar *vwork;

4314:   PetscFunctionBegin;
4315:   if (B->assembled) PetscCall(MatZeroEntries(B));
4316:   if (str == SAME_NONZERO_PATTERN) {
4317:     PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4318:     for (i = rstart; i < rend; i++) {
4319:       PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4320:       PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4321:       PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4322:     }
4323:   } else {
4324:     PetscCall(MatAYPX(B, 0.0, A, str));
4325:   }
4326:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4327:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4328:   PetscFunctionReturn(PETSC_SUCCESS);
4329: }

4331: /*@
4332:   MatCopy - Copies a matrix to another matrix.

4334:   Collective

4336:   Input Parameters:
4337: + A   - the matrix
4338: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

4340:   Output Parameter:
4341: . B - where the copy is put

4343:   Level: intermediate

4345:   Notes:
4346:   If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.

4348:   `MatCopy()` copies the matrix entries of a matrix to another existing
4349:   matrix (after first zeroing the second matrix).  A related routine is
4350:   `MatConvert()`, which first creates a new matrix and then copies the data.

4352: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4353: @*/
4354: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4355: {
4356:   PetscInt i;

4358:   PetscFunctionBegin;
4363:   PetscCheckSameComm(A, 1, B, 2);
4364:   MatCheckPreallocated(B, 2);
4365:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4366:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4367:   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,
4368:              A->cmap->N, B->cmap->N);
4369:   MatCheckPreallocated(A, 1);
4370:   if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

4372:   PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4373:   if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4374:   else PetscCall(MatCopy_Basic(A, B, str));

4376:   B->stencil.dim = A->stencil.dim;
4377:   B->stencil.noc = A->stencil.noc;
4378:   for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4379:     B->stencil.dims[i]   = A->stencil.dims[i];
4380:     B->stencil.starts[i] = A->stencil.starts[i];
4381:   }

4383:   PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4384:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4385:   PetscFunctionReturn(PETSC_SUCCESS);
4386: }

4388: /*@
4389:   MatConvert - Converts a matrix to another matrix, either of the same
4390:   or different type.

4392:   Collective

4394:   Input Parameters:
4395: + mat     - the matrix
4396: . newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
4397:             same type as the original matrix.
4398: - reuse   - denotes if the destination matrix is to be created or reused.
4399:             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
4400:             `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).

4402:   Output Parameter:
4403: . M - pointer to place new matrix

4405:   Level: intermediate

4407:   Notes:
4408:   `MatConvert()` first creates a new matrix and then copies the data from
4409:   the first matrix.  A related routine is `MatCopy()`, which copies the matrix
4410:   entries of one matrix to another already existing matrix context.

4412:   Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4413:   the MPI communicator of the generated matrix is always the same as the communicator
4414:   of the input matrix.

4416: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4417: @*/
4418: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4419: {
4420:   PetscBool  sametype, issame, flg;
4421:   PetscBool3 issymmetric, ishermitian;
4422:   char       convname[256], mtype[256];
4423:   Mat        B;

4425:   PetscFunctionBegin;
4428:   PetscAssertPointer(M, 4);
4429:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4430:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4431:   MatCheckPreallocated(mat, 1);

4433:   PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4434:   if (flg) newtype = mtype;

4436:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4437:   PetscCall(PetscStrcmp(newtype, "same", &issame));
4438:   PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4439:   if (reuse == MAT_REUSE_MATRIX) {
4441:     PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4442:   }

4444:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4445:     PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4446:     PetscFunctionReturn(PETSC_SUCCESS);
4447:   }

4449:   /* Cache Mat options because some converters use MatHeaderReplace  */
4450:   issymmetric = mat->symmetric;
4451:   ishermitian = mat->hermitian;

4453:   if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4454:     PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4455:     PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4456:   } else {
4457:     PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4458:     const char *prefix[3]                                 = {"seq", "mpi", ""};
4459:     PetscInt    i;
4460:     /*
4461:        Order of precedence:
4462:        0) See if newtype is a superclass of the current matrix.
4463:        1) See if a specialized converter is known to the current matrix.
4464:        2) See if a specialized converter is known to the desired matrix class.
4465:        3) See if a good general converter is registered for the desired class
4466:           (as of 6/27/03 only MATMPIADJ falls into this category).
4467:        4) See if a good general converter is known for the current matrix.
4468:        5) Use a really basic converter.
4469:     */

4471:     /* 0) See if newtype is a superclass of the current matrix.
4472:           i.e mat is mpiaij and newtype is aij */
4473:     for (i = 0; i < 2; i++) {
4474:       PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4475:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4476:       PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4477:       PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4478:       if (flg) {
4479:         if (reuse == MAT_INPLACE_MATRIX) {
4480:           PetscCall(PetscInfo(mat, "Early return\n"));
4481:           PetscFunctionReturn(PETSC_SUCCESS);
4482:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4483:           PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4484:           PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4485:           PetscFunctionReturn(PETSC_SUCCESS);
4486:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4487:           PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4488:           PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4489:           PetscFunctionReturn(PETSC_SUCCESS);
4490:         }
4491:       }
4492:     }
4493:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4494:     for (i = 0; i < 3; i++) {
4495:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4496:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4497:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4498:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4499:       PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4500:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4501:       PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4502:       PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4503:       if (conv) goto foundconv;
4504:     }

4506:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4507:     PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4508:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4509:     PetscCall(MatSetType(B, newtype));
4510:     for (i = 0; i < 3; i++) {
4511:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4512:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4513:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4514:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4515:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4516:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4517:       PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4518:       PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4519:       if (conv) {
4520:         PetscCall(MatDestroy(&B));
4521:         goto foundconv;
4522:       }
4523:     }

4525:     /* 3) See if a good general converter is registered for the desired class */
4526:     conv = B->ops->convertfrom;
4527:     PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4528:     PetscCall(MatDestroy(&B));
4529:     if (conv) goto foundconv;

4531:     /* 4) See if a good general converter is known for the current matrix */
4532:     if (mat->ops->convert) conv = mat->ops->convert;
4533:     PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4534:     if (conv) goto foundconv;

4536:     /* 5) Use a really basic converter. */
4537:     PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4538:     conv = MatConvert_Basic;

4540:   foundconv:
4541:     PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4542:     PetscCall((*conv)(mat, newtype, reuse, M));
4543:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4544:       /* the block sizes must be same if the mappings are copied over */
4545:       (*M)->rmap->bs = mat->rmap->bs;
4546:       (*M)->cmap->bs = mat->cmap->bs;
4547:       PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4548:       PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4549:       (*M)->rmap->mapping = mat->rmap->mapping;
4550:       (*M)->cmap->mapping = mat->cmap->mapping;
4551:     }
4552:     (*M)->stencil.dim = mat->stencil.dim;
4553:     (*M)->stencil.noc = mat->stencil.noc;
4554:     for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4555:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4556:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4557:     }
4558:     PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4559:   }
4560:   PetscCall(PetscObjectStateIncrease((PetscObject)*M));

4562:   /* Copy Mat options */
4563:   if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4564:   else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4565:   if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4566:   else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4567:   PetscFunctionReturn(PETSC_SUCCESS);
4568: }

4570: /*@
4571:   MatFactorGetSolverType - Returns name of the package providing the factorization routines

4573:   Not Collective

4575:   Input Parameter:
4576: . mat - the matrix, must be a factored matrix

4578:   Output Parameter:
4579: . type - the string name of the package (do not free this string)

4581:   Level: intermediate

4583:   Fortran Note:
4584:   Pass in an empty string that is long enough and the package name will be copied into it.

4586: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4587: @*/
4588: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4589: {
4590:   PetscErrorCode (*conv)(Mat, MatSolverType *);

4592:   PetscFunctionBegin;
4595:   PetscAssertPointer(type, 2);
4596:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4597:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4598:   if (conv) PetscCall((*conv)(mat, type));
4599:   else *type = MATSOLVERPETSC;
4600:   PetscFunctionReturn(PETSC_SUCCESS);
4601: }

4603: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4604: struct _MatSolverTypeForSpecifcType {
4605:   MatType mtype;
4606:   /* no entry for MAT_FACTOR_NONE */
4607:   PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4608:   MatSolverTypeForSpecifcType next;
4609: };

4611: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4612: struct _MatSolverTypeHolder {
4613:   char                       *name;
4614:   MatSolverTypeForSpecifcType handlers;
4615:   MatSolverTypeHolder         next;
4616: };

4618: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4620: /*@C
4621:   MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

4623:   Logically Collective, No Fortran Support

4625:   Input Parameters:
4626: + package      - name of the package, for example petsc or superlu
4627: . mtype        - the matrix type that works with this package
4628: . ftype        - the type of factorization supported by the package
4629: - createfactor - routine that will create the factored matrix ready to be used

4631:   Level: developer

4633: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4634:   `MatGetFactor()`
4635: @*/
4636: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4637: {
4638:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
4639:   PetscBool                   flg;
4640:   MatSolverTypeForSpecifcType inext, iprev = NULL;

4642:   PetscFunctionBegin;
4643:   PetscCall(MatInitializePackage());
4644:   if (!next) {
4645:     PetscCall(PetscNew(&MatSolverTypeHolders));
4646:     PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4647:     PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4648:     PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4649:     MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4650:     PetscFunctionReturn(PETSC_SUCCESS);
4651:   }
4652:   while (next) {
4653:     PetscCall(PetscStrcasecmp(package, next->name, &flg));
4654:     if (flg) {
4655:       PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4656:       inext = next->handlers;
4657:       while (inext) {
4658:         PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4659:         if (flg) {
4660:           inext->createfactor[(int)ftype - 1] = createfactor;
4661:           PetscFunctionReturn(PETSC_SUCCESS);
4662:         }
4663:         iprev = inext;
4664:         inext = inext->next;
4665:       }
4666:       PetscCall(PetscNew(&iprev->next));
4667:       PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4668:       iprev->next->createfactor[(int)ftype - 1] = createfactor;
4669:       PetscFunctionReturn(PETSC_SUCCESS);
4670:     }
4671:     prev = next;
4672:     next = next->next;
4673:   }
4674:   PetscCall(PetscNew(&prev->next));
4675:   PetscCall(PetscStrallocpy(package, &prev->next->name));
4676:   PetscCall(PetscNew(&prev->next->handlers));
4677:   PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4678:   prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4679:   PetscFunctionReturn(PETSC_SUCCESS);
4680: }

4682: /*@C
4683:   MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

4685:   Input Parameters:
4686: + 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
4687: . ftype - the type of factorization supported by the type
4688: - mtype - the matrix type that works with this type

4690:   Output Parameters:
4691: + foundtype    - `PETSC_TRUE` if the type was registered
4692: . foundmtype   - `PETSC_TRUE` if the type supports the requested mtype
4693: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

4695:   Calling sequence of `createfactor`:
4696: + A     - the matrix providing the factor matrix
4697: . ftype - the `MatFactorType` of the factor requested
4698: - B     - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`

4700:   Level: developer

4702:   Note:
4703:   When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4704:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4705:   For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.

4707: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4708:           `MatInitializePackage()`
4709: @*/
4710: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4711: {
4712:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4713:   PetscBool                   flg;
4714:   MatSolverTypeForSpecifcType inext;

4716:   PetscFunctionBegin;
4717:   if (foundtype) *foundtype = PETSC_FALSE;
4718:   if (foundmtype) *foundmtype = PETSC_FALSE;
4719:   if (createfactor) *createfactor = NULL;

4721:   if (type) {
4722:     while (next) {
4723:       PetscCall(PetscStrcasecmp(type, next->name, &flg));
4724:       if (flg) {
4725:         if (foundtype) *foundtype = PETSC_TRUE;
4726:         inext = next->handlers;
4727:         while (inext) {
4728:           PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4729:           if (flg) {
4730:             if (foundmtype) *foundmtype = PETSC_TRUE;
4731:             if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4732:             PetscFunctionReturn(PETSC_SUCCESS);
4733:           }
4734:           inext = inext->next;
4735:         }
4736:       }
4737:       next = next->next;
4738:     }
4739:   } else {
4740:     while (next) {
4741:       inext = next->handlers;
4742:       while (inext) {
4743:         PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4744:         if (flg && inext->createfactor[(int)ftype - 1]) {
4745:           if (foundtype) *foundtype = PETSC_TRUE;
4746:           if (foundmtype) *foundmtype = PETSC_TRUE;
4747:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4748:           PetscFunctionReturn(PETSC_SUCCESS);
4749:         }
4750:         inext = inext->next;
4751:       }
4752:       next = next->next;
4753:     }
4754:     /* try with base classes inext->mtype */
4755:     next = MatSolverTypeHolders;
4756:     while (next) {
4757:       inext = next->handlers;
4758:       while (inext) {
4759:         PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4760:         if (flg && inext->createfactor[(int)ftype - 1]) {
4761:           if (foundtype) *foundtype = PETSC_TRUE;
4762:           if (foundmtype) *foundmtype = PETSC_TRUE;
4763:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4764:           PetscFunctionReturn(PETSC_SUCCESS);
4765:         }
4766:         inext = inext->next;
4767:       }
4768:       next = next->next;
4769:     }
4770:   }
4771:   PetscFunctionReturn(PETSC_SUCCESS);
4772: }

4774: PetscErrorCode MatSolverTypeDestroy(void)
4775: {
4776:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
4777:   MatSolverTypeForSpecifcType inext, iprev;

4779:   PetscFunctionBegin;
4780:   while (next) {
4781:     PetscCall(PetscFree(next->name));
4782:     inext = next->handlers;
4783:     while (inext) {
4784:       PetscCall(PetscFree(inext->mtype));
4785:       iprev = inext;
4786:       inext = inext->next;
4787:       PetscCall(PetscFree(iprev));
4788:     }
4789:     prev = next;
4790:     next = next->next;
4791:     PetscCall(PetscFree(prev));
4792:   }
4793:   MatSolverTypeHolders = NULL;
4794:   PetscFunctionReturn(PETSC_SUCCESS);
4795: }

4797: /*@
4798:   MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

4800:   Logically Collective

4802:   Input Parameter:
4803: . mat - the matrix

4805:   Output Parameter:
4806: . flg - `PETSC_TRUE` if uses the ordering

4808:   Level: developer

4810:   Note:
4811:   Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4812:   packages do not, thus we want to skip generating the ordering when it is not needed or used.

4814: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4815: @*/
4816: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4817: {
4818:   PetscFunctionBegin;
4819:   *flg = mat->canuseordering;
4820:   PetscFunctionReturn(PETSC_SUCCESS);
4821: }

4823: /*@
4824:   MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

4826:   Logically Collective

4828:   Input Parameters:
4829: + mat   - the matrix obtained with `MatGetFactor()`
4830: - ftype - the factorization type to be used

4832:   Output Parameter:
4833: . otype - the preferred ordering type

4835:   Level: developer

4837: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4838: @*/
4839: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4840: {
4841:   PetscFunctionBegin;
4842:   *otype = mat->preferredordering[ftype];
4843:   PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4844:   PetscFunctionReturn(PETSC_SUCCESS);
4845: }

4847: /*@
4848:   MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()

4850:   Collective

4852:   Input Parameters:
4853: + mat   - the matrix
4854: . 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
4855:           the other criteria is returned
4856: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4858:   Output Parameter:
4859: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.

4861:   Options Database Keys:
4862: + -pc_factor_mat_solver_type <type>             - choose the type at run time. When using `KSP` solvers
4863: - -mat_factor_bind_factorization <host, device> - Where to do matrix factorization? Default is device (might consume more device memory.
4864:                                                   One can choose host to save device memory). Currently only supported with `MATSEQAIJCUSPARSE` matrices.

4866:   Level: intermediate

4868:   Notes:
4869:   The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4870:   types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.

4872:   Users usually access the factorization solvers via `KSP`

4874:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4875:   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

4877:   When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4878:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4879:   For example if one configuration had --download-mumps while a different one had --download-superlu_dist.

4881:   Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4882:   where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4883:   call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

4885:   Developer Note:
4886:   This should actually be called `MatCreateFactor()` since it creates a new factor object

4888: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4889:           `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4890:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4891: @*/
4892: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4893: {
4894:   PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4895:   PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

4897:   PetscFunctionBegin;

4901:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4902:   MatCheckPreallocated(mat, 1);

4904:   PetscCall(MatIsShell(mat, &shell));
4905:   if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4906:   if (hasop) {
4907:     PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4908:     PetscFunctionReturn(PETSC_SUCCESS);
4909:   }

4911:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4912:   if (!foundtype) {
4913:     if (type) {
4914:       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],
4915:               ((PetscObject)mat)->type_name, type);
4916:     } else {
4917:       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);
4918:     }
4919:   }
4920:   PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4921:   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);

4923:   PetscCall((*conv)(mat, ftype, f));
4924:   if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4925:   PetscFunctionReturn(PETSC_SUCCESS);
4926: }

4928: /*@
4929:   MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type

4931:   Not Collective

4933:   Input Parameters:
4934: + mat   - the matrix
4935: . type  - name of solver type, for example, superlu, petsc (to use PETSc's default)
4936: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4938:   Output Parameter:
4939: . flg - PETSC_TRUE if the factorization is available

4941:   Level: intermediate

4943:   Notes:
4944:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4945:   such as pastix, superlu, mumps etc.

4947:   PETSc must have been ./configure to use the external solver, using the option --download-package

4949:   Developer Note:
4950:   This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object

4952: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4953:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4954: @*/
4955: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4956: {
4957:   PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

4959:   PetscFunctionBegin;
4961:   PetscAssertPointer(flg, 4);

4963:   *flg = PETSC_FALSE;
4964:   if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);

4966:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4967:   MatCheckPreallocated(mat, 1);

4969:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4970:   *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4971:   PetscFunctionReturn(PETSC_SUCCESS);
4972: }

4974: /*@
4975:   MatDuplicate - Duplicates a matrix including the non-zero structure.

4977:   Collective

4979:   Input Parameters:
4980: + mat - the matrix
4981: - op  - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4982:         See the manual page for `MatDuplicateOption()` for an explanation of these options.

4984:   Output Parameter:
4985: . M - pointer to place new matrix

4987:   Level: intermediate

4989:   Notes:
4990:   You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.

4992:   If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.

4994:   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.

4996:   When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4997:   is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4998:   User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.

5000: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
5001: @*/
5002: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
5003: {
5004:   Mat         B;
5005:   VecType     vtype;
5006:   PetscInt    i;
5007:   PetscObject dm, container_h, container_d;
5008:   void (*viewf)(void);

5010:   PetscFunctionBegin;
5013:   PetscAssertPointer(M, 3);
5014:   PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
5015:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5016:   MatCheckPreallocated(mat, 1);

5018:   *M = NULL;
5019:   PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
5020:   PetscUseTypeMethod(mat, duplicate, op, M);
5021:   PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
5022:   B = *M;

5024:   PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
5025:   if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
5026:   PetscCall(MatGetVecType(mat, &vtype));
5027:   PetscCall(MatSetVecType(B, vtype));

5029:   B->stencil.dim = mat->stencil.dim;
5030:   B->stencil.noc = mat->stencil.noc;
5031:   for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
5032:     B->stencil.dims[i]   = mat->stencil.dims[i];
5033:     B->stencil.starts[i] = mat->stencil.starts[i];
5034:   }

5036:   B->nooffproczerorows = mat->nooffproczerorows;
5037:   B->nooffprocentries  = mat->nooffprocentries;

5039:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5040:   if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5041:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5042:   if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5043:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5044:   if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5045:   if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5046:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
5047:   PetscFunctionReturn(PETSC_SUCCESS);
5048: }

5050: /*@
5051:   MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`

5053:   Logically Collective

5055:   Input Parameter:
5056: . mat - the matrix

5058:   Output Parameter:
5059: . v - the diagonal of the matrix

5061:   Level: intermediate

5063:   Note:
5064:   If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5065:   of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5066:   is larger than `ndiag`, the values of the remaining entries are unspecified.

5068:   Currently only correct in parallel for square matrices.

5070: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5071: @*/
5072: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5073: {
5074:   PetscFunctionBegin;
5078:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5079:   MatCheckPreallocated(mat, 1);
5080:   if (PetscDefined(USE_DEBUG)) {
5081:     PetscInt nv, row, col, ndiag;

5083:     PetscCall(VecGetLocalSize(v, &nv));
5084:     PetscCall(MatGetLocalSize(mat, &row, &col));
5085:     ndiag = PetscMin(row, col);
5086:     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);
5087:   }

5089:   PetscUseTypeMethod(mat, getdiagonal, v);
5090:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5091:   PetscFunctionReturn(PETSC_SUCCESS);
5092: }

5094: /*@
5095:   MatGetRowMin - Gets the minimum value (of the real part) of each
5096:   row of the matrix

5098:   Logically Collective

5100:   Input Parameter:
5101: . mat - the matrix

5103:   Output Parameters:
5104: + v   - the vector for storing the maximums
5105: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)

5107:   Level: intermediate

5109:   Note:
5110:   The result of this call are the same as if one converted the matrix to dense format
5111:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5113:   This code is only implemented for a couple of matrix formats.

5115: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5116:           `MatGetRowMax()`
5117: @*/
5118: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5119: {
5120:   PetscFunctionBegin;
5124:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5126:   if (!mat->cmap->N) {
5127:     PetscCall(VecSet(v, PETSC_MAX_REAL));
5128:     if (idx) {
5129:       PetscInt i, m = mat->rmap->n;
5130:       for (i = 0; i < m; i++) idx[i] = -1;
5131:     }
5132:   } else {
5133:     MatCheckPreallocated(mat, 1);
5134:   }
5135:   PetscUseTypeMethod(mat, getrowmin, v, idx);
5136:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5137:   PetscFunctionReturn(PETSC_SUCCESS);
5138: }

5140: /*@
5141:   MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5142:   row of the matrix

5144:   Logically Collective

5146:   Input Parameter:
5147: . mat - the matrix

5149:   Output Parameters:
5150: + v   - the vector for storing the minimums
5151: - idx - the indices of the column found for each row (or `NULL` if not needed)

5153:   Level: intermediate

5155:   Notes:
5156:   if a row is completely empty or has only 0.0 values, then the `idx` value for that
5157:   row is 0 (the first column).

5159:   This code is only implemented for a couple of matrix formats.

5161: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5162: @*/
5163: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5164: {
5165:   PetscFunctionBegin;
5169:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5170:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

5172:   if (!mat->cmap->N) {
5173:     PetscCall(VecSet(v, 0.0));
5174:     if (idx) {
5175:       PetscInt i, m = mat->rmap->n;
5176:       for (i = 0; i < m; i++) idx[i] = -1;
5177:     }
5178:   } else {
5179:     MatCheckPreallocated(mat, 1);
5180:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5181:     PetscUseTypeMethod(mat, getrowminabs, v, idx);
5182:   }
5183:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5184:   PetscFunctionReturn(PETSC_SUCCESS);
5185: }

5187: /*@
5188:   MatGetRowMax - Gets the maximum value (of the real part) of each
5189:   row of the matrix

5191:   Logically Collective

5193:   Input Parameter:
5194: . mat - the matrix

5196:   Output Parameters:
5197: + v   - the vector for storing the maximums
5198: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)

5200:   Level: intermediate

5202:   Notes:
5203:   The result of this call are the same as if one converted the matrix to dense format
5204:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5206:   This code is only implemented for a couple of matrix formats.

5208: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5209: @*/
5210: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5211: {
5212:   PetscFunctionBegin;
5216:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5218:   if (!mat->cmap->N) {
5219:     PetscCall(VecSet(v, PETSC_MIN_REAL));
5220:     if (idx) {
5221:       PetscInt i, m = mat->rmap->n;
5222:       for (i = 0; i < m; i++) idx[i] = -1;
5223:     }
5224:   } else {
5225:     MatCheckPreallocated(mat, 1);
5226:     PetscUseTypeMethod(mat, getrowmax, v, idx);
5227:   }
5228:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5229:   PetscFunctionReturn(PETSC_SUCCESS);
5230: }

5232: /*@
5233:   MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5234:   row of the matrix

5236:   Logically Collective

5238:   Input Parameter:
5239: . mat - the matrix

5241:   Output Parameters:
5242: + v   - the vector for storing the maximums
5243: - idx - the indices of the column found for each row (or `NULL` if not needed)

5245:   Level: intermediate

5247:   Notes:
5248:   if a row is completely empty or has only 0.0 values, then the `idx` value for that
5249:   row is 0 (the first column).

5251:   This code is only implemented for a couple of matrix formats.

5253: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5254: @*/
5255: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5256: {
5257:   PetscFunctionBegin;
5261:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5263:   if (!mat->cmap->N) {
5264:     PetscCall(VecSet(v, 0.0));
5265:     if (idx) {
5266:       PetscInt i, m = mat->rmap->n;
5267:       for (i = 0; i < m; i++) idx[i] = -1;
5268:     }
5269:   } else {
5270:     MatCheckPreallocated(mat, 1);
5271:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5272:     PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5273:   }
5274:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5275:   PetscFunctionReturn(PETSC_SUCCESS);
5276: }

5278: /*@
5279:   MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix

5281:   Logically Collective

5283:   Input Parameter:
5284: . mat - the matrix

5286:   Output Parameter:
5287: . v - the vector for storing the sum

5289:   Level: intermediate

5291:   This code is only implemented for a couple of matrix formats.

5293: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5294: @*/
5295: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5296: {
5297:   PetscFunctionBegin;
5301:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5303:   if (!mat->cmap->N) {
5304:     PetscCall(VecSet(v, 0.0));
5305:   } else {
5306:     MatCheckPreallocated(mat, 1);
5307:     PetscUseTypeMethod(mat, getrowsumabs, v);
5308:   }
5309:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5310:   PetscFunctionReturn(PETSC_SUCCESS);
5311: }

5313: /*@
5314:   MatGetRowSum - Gets the sum of each row of the matrix

5316:   Logically or Neighborhood Collective

5318:   Input Parameter:
5319: . mat - the matrix

5321:   Output Parameter:
5322: . v - the vector for storing the sum of rows

5324:   Level: intermediate

5326:   Note:
5327:   This code is slow since it is not currently specialized for different formats

5329: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5330: @*/
5331: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5332: {
5333:   Vec ones;

5335:   PetscFunctionBegin;
5339:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5340:   MatCheckPreallocated(mat, 1);
5341:   PetscCall(MatCreateVecs(mat, &ones, NULL));
5342:   PetscCall(VecSet(ones, 1.));
5343:   PetscCall(MatMult(mat, ones, v));
5344:   PetscCall(VecDestroy(&ones));
5345:   PetscFunctionReturn(PETSC_SUCCESS);
5346: }

5348: /*@
5349:   MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5350:   when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)

5352:   Collective

5354:   Input Parameter:
5355: . mat - the matrix to provide the transpose

5357:   Output Parameter:
5358: . 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

5360:   Level: advanced

5362:   Note:
5363:   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
5364:   routine allows bypassing that call.

5366: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5367: @*/
5368: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5369: {
5370:   MatParentState *rb = NULL;

5372:   PetscFunctionBegin;
5373:   PetscCall(PetscNew(&rb));
5374:   rb->id    = ((PetscObject)mat)->id;
5375:   rb->state = 0;
5376:   PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5377:   PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5378:   PetscFunctionReturn(PETSC_SUCCESS);
5379: }

5381: /*@
5382:   MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.

5384:   Collective

5386:   Input Parameters:
5387: + mat   - the matrix to transpose
5388: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5390:   Output Parameter:
5391: . B - the transpose of the matrix

5393:   Level: intermediate

5395:   Notes:
5396:   If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`

5398:   `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
5399:   transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.

5401:   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.

5403:   Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5404:   For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.

5406:   If `mat` is unchanged from the last call this function returns immediately without recomputing the result

5408:   If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`

5410: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5411:           `MatTransposeSymbolic()`, `MatCreateTranspose()`
5412: @*/
5413: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5414: {
5415:   PetscContainer  rB = NULL;
5416:   MatParentState *rb = NULL;

5418:   PetscFunctionBegin;
5421:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5422:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5423:   PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5424:   PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5425:   MatCheckPreallocated(mat, 1);
5426:   if (reuse == MAT_REUSE_MATRIX) {
5427:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5428:     PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5429:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5430:     PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5431:     if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5432:   }

5434:   PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5435:   if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5436:     PetscUseTypeMethod(mat, transpose, reuse, B);
5437:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5438:   }
5439:   PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

5441:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5442:   if (reuse != MAT_INPLACE_MATRIX) {
5443:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5444:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5445:     rb->state        = ((PetscObject)mat)->state;
5446:     rb->nonzerostate = mat->nonzerostate;
5447:   }
5448:   PetscFunctionReturn(PETSC_SUCCESS);
5449: }

5451: /*@
5452:   MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

5454:   Collective

5456:   Input Parameter:
5457: . A - the matrix to transpose

5459:   Output Parameter:
5460: . 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
5461:       numerical portion.

5463:   Level: intermediate

5465:   Note:
5466:   This is not supported for many matrix types, use `MatTranspose()` in those cases

5468: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5469: @*/
5470: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5471: {
5472:   PetscFunctionBegin;
5475:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5476:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5477:   PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5478:   PetscUseTypeMethod(A, transposesymbolic, B);
5479:   PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

5481:   PetscCall(MatTransposeSetPrecursor(A, *B));
5482:   PetscFunctionReturn(PETSC_SUCCESS);
5483: }

5485: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5486: {
5487:   PetscContainer  rB;
5488:   MatParentState *rb;

5490:   PetscFunctionBegin;
5493:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5494:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5495:   PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5496:   PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5497:   PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5498:   PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5499:   PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5500:   PetscFunctionReturn(PETSC_SUCCESS);
5501: }

5503: /*@
5504:   MatIsTranspose - Test whether a matrix is another one's transpose,
5505:   or its own, in which case it tests symmetry.

5507:   Collective

5509:   Input Parameters:
5510: + A   - the matrix to test
5511: . B   - the matrix to test against, this can equal the first parameter
5512: - tol - tolerance, differences between entries smaller than this are counted as zero

5514:   Output Parameter:
5515: . flg - the result

5517:   Level: intermediate

5519:   Notes:
5520:   The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5521:   test involves parallel copies of the block off-diagonal parts of the matrix.

5523: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5524: @*/
5525: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5526: {
5527:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5529:   PetscFunctionBegin;
5532:   PetscAssertPointer(flg, 4);
5533:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5534:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5535:   *flg = PETSC_FALSE;
5536:   if (f && g) {
5537:     PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5538:     PetscCall((*f)(A, B, tol, flg));
5539:   } else {
5540:     MatType mattype;

5542:     PetscCall(MatGetType(f ? B : A, &mattype));
5543:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5544:   }
5545:   PetscFunctionReturn(PETSC_SUCCESS);
5546: }

5548: /*@
5549:   MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

5551:   Collective

5553:   Input Parameters:
5554: + mat   - the matrix to transpose and complex conjugate
5555: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5557:   Output Parameter:
5558: . B - the Hermitian transpose

5560:   Level: intermediate

5562: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5563: @*/
5564: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5565: {
5566:   PetscFunctionBegin;
5567:   PetscCall(MatTranspose(mat, reuse, B));
5568: #if defined(PETSC_USE_COMPLEX)
5569:   PetscCall(MatConjugate(*B));
5570: #endif
5571:   PetscFunctionReturn(PETSC_SUCCESS);
5572: }

5574: /*@
5575:   MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

5577:   Collective

5579:   Input Parameters:
5580: + A   - the matrix to test
5581: . B   - the matrix to test against, this can equal the first parameter
5582: - tol - tolerance, differences between entries smaller than this are counted as zero

5584:   Output Parameter:
5585: . flg - the result

5587:   Level: intermediate

5589:   Notes:
5590:   Only available for `MATAIJ` matrices.

5592:   The sequential algorithm
5593:   has a running time of the order of the number of nonzeros; the parallel
5594:   test involves parallel copies of the block off-diagonal parts of the matrix.

5596: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5597: @*/
5598: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5599: {
5600:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5602:   PetscFunctionBegin;
5605:   PetscAssertPointer(flg, 4);
5606:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5607:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5608:   if (f && g) {
5609:     PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5610:     PetscCall((*f)(A, B, tol, flg));
5611:   }
5612:   PetscFunctionReturn(PETSC_SUCCESS);
5613: }

5615: /*@
5616:   MatPermute - Creates a new matrix with rows and columns permuted from the
5617:   original.

5619:   Collective

5621:   Input Parameters:
5622: + mat - the matrix to permute
5623: . row - row permutation, each processor supplies only the permutation for its rows
5624: - col - column permutation, each processor supplies only the permutation for its columns

5626:   Output Parameter:
5627: . B - the permuted matrix

5629:   Level: advanced

5631:   Note:
5632:   The index sets map from row/col of permuted matrix to row/col of original matrix.
5633:   The index sets should be on the same communicator as mat and have the same local sizes.

5635:   Developer Note:
5636:   If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5637:   exploit the fact that row and col are permutations, consider implementing the
5638:   more general `MatCreateSubMatrix()` instead.

5640: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5641: @*/
5642: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5643: {
5644:   PetscFunctionBegin;
5649:   PetscAssertPointer(B, 4);
5650:   PetscCheckSameComm(mat, 1, row, 2);
5651:   if (row != col) PetscCheckSameComm(row, 2, col, 3);
5652:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5653:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5654:   PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5655:   MatCheckPreallocated(mat, 1);

5657:   if (mat->ops->permute) {
5658:     PetscUseTypeMethod(mat, permute, row, col, B);
5659:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5660:   } else {
5661:     PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5662:   }
5663:   PetscFunctionReturn(PETSC_SUCCESS);
5664: }

5666: /*@
5667:   MatEqual - Compares two matrices.

5669:   Collective

5671:   Input Parameters:
5672: + A - the first matrix
5673: - B - the second matrix

5675:   Output Parameter:
5676: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

5678:   Level: intermediate

5680:   Note:
5681:   If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing the results of several matrix-vector product
5682:   using several randomly created vectors, see `MatMultEqual()`.

5684: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5685: @*/
5686: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5687: {
5688:   PetscFunctionBegin;
5693:   PetscAssertPointer(flg, 3);
5694:   PetscCheckSameComm(A, 1, B, 2);
5695:   MatCheckPreallocated(A, 1);
5696:   MatCheckPreallocated(B, 2);
5697:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5698:   PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5699:   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,
5700:              B->cmap->N);
5701:   if (A->ops->equal && A->ops->equal == B->ops->equal) {
5702:     PetscUseTypeMethod(A, equal, B, flg);
5703:   } else {
5704:     PetscCall(MatMultEqual(A, B, 10, flg));
5705:   }
5706:   PetscFunctionReturn(PETSC_SUCCESS);
5707: }

5709: /*@
5710:   MatDiagonalScale - Scales a matrix on the left and right by diagonal
5711:   matrices that are stored as vectors.  Either of the two scaling
5712:   matrices can be `NULL`.

5714:   Collective

5716:   Input Parameters:
5717: + mat - the matrix to be scaled
5718: . l   - the left scaling vector (or `NULL`)
5719: - r   - the right scaling vector (or `NULL`)

5721:   Level: intermediate

5723:   Note:
5724:   `MatDiagonalScale()` computes $A = LAR$, where
5725:   L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5726:   The L scales the rows of the matrix, the R scales the columns of the matrix.

5728: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5729: @*/
5730: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5731: {
5732:   PetscFunctionBegin;
5735:   if (l) {
5737:     PetscCheckSameComm(mat, 1, l, 2);
5738:   }
5739:   if (r) {
5741:     PetscCheckSameComm(mat, 1, r, 3);
5742:   }
5743:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5744:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5745:   MatCheckPreallocated(mat, 1);
5746:   if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

5748:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5749:   PetscUseTypeMethod(mat, diagonalscale, l, r);
5750:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5751:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5752:   if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5753:   PetscFunctionReturn(PETSC_SUCCESS);
5754: }

5756: /*@
5757:   MatScale - Scales all elements of a matrix by a given number.

5759:   Logically Collective

5761:   Input Parameters:
5762: + mat - the matrix to be scaled
5763: - a   - the scaling value

5765:   Level: intermediate

5767: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5768: @*/
5769: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5770: {
5771:   PetscFunctionBegin;
5774:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5775:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5777:   MatCheckPreallocated(mat, 1);

5779:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5780:   if (a != (PetscScalar)1.0) {
5781:     PetscUseTypeMethod(mat, scale, a);
5782:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5783:   }
5784:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5785:   PetscFunctionReturn(PETSC_SUCCESS);
5786: }

5788: /*@
5789:   MatNorm - Calculates various norms of a matrix.

5791:   Collective

5793:   Input Parameters:
5794: + mat  - the matrix
5795: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

5797:   Output Parameter:
5798: . nrm - the resulting norm

5800:   Level: intermediate

5802: .seealso: [](ch_matrices), `Mat`
5803: @*/
5804: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5805: {
5806:   PetscFunctionBegin;
5809:   PetscAssertPointer(nrm, 3);

5811:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5812:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5813:   MatCheckPreallocated(mat, 1);

5815:   PetscUseTypeMethod(mat, norm, type, nrm);
5816:   PetscFunctionReturn(PETSC_SUCCESS);
5817: }

5819: /*
5820:      This variable is used to prevent counting of MatAssemblyBegin() that
5821:    are called from within a MatAssemblyEnd().
5822: */
5823: static PetscInt MatAssemblyEnd_InUse = 0;
5824: /*@
5825:   MatAssemblyBegin - Begins assembling the matrix.  This routine should
5826:   be called after completing all calls to `MatSetValues()`.

5828:   Collective

5830:   Input Parameters:
5831: + mat  - the matrix
5832: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5834:   Level: beginner

5836:   Notes:
5837:   `MatSetValues()` generally caches the values that belong to other MPI processes.  The matrix is ready to
5838:   use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

5840:   Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5841:   in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5842:   using the matrix.

5844:   ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5845:   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
5846:   a global collective operation requiring all processes that share the matrix.

5848:   Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5849:   out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5850:   before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

5852: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5853: @*/
5854: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5855: {
5856:   PetscFunctionBegin;
5859:   MatCheckPreallocated(mat, 1);
5860:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5861:   if (mat->assembled) {
5862:     mat->was_assembled = PETSC_TRUE;
5863:     mat->assembled     = PETSC_FALSE;
5864:   }

5866:   if (!MatAssemblyEnd_InUse) {
5867:     PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5868:     PetscTryTypeMethod(mat, assemblybegin, type);
5869:     PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5870:   } else PetscTryTypeMethod(mat, assemblybegin, type);
5871:   PetscFunctionReturn(PETSC_SUCCESS);
5872: }

5874: /*@
5875:   MatAssembled - Indicates if a matrix has been assembled and is ready for
5876:   use; for example, in matrix-vector product.

5878:   Not Collective

5880:   Input Parameter:
5881: . mat - the matrix

5883:   Output Parameter:
5884: . assembled - `PETSC_TRUE` or `PETSC_FALSE`

5886:   Level: advanced

5888: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5889: @*/
5890: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5891: {
5892:   PetscFunctionBegin;
5894:   PetscAssertPointer(assembled, 2);
5895:   *assembled = mat->assembled;
5896:   PetscFunctionReturn(PETSC_SUCCESS);
5897: }

5899: /*@
5900:   MatAssemblyEnd - Completes assembling the matrix.  This routine should
5901:   be called after `MatAssemblyBegin()`.

5903:   Collective

5905:   Input Parameters:
5906: + mat  - the matrix
5907: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5909:   Options Database Keys:
5910: + -mat_view ::ascii_info             - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5911: . -mat_view ::ascii_info_detail      - Prints more detailed info
5912: . -mat_view                          - Prints matrix in ASCII format
5913: . -mat_view ::ascii_matlab           - Prints matrix in MATLAB format
5914: . -mat_view draw                     - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5915: . -display <name>                    - Sets display name (default is host)
5916: . -draw_pause <sec>                  - Sets number of seconds to pause after display
5917: . -mat_view socket                   - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5918: . -viewer_socket_machine <machine>   - Machine to use for socket
5919: . -viewer_socket_port <port>         - Port number to use for socket
5920: - -mat_view binary:filename[:append] - Save matrix to file in binary format

5922:   Level: beginner

5924: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5925: @*/
5926: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5927: {
5928:   static PetscInt inassm = 0;
5929:   PetscBool       flg    = PETSC_FALSE;

5931:   PetscFunctionBegin;

5935:   inassm++;
5936:   MatAssemblyEnd_InUse++;
5937:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5938:     PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5939:     PetscTryTypeMethod(mat, assemblyend, type);
5940:     PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5941:   } else PetscTryTypeMethod(mat, assemblyend, type);

5943:   /* Flush assembly is not a true assembly */
5944:   if (type != MAT_FLUSH_ASSEMBLY) {
5945:     if (mat->num_ass) {
5946:       if (!mat->symmetry_eternal) {
5947:         mat->symmetric = PETSC_BOOL3_UNKNOWN;
5948:         mat->hermitian = PETSC_BOOL3_UNKNOWN;
5949:       }
5950:       if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5951:       if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5952:     }
5953:     mat->num_ass++;
5954:     mat->assembled        = PETSC_TRUE;
5955:     mat->ass_nonzerostate = mat->nonzerostate;
5956:   }

5958:   mat->insertmode = NOT_SET_VALUES;
5959:   MatAssemblyEnd_InUse--;
5960:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5961:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5962:     PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

5964:     if (mat->checksymmetryonassembly) {
5965:       PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5966:       if (flg) {
5967:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5968:       } else {
5969:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5970:       }
5971:     }
5972:     if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5973:   }
5974:   inassm--;
5975:   PetscFunctionReturn(PETSC_SUCCESS);
5976: }

5978: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5979: /*@
5980:   MatSetOption - Sets a parameter option for a matrix. Some options
5981:   may be specific to certain storage formats.  Some options
5982:   determine how values will be inserted (or added). Sorted,
5983:   row-oriented input will generally assemble the fastest. The default
5984:   is row-oriented.

5986:   Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

5988:   Input Parameters:
5989: + mat - the matrix
5990: . op  - the option, one of those listed below (and possibly others),
5991: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5993:   Options Describing Matrix Structure:
5994: + `MAT_SPD`                         - symmetric positive definite
5995: . `MAT_SYMMETRIC`                   - symmetric in terms of both structure and value
5996: . `MAT_HERMITIAN`                   - transpose is the complex conjugation
5997: . `MAT_STRUCTURALLY_SYMMETRIC`      - symmetric nonzero structure
5998: . `MAT_SYMMETRY_ETERNAL`            - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5999: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
6000: . `MAT_SPD_ETERNAL`                 - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

6002:    These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
6003:    do not need to be computed (usually at a high cost)

6005:    Options For Use with `MatSetValues()`:
6006:    Insert a logically dense subblock, which can be
6007: . `MAT_ROW_ORIENTED`                - row-oriented (default)

6009:    These options reflect the data you pass in with `MatSetValues()`; it has
6010:    nothing to do with how the data is stored internally in the matrix
6011:    data structure.

6013:    When (re)assembling a matrix, we can restrict the input for
6014:    efficiency/debugging purposes.  These options include
6015: . `MAT_NEW_NONZERO_LOCATIONS`       - additional insertions will be allowed if they generate a new nonzero (slow)
6016: . `MAT_FORCE_DIAGONAL_ENTRIES`      - forces diagonal entries to be allocated
6017: . `MAT_IGNORE_OFF_PROC_ENTRIES`     - drops off-processor entries
6018: . `MAT_NEW_NONZERO_LOCATION_ERR`    - generates an error for new matrix entry
6019: . `MAT_USE_HASH_TABLE`              - uses a hash table to speed up matrix assembly
6020: . `MAT_NO_OFF_PROC_ENTRIES`         - you know each process will only set values for its own rows, will generate an error if
6021:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
6022:         performance for very large process counts.
6023: - `MAT_SUBSET_OFF_PROC_ENTRIES`     - you know that the first assembly after setting this flag will set a superset
6024:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
6025:         functions, instead sending only neighbor messages.

6027:   Level: intermediate

6029:   Notes:
6030:   Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

6032:   Some options are relevant only for particular matrix types and
6033:   are thus ignored by others.  Other options are not supported by
6034:   certain matrix types and will generate an error message if set.

6036:   If using Fortran to compute a matrix, one may need to
6037:   use the column-oriented option (or convert to the row-oriented
6038:   format).

6040:   `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6041:   that would generate a new entry in the nonzero structure is instead
6042:   ignored.  Thus, if memory has not already been allocated for this particular
6043:   data, then the insertion is ignored. For dense matrices, in which
6044:   the entire array is allocated, no entries are ever ignored.
6045:   Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

6047:   `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6048:   that would generate a new entry in the nonzero structure instead produces
6049:   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

6051:   `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6052:   that would generate a new entry that has not been preallocated will
6053:   instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6054:   only.) This is a useful flag when debugging matrix memory preallocation.
6055:   If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

6057:   `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6058:   other processors should be dropped, rather than stashed.
6059:   This is useful if you know that the "owning" processor is also
6060:   always generating the correct matrix entries, so that PETSc need
6061:   not transfer duplicate entries generated on another processor.

6063:   `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6064:   searches during matrix assembly. When this flag is set, the hash table
6065:   is created during the first matrix assembly. This hash table is
6066:   used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6067:   to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6068:   should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6069:   supported by `MATMPIBAIJ` format only.

6071:   `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6072:   are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`

6074:   `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6075:   a zero location in the matrix

6077:   `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

6079:   `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6080:   zero row routines and thus improves performance for very large process counts.

6082:   `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6083:   part of the matrix (since they should match the upper triangular part).

6085:   `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6086:   single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6087:   with finite difference schemes with non-periodic boundary conditions.

6089:   Developer Note:
6090:   `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6091:   places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6092:   to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6093:   not changed.

6095: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6096: @*/
6097: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6098: {
6099:   PetscFunctionBegin;
6101:   if (op > 0) {
6104:   }

6106:   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);

6108:   switch (op) {
6109:   case MAT_FORCE_DIAGONAL_ENTRIES:
6110:     mat->force_diagonals = flg;
6111:     PetscFunctionReturn(PETSC_SUCCESS);
6112:   case MAT_NO_OFF_PROC_ENTRIES:
6113:     mat->nooffprocentries = flg;
6114:     PetscFunctionReturn(PETSC_SUCCESS);
6115:   case MAT_SUBSET_OFF_PROC_ENTRIES:
6116:     mat->assembly_subset = flg;
6117:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6118: #if !defined(PETSC_HAVE_MPIUNI)
6119:       PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6120: #endif
6121:       mat->stash.first_assembly_done = PETSC_FALSE;
6122:     }
6123:     PetscFunctionReturn(PETSC_SUCCESS);
6124:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6125:     mat->nooffproczerorows = flg;
6126:     PetscFunctionReturn(PETSC_SUCCESS);
6127:   case MAT_SPD:
6128:     if (flg) {
6129:       mat->spd                    = PETSC_BOOL3_TRUE;
6130:       mat->symmetric              = PETSC_BOOL3_TRUE;
6131:       mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6132:     } else {
6133:       mat->spd = PETSC_BOOL3_FALSE;
6134:     }
6135:     break;
6136:   case MAT_SYMMETRIC:
6137:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6138:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6139: #if !defined(PETSC_USE_COMPLEX)
6140:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6141: #endif
6142:     break;
6143:   case MAT_HERMITIAN:
6144:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6145:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6146: #if !defined(PETSC_USE_COMPLEX)
6147:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6148: #endif
6149:     break;
6150:   case MAT_STRUCTURALLY_SYMMETRIC:
6151:     mat->structurally_symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6152:     break;
6153:   case MAT_SYMMETRY_ETERNAL:
6154:     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");
6155:     mat->symmetry_eternal = flg;
6156:     if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6157:     break;
6158:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6159:     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");
6160:     mat->structural_symmetry_eternal = flg;
6161:     break;
6162:   case MAT_SPD_ETERNAL:
6163:     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");
6164:     mat->spd_eternal = flg;
6165:     if (flg) {
6166:       mat->structural_symmetry_eternal = PETSC_TRUE;
6167:       mat->symmetry_eternal            = PETSC_TRUE;
6168:     }
6169:     break;
6170:   case MAT_STRUCTURE_ONLY:
6171:     mat->structure_only = flg;
6172:     break;
6173:   case MAT_SORTED_FULL:
6174:     mat->sortedfull = flg;
6175:     break;
6176:   default:
6177:     break;
6178:   }
6179:   PetscTryTypeMethod(mat, setoption, op, flg);
6180:   PetscFunctionReturn(PETSC_SUCCESS);
6181: }

6183: /*@
6184:   MatGetOption - Gets a parameter option that has been set for a matrix.

6186:   Logically Collective

6188:   Input Parameters:
6189: + mat - the matrix
6190: - op  - the option, this only responds to certain options, check the code for which ones

6192:   Output Parameter:
6193: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

6195:   Level: intermediate

6197:   Notes:
6198:   Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

6200:   Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6201:   `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

6203: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6204:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6205: @*/
6206: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6207: {
6208:   PetscFunctionBegin;

6212:   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);
6213:   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()");

6215:   switch (op) {
6216:   case MAT_NO_OFF_PROC_ENTRIES:
6217:     *flg = mat->nooffprocentries;
6218:     break;
6219:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6220:     *flg = mat->nooffproczerorows;
6221:     break;
6222:   case MAT_SYMMETRIC:
6223:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6224:     break;
6225:   case MAT_HERMITIAN:
6226:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6227:     break;
6228:   case MAT_STRUCTURALLY_SYMMETRIC:
6229:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6230:     break;
6231:   case MAT_SPD:
6232:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6233:     break;
6234:   case MAT_SYMMETRY_ETERNAL:
6235:     *flg = mat->symmetry_eternal;
6236:     break;
6237:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6238:     *flg = mat->symmetry_eternal;
6239:     break;
6240:   default:
6241:     break;
6242:   }
6243:   PetscFunctionReturn(PETSC_SUCCESS);
6244: }

6246: /*@
6247:   MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
6248:   this routine retains the old nonzero structure.

6250:   Logically Collective

6252:   Input Parameter:
6253: . mat - the matrix

6255:   Level: intermediate

6257:   Note:
6258:   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.
6259:   See the Performance chapter of the users manual for information on preallocating matrices.

6261: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6262: @*/
6263: PetscErrorCode MatZeroEntries(Mat mat)
6264: {
6265:   PetscFunctionBegin;
6268:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6269:   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");
6270:   MatCheckPreallocated(mat, 1);

6272:   PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6273:   PetscUseTypeMethod(mat, zeroentries);
6274:   PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6275:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6276:   PetscFunctionReturn(PETSC_SUCCESS);
6277: }

6279: /*@
6280:   MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6281:   of a set of rows and columns of a matrix.

6283:   Collective

6285:   Input Parameters:
6286: + mat     - the matrix
6287: . numRows - the number of rows/columns to zero
6288: . rows    - the global row indices
6289: . diag    - value put in the diagonal of the eliminated rows
6290: . x       - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6291: - b       - optional vector of the right-hand side, that will be adjusted by provided solution entries

6293:   Level: intermediate

6295:   Notes:
6296:   This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6298:   For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6299:   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

6301:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6302:   Krylov method to take advantage of the known solution on the zeroed rows.

6304:   For the parallel case, all processes that share the matrix (i.e.,
6305:   those in the communicator used for matrix creation) MUST call this
6306:   routine, regardless of whether any rows being zeroed are owned by
6307:   them.

6309:   Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6310:   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
6311:   missing.

6313:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6314:   list only rows local to itself).

6316:   The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

6318: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6319:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6320: @*/
6321: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6322: {
6323:   PetscFunctionBegin;
6326:   if (numRows) PetscAssertPointer(rows, 3);
6327:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6328:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6329:   MatCheckPreallocated(mat, 1);

6331:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6332:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6333:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6334:   PetscFunctionReturn(PETSC_SUCCESS);
6335: }

6337: /*@
6338:   MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6339:   of a set of rows and columns of a matrix.

6341:   Collective

6343:   Input Parameters:
6344: + mat  - the matrix
6345: . is   - the rows to zero
6346: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6347: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6348: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6350:   Level: intermediate

6352:   Note:
6353:   See `MatZeroRowsColumns()` for details on how this routine operates.

6355: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6356:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6357: @*/
6358: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6359: {
6360:   PetscInt        numRows;
6361:   const PetscInt *rows;

6363:   PetscFunctionBegin;
6368:   PetscCall(ISGetLocalSize(is, &numRows));
6369:   PetscCall(ISGetIndices(is, &rows));
6370:   PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6371:   PetscCall(ISRestoreIndices(is, &rows));
6372:   PetscFunctionReturn(PETSC_SUCCESS);
6373: }

6375: /*@
6376:   MatZeroRows - Zeros all entries (except possibly the main diagonal)
6377:   of a set of rows of a matrix.

6379:   Collective

6381:   Input Parameters:
6382: + mat     - the matrix
6383: . numRows - the number of rows to zero
6384: . rows    - the global row indices
6385: . diag    - value put in the diagonal of the zeroed rows
6386: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6387: - b       - optional vector of right-hand side, that will be adjusted by provided solution entries

6389:   Level: intermediate

6391:   Notes:
6392:   This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6394:   For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

6396:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6397:   Krylov method to take advantage of the known solution on the zeroed rows.

6399:   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)
6400:   from the matrix.

6402:   Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6403:   but does not release memory.  Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6404:   formats this does not alter the nonzero structure.

6406:   If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6407:   of the matrix is not changed the values are
6408:   merely zeroed.

6410:   The user can set a value in the diagonal entry (or for the `MATAIJ` format
6411:   formats can optionally remove the main diagonal entry from the
6412:   nonzero structure as well, by passing 0.0 as the final argument).

6414:   For the parallel case, all processes that share the matrix (i.e.,
6415:   those in the communicator used for matrix creation) MUST call this
6416:   routine, regardless of whether any rows being zeroed are owned by
6417:   them.

6419:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6420:   list only rows local to itself).

6422:   You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6423:   owns that are to be zeroed. This saves a global synchronization in the implementation.

6425: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6426:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6427: @*/
6428: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6429: {
6430:   PetscFunctionBegin;
6433:   if (numRows) PetscAssertPointer(rows, 3);
6434:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6435:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6436:   MatCheckPreallocated(mat, 1);

6438:   PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6439:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6440:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6441:   PetscFunctionReturn(PETSC_SUCCESS);
6442: }

6444: /*@
6445:   MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6446:   of a set of rows of a matrix indicated by an `IS`

6448:   Collective

6450:   Input Parameters:
6451: + mat  - the matrix
6452: . is   - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6453: . diag - value put in all diagonals of eliminated rows
6454: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6455: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6457:   Level: intermediate

6459:   Note:
6460:   See `MatZeroRows()` for details on how this routine operates.

6462: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6463:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6464: @*/
6465: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6466: {
6467:   PetscInt        numRows = 0;
6468:   const PetscInt *rows    = NULL;

6470:   PetscFunctionBegin;
6473:   if (is) {
6475:     PetscCall(ISGetLocalSize(is, &numRows));
6476:     PetscCall(ISGetIndices(is, &rows));
6477:   }
6478:   PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6479:   if (is) PetscCall(ISRestoreIndices(is, &rows));
6480:   PetscFunctionReturn(PETSC_SUCCESS);
6481: }

6483: /*@
6484:   MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6485:   of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.

6487:   Collective

6489:   Input Parameters:
6490: + mat     - the matrix
6491: . numRows - the number of rows to remove
6492: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6493: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6494: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6495: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6497:   Level: intermediate

6499:   Notes:
6500:   See `MatZeroRows()` for details on how this routine operates.

6502:   The grid coordinates are across the entire grid, not just the local portion

6504:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6505:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6506:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6507:   `DM_BOUNDARY_PERIODIC` boundary type.

6509:   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
6510:   a single value per point) you can skip filling those indices.

6512:   Fortran Note:
6513:   `idxm` and `idxn` should be declared as
6514: $     MatStencil idxm(4, m)
6515:   and the values inserted using
6516: .vb
6517:     idxm(MatStencil_i, 1) = i
6518:     idxm(MatStencil_j, 1) = j
6519:     idxm(MatStencil_k, 1) = k
6520:     idxm(MatStencil_c, 1) = c
6521:    etc
6522: .ve

6524: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6525:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6526: @*/
6527: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6528: {
6529:   PetscInt  dim    = mat->stencil.dim;
6530:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6531:   PetscInt *dims   = mat->stencil.dims + 1;
6532:   PetscInt *starts = mat->stencil.starts;
6533:   PetscInt *dxm    = (PetscInt *)rows;
6534:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6536:   PetscFunctionBegin;
6539:   if (numRows) PetscAssertPointer(rows, 3);

6541:   PetscCall(PetscMalloc1(numRows, &jdxm));
6542:   for (i = 0; i < numRows; ++i) {
6543:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6544:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6545:     /* Local index in X dir */
6546:     tmp = *dxm++ - starts[0];
6547:     /* Loop over remaining dimensions */
6548:     for (j = 0; j < dim - 1; ++j) {
6549:       /* If nonlocal, set index to be negative */
6550:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6551:       /* Update local index */
6552:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6553:     }
6554:     /* Skip component slot if necessary */
6555:     if (mat->stencil.noc) dxm++;
6556:     /* Local row number */
6557:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6558:   }
6559:   PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6560:   PetscCall(PetscFree(jdxm));
6561:   PetscFunctionReturn(PETSC_SUCCESS);
6562: }

6564: /*@
6565:   MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6566:   of a set of rows and columns of a matrix.

6568:   Collective

6570:   Input Parameters:
6571: + mat     - the matrix
6572: . numRows - the number of rows/columns to remove
6573: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6574: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6575: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6576: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6578:   Level: intermediate

6580:   Notes:
6581:   See `MatZeroRowsColumns()` for details on how this routine operates.

6583:   The grid coordinates are across the entire grid, not just the local portion

6585:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6586:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6587:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6588:   `DM_BOUNDARY_PERIODIC` boundary type.

6590:   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
6591:   a single value per point) you can skip filling those indices.

6593:   Fortran Note:
6594:   `idxm` and `idxn` should be declared as
6595: $     MatStencil idxm(4, m)
6596:   and the values inserted using
6597: .vb
6598:     idxm(MatStencil_i, 1) = i
6599:     idxm(MatStencil_j, 1) = j
6600:     idxm(MatStencil_k, 1) = k
6601:     idxm(MatStencil_c, 1) = c
6602:     etc
6603: .ve

6605: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6606:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6607: @*/
6608: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6609: {
6610:   PetscInt  dim    = mat->stencil.dim;
6611:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6612:   PetscInt *dims   = mat->stencil.dims + 1;
6613:   PetscInt *starts = mat->stencil.starts;
6614:   PetscInt *dxm    = (PetscInt *)rows;
6615:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6617:   PetscFunctionBegin;
6620:   if (numRows) PetscAssertPointer(rows, 3);

6622:   PetscCall(PetscMalloc1(numRows, &jdxm));
6623:   for (i = 0; i < numRows; ++i) {
6624:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6625:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6626:     /* Local index in X dir */
6627:     tmp = *dxm++ - starts[0];
6628:     /* Loop over remaining dimensions */
6629:     for (j = 0; j < dim - 1; ++j) {
6630:       /* If nonlocal, set index to be negative */
6631:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6632:       /* Update local index */
6633:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6634:     }
6635:     /* Skip component slot if necessary */
6636:     if (mat->stencil.noc) dxm++;
6637:     /* Local row number */
6638:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6639:   }
6640:   PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6641:   PetscCall(PetscFree(jdxm));
6642:   PetscFunctionReturn(PETSC_SUCCESS);
6643: }

6645: /*@
6646:   MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6647:   of a set of rows of a matrix; using local numbering of rows.

6649:   Collective

6651:   Input Parameters:
6652: + mat     - the matrix
6653: . numRows - the number of rows to remove
6654: . rows    - the local row indices
6655: . diag    - value put in all diagonals of eliminated rows
6656: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6657: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6659:   Level: intermediate

6661:   Notes:
6662:   Before calling `MatZeroRowsLocal()`, the user must first set the
6663:   local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

6665:   See `MatZeroRows()` for details on how this routine operates.

6667: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6668:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6669: @*/
6670: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6671: {
6672:   PetscFunctionBegin;
6675:   if (numRows) PetscAssertPointer(rows, 3);
6676:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6677:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6678:   MatCheckPreallocated(mat, 1);

6680:   if (mat->ops->zerorowslocal) {
6681:     PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6682:   } else {
6683:     IS              is, newis;
6684:     const PetscInt *newRows;

6686:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6687:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6688:     PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6689:     PetscCall(ISGetIndices(newis, &newRows));
6690:     PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6691:     PetscCall(ISRestoreIndices(newis, &newRows));
6692:     PetscCall(ISDestroy(&newis));
6693:     PetscCall(ISDestroy(&is));
6694:   }
6695:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6696:   PetscFunctionReturn(PETSC_SUCCESS);
6697: }

6699: /*@
6700:   MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6701:   of a set of rows of a matrix; using local numbering of rows.

6703:   Collective

6705:   Input Parameters:
6706: + mat  - the matrix
6707: . is   - index set of rows to remove
6708: . diag - value put in all diagonals of eliminated rows
6709: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6710: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6712:   Level: intermediate

6714:   Notes:
6715:   Before calling `MatZeroRowsLocalIS()`, the user must first set the
6716:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6718:   See `MatZeroRows()` for details on how this routine operates.

6720: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6721:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6722: @*/
6723: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6724: {
6725:   PetscInt        numRows;
6726:   const PetscInt *rows;

6728:   PetscFunctionBegin;
6732:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6733:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6734:   MatCheckPreallocated(mat, 1);

6736:   PetscCall(ISGetLocalSize(is, &numRows));
6737:   PetscCall(ISGetIndices(is, &rows));
6738:   PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6739:   PetscCall(ISRestoreIndices(is, &rows));
6740:   PetscFunctionReturn(PETSC_SUCCESS);
6741: }

6743: /*@
6744:   MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6745:   of a set of rows and columns of a matrix; using local numbering of rows.

6747:   Collective

6749:   Input Parameters:
6750: + mat     - the matrix
6751: . numRows - the number of rows to remove
6752: . rows    - the global row indices
6753: . diag    - value put in all diagonals of eliminated rows
6754: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6755: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6757:   Level: intermediate

6759:   Notes:
6760:   Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6761:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6763:   See `MatZeroRowsColumns()` for details on how this routine operates.

6765: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6766:           `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6767: @*/
6768: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6769: {
6770:   IS              is, newis;
6771:   const PetscInt *newRows;

6773:   PetscFunctionBegin;
6776:   if (numRows) PetscAssertPointer(rows, 3);
6777:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6778:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6779:   MatCheckPreallocated(mat, 1);

6781:   PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6782:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6783:   PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6784:   PetscCall(ISGetIndices(newis, &newRows));
6785:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6786:   PetscCall(ISRestoreIndices(newis, &newRows));
6787:   PetscCall(ISDestroy(&newis));
6788:   PetscCall(ISDestroy(&is));
6789:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6790:   PetscFunctionReturn(PETSC_SUCCESS);
6791: }

6793: /*@
6794:   MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6795:   of a set of rows and columns of a matrix; using local numbering of rows.

6797:   Collective

6799:   Input Parameters:
6800: + mat  - the matrix
6801: . is   - index set of rows to remove
6802: . diag - value put in all diagonals of eliminated rows
6803: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6804: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6806:   Level: intermediate

6808:   Notes:
6809:   Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6810:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6812:   See `MatZeroRowsColumns()` for details on how this routine operates.

6814: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6815:           `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6816: @*/
6817: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6818: {
6819:   PetscInt        numRows;
6820:   const PetscInt *rows;

6822:   PetscFunctionBegin;
6826:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6827:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6828:   MatCheckPreallocated(mat, 1);

6830:   PetscCall(ISGetLocalSize(is, &numRows));
6831:   PetscCall(ISGetIndices(is, &rows));
6832:   PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6833:   PetscCall(ISRestoreIndices(is, &rows));
6834:   PetscFunctionReturn(PETSC_SUCCESS);
6835: }

6837: /*@
6838:   MatGetSize - Returns the numbers of rows and columns in a matrix.

6840:   Not Collective

6842:   Input Parameter:
6843: . mat - the matrix

6845:   Output Parameters:
6846: + m - the number of global rows
6847: - n - the number of global columns

6849:   Level: beginner

6851:   Note:
6852:   Both output parameters can be `NULL` on input.

6854: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6855: @*/
6856: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6857: {
6858:   PetscFunctionBegin;
6860:   if (m) *m = mat->rmap->N;
6861:   if (n) *n = mat->cmap->N;
6862:   PetscFunctionReturn(PETSC_SUCCESS);
6863: }

6865: /*@
6866:   MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6867:   of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

6869:   Not Collective

6871:   Input Parameter:
6872: . mat - the matrix

6874:   Output Parameters:
6875: + m - the number of local rows, use `NULL` to not obtain this value
6876: - n - the number of local columns, use `NULL` to not obtain this value

6878:   Level: beginner

6880: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6881: @*/
6882: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6883: {
6884:   PetscFunctionBegin;
6886:   if (m) PetscAssertPointer(m, 2);
6887:   if (n) PetscAssertPointer(n, 3);
6888:   if (m) *m = mat->rmap->n;
6889:   if (n) *n = mat->cmap->n;
6890:   PetscFunctionReturn(PETSC_SUCCESS);
6891: }

6893: /*@
6894:   MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6895:   vector one multiplies this matrix by that are owned by this processor.

6897:   Not Collective, unless matrix has not been allocated, then collective

6899:   Input Parameter:
6900: . mat - the matrix

6902:   Output Parameters:
6903: + m - the global index of the first local column, use `NULL` to not obtain this value
6904: - n - one more than the global index of the last local column, use `NULL` to not obtain this value

6906:   Level: developer

6908:   Notes:
6909:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6911:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6912:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6914:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6915:   the local values in the matrix.

6917:   Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6918:   Layouts](sec_matlayout) for details on matrix layouts.

6920: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6921:           `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6922: @*/
6923: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6924: {
6925:   PetscFunctionBegin;
6928:   if (m) PetscAssertPointer(m, 2);
6929:   if (n) PetscAssertPointer(n, 3);
6930:   MatCheckPreallocated(mat, 1);
6931:   if (m) *m = mat->cmap->rstart;
6932:   if (n) *n = mat->cmap->rend;
6933:   PetscFunctionReturn(PETSC_SUCCESS);
6934: }

6936: /*@
6937:   MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6938:   this MPI process.

6940:   Not Collective

6942:   Input Parameter:
6943: . mat - the matrix

6945:   Output Parameters:
6946: + m - the global index of the first local row, use `NULL` to not obtain this value
6947: - n - one more than the global index of the last local row, use `NULL` to not obtain this value

6949:   Level: beginner

6951:   Notes:
6952:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6954:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6955:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6957:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6958:   the local values in the matrix.

6960:   The high argument is one more than the last element stored locally.

6962:   For all matrices  it returns the range of matrix rows associated with rows of a vector that
6963:   would contain the result of a matrix vector product with this matrix. See [Matrix
6964:   Layouts](sec_matlayout) for details on matrix layouts.

6966: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6967:           `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6968: @*/
6969: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6970: {
6971:   PetscFunctionBegin;
6974:   if (m) PetscAssertPointer(m, 2);
6975:   if (n) PetscAssertPointer(n, 3);
6976:   MatCheckPreallocated(mat, 1);
6977:   if (m) *m = mat->rmap->rstart;
6978:   if (n) *n = mat->rmap->rend;
6979:   PetscFunctionReturn(PETSC_SUCCESS);
6980: }

6982: /*@C
6983:   MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6984:   `MATSCALAPACK`, returns the range of matrix rows owned by each process.

6986:   Not Collective, unless matrix has not been allocated

6988:   Input Parameter:
6989: . mat - the matrix

6991:   Output Parameter:
6992: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6993:            where `size` is the number of MPI processes used by `mat`

6995:   Level: beginner

6997:   Notes:
6998:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

7000:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7001:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

7003:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7004:   the local values in the matrix.

7006:   For all matrices  it returns the ranges of matrix rows associated with rows of a vector that
7007:   would contain the result of a matrix vector product with this matrix. See [Matrix
7008:   Layouts](sec_matlayout) for details on matrix layouts.

7010: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7011:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
7012:           `DMDAGetGhostCorners()`, `DM`
7013: @*/
7014: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
7015: {
7016:   PetscFunctionBegin;
7019:   MatCheckPreallocated(mat, 1);
7020:   PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
7021:   PetscFunctionReturn(PETSC_SUCCESS);
7022: }

7024: /*@C
7025:   MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
7026:   vector one multiplies this vector by that are owned by each processor.

7028:   Not Collective, unless matrix has not been allocated

7030:   Input Parameter:
7031: . mat - the matrix

7033:   Output Parameter:
7034: . ranges - start of each processors portion plus one more than the total length at the end

7036:   Level: beginner

7038:   Notes:
7039:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

7041:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7042:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

7044:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7045:   the local values in the matrix.

7047:   Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7048:   Layouts](sec_matlayout) for details on matrix layouts.

7050: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7051:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7052:           `DMDAGetGhostCorners()`, `DM`
7053: @*/
7054: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7055: {
7056:   PetscFunctionBegin;
7059:   MatCheckPreallocated(mat, 1);
7060:   PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7061:   PetscFunctionReturn(PETSC_SUCCESS);
7062: }

7064: /*@
7065:   MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.

7067:   Not Collective

7069:   Input Parameter:
7070: . A - matrix

7072:   Output Parameters:
7073: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7074: - cols - columns in which this process owns elements, use `NULL` to not obtain this value

7076:   Level: intermediate

7078:   Note:
7079:   You should call `ISDestroy()` on the returned `IS`

7081:   For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7082:   returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7083:   `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7084:   details on matrix layouts.

7086: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7087: @*/
7088: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7089: {
7090:   PetscErrorCode (*f)(Mat, IS *, IS *);

7092:   PetscFunctionBegin;
7095:   MatCheckPreallocated(A, 1);
7096:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7097:   if (f) {
7098:     PetscCall((*f)(A, rows, cols));
7099:   } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7100:     if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7101:     if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7102:   }
7103:   PetscFunctionReturn(PETSC_SUCCESS);
7104: }

7106: /*@
7107:   MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7108:   Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7109:   to complete the factorization.

7111:   Collective

7113:   Input Parameters:
7114: + fact - the factorized matrix obtained with `MatGetFactor()`
7115: . mat  - the matrix
7116: . row  - row permutation
7117: . col  - column permutation
7118: - info - structure containing
7119: .vb
7120:       levels - number of levels of fill.
7121:       expected fill - as ratio of original fill.
7122:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7123:                 missing diagonal entries)
7124: .ve

7126:   Level: developer

7128:   Notes:
7129:   See [Matrix Factorization](sec_matfactor) for additional information.

7131:   Most users should employ the `KSP` interface for linear solvers
7132:   instead of working directly with matrix algebra routines such as this.
7133:   See, e.g., `KSPCreate()`.

7135:   Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`

7137:   Developer Note:
7138:   The Fortran interface is not autogenerated as the
7139:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

7141: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7142:           `MatGetOrdering()`, `MatFactorInfo`
7143: @*/
7144: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7145: {
7146:   PetscFunctionBegin;
7151:   PetscAssertPointer(info, 5);
7152:   PetscAssertPointer(fact, 1);
7153:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7154:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7155:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7156:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7157:   MatCheckPreallocated(mat, 2);

7159:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7160:   PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7161:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7162:   PetscFunctionReturn(PETSC_SUCCESS);
7163: }

7165: /*@
7166:   MatICCFactorSymbolic - Performs symbolic incomplete
7167:   Cholesky factorization for a symmetric matrix.  Use
7168:   `MatCholeskyFactorNumeric()` to complete the factorization.

7170:   Collective

7172:   Input Parameters:
7173: + fact - the factorized matrix obtained with `MatGetFactor()`
7174: . mat  - the matrix to be factored
7175: . perm - row and column permutation
7176: - info - structure containing
7177: .vb
7178:       levels - number of levels of fill.
7179:       expected fill - as ratio of original fill.
7180: .ve

7182:   Level: developer

7184:   Notes:
7185:   Most users should employ the `KSP` interface for linear solvers
7186:   instead of working directly with matrix algebra routines such as this.
7187:   See, e.g., `KSPCreate()`.

7189:   This uses the definition of level of fill as in Y. Saad {cite}`saad2003`

7191:   Developer Note:
7192:   The Fortran interface is not autogenerated as the
7193:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

7195: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7196: @*/
7197: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7198: {
7199:   PetscFunctionBegin;
7203:   PetscAssertPointer(info, 4);
7204:   PetscAssertPointer(fact, 1);
7205:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7206:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7207:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7208:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7209:   MatCheckPreallocated(mat, 2);

7211:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7212:   PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7213:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7214:   PetscFunctionReturn(PETSC_SUCCESS);
7215: }

7217: /*@C
7218:   MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7219:   points to an array of valid matrices, they may be reused to store the new
7220:   submatrices.

7222:   Collective

7224:   Input Parameters:
7225: + mat   - the matrix
7226: . n     - the number of submatrixes to be extracted (on this processor, may be zero)
7227: . irow  - index set of rows to extract
7228: . icol  - index set of columns to extract
7229: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7231:   Output Parameter:
7232: . submat - the array of submatrices

7234:   Level: advanced

7236:   Notes:
7237:   `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7238:   (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7239:   to extract a parallel submatrix.

7241:   Some matrix types place restrictions on the row and column
7242:   indices, such as that they be sorted or that they be equal to each other.

7244:   The index sets may not have duplicate entries.

7246:   When extracting submatrices from a parallel matrix, each processor can
7247:   form a different submatrix by setting the rows and columns of its
7248:   individual index sets according to the local submatrix desired.

7250:   When finished using the submatrices, the user should destroy
7251:   them with `MatDestroySubMatrices()`.

7253:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7254:   original matrix has not changed from that last call to `MatCreateSubMatrices()`.

7256:   This routine creates the matrices in submat; you should NOT create them before
7257:   calling it. It also allocates the array of matrix pointers submat.

7259:   For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7260:   request one row/column in a block, they must request all rows/columns that are in
7261:   that block. For example, if the block size is 2 you cannot request just row 0 and
7262:   column 0.

7264:   Fortran Note:
7265:   One must pass in as `submat` a `Mat` array of size at least `n`+1.

7267: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7268: @*/
7269: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7270: {
7271:   PetscInt  i;
7272:   PetscBool eq;

7274:   PetscFunctionBegin;
7277:   if (n) {
7278:     PetscAssertPointer(irow, 3);
7280:     PetscAssertPointer(icol, 4);
7282:   }
7283:   PetscAssertPointer(submat, 6);
7284:   if (n && scall == MAT_REUSE_MATRIX) {
7285:     PetscAssertPointer(*submat, 6);
7287:   }
7288:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7289:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7290:   MatCheckPreallocated(mat, 1);
7291:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7292:   PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7293:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7294:   for (i = 0; i < n; i++) {
7295:     (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7296:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7297:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7298: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7299:     if (mat->boundtocpu && mat->bindingpropagates) {
7300:       PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7301:       PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7302:     }
7303: #endif
7304:   }
7305:   PetscFunctionReturn(PETSC_SUCCESS);
7306: }

7308: /*@C
7309:   MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of `IS` that may live on subcomms).

7311:   Collective

7313:   Input Parameters:
7314: + mat   - the matrix
7315: . n     - the number of submatrixes to be extracted
7316: . irow  - index set of rows to extract
7317: . icol  - index set of columns to extract
7318: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7320:   Output Parameter:
7321: . submat - the array of submatrices

7323:   Level: advanced

7325:   Note:
7326:   This is used by `PCGASM`

7328: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7329: @*/
7330: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7331: {
7332:   PetscInt  i;
7333:   PetscBool eq;

7335:   PetscFunctionBegin;
7338:   if (n) {
7339:     PetscAssertPointer(irow, 3);
7341:     PetscAssertPointer(icol, 4);
7343:   }
7344:   PetscAssertPointer(submat, 6);
7345:   if (n && scall == MAT_REUSE_MATRIX) {
7346:     PetscAssertPointer(*submat, 6);
7348:   }
7349:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7350:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7351:   MatCheckPreallocated(mat, 1);

7353:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7354:   PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7355:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7356:   for (i = 0; i < n; i++) {
7357:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7358:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7359:   }
7360:   PetscFunctionReturn(PETSC_SUCCESS);
7361: }

7363: /*@C
7364:   MatDestroyMatrices - Destroys an array of matrices.

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)

7372:   Level: advanced

7374:   Notes:
7375:   Frees not only the matrices, but also the array that contains the matrices

7377:   For matrices obtained with  `MatCreateSubMatrices()` use `MatDestroySubMatrices()`

7379:   Fortran Note:
7380:   Does not free the `mat` array.

7382: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7383: @*/
7384: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7385: {
7386:   PetscInt i;

7388:   PetscFunctionBegin;
7389:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7390:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7391:   PetscAssertPointer(mat, 2);

7393:   for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

7395:   /* memory is allocated even if n = 0 */
7396:   PetscCall(PetscFree(*mat));
7397:   PetscFunctionReturn(PETSC_SUCCESS);
7398: }

7400: /*@C
7401:   MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

7403:   Collective

7405:   Input Parameters:
7406: + n   - the number of local matrices
7407: - mat - the matrices (this is a pointer to the array of matrices, just to match the calling
7408:                        sequence of `MatCreateSubMatrices()`)

7410:   Level: advanced

7412:   Note:
7413:   Frees not only the matrices, but also the array that contains the matrices

7415:   Fortran Note:
7416:   Does not free the `mat` array.

7418: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7419: @*/
7420: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7421: {
7422:   Mat mat0;

7424:   PetscFunctionBegin;
7425:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7426:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7427:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7428:   PetscAssertPointer(mat, 2);

7430:   mat0 = (*mat)[0];
7431:   if (mat0 && mat0->ops->destroysubmatrices) {
7432:     PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7433:   } else {
7434:     PetscCall(MatDestroyMatrices(n, mat));
7435:   }
7436:   PetscFunctionReturn(PETSC_SUCCESS);
7437: }

7439: /*@
7440:   MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

7442:   Collective

7444:   Input Parameter:
7445: . mat - the matrix

7447:   Output Parameter:
7448: . matstruct - the sequential matrix with the nonzero structure of `mat`

7450:   Level: developer

7452: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7453: @*/
7454: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7455: {
7456:   PetscFunctionBegin;
7458:   PetscAssertPointer(matstruct, 2);

7461:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7462:   MatCheckPreallocated(mat, 1);

7464:   PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7465:   PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7466:   PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7467:   PetscFunctionReturn(PETSC_SUCCESS);
7468: }

7470: /*@C
7471:   MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

7473:   Collective

7475:   Input Parameter:
7476: . mat - the matrix

7478:   Level: advanced

7480:   Note:
7481:   This is not needed, one can just call `MatDestroy()`

7483: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7484: @*/
7485: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7486: {
7487:   PetscFunctionBegin;
7488:   PetscAssertPointer(mat, 1);
7489:   PetscCall(MatDestroy(mat));
7490:   PetscFunctionReturn(PETSC_SUCCESS);
7491: }

7493: /*@
7494:   MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7495:   replaces the index sets by larger ones that represent submatrices with
7496:   additional overlap.

7498:   Collective

7500:   Input Parameters:
7501: + mat - the matrix
7502: . n   - the number of index sets
7503: . is  - the array of index sets (these index sets will changed during the call)
7504: - ov  - the additional overlap requested

7506:   Options Database Key:
7507: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7509:   Level: developer

7511:   Note:
7512:   The computed overlap preserves the matrix block sizes when the blocks are square.
7513:   That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7514:   that block are included in the overlap regardless of whether each specific column would increase the overlap.

7516: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7517: @*/
7518: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7519: {
7520:   PetscInt i, bs, cbs;

7522:   PetscFunctionBegin;
7526:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7527:   if (n) {
7528:     PetscAssertPointer(is, 3);
7530:   }
7531:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7532:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7533:   MatCheckPreallocated(mat, 1);

7535:   if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7536:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7537:   PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7538:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7539:   PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7540:   if (bs == cbs) {
7541:     for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7542:   }
7543:   PetscFunctionReturn(PETSC_SUCCESS);
7544: }

7546: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

7548: /*@
7549:   MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7550:   a sub communicator, replaces the index sets by larger ones that represent submatrices with
7551:   additional overlap.

7553:   Collective

7555:   Input Parameters:
7556: + mat - the matrix
7557: . n   - the number of index sets
7558: . is  - the array of index sets (these index sets will changed during the call)
7559: - ov  - the additional overlap requested

7561:   `   Options Database Key:
7562: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7564:   Level: developer

7566: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7567: @*/
7568: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7569: {
7570:   PetscInt i;

7572:   PetscFunctionBegin;
7575:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7576:   if (n) {
7577:     PetscAssertPointer(is, 3);
7579:   }
7580:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7581:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7582:   MatCheckPreallocated(mat, 1);
7583:   if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7584:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7585:   for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7586:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7587:   PetscFunctionReturn(PETSC_SUCCESS);
7588: }

7590: /*@
7591:   MatGetBlockSize - Returns the matrix block size.

7593:   Not Collective

7595:   Input Parameter:
7596: . mat - the matrix

7598:   Output Parameter:
7599: . bs - block size

7601:   Level: intermediate

7603:   Notes:
7604:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

7606:   If the block size has not been set yet this routine returns 1.

7608: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7609: @*/
7610: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7611: {
7612:   PetscFunctionBegin;
7614:   PetscAssertPointer(bs, 2);
7615:   *bs = PetscAbs(mat->rmap->bs);
7616:   PetscFunctionReturn(PETSC_SUCCESS);
7617: }

7619: /*@
7620:   MatGetBlockSizes - Returns the matrix block row and column sizes.

7622:   Not Collective

7624:   Input Parameter:
7625: . mat - the matrix

7627:   Output Parameters:
7628: + rbs - row block size
7629: - cbs - column block size

7631:   Level: intermediate

7633:   Notes:
7634:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7635:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7637:   If a block size has not been set yet this routine returns 1.

7639: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7640: @*/
7641: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7642: {
7643:   PetscFunctionBegin;
7645:   if (rbs) PetscAssertPointer(rbs, 2);
7646:   if (cbs) PetscAssertPointer(cbs, 3);
7647:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7648:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7649:   PetscFunctionReturn(PETSC_SUCCESS);
7650: }

7652: /*@
7653:   MatSetBlockSize - Sets the matrix block size.

7655:   Logically Collective

7657:   Input Parameters:
7658: + mat - the matrix
7659: - bs  - block size

7661:   Level: intermediate

7663:   Notes:
7664:   Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7665:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7667:   For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7668:   is compatible with the matrix local sizes.

7670: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7671: @*/
7672: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7673: {
7674:   PetscFunctionBegin;
7677:   PetscCall(MatSetBlockSizes(mat, bs, bs));
7678:   PetscFunctionReturn(PETSC_SUCCESS);
7679: }

7681: typedef struct {
7682:   PetscInt         n;
7683:   IS              *is;
7684:   Mat             *mat;
7685:   PetscObjectState nonzerostate;
7686:   Mat              C;
7687: } EnvelopeData;

7689: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7690: {
7691:   EnvelopeData *edata = (EnvelopeData *)*ptr;

7693:   PetscFunctionBegin;
7694:   for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7695:   PetscCall(PetscFree(edata->is));
7696:   PetscCall(PetscFree(edata));
7697:   PetscFunctionReturn(PETSC_SUCCESS);
7698: }

7700: /*@
7701:   MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7702:   the sizes of these blocks in the matrix. An individual block may lie over several processes.

7704:   Collective

7706:   Input Parameter:
7707: . mat - the matrix

7709:   Level: intermediate

7711:   Notes:
7712:   There can be zeros within the blocks

7714:   The blocks can overlap between processes, including laying on more than two processes

7716: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7717: @*/
7718: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7719: {
7720:   PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7721:   PetscInt          *diag, *odiag, sc;
7722:   VecScatter         scatter;
7723:   PetscScalar       *seqv;
7724:   const PetscScalar *parv;
7725:   const PetscInt    *ia, *ja;
7726:   PetscBool          set, flag, done;
7727:   Mat                AA = mat, A;
7728:   MPI_Comm           comm;
7729:   PetscMPIInt        rank, size, tag;
7730:   MPI_Status         status;
7731:   PetscContainer     container;
7732:   EnvelopeData      *edata;
7733:   Vec                seq, par;
7734:   IS                 isglobal;

7736:   PetscFunctionBegin;
7738:   PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7739:   if (!set || !flag) {
7740:     /* TODO: only needs nonzero structure of transpose */
7741:     PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7742:     PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7743:   }
7744:   PetscCall(MatAIJGetLocalMat(AA, &A));
7745:   PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7746:   PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

7748:   PetscCall(MatGetLocalSize(mat, &n, NULL));
7749:   PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7750:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7751:   PetscCallMPI(MPI_Comm_size(comm, &size));
7752:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

7754:   PetscCall(PetscMalloc2(n, &sizes, n, &starts));

7756:   if (rank > 0) {
7757:     PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7758:     PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7759:   }
7760:   PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7761:   for (i = 0; i < n; i++) {
7762:     env = PetscMax(env, ja[ia[i + 1] - 1]);
7763:     II  = rstart + i;
7764:     if (env == II) {
7765:       starts[lblocks]  = tbs;
7766:       sizes[lblocks++] = 1 + II - tbs;
7767:       tbs              = 1 + II;
7768:     }
7769:   }
7770:   if (rank < size - 1) {
7771:     PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7772:     PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7773:   }

7775:   PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7776:   if (!set || !flag) PetscCall(MatDestroy(&AA));
7777:   PetscCall(MatDestroy(&A));

7779:   PetscCall(PetscNew(&edata));
7780:   PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7781:   edata->n = lblocks;
7782:   /* create IS needed for extracting blocks from the original matrix */
7783:   PetscCall(PetscMalloc1(lblocks, &edata->is));
7784:   for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

7786:   /* Create the resulting inverse matrix nonzero structure with preallocation information */
7787:   PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7788:   PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7789:   PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7790:   PetscCall(MatSetType(edata->C, MATAIJ));

7792:   /* Communicate the start and end of each row, from each block to the correct rank */
7793:   /* TODO: Use PetscSF instead of VecScatter */
7794:   for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7795:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7796:   PetscCall(VecGetArrayWrite(seq, &seqv));
7797:   for (PetscInt i = 0; i < lblocks; i++) {
7798:     for (PetscInt j = 0; j < sizes[i]; j++) {
7799:       seqv[cnt]     = starts[i];
7800:       seqv[cnt + 1] = starts[i] + sizes[i];
7801:       cnt += 2;
7802:     }
7803:   }
7804:   PetscCall(VecRestoreArrayWrite(seq, &seqv));
7805:   PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7806:   sc -= cnt;
7807:   PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7808:   PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7809:   PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7810:   PetscCall(ISDestroy(&isglobal));
7811:   PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7812:   PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7813:   PetscCall(VecScatterDestroy(&scatter));
7814:   PetscCall(VecDestroy(&seq));
7815:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7816:   PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7817:   PetscCall(VecGetArrayRead(par, &parv));
7818:   cnt = 0;
7819:   PetscCall(MatGetSize(mat, NULL, &n));
7820:   for (PetscInt i = 0; i < mat->rmap->n; i++) {
7821:     PetscInt start, end, d = 0, od = 0;

7823:     start = (PetscInt)PetscRealPart(parv[cnt]);
7824:     end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
7825:     cnt += 2;

7827:     if (start < cstart) {
7828:       od += cstart - start + n - cend;
7829:       d += cend - cstart;
7830:     } else if (start < cend) {
7831:       od += n - cend;
7832:       d += cend - start;
7833:     } else od += n - start;
7834:     if (end <= cstart) {
7835:       od -= cstart - end + n - cend;
7836:       d -= cend - cstart;
7837:     } else if (end < cend) {
7838:       od -= n - cend;
7839:       d -= cend - end;
7840:     } else od -= n - end;

7842:     odiag[i] = od;
7843:     diag[i]  = d;
7844:   }
7845:   PetscCall(VecRestoreArrayRead(par, &parv));
7846:   PetscCall(VecDestroy(&par));
7847:   PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7848:   PetscCall(PetscFree2(diag, odiag));
7849:   PetscCall(PetscFree2(sizes, starts));

7851:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7852:   PetscCall(PetscContainerSetPointer(container, edata));
7853:   PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7854:   PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7855:   PetscCall(PetscObjectDereference((PetscObject)container));
7856:   PetscFunctionReturn(PETSC_SUCCESS);
7857: }

7859: /*@
7860:   MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

7862:   Collective

7864:   Input Parameters:
7865: + A     - the matrix
7866: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine

7868:   Output Parameter:
7869: . C - matrix with inverted block diagonal of `A`

7871:   Level: advanced

7873:   Note:
7874:   For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

7876: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7877: @*/
7878: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7879: {
7880:   PetscContainer   container;
7881:   EnvelopeData    *edata;
7882:   PetscObjectState nonzerostate;

7884:   PetscFunctionBegin;
7885:   PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7886:   if (!container) {
7887:     PetscCall(MatComputeVariableBlockEnvelope(A));
7888:     PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7889:   }
7890:   PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7891:   PetscCall(MatGetNonzeroState(A, &nonzerostate));
7892:   PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7893:   PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

7895:   PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7896:   *C = edata->C;

7898:   for (PetscInt i = 0; i < edata->n; i++) {
7899:     Mat          D;
7900:     PetscScalar *dvalues;

7902:     PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7903:     PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7904:     PetscCall(MatSeqDenseInvert(D));
7905:     PetscCall(MatDenseGetArray(D, &dvalues));
7906:     PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7907:     PetscCall(MatDestroy(&D));
7908:   }
7909:   PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7910:   PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7911:   PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7912:   PetscFunctionReturn(PETSC_SUCCESS);
7913: }

7915: /*@
7916:   MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

7918:   Not Collective

7920:   Input Parameters:
7921: + mat     - the matrix
7922: . nblocks - the number of blocks on this process, each block can only exist on a single process
7923: - bsizes  - the block sizes

7925:   Level: intermediate

7927:   Notes:
7928:   Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

7930:   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.

7932: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7933:           `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7934: @*/
7935: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7936: {
7937:   PetscInt ncnt = 0, nlocal;

7939:   PetscFunctionBegin;
7941:   PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7942:   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);
7943:   for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7944:   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);
7945:   PetscCall(PetscFree(mat->bsizes));
7946:   mat->nblocks = nblocks;
7947:   PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7948:   PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7949:   PetscFunctionReturn(PETSC_SUCCESS);
7950: }

7952: /*@C
7953:   MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7955:   Not Collective; No Fortran Support

7957:   Input Parameter:
7958: . mat - the matrix

7960:   Output Parameters:
7961: + nblocks - the number of blocks on this process
7962: - bsizes  - the block sizes

7964:   Level: intermediate

7966: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7967: @*/
7968: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7969: {
7970:   PetscFunctionBegin;
7972:   if (nblocks) *nblocks = mat->nblocks;
7973:   if (bsizes) *bsizes = mat->bsizes;
7974:   PetscFunctionReturn(PETSC_SUCCESS);
7975: }

7977: /*@
7978:   MatSetBlockSizes - Sets the matrix block row and column sizes.

7980:   Logically Collective

7982:   Input Parameters:
7983: + mat - the matrix
7984: . rbs - row block size
7985: - cbs - column block size

7987:   Level: intermediate

7989:   Notes:
7990:   Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7991:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7992:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7994:   For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7995:   are compatible with the matrix local sizes.

7997:   The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

7999: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8000: @*/
8001: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8002: {
8003:   PetscFunctionBegin;
8007:   PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8008:   if (mat->rmap->refcnt) {
8009:     ISLocalToGlobalMapping l2g  = NULL;
8010:     PetscLayout            nmap = NULL;

8012:     PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8013:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8014:     PetscCall(PetscLayoutDestroy(&mat->rmap));
8015:     mat->rmap          = nmap;
8016:     mat->rmap->mapping = l2g;
8017:   }
8018:   if (mat->cmap->refcnt) {
8019:     ISLocalToGlobalMapping l2g  = NULL;
8020:     PetscLayout            nmap = NULL;

8022:     PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8023:     if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8024:     PetscCall(PetscLayoutDestroy(&mat->cmap));
8025:     mat->cmap          = nmap;
8026:     mat->cmap->mapping = l2g;
8027:   }
8028:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8029:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8030:   PetscFunctionReturn(PETSC_SUCCESS);
8031: }

8033: /*@
8034:   MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

8036:   Logically Collective

8038:   Input Parameters:
8039: + mat     - the matrix
8040: . fromRow - matrix from which to copy row block size
8041: - fromCol - matrix from which to copy column block size (can be same as fromRow)

8043:   Level: developer

8045: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8046: @*/
8047: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8048: {
8049:   PetscFunctionBegin;
8053:   if (fromRow->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8054:   if (fromCol->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8055:   PetscFunctionReturn(PETSC_SUCCESS);
8056: }

8058: /*@
8059:   MatResidual - Default routine to calculate the residual r = b - Ax

8061:   Collective

8063:   Input Parameters:
8064: + mat - the matrix
8065: . b   - the right-hand-side
8066: - x   - the approximate solution

8068:   Output Parameter:
8069: . r - location to store the residual

8071:   Level: developer

8073: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8074: @*/
8075: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8076: {
8077:   PetscFunctionBegin;
8083:   MatCheckPreallocated(mat, 1);
8084:   PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8085:   if (!mat->ops->residual) {
8086:     PetscCall(MatMult(mat, x, r));
8087:     PetscCall(VecAYPX(r, -1.0, b));
8088:   } else {
8089:     PetscUseTypeMethod(mat, residual, b, x, r);
8090:   }
8091:   PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8092:   PetscFunctionReturn(PETSC_SUCCESS);
8093: }

8095: /*MC
8096:     MatGetRowIJF90 - Obtains the compressed row storage i and j indices for the local rows of a sparse matrix

8098:     Synopsis:
8099:     MatGetRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

8101:     Not Collective

8103:     Input Parameters:
8104: +   A - the matrix
8105: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
8106: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8107: -   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8108:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8109:                  always used.

8111:     Output Parameters:
8112: +   n - number of local rows in the (possibly compressed) matrix
8113: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
8114: .   ja - the column indices
8115: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8116:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8118:     Level: developer

8120:     Note:
8121:     Use  `MatRestoreRowIJF90()` when you no longer need access to the data

8123: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatRestoreRowIJF90()`
8124: M*/

8126: /*MC
8127:     MatRestoreRowIJF90 - restores the compressed row storage i and j indices for the local rows of a sparse matrix obtained with `MatGetRowIJF90()`

8129:     Synopsis:
8130:     MatRestoreRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

8132:     Not Collective

8134:     Input Parameters:
8135: +   A - the  matrix
8136: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
8137: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8138:     inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8139:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8140:                  always used.
8141: .   n - number of local rows in the (possibly compressed) matrix
8142: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
8143: .   ja - the column indices
8144: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8145:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8147:     Level: developer

8149: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatGetRowIJF90()`
8150: M*/

8152: /*@C
8153:   MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

8155:   Collective

8157:   Input Parameters:
8158: + mat             - the matrix
8159: . shift           - 0 or 1 indicating we want the indices starting at 0 or 1
8160: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8161: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8162:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8163:                  always used.

8165:   Output Parameters:
8166: + n    - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8167: . 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
8168: . ja   - the column indices, use `NULL` if not needed
8169: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8170:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8172:   Level: developer

8174:   Notes:
8175:   You CANNOT change any of the ia[] or ja[] values.

8177:   Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

8179:   Fortran Notes:
8180:   Use
8181: .vb
8182:     PetscInt, pointer :: ia(:),ja(:)
8183:     call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8184:     ! Access the ith and jth entries via ia(i) and ja(j)
8185: .ve

8187:   `MatGetRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatGetRowIJF90()`

8189: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetRowIJF90()`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8190: @*/
8191: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8192: {
8193:   PetscFunctionBegin;
8196:   if (n) PetscAssertPointer(n, 5);
8197:   if (ia) PetscAssertPointer(ia, 6);
8198:   if (ja) PetscAssertPointer(ja, 7);
8199:   if (done) PetscAssertPointer(done, 8);
8200:   MatCheckPreallocated(mat, 1);
8201:   if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8202:   else {
8203:     if (done) *done = PETSC_TRUE;
8204:     PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8205:     PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8206:     PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8207:   }
8208:   PetscFunctionReturn(PETSC_SUCCESS);
8209: }

8211: /*@C
8212:   MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

8214:   Collective

8216:   Input Parameters:
8217: + mat             - the matrix
8218: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8219: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8220:                 symmetrized
8221: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8222:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8223:                  always used.
8224: . n               - number of columns in the (possibly compressed) matrix
8225: . ia              - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8226: - ja              - the row indices

8228:   Output Parameter:
8229: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

8231:   Level: developer

8233: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8234: @*/
8235: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8236: {
8237:   PetscFunctionBegin;
8240:   PetscAssertPointer(n, 5);
8241:   if (ia) PetscAssertPointer(ia, 6);
8242:   if (ja) PetscAssertPointer(ja, 7);
8243:   PetscAssertPointer(done, 8);
8244:   MatCheckPreallocated(mat, 1);
8245:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8246:   else {
8247:     *done = PETSC_TRUE;
8248:     PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8249:   }
8250:   PetscFunctionReturn(PETSC_SUCCESS);
8251: }

8253: /*@C
8254:   MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

8256:   Collective

8258:   Input Parameters:
8259: + mat             - the matrix
8260: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8261: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8262: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8263:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8264:                     always used.
8265: . n               - size of (possibly compressed) matrix
8266: . ia              - the row pointers
8267: - ja              - the column indices

8269:   Output Parameter:
8270: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8272:   Level: developer

8274:   Note:
8275:   This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8276:   us of the array after it has been restored. If you pass `NULL`, it will
8277:   not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

8279:   Fortran Note:
8280:   `MatRestoreRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatRestoreRowIJF90()`

8282: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreRowIJF90()`, `MatRestoreColumnIJ()`
8283: @*/
8284: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8285: {
8286:   PetscFunctionBegin;
8289:   if (ia) PetscAssertPointer(ia, 6);
8290:   if (ja) PetscAssertPointer(ja, 7);
8291:   if (done) PetscAssertPointer(done, 8);
8292:   MatCheckPreallocated(mat, 1);

8294:   if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8295:   else {
8296:     if (done) *done = PETSC_TRUE;
8297:     PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8298:     if (n) *n = 0;
8299:     if (ia) *ia = NULL;
8300:     if (ja) *ja = NULL;
8301:   }
8302:   PetscFunctionReturn(PETSC_SUCCESS);
8303: }

8305: /*@C
8306:   MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

8308:   Collective

8310:   Input Parameters:
8311: + mat             - the matrix
8312: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8313: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8314: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8315:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8316:                     always used.

8318:   Output Parameters:
8319: + n    - size of (possibly compressed) matrix
8320: . ia   - the column pointers
8321: . ja   - the row indices
8322: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8324:   Level: developer

8326: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8327: @*/
8328: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8329: {
8330:   PetscFunctionBegin;
8333:   if (ia) PetscAssertPointer(ia, 6);
8334:   if (ja) PetscAssertPointer(ja, 7);
8335:   PetscAssertPointer(done, 8);
8336:   MatCheckPreallocated(mat, 1);

8338:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8339:   else {
8340:     *done = PETSC_TRUE;
8341:     PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8342:     if (n) *n = 0;
8343:     if (ia) *ia = NULL;
8344:     if (ja) *ja = NULL;
8345:   }
8346:   PetscFunctionReturn(PETSC_SUCCESS);
8347: }

8349: /*@
8350:   MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8351:   `MatGetColumnIJ()`.

8353:   Collective

8355:   Input Parameters:
8356: + mat        - the matrix
8357: . ncolors    - maximum color value
8358: . n          - number of entries in colorarray
8359: - colorarray - array indicating color for each column

8361:   Output Parameter:
8362: . iscoloring - coloring generated using colorarray information

8364:   Level: developer

8366: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8367: @*/
8368: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8369: {
8370:   PetscFunctionBegin;
8373:   PetscAssertPointer(colorarray, 4);
8374:   PetscAssertPointer(iscoloring, 5);
8375:   MatCheckPreallocated(mat, 1);

8377:   if (!mat->ops->coloringpatch) {
8378:     PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8379:   } else {
8380:     PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8381:   }
8382:   PetscFunctionReturn(PETSC_SUCCESS);
8383: }

8385: /*@
8386:   MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

8388:   Logically Collective

8390:   Input Parameter:
8391: . mat - the factored matrix to be reset

8393:   Level: developer

8395:   Notes:
8396:   This routine should be used only with factored matrices formed by in-place
8397:   factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8398:   format).  This option can save memory, for example, when solving nonlinear
8399:   systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8400:   ILU(0) preconditioner.

8402:   One can specify in-place ILU(0) factorization by calling
8403: .vb
8404:      PCType(pc,PCILU);
8405:      PCFactorSeUseInPlace(pc);
8406: .ve
8407:   or by using the options -pc_type ilu -pc_factor_in_place

8409:   In-place factorization ILU(0) can also be used as a local
8410:   solver for the blocks within the block Jacobi or additive Schwarz
8411:   methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
8412:   for details on setting local solver options.

8414:   Most users should employ the `KSP` interface for linear solvers
8415:   instead of working directly with matrix algebra routines such as this.
8416:   See, e.g., `KSPCreate()`.

8418: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8419: @*/
8420: PetscErrorCode MatSetUnfactored(Mat mat)
8421: {
8422:   PetscFunctionBegin;
8425:   MatCheckPreallocated(mat, 1);
8426:   mat->factortype = MAT_FACTOR_NONE;
8427:   if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8428:   PetscUseTypeMethod(mat, setunfactored);
8429:   PetscFunctionReturn(PETSC_SUCCESS);
8430: }

8432: /*MC
8433:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran

8435:     Synopsis:
8436:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8438:     Not Collective

8440:     Input Parameter:
8441: .   x - matrix

8443:     Output Parameters:
8444: +   xx_v - the Fortran pointer to the array
8445: -   ierr - error code

8447:     Example of Usage:
8448: .vb
8449:       PetscScalar, pointer xx_v(:,:)
8450:       ....
8451:       call MatDenseGetArrayF90(x,xx_v,ierr)
8452:       a = xx_v(3)
8453:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
8454: .ve

8456:     Level: advanced

8458: .seealso: [](ch_matrices), `Mat`, `MatDenseRestoreArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJGetArrayF90()`
8459: M*/

8461: /*MC
8462:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
8463:     accessed with `MatDenseGetArrayF90()`.

8465:     Synopsis:
8466:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8468:     Not Collective

8470:     Input Parameters:
8471: +   x - matrix
8472: -   xx_v - the Fortran90 pointer to the array

8474:     Output Parameter:
8475: .   ierr - error code

8477:     Example of Usage:
8478: .vb
8479:        PetscScalar, pointer xx_v(:,:)
8480:        ....
8481:        call MatDenseGetArrayF90(x,xx_v,ierr)
8482:        a = xx_v(3)
8483:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
8484: .ve

8486:     Level: advanced

8488: .seealso: [](ch_matrices), `Mat`, `MatDenseGetArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJRestoreArrayF90()`
8489: M*/

8491: /*MC
8492:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran.

8494:     Synopsis:
8495:     MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

8497:     Not Collective

8499:     Input Parameter:
8500: .   x - matrix

8502:     Output Parameters:
8503: +   xx_v - the Fortran pointer to the array
8504: -   ierr - error code

8506:     Example of Usage:
8507: .vb
8508:       PetscScalar, pointer xx_v(:)
8509:       ....
8510:       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8511:       a = xx_v(3)
8512:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8513: .ve

8515:     Level: advanced

8517: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseGetArrayF90()`
8518: M*/

8520: /*MC
8521:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
8522:     accessed with `MatSeqAIJGetArrayF90()`.

8524:     Synopsis:
8525:     MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

8527:     Not Collective

8529:     Input Parameters:
8530: +   x - matrix
8531: -   xx_v - the Fortran90 pointer to the array

8533:     Output Parameter:
8534: .   ierr - error code

8536:     Example of Usage:
8537: .vb
8538:        PetscScalar, pointer xx_v(:)
8539:        ....
8540:        call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8541:        a = xx_v(3)
8542:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8543: .ve

8545:     Level: advanced

8547: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseRestoreArrayF90()`
8548: M*/

8550: /*@
8551:   MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8552:   as the original matrix.

8554:   Collective

8556:   Input Parameters:
8557: + mat   - the original matrix
8558: . isrow - parallel `IS` containing the rows this processor should obtain
8559: . 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.
8560: - cll   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

8562:   Output Parameter:
8563: . newmat - the new submatrix, of the same type as the original matrix

8565:   Level: advanced

8567:   Notes:
8568:   The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

8570:   Some matrix types place restrictions on the row and column indices, such
8571:   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;
8572:   for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

8574:   The index sets may not have duplicate entries.

8576:   The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8577:   the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8578:   to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8579:   will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
8580:   you are finished using it.

8582:   The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8583:   the input matrix.

8585:   If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

8587:   If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8588:   is used by `PCFIELDSPLIT` to allow easy nesting of its use.

8590:   Example usage:
8591:   Consider the following 8x8 matrix with 34 non-zero values, that is
8592:   assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8593:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8594:   as follows
8595: .vb
8596:             1  2  0  |  0  3  0  |  0  4
8597:     Proc0   0  5  6  |  7  0  0  |  8  0
8598:             9  0 10  | 11  0  0  | 12  0
8599:     -------------------------------------
8600:            13  0 14  | 15 16 17  |  0  0
8601:     Proc1   0 18  0  | 19 20 21  |  0  0
8602:             0  0  0  | 22 23  0  | 24  0
8603:     -------------------------------------
8604:     Proc2  25 26 27  |  0  0 28  | 29  0
8605:            30  0  0  | 31 32 33  |  0 34
8606: .ve

8608:   Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

8610: .vb
8611:             2  0  |  0  3  0  |  0
8612:     Proc0   5  6  |  7  0  0  |  8
8613:     -------------------------------
8614:     Proc1  18  0  | 19 20 21  |  0
8615:     -------------------------------
8616:     Proc2  26 27  |  0  0 28  | 29
8617:             0  0  | 31 32 33  |  0
8618: .ve

8620: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8621: @*/
8622: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8623: {
8624:   PetscMPIInt size;
8625:   Mat        *local;
8626:   IS          iscoltmp;
8627:   PetscBool   flg;

8629:   PetscFunctionBegin;
8633:   PetscAssertPointer(newmat, 5);
8636:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8637:   PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");

8639:   MatCheckPreallocated(mat, 1);
8640:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));

8642:   if (!iscol || isrow == iscol) {
8643:     PetscBool   stride;
8644:     PetscMPIInt grabentirematrix = 0, grab;
8645:     PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8646:     if (stride) {
8647:       PetscInt first, step, n, rstart, rend;
8648:       PetscCall(ISStrideGetInfo(isrow, &first, &step));
8649:       if (step == 1) {
8650:         PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8651:         if (rstart == first) {
8652:           PetscCall(ISGetLocalSize(isrow, &n));
8653:           if (n == rend - rstart) grabentirematrix = 1;
8654:         }
8655:       }
8656:     }
8657:     PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8658:     if (grab) {
8659:       PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8660:       if (cll == MAT_INITIAL_MATRIX) {
8661:         *newmat = mat;
8662:         PetscCall(PetscObjectReference((PetscObject)mat));
8663:       }
8664:       PetscFunctionReturn(PETSC_SUCCESS);
8665:     }
8666:   }

8668:   if (!iscol) {
8669:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8670:   } else {
8671:     iscoltmp = iscol;
8672:   }

8674:   /* if original matrix is on just one processor then use submatrix generated */
8675:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8676:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8677:     goto setproperties;
8678:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8679:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8680:     *newmat = *local;
8681:     PetscCall(PetscFree(local));
8682:     goto setproperties;
8683:   } else if (!mat->ops->createsubmatrix) {
8684:     /* Create a new matrix type that implements the operation using the full matrix */
8685:     PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8686:     switch (cll) {
8687:     case MAT_INITIAL_MATRIX:
8688:       PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8689:       break;
8690:     case MAT_REUSE_MATRIX:
8691:       PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8692:       break;
8693:     default:
8694:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8695:     }
8696:     PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8697:     goto setproperties;
8698:   }

8700:   PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8701:   PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8702:   PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));

8704: setproperties:
8705:   if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8706:     PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8707:     if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8708:   }
8709:   if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8710:   if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8711:   PetscFunctionReturn(PETSC_SUCCESS);
8712: }

8714: /*@
8715:   MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

8717:   Not Collective

8719:   Input Parameters:
8720: + A - the matrix we wish to propagate options from
8721: - B - the matrix we wish to propagate options to

8723:   Level: beginner

8725:   Note:
8726:   Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`

8728: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8729: @*/
8730: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8731: {
8732:   PetscFunctionBegin;
8735:   B->symmetry_eternal            = A->symmetry_eternal;
8736:   B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8737:   B->symmetric                   = A->symmetric;
8738:   B->structurally_symmetric      = A->structurally_symmetric;
8739:   B->spd                         = A->spd;
8740:   B->hermitian                   = A->hermitian;
8741:   PetscFunctionReturn(PETSC_SUCCESS);
8742: }

8744: /*@
8745:   MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8746:   used during the assembly process to store values that belong to
8747:   other processors.

8749:   Not Collective

8751:   Input Parameters:
8752: + mat   - the matrix
8753: . size  - the initial size of the stash.
8754: - bsize - the initial size of the block-stash(if used).

8756:   Options Database Keys:
8757: + -matstash_initial_size <size> or <size0,size1,...sizep-1>            - set initial size
8758: - -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1> - set initial block size

8760:   Level: intermediate

8762:   Notes:
8763:   The block-stash is used for values set with `MatSetValuesBlocked()` while
8764:   the stash is used for values set with `MatSetValues()`

8766:   Run with the option -info and look for output of the form
8767:   MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8768:   to determine the appropriate value, MM, to use for size and
8769:   MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8770:   to determine the value, BMM to use for bsize

8772: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8773: @*/
8774: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8775: {
8776:   PetscFunctionBegin;
8779:   PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8780:   PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8781:   PetscFunctionReturn(PETSC_SUCCESS);
8782: }

8784: /*@
8785:   MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8786:   the matrix

8788:   Neighbor-wise Collective

8790:   Input Parameters:
8791: + A - the matrix
8792: . x - the vector to be multiplied by the interpolation operator
8793: - y - the vector to be added to the result

8795:   Output Parameter:
8796: . w - the resulting vector

8798:   Level: intermediate

8800:   Notes:
8801:   `w` may be the same vector as `y`.

8803:   This allows one to use either the restriction or interpolation (its transpose)
8804:   matrix to do the interpolation

8806: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8807: @*/
8808: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8809: {
8810:   PetscInt M, N, Ny;

8812:   PetscFunctionBegin;
8817:   PetscCall(MatGetSize(A, &M, &N));
8818:   PetscCall(VecGetSize(y, &Ny));
8819:   if (M == Ny) {
8820:     PetscCall(MatMultAdd(A, x, y, w));
8821:   } else {
8822:     PetscCall(MatMultTransposeAdd(A, x, y, w));
8823:   }
8824:   PetscFunctionReturn(PETSC_SUCCESS);
8825: }

8827: /*@
8828:   MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8829:   the matrix

8831:   Neighbor-wise Collective

8833:   Input Parameters:
8834: + A - the matrix
8835: - x - the vector to be interpolated

8837:   Output Parameter:
8838: . y - the resulting vector

8840:   Level: intermediate

8842:   Note:
8843:   This allows one to use either the restriction or interpolation (its transpose)
8844:   matrix to do the interpolation

8846: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8847: @*/
8848: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8849: {
8850:   PetscInt M, N, Ny;

8852:   PetscFunctionBegin;
8856:   PetscCall(MatGetSize(A, &M, &N));
8857:   PetscCall(VecGetSize(y, &Ny));
8858:   if (M == Ny) {
8859:     PetscCall(MatMult(A, x, y));
8860:   } else {
8861:     PetscCall(MatMultTranspose(A, x, y));
8862:   }
8863:   PetscFunctionReturn(PETSC_SUCCESS);
8864: }

8866: /*@
8867:   MatRestrict - $y = A*x$ or $A^T*x$

8869:   Neighbor-wise Collective

8871:   Input Parameters:
8872: + A - the matrix
8873: - x - the vector to be restricted

8875:   Output Parameter:
8876: . y - the resulting vector

8878:   Level: intermediate

8880:   Note:
8881:   This allows one to use either the restriction or interpolation (its transpose)
8882:   matrix to do the restriction

8884: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8885: @*/
8886: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8887: {
8888:   PetscInt M, N, Nx;

8890:   PetscFunctionBegin;
8894:   PetscCall(MatGetSize(A, &M, &N));
8895:   PetscCall(VecGetSize(x, &Nx));
8896:   if (M == Nx) {
8897:     PetscCall(MatMultTranspose(A, x, y));
8898:   } else {
8899:     PetscCall(MatMult(A, x, y));
8900:   }
8901:   PetscFunctionReturn(PETSC_SUCCESS);
8902: }

8904: /*@
8905:   MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`

8907:   Neighbor-wise Collective

8909:   Input Parameters:
8910: + A - the matrix
8911: . x - the input dense matrix to be multiplied
8912: - w - the input dense matrix to be added to the result

8914:   Output Parameter:
8915: . y - the output dense matrix

8917:   Level: intermediate

8919:   Note:
8920:   This allows one to use either the restriction or interpolation (its transpose)
8921:   matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8922:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8924: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8925: @*/
8926: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8927: {
8928:   PetscInt  M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8929:   PetscBool trans = PETSC_TRUE;
8930:   MatReuse  reuse = MAT_INITIAL_MATRIX;

8932:   PetscFunctionBegin;
8938:   PetscCall(MatGetSize(A, &M, &N));
8939:   PetscCall(MatGetSize(x, &Mx, &Nx));
8940:   if (N == Mx) trans = PETSC_FALSE;
8941:   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);
8942:   Mo = trans ? N : M;
8943:   if (*y) {
8944:     PetscCall(MatGetSize(*y, &My, &Ny));
8945:     if (Mo == My && Nx == Ny) {
8946:       reuse = MAT_REUSE_MATRIX;
8947:     } else {
8948:       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);
8949:       PetscCall(MatDestroy(y));
8950:     }
8951:   }

8953:   if (w && *y == w) { /* this is to minimize changes in PCMG */
8954:     PetscBool flg;

8956:     PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8957:     if (w) {
8958:       PetscInt My, Ny, Mw, Nw;

8960:       PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8961:       PetscCall(MatGetSize(*y, &My, &Ny));
8962:       PetscCall(MatGetSize(w, &Mw, &Nw));
8963:       if (!flg || My != Mw || Ny != Nw) w = NULL;
8964:     }
8965:     if (!w) {
8966:       PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8967:       PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8968:       PetscCall(PetscObjectDereference((PetscObject)w));
8969:     } else {
8970:       PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8971:     }
8972:   }
8973:   if (!trans) {
8974:     PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8975:   } else {
8976:     PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8977:   }
8978:   if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8979:   PetscFunctionReturn(PETSC_SUCCESS);
8980: }

8982: /*@
8983:   MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

8985:   Neighbor-wise Collective

8987:   Input Parameters:
8988: + A - the matrix
8989: - x - the input dense matrix

8991:   Output Parameter:
8992: . y - the output dense matrix

8994:   Level: intermediate

8996:   Note:
8997:   This allows one to use either the restriction or interpolation (its transpose)
8998:   matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8999:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

9001: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
9002: @*/
9003: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
9004: {
9005:   PetscFunctionBegin;
9006:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9007:   PetscFunctionReturn(PETSC_SUCCESS);
9008: }

9010: /*@
9011:   MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

9013:   Neighbor-wise Collective

9015:   Input Parameters:
9016: + A - the matrix
9017: - x - the input dense matrix

9019:   Output Parameter:
9020: . y - the output dense matrix

9022:   Level: intermediate

9024:   Note:
9025:   This allows one to use either the restriction or interpolation (its transpose)
9026:   matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
9027:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

9029: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
9030: @*/
9031: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
9032: {
9033:   PetscFunctionBegin;
9034:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9035:   PetscFunctionReturn(PETSC_SUCCESS);
9036: }

9038: /*@
9039:   MatGetNullSpace - retrieves the null space of a matrix.

9041:   Logically Collective

9043:   Input Parameters:
9044: + mat    - the matrix
9045: - nullsp - the null space object

9047:   Level: developer

9049: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
9050: @*/
9051: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
9052: {
9053:   PetscFunctionBegin;
9055:   PetscAssertPointer(nullsp, 2);
9056:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
9057:   PetscFunctionReturn(PETSC_SUCCESS);
9058: }

9060: /*@C
9061:   MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices

9063:   Logically Collective

9065:   Input Parameters:
9066: + n   - the number of matrices
9067: - mat - the array of matrices

9069:   Output Parameters:
9070: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`

9072:   Level: developer

9074:   Note:
9075:   Call `MatRestoreNullspaces()` to provide these to another array of matrices

9077: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9078:           `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
9079: @*/
9080: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9081: {
9082:   PetscFunctionBegin;
9083:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9084:   PetscAssertPointer(mat, 2);
9085:   PetscAssertPointer(nullsp, 3);

9087:   PetscCall(PetscCalloc1(3 * n, nullsp));
9088:   for (PetscInt i = 0; i < n; i++) {
9090:     (*nullsp)[i] = mat[i]->nullsp;
9091:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
9092:     (*nullsp)[n + i] = mat[i]->nearnullsp;
9093:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
9094:     (*nullsp)[2 * n + i] = mat[i]->transnullsp;
9095:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
9096:   }
9097:   PetscFunctionReturn(PETSC_SUCCESS);
9098: }

9100: /*@C
9101:   MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices

9103:   Logically Collective

9105:   Input Parameters:
9106: + n      - the number of matrices
9107: . mat    - the array of matrices
9108: - nullsp - an array of null spaces

9110:   Level: developer

9112:   Note:
9113:   Call `MatGetNullSpaces()` to create `nullsp`

9115: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9116:           `MatNullSpaceRemove()`, `MatGetNullSpaces()`
9117: @*/
9118: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9119: {
9120:   PetscFunctionBegin;
9121:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9122:   PetscAssertPointer(mat, 2);
9123:   PetscAssertPointer(nullsp, 3);
9124:   PetscAssertPointer(*nullsp, 3);

9126:   for (PetscInt i = 0; i < n; i++) {
9128:     PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
9129:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9130:     PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9131:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9132:     PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9133:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9134:   }
9135:   PetscCall(PetscFree(*nullsp));
9136:   PetscFunctionReturn(PETSC_SUCCESS);
9137: }

9139: /*@
9140:   MatSetNullSpace - attaches a null space to a matrix.

9142:   Logically Collective

9144:   Input Parameters:
9145: + mat    - the matrix
9146: - nullsp - the null space object

9148:   Level: advanced

9150:   Notes:
9151:   This null space is used by the `KSP` linear solvers to solve singular systems.

9153:   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`

9155:   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
9156:   to zero but the linear system will still be solved in a least squares sense.

9158:   The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9159:   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), + the range of $A^T$, $R(A^T)$.
9160:   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
9161:   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
9162:   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($A^T$).
9163:   This  \hat{b} can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.

9165:   If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one as called
9166:   `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9167:   routine also automatically calls `MatSetTransposeNullSpace()`.

9169:   The user should call `MatNullSpaceDestroy()`.

9171: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9172:           `KSPSetPCSide()`
9173: @*/
9174: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9175: {
9176:   PetscFunctionBegin;
9179:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9180:   PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9181:   mat->nullsp = nullsp;
9182:   if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9183:   PetscFunctionReturn(PETSC_SUCCESS);
9184: }

9186: /*@
9187:   MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

9189:   Logically Collective

9191:   Input Parameters:
9192: + mat    - the matrix
9193: - nullsp - the null space object

9195:   Level: developer

9197: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9198: @*/
9199: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9200: {
9201:   PetscFunctionBegin;
9204:   PetscAssertPointer(nullsp, 2);
9205:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9206:   PetscFunctionReturn(PETSC_SUCCESS);
9207: }

9209: /*@
9210:   MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix

9212:   Logically Collective

9214:   Input Parameters:
9215: + mat    - the matrix
9216: - nullsp - the null space object

9218:   Level: advanced

9220:   Notes:
9221:   This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.

9223:   See `MatSetNullSpace()`

9225: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9226: @*/
9227: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9228: {
9229:   PetscFunctionBegin;
9232:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9233:   PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9234:   mat->transnullsp = nullsp;
9235:   PetscFunctionReturn(PETSC_SUCCESS);
9236: }

9238: /*@
9239:   MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9240:   This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

9242:   Logically Collective

9244:   Input Parameters:
9245: + mat    - the matrix
9246: - nullsp - the null space object

9248:   Level: advanced

9250:   Notes:
9251:   Overwrites any previous near null space that may have been attached

9253:   You can remove the null space by calling this routine with an `nullsp` of `NULL`

9255: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9256: @*/
9257: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9258: {
9259:   PetscFunctionBegin;
9263:   MatCheckPreallocated(mat, 1);
9264:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9265:   PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9266:   mat->nearnullsp = nullsp;
9267:   PetscFunctionReturn(PETSC_SUCCESS);
9268: }

9270: /*@
9271:   MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`

9273:   Not Collective

9275:   Input Parameter:
9276: . mat - the matrix

9278:   Output Parameter:
9279: . nullsp - the null space object, `NULL` if not set

9281:   Level: advanced

9283: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9284: @*/
9285: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9286: {
9287:   PetscFunctionBegin;
9290:   PetscAssertPointer(nullsp, 2);
9291:   MatCheckPreallocated(mat, 1);
9292:   *nullsp = mat->nearnullsp;
9293:   PetscFunctionReturn(PETSC_SUCCESS);
9294: }

9296: /*@
9297:   MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

9299:   Collective

9301:   Input Parameters:
9302: + mat  - the matrix
9303: . row  - row/column permutation
9304: - info - information on desired factorization process

9306:   Level: developer

9308:   Notes:
9309:   Probably really in-place only when level of fill is zero, otherwise allocates
9310:   new space to store factored matrix and deletes previous memory.

9312:   Most users should employ the `KSP` interface for linear solvers
9313:   instead of working directly with matrix algebra routines such as this.
9314:   See, e.g., `KSPCreate()`.

9316:   Developer Note:
9317:   The Fortran interface is not autogenerated as the
9318:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

9320: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9321: @*/
9322: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9323: {
9324:   PetscFunctionBegin;
9328:   PetscAssertPointer(info, 3);
9329:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9330:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9331:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9332:   MatCheckPreallocated(mat, 1);
9333:   PetscUseTypeMethod(mat, iccfactor, row, info);
9334:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9335:   PetscFunctionReturn(PETSC_SUCCESS);
9336: }

9338: /*@
9339:   MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9340:   ghosted ones.

9342:   Not Collective

9344:   Input Parameters:
9345: + mat  - the matrix
9346: - diag - the diagonal values, including ghost ones

9348:   Level: developer

9350:   Notes:
9351:   Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices

9353:   This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`

9355: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9356: @*/
9357: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9358: {
9359:   PetscMPIInt size;

9361:   PetscFunctionBegin;

9366:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9367:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9368:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9369:   if (size == 1) {
9370:     PetscInt n, m;
9371:     PetscCall(VecGetSize(diag, &n));
9372:     PetscCall(MatGetSize(mat, NULL, &m));
9373:     PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9374:     PetscCall(MatDiagonalScale(mat, NULL, diag));
9375:   } else {
9376:     PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9377:   }
9378:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9379:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9380:   PetscFunctionReturn(PETSC_SUCCESS);
9381: }

9383: /*@
9384:   MatGetInertia - Gets the inertia from a factored matrix

9386:   Collective

9388:   Input Parameter:
9389: . mat - the matrix

9391:   Output Parameters:
9392: + nneg  - number of negative eigenvalues
9393: . nzero - number of zero eigenvalues
9394: - npos  - number of positive eigenvalues

9396:   Level: advanced

9398:   Note:
9399:   Matrix must have been factored by `MatCholeskyFactor()`

9401: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9402: @*/
9403: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9404: {
9405:   PetscFunctionBegin;
9408:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9409:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9410:   PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9411:   PetscFunctionReturn(PETSC_SUCCESS);
9412: }

9414: /*@C
9415:   MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors

9417:   Neighbor-wise Collective

9419:   Input Parameters:
9420: + mat - the factored matrix obtained with `MatGetFactor()`
9421: - b   - the right-hand-side vectors

9423:   Output Parameter:
9424: . x - the result vectors

9426:   Level: developer

9428:   Note:
9429:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
9430:   call `MatSolves`(A,x,x).

9432: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9433: @*/
9434: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9435: {
9436:   PetscFunctionBegin;
9439:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9440:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9441:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);

9443:   MatCheckPreallocated(mat, 1);
9444:   PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9445:   PetscUseTypeMethod(mat, solves, b, x);
9446:   PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9447:   PetscFunctionReturn(PETSC_SUCCESS);
9448: }

9450: /*@
9451:   MatIsSymmetric - Test whether a matrix is symmetric

9453:   Collective

9455:   Input Parameters:
9456: + A   - the matrix to test
9457: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

9459:   Output Parameter:
9460: . flg - the result

9462:   Level: intermediate

9464:   Notes:
9465:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9467:   If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`

9469:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9470:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9472: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9473:           `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9474: @*/
9475: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9476: {
9477:   PetscFunctionBegin;
9479:   PetscAssertPointer(flg, 3);
9480:   if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9481:   else {
9482:     if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9483:     else PetscCall(MatIsTranspose(A, A, tol, flg));
9484:     if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9485:   }
9486:   PetscFunctionReturn(PETSC_SUCCESS);
9487: }

9489: /*@
9490:   MatIsHermitian - Test whether a matrix is Hermitian

9492:   Collective

9494:   Input Parameters:
9495: + A   - the matrix to test
9496: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

9498:   Output Parameter:
9499: . flg - the result

9501:   Level: intermediate

9503:   Notes:
9504:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9506:   If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`

9508:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9509:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)

9511: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9512:           `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9513: @*/
9514: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9515: {
9516:   PetscFunctionBegin;
9518:   PetscAssertPointer(flg, 3);
9519:   if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9520:   else {
9521:     if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9522:     else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9523:     if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9524:   }
9525:   PetscFunctionReturn(PETSC_SUCCESS);
9526: }

9528: /*@
9529:   MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state

9531:   Not Collective

9533:   Input Parameter:
9534: . A - the matrix to check

9536:   Output Parameters:
9537: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9538: - flg - the result (only valid if set is `PETSC_TRUE`)

9540:   Level: advanced

9542:   Notes:
9543:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9544:   if you want it explicitly checked

9546:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9547:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9549: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9550: @*/
9551: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9552: {
9553:   PetscFunctionBegin;
9555:   PetscAssertPointer(set, 2);
9556:   PetscAssertPointer(flg, 3);
9557:   if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9558:     *set = PETSC_TRUE;
9559:     *flg = PetscBool3ToBool(A->symmetric);
9560:   } else {
9561:     *set = PETSC_FALSE;
9562:   }
9563:   PetscFunctionReturn(PETSC_SUCCESS);
9564: }

9566: /*@
9567:   MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state

9569:   Not Collective

9571:   Input Parameter:
9572: . A - the matrix to check

9574:   Output Parameters:
9575: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9576: - flg - the result (only valid if set is `PETSC_TRUE`)

9578:   Level: advanced

9580:   Notes:
9581:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).

9583:   One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9584:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)

9586: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9587: @*/
9588: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9589: {
9590:   PetscFunctionBegin;
9592:   PetscAssertPointer(set, 2);
9593:   PetscAssertPointer(flg, 3);
9594:   if (A->spd != PETSC_BOOL3_UNKNOWN) {
9595:     *set = PETSC_TRUE;
9596:     *flg = PetscBool3ToBool(A->spd);
9597:   } else {
9598:     *set = PETSC_FALSE;
9599:   }
9600:   PetscFunctionReturn(PETSC_SUCCESS);
9601: }

9603: /*@
9604:   MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state

9606:   Not Collective

9608:   Input Parameter:
9609: . A - the matrix to check

9611:   Output Parameters:
9612: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9613: - flg - the result (only valid if set is `PETSC_TRUE`)

9615:   Level: advanced

9617:   Notes:
9618:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9619:   if you want it explicitly checked

9621:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9622:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9624: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9625: @*/
9626: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9627: {
9628:   PetscFunctionBegin;
9630:   PetscAssertPointer(set, 2);
9631:   PetscAssertPointer(flg, 3);
9632:   if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9633:     *set = PETSC_TRUE;
9634:     *flg = PetscBool3ToBool(A->hermitian);
9635:   } else {
9636:     *set = PETSC_FALSE;
9637:   }
9638:   PetscFunctionReturn(PETSC_SUCCESS);
9639: }

9641: /*@
9642:   MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

9644:   Collective

9646:   Input Parameter:
9647: . A - the matrix to test

9649:   Output Parameter:
9650: . flg - the result

9652:   Level: intermediate

9654:   Notes:
9655:   If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`

9657:   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
9658:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9660: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9661: @*/
9662: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9663: {
9664:   PetscFunctionBegin;
9666:   PetscAssertPointer(flg, 2);
9667:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9668:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9669:   } else {
9670:     PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9671:     PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9672:   }
9673:   PetscFunctionReturn(PETSC_SUCCESS);
9674: }

9676: /*@
9677:   MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state

9679:   Not Collective

9681:   Input Parameter:
9682: . A - the matrix to check

9684:   Output Parameters:
9685: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9686: - flg - the result (only valid if set is PETSC_TRUE)

9688:   Level: advanced

9690:   Notes:
9691:   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
9692:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9694:   Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)

9696: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9697: @*/
9698: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9699: {
9700:   PetscFunctionBegin;
9702:   PetscAssertPointer(set, 2);
9703:   PetscAssertPointer(flg, 3);
9704:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9705:     *set = PETSC_TRUE;
9706:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9707:   } else {
9708:     *set = PETSC_FALSE;
9709:   }
9710:   PetscFunctionReturn(PETSC_SUCCESS);
9711: }

9713: /*@
9714:   MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9715:   to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process

9717:   Not Collective

9719:   Input Parameter:
9720: . mat - the matrix

9722:   Output Parameters:
9723: + nstash    - the size of the stash
9724: . reallocs  - the number of additional mallocs incurred.
9725: . bnstash   - the size of the block stash
9726: - breallocs - the number of additional mallocs incurred.in the block stash

9728:   Level: advanced

9730: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9731: @*/
9732: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9733: {
9734:   PetscFunctionBegin;
9735:   PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9736:   PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9737:   PetscFunctionReturn(PETSC_SUCCESS);
9738: }

9740: /*@
9741:   MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9742:   parallel layout, `PetscLayout` for rows and columns

9744:   Collective

9746:   Input Parameter:
9747: . mat - the matrix

9749:   Output Parameters:
9750: + right - (optional) vector that the matrix can be multiplied against
9751: - left  - (optional) vector that the matrix vector product can be stored in

9753:   Level: advanced

9755:   Notes:
9756:   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()`.

9758:   These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed

9760: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9761: @*/
9762: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9763: {
9764:   PetscFunctionBegin;
9767:   if (mat->ops->getvecs) {
9768:     PetscUseTypeMethod(mat, getvecs, right, left);
9769:   } else {
9770:     if (right) {
9771:       PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9772:       PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9773:       PetscCall(VecSetType(*right, mat->defaultvectype));
9774: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9775:       if (mat->boundtocpu && mat->bindingpropagates) {
9776:         PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9777:         PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9778:       }
9779: #endif
9780:     }
9781:     if (left) {
9782:       PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9783:       PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9784:       PetscCall(VecSetType(*left, mat->defaultvectype));
9785: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9786:       if (mat->boundtocpu && mat->bindingpropagates) {
9787:         PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9788:         PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9789:       }
9790: #endif
9791:     }
9792:   }
9793:   PetscFunctionReturn(PETSC_SUCCESS);
9794: }

9796: /*@
9797:   MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9798:   with default values.

9800:   Not Collective

9802:   Input Parameter:
9803: . info - the `MatFactorInfo` data structure

9805:   Level: developer

9807:   Notes:
9808:   The solvers are generally used through the `KSP` and `PC` objects, for example
9809:   `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`

9811:   Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed

9813: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9814: @*/
9815: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9816: {
9817:   PetscFunctionBegin;
9818:   PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9819:   PetscFunctionReturn(PETSC_SUCCESS);
9820: }

9822: /*@
9823:   MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

9825:   Collective

9827:   Input Parameters:
9828: + mat - the factored matrix
9829: - is  - the index set defining the Schur indices (0-based)

9831:   Level: advanced

9833:   Notes:
9834:   Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.

9836:   You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.

9838:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9840: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9841:           `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9842: @*/
9843: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9844: {
9845:   PetscErrorCode (*f)(Mat, IS);

9847:   PetscFunctionBegin;
9852:   PetscCheckSameComm(mat, 1, is, 2);
9853:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9854:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9855:   PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9856:   PetscCall(MatDestroy(&mat->schur));
9857:   PetscCall((*f)(mat, is));
9858:   PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9859:   PetscFunctionReturn(PETSC_SUCCESS);
9860: }

9862: /*@
9863:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

9865:   Logically Collective

9867:   Input Parameters:
9868: + F      - the factored matrix obtained by calling `MatGetFactor()`
9869: . S      - location where to return the Schur complement, can be `NULL`
9870: - status - the status of the Schur complement matrix, can be `NULL`

9872:   Level: advanced

9874:   Notes:
9875:   You must call `MatFactorSetSchurIS()` before calling this routine.

9877:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9879:   The routine provides a copy of the Schur matrix stored within the solver data structures.
9880:   The caller must destroy the object when it is no longer needed.
9881:   If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.

9883:   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)

9885:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9887:   Developer Note:
9888:   The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9889:   matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

9891: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9892: @*/
9893: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9894: {
9895:   PetscFunctionBegin;
9897:   if (S) PetscAssertPointer(S, 2);
9898:   if (status) PetscAssertPointer(status, 3);
9899:   if (S) {
9900:     PetscErrorCode (*f)(Mat, Mat *);

9902:     PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9903:     if (f) {
9904:       PetscCall((*f)(F, S));
9905:     } else {
9906:       PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9907:     }
9908:   }
9909:   if (status) *status = F->schur_status;
9910:   PetscFunctionReturn(PETSC_SUCCESS);
9911: }

9913: /*@
9914:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

9916:   Logically Collective

9918:   Input Parameters:
9919: + F      - the factored matrix obtained by calling `MatGetFactor()`
9920: . S      - location where to return the Schur complement, can be `NULL`
9921: - status - the status of the Schur complement matrix, can be `NULL`

9923:   Level: advanced

9925:   Notes:
9926:   You must call `MatFactorSetSchurIS()` before calling this routine.

9928:   Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`

9930:   The routine returns a the Schur Complement stored within the data structures of the solver.

9932:   If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.

9934:   The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.

9936:   Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix

9938:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9940: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9941: @*/
9942: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9943: {
9944:   PetscFunctionBegin;
9946:   if (S) {
9947:     PetscAssertPointer(S, 2);
9948:     *S = F->schur;
9949:   }
9950:   if (status) {
9951:     PetscAssertPointer(status, 3);
9952:     *status = F->schur_status;
9953:   }
9954:   PetscFunctionReturn(PETSC_SUCCESS);
9955: }

9957: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9958: {
9959:   Mat S = F->schur;

9961:   PetscFunctionBegin;
9962:   switch (F->schur_status) {
9963:   case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9964:   case MAT_FACTOR_SCHUR_INVERTED:
9965:     if (S) {
9966:       S->ops->solve             = NULL;
9967:       S->ops->matsolve          = NULL;
9968:       S->ops->solvetranspose    = NULL;
9969:       S->ops->matsolvetranspose = NULL;
9970:       S->ops->solveadd          = NULL;
9971:       S->ops->solvetransposeadd = NULL;
9972:       S->factortype             = MAT_FACTOR_NONE;
9973:       PetscCall(PetscFree(S->solvertype));
9974:     }
9975:   case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9976:     break;
9977:   default:
9978:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9979:   }
9980:   PetscFunctionReturn(PETSC_SUCCESS);
9981: }

9983: /*@
9984:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`

9986:   Logically Collective

9988:   Input Parameters:
9989: + F      - the factored matrix obtained by calling `MatGetFactor()`
9990: . S      - location where the Schur complement is stored
9991: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)

9993:   Level: advanced

9995: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9996: @*/
9997: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9998: {
9999:   PetscFunctionBegin;
10001:   if (S) {
10003:     *S = NULL;
10004:   }
10005:   F->schur_status = status;
10006:   PetscCall(MatFactorUpdateSchurStatus_Private(F));
10007:   PetscFunctionReturn(PETSC_SUCCESS);
10008: }

10010: /*@
10011:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

10013:   Logically Collective

10015:   Input Parameters:
10016: + F   - the factored matrix obtained by calling `MatGetFactor()`
10017: . rhs - location where the right-hand side of the Schur complement system is stored
10018: - sol - location where the solution of the Schur complement system has to be returned

10020:   Level: advanced

10022:   Notes:
10023:   The sizes of the vectors should match the size of the Schur complement

10025:   Must be called after `MatFactorSetSchurIS()`

10027: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
10028: @*/
10029: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
10030: {
10031:   PetscFunctionBegin;
10038:   PetscCheckSameComm(F, 1, rhs, 2);
10039:   PetscCheckSameComm(F, 1, sol, 3);
10040:   PetscCall(MatFactorFactorizeSchurComplement(F));
10041:   switch (F->schur_status) {
10042:   case MAT_FACTOR_SCHUR_FACTORED:
10043:     PetscCall(MatSolveTranspose(F->schur, rhs, sol));
10044:     break;
10045:   case MAT_FACTOR_SCHUR_INVERTED:
10046:     PetscCall(MatMultTranspose(F->schur, rhs, sol));
10047:     break;
10048:   default:
10049:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10050:   }
10051:   PetscFunctionReturn(PETSC_SUCCESS);
10052: }

10054: /*@
10055:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

10057:   Logically Collective

10059:   Input Parameters:
10060: + F   - the factored matrix obtained by calling `MatGetFactor()`
10061: . rhs - location where the right-hand side of the Schur complement system is stored
10062: - sol - location where the solution of the Schur complement system has to be returned

10064:   Level: advanced

10066:   Notes:
10067:   The sizes of the vectors should match the size of the Schur complement

10069:   Must be called after `MatFactorSetSchurIS()`

10071: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
10072: @*/
10073: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
10074: {
10075:   PetscFunctionBegin;
10082:   PetscCheckSameComm(F, 1, rhs, 2);
10083:   PetscCheckSameComm(F, 1, sol, 3);
10084:   PetscCall(MatFactorFactorizeSchurComplement(F));
10085:   switch (F->schur_status) {
10086:   case MAT_FACTOR_SCHUR_FACTORED:
10087:     PetscCall(MatSolve(F->schur, rhs, sol));
10088:     break;
10089:   case MAT_FACTOR_SCHUR_INVERTED:
10090:     PetscCall(MatMult(F->schur, rhs, sol));
10091:     break;
10092:   default:
10093:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10094:   }
10095:   PetscFunctionReturn(PETSC_SUCCESS);
10096: }

10098: PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
10099: #if PetscDefined(HAVE_CUDA)
10100: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
10101: #endif

10103: /* Schur status updated in the interface */
10104: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
10105: {
10106:   Mat S = F->schur;

10108:   PetscFunctionBegin;
10109:   if (S) {
10110:     PetscMPIInt size;
10111:     PetscBool   isdense, isdensecuda;

10113:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
10114:     PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
10115:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
10116:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
10117:     PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
10118:     PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
10119:     if (isdense) {
10120:       PetscCall(MatSeqDenseInvertFactors_Private(S));
10121:     } else if (isdensecuda) {
10122: #if defined(PETSC_HAVE_CUDA)
10123:       PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
10124: #endif
10125:     }
10126:     // HIP??????????????
10127:     PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
10128:   }
10129:   PetscFunctionReturn(PETSC_SUCCESS);
10130: }

10132: /*@
10133:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

10135:   Logically Collective

10137:   Input Parameter:
10138: . F - the factored matrix obtained by calling `MatGetFactor()`

10140:   Level: advanced

10142:   Notes:
10143:   Must be called after `MatFactorSetSchurIS()`.

10145:   Call `MatFactorGetSchurComplement()` or  `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.

10147: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10148: @*/
10149: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10150: {
10151:   PetscFunctionBegin;
10154:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10155:   PetscCall(MatFactorFactorizeSchurComplement(F));
10156:   PetscCall(MatFactorInvertSchurComplement_Private(F));
10157:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10158:   PetscFunctionReturn(PETSC_SUCCESS);
10159: }

10161: /*@
10162:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

10164:   Logically Collective

10166:   Input Parameter:
10167: . F - the factored matrix obtained by calling `MatGetFactor()`

10169:   Level: advanced

10171:   Note:
10172:   Must be called after `MatFactorSetSchurIS()`

10174: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10175: @*/
10176: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10177: {
10178:   MatFactorInfo info;

10180:   PetscFunctionBegin;
10183:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10184:   PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10185:   PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10186:   if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10187:     PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10188:   } else {
10189:     PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10190:   }
10191:   PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10192:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10193:   PetscFunctionReturn(PETSC_SUCCESS);
10194: }

10196: /*@
10197:   MatPtAP - Creates the matrix product $C = P^T * A * P$

10199:   Neighbor-wise Collective

10201:   Input Parameters:
10202: + A     - the matrix
10203: . P     - the projection matrix
10204: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10205: - 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
10206:           if the result is a dense matrix this is irrelevant

10208:   Output Parameter:
10209: . C - the product matrix

10211:   Level: intermediate

10213:   Notes:
10214:   C will be created and must be destroyed by the user with `MatDestroy()`.

10216:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

10218:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10220:   Developer Note:
10221:   For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.

10223: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10224: @*/
10225: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10226: {
10227:   PetscFunctionBegin;
10228:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10229:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10231:   if (scall == MAT_INITIAL_MATRIX) {
10232:     PetscCall(MatProductCreate(A, P, NULL, C));
10233:     PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10234:     PetscCall(MatProductSetAlgorithm(*C, "default"));
10235:     PetscCall(MatProductSetFill(*C, fill));

10237:     (*C)->product->api_user = PETSC_TRUE;
10238:     PetscCall(MatProductSetFromOptions(*C));
10239:     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);
10240:     PetscCall(MatProductSymbolic(*C));
10241:   } else { /* scall == MAT_REUSE_MATRIX */
10242:     PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10243:   }

10245:   PetscCall(MatProductNumeric(*C));
10246:   (*C)->symmetric = A->symmetric;
10247:   (*C)->spd       = A->spd;
10248:   PetscFunctionReturn(PETSC_SUCCESS);
10249: }

10251: /*@
10252:   MatRARt - Creates the matrix product $C = R * A * R^T$

10254:   Neighbor-wise Collective

10256:   Input Parameters:
10257: + A     - the matrix
10258: . R     - the projection matrix
10259: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10260: - fill  - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10261:           if the result is a dense matrix this is irrelevant

10263:   Output Parameter:
10264: . C - the product matrix

10266:   Level: intermediate

10268:   Notes:
10269:   `C` will be created and must be destroyed by the user with `MatDestroy()`.

10271:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

10273:   This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10274:   which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10275:   the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10276:   We recommend using `MatPtAP()` when possible.

10278:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10280: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10281: @*/
10282: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10283: {
10284:   PetscFunctionBegin;
10285:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10286:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10288:   if (scall == MAT_INITIAL_MATRIX) {
10289:     PetscCall(MatProductCreate(A, R, NULL, C));
10290:     PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10291:     PetscCall(MatProductSetAlgorithm(*C, "default"));
10292:     PetscCall(MatProductSetFill(*C, fill));

10294:     (*C)->product->api_user = PETSC_TRUE;
10295:     PetscCall(MatProductSetFromOptions(*C));
10296:     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);
10297:     PetscCall(MatProductSymbolic(*C));
10298:   } else { /* scall == MAT_REUSE_MATRIX */
10299:     PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10300:   }

10302:   PetscCall(MatProductNumeric(*C));
10303:   if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10304:   PetscFunctionReturn(PETSC_SUCCESS);
10305: }

10307: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10308: {
10309:   PetscBool flg = PETSC_TRUE;

10311:   PetscFunctionBegin;
10312:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10313:   if (scall == MAT_INITIAL_MATRIX) {
10314:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10315:     PetscCall(MatProductCreate(A, B, NULL, C));
10316:     PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10317:     PetscCall(MatProductSetFill(*C, fill));
10318:   } else { /* scall == MAT_REUSE_MATRIX */
10319:     Mat_Product *product = (*C)->product;

10321:     PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10322:     if (flg && product && product->type != ptype) {
10323:       PetscCall(MatProductClear(*C));
10324:       product = NULL;
10325:     }
10326:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10327:     if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10328:       PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10329:       PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10330:       product        = (*C)->product;
10331:       product->fill  = fill;
10332:       product->clear = PETSC_TRUE;
10333:     } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10334:       flg = PETSC_FALSE;
10335:       PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10336:     }
10337:   }
10338:   if (flg) {
10339:     (*C)->product->api_user = PETSC_TRUE;
10340:     PetscCall(MatProductSetType(*C, ptype));
10341:     PetscCall(MatProductSetFromOptions(*C));
10342:     PetscCall(MatProductSymbolic(*C));
10343:   }
10344:   PetscCall(MatProductNumeric(*C));
10345:   PetscFunctionReturn(PETSC_SUCCESS);
10346: }

10348: /*@
10349:   MatMatMult - Performs matrix-matrix multiplication C=A*B.

10351:   Neighbor-wise Collective

10353:   Input Parameters:
10354: + A     - the left matrix
10355: . B     - the right matrix
10356: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10357: - 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
10358:           if the result is a dense matrix this is irrelevant

10360:   Output Parameter:
10361: . C - the product matrix

10363:   Notes:
10364:   Unless scall is `MAT_REUSE_MATRIX` C will be created.

10366:   `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
10367:   call to this function with `MAT_INITIAL_MATRIX`.

10369:   To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.

10371:   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`,
10372:   rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.

10374:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10376:   Example of Usage:
10377: .vb
10378:      MatProductCreate(A,B,NULL,&C);
10379:      MatProductSetType(C,MATPRODUCT_AB);
10380:      MatProductSymbolic(C);
10381:      MatProductNumeric(C); // compute C=A * B
10382:      MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10383:      MatProductNumeric(C);
10384:      MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10385:      MatProductNumeric(C);
10386: .ve

10388:   Level: intermediate

10390: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10391: @*/
10392: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10393: {
10394:   PetscFunctionBegin;
10395:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10396:   PetscFunctionReturn(PETSC_SUCCESS);
10397: }

10399: /*@
10400:   MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.

10402:   Neighbor-wise Collective

10404:   Input Parameters:
10405: + A     - the left matrix
10406: . B     - the right matrix
10407: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10408: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

10410:   Output Parameter:
10411: . C - the product matrix

10413:   Options Database Key:
10414: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10415:               first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10416:               the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.

10418:   Level: intermediate

10420:   Notes:
10421:   C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10423:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call

10425:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10426:   actually needed.

10428:   This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10429:   and for pairs of `MATMPIDENSE` matrices.

10431:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`

10433:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10435: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10436: @*/
10437: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10438: {
10439:   PetscFunctionBegin;
10440:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10441:   if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10442:   PetscFunctionReturn(PETSC_SUCCESS);
10443: }

10445: /*@
10446:   MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.

10448:   Neighbor-wise Collective

10450:   Input Parameters:
10451: + A     - the left matrix
10452: . B     - the right matrix
10453: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10454: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

10456:   Output Parameter:
10457: . C - the product matrix

10459:   Level: intermediate

10461:   Notes:
10462:   `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10464:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.

10466:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`

10468:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10469:   actually needed.

10471:   This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10472:   which inherit from `MATSEQAIJ`.  `C` will be of the same type as the input matrices.

10474:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10476: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10477: @*/
10478: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10479: {
10480:   PetscFunctionBegin;
10481:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10482:   PetscFunctionReturn(PETSC_SUCCESS);
10483: }

10485: /*@
10486:   MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.

10488:   Neighbor-wise Collective

10490:   Input Parameters:
10491: + A     - the left matrix
10492: . B     - the middle matrix
10493: . C     - the right matrix
10494: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10495: - 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
10496:           if the result is a dense matrix this is irrelevant

10498:   Output Parameter:
10499: . D - the product matrix

10501:   Level: intermediate

10503:   Notes:
10504:   Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.

10506:   `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call

10508:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`

10510:   To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10511:   actually needed.

10513:   If you have many matrices with the same non-zero structure to multiply, you
10514:   should use `MAT_REUSE_MATRIX` in all calls but the first

10516:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10518: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10519: @*/
10520: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10521: {
10522:   PetscFunctionBegin;
10523:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10524:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10526:   if (scall == MAT_INITIAL_MATRIX) {
10527:     PetscCall(MatProductCreate(A, B, C, D));
10528:     PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10529:     PetscCall(MatProductSetAlgorithm(*D, "default"));
10530:     PetscCall(MatProductSetFill(*D, fill));

10532:     (*D)->product->api_user = PETSC_TRUE;
10533:     PetscCall(MatProductSetFromOptions(*D));
10534:     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,
10535:                ((PetscObject)C)->type_name);
10536:     PetscCall(MatProductSymbolic(*D));
10537:   } else { /* user may change input matrices when REUSE */
10538:     PetscCall(MatProductReplaceMats(A, B, C, *D));
10539:   }
10540:   PetscCall(MatProductNumeric(*D));
10541:   PetscFunctionReturn(PETSC_SUCCESS);
10542: }

10544: /*@
10545:   MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.

10547:   Collective

10549:   Input Parameters:
10550: + mat      - the matrix
10551: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10552: . subcomm  - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10553: - reuse    - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10555:   Output Parameter:
10556: . matredundant - redundant matrix

10558:   Level: advanced

10560:   Notes:
10561:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10562:   original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.

10564:   This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10565:   calling it.

10567:   `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.

10569: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10570: @*/
10571: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10572: {
10573:   MPI_Comm       comm;
10574:   PetscMPIInt    size;
10575:   PetscInt       mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10576:   Mat_Redundant *redund     = NULL;
10577:   PetscSubcomm   psubcomm   = NULL;
10578:   MPI_Comm       subcomm_in = subcomm;
10579:   Mat           *matseq;
10580:   IS             isrow, iscol;
10581:   PetscBool      newsubcomm = PETSC_FALSE;

10583:   PetscFunctionBegin;
10585:   if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10586:     PetscAssertPointer(*matredundant, 5);
10588:   }

10590:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10591:   if (size == 1 || nsubcomm == 1) {
10592:     if (reuse == MAT_INITIAL_MATRIX) {
10593:       PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10594:     } else {
10595:       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");
10596:       PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10597:     }
10598:     PetscFunctionReturn(PETSC_SUCCESS);
10599:   }

10601:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10602:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10603:   MatCheckPreallocated(mat, 1);

10605:   PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10606:   if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10607:     /* create psubcomm, then get subcomm */
10608:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10609:     PetscCallMPI(MPI_Comm_size(comm, &size));
10610:     PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);

10612:     PetscCall(PetscSubcommCreate(comm, &psubcomm));
10613:     PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10614:     PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10615:     PetscCall(PetscSubcommSetFromOptions(psubcomm));
10616:     PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10617:     newsubcomm = PETSC_TRUE;
10618:     PetscCall(PetscSubcommDestroy(&psubcomm));
10619:   }

10621:   /* get isrow, iscol and a local sequential matrix matseq[0] */
10622:   if (reuse == MAT_INITIAL_MATRIX) {
10623:     mloc_sub = PETSC_DECIDE;
10624:     nloc_sub = PETSC_DECIDE;
10625:     if (bs < 1) {
10626:       PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10627:       PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10628:     } else {
10629:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10630:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10631:     }
10632:     PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10633:     rstart = rend - mloc_sub;
10634:     PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10635:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10636:     PetscCall(ISSetIdentity(iscol));
10637:   } else { /* reuse == MAT_REUSE_MATRIX */
10638:     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");
10639:     /* retrieve subcomm */
10640:     PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10641:     redund = (*matredundant)->redundant;
10642:     isrow  = redund->isrow;
10643:     iscol  = redund->iscol;
10644:     matseq = redund->matseq;
10645:   }
10646:   PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));

10648:   /* get matredundant over subcomm */
10649:   if (reuse == MAT_INITIAL_MATRIX) {
10650:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));

10652:     /* create a supporting struct and attach it to C for reuse */
10653:     PetscCall(PetscNew(&redund));
10654:     (*matredundant)->redundant = redund;
10655:     redund->isrow              = isrow;
10656:     redund->iscol              = iscol;
10657:     redund->matseq             = matseq;
10658:     if (newsubcomm) {
10659:       redund->subcomm = subcomm;
10660:     } else {
10661:       redund->subcomm = MPI_COMM_NULL;
10662:     }
10663:   } else {
10664:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10665:   }
10666: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10667:   if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10668:     PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10669:     PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10670:   }
10671: #endif
10672:   PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10673:   PetscFunctionReturn(PETSC_SUCCESS);
10674: }

10676: /*@C
10677:   MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10678:   a given `Mat`. Each submatrix can span multiple procs.

10680:   Collective

10682:   Input Parameters:
10683: + mat     - the matrix
10684: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10685: - scall   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10687:   Output Parameter:
10688: . subMat - parallel sub-matrices each spanning a given `subcomm`

10690:   Level: advanced

10692:   Notes:
10693:   The submatrix partition across processors is dictated by `subComm` a
10694:   communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10695:   is not restricted to be grouped with consecutive original MPI processes.

10697:   Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10698:   map directly to the layout of the original matrix [wrt the local
10699:   row,col partitioning]. So the original 'DiagonalMat' naturally maps
10700:   into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10701:   the `subMat`. However the offDiagMat looses some columns - and this is
10702:   reconstructed with `MatSetValues()`

10704:   This is used by `PCBJACOBI` when a single block spans multiple MPI processes.

10706: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10707: @*/
10708: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10709: {
10710:   PetscMPIInt commsize, subCommSize;

10712:   PetscFunctionBegin;
10713:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10714:   PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10715:   PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);

10717:   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");
10718:   PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10719:   PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10720:   PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10721:   PetscFunctionReturn(PETSC_SUCCESS);
10722: }

10724: /*@
10725:   MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering

10727:   Not Collective

10729:   Input Parameters:
10730: + mat   - matrix to extract local submatrix from
10731: . isrow - local row indices for submatrix
10732: - iscol - local column indices for submatrix

10734:   Output Parameter:
10735: . submat - the submatrix

10737:   Level: intermediate

10739:   Notes:
10740:   `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.

10742:   Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`.  Its communicator may be
10743:   the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.

10745:   `submat` always implements `MatSetValuesLocal()`.  If `isrow` and `iscol` have the same block size, then
10746:   `MatSetValuesBlockedLocal()` will also be implemented.

10748:   `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10749:   Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.

10751: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10752: @*/
10753: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10754: {
10755:   PetscFunctionBegin;
10759:   PetscCheckSameComm(isrow, 2, iscol, 3);
10760:   PetscAssertPointer(submat, 4);
10761:   PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");

10763:   if (mat->ops->getlocalsubmatrix) {
10764:     PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10765:   } else {
10766:     PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10767:   }
10768:   PetscFunctionReturn(PETSC_SUCCESS);
10769: }

10771: /*@
10772:   MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`

10774:   Not Collective

10776:   Input Parameters:
10777: + mat    - matrix to extract local submatrix from
10778: . isrow  - local row indices for submatrix
10779: . iscol  - local column indices for submatrix
10780: - submat - the submatrix

10782:   Level: intermediate

10784: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10785: @*/
10786: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10787: {
10788:   PetscFunctionBegin;
10792:   PetscCheckSameComm(isrow, 2, iscol, 3);
10793:   PetscAssertPointer(submat, 4);

10796:   if (mat->ops->restorelocalsubmatrix) {
10797:     PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10798:   } else {
10799:     PetscCall(MatDestroy(submat));
10800:   }
10801:   *submat = NULL;
10802:   PetscFunctionReturn(PETSC_SUCCESS);
10803: }

10805: /*@
10806:   MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix

10808:   Collective

10810:   Input Parameter:
10811: . mat - the matrix

10813:   Output Parameter:
10814: . is - if any rows have zero diagonals this contains the list of them

10816:   Level: developer

10818: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10819: @*/
10820: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10821: {
10822:   PetscFunctionBegin;
10825:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10826:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10828:   if (!mat->ops->findzerodiagonals) {
10829:     Vec                diag;
10830:     const PetscScalar *a;
10831:     PetscInt          *rows;
10832:     PetscInt           rStart, rEnd, r, nrow = 0;

10834:     PetscCall(MatCreateVecs(mat, &diag, NULL));
10835:     PetscCall(MatGetDiagonal(mat, diag));
10836:     PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10837:     PetscCall(VecGetArrayRead(diag, &a));
10838:     for (r = 0; r < rEnd - rStart; ++r)
10839:       if (a[r] == 0.0) ++nrow;
10840:     PetscCall(PetscMalloc1(nrow, &rows));
10841:     nrow = 0;
10842:     for (r = 0; r < rEnd - rStart; ++r)
10843:       if (a[r] == 0.0) rows[nrow++] = r + rStart;
10844:     PetscCall(VecRestoreArrayRead(diag, &a));
10845:     PetscCall(VecDestroy(&diag));
10846:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10847:   } else {
10848:     PetscUseTypeMethod(mat, findzerodiagonals, is);
10849:   }
10850:   PetscFunctionReturn(PETSC_SUCCESS);
10851: }

10853: /*@
10854:   MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)

10856:   Collective

10858:   Input Parameter:
10859: . mat - the matrix

10861:   Output Parameter:
10862: . is - contains the list of rows with off block diagonal entries

10864:   Level: developer

10866: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10867: @*/
10868: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10869: {
10870:   PetscFunctionBegin;
10873:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10874:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10876:   PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10877:   PetscFunctionReturn(PETSC_SUCCESS);
10878: }

10880: /*@C
10881:   MatInvertBlockDiagonal - Inverts the block diagonal entries.

10883:   Collective; No Fortran Support

10885:   Input Parameter:
10886: . mat - the matrix

10888:   Output Parameter:
10889: . values - the block inverses in column major order (FORTRAN-like)

10891:   Level: advanced

10893:   Notes:
10894:   The size of the blocks is determined by the block size of the matrix.

10896:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10898:   The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size

10900: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10901: @*/
10902: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10903: {
10904:   PetscFunctionBegin;
10906:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10907:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10908:   PetscUseTypeMethod(mat, invertblockdiagonal, values);
10909:   PetscFunctionReturn(PETSC_SUCCESS);
10910: }

10912: /*@
10913:   MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.

10915:   Collective; No Fortran Support

10917:   Input Parameters:
10918: + mat     - the matrix
10919: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10920: - bsizes  - the size of each block on the process, set with `MatSetVariableBlockSizes()`

10922:   Output Parameter:
10923: . values - the block inverses in column major order (FORTRAN-like)

10925:   Level: advanced

10927:   Notes:
10928:   Use `MatInvertBlockDiagonal()` if all blocks have the same size

10930:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10932: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10933: @*/
10934: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10935: {
10936:   PetscFunctionBegin;
10938:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10939:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10940:   PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10941:   PetscFunctionReturn(PETSC_SUCCESS);
10942: }

10944: /*@
10945:   MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A

10947:   Collective

10949:   Input Parameters:
10950: + A - the matrix
10951: - C - matrix with inverted block diagonal of `A`.  This matrix should be created and may have its type set.

10953:   Level: advanced

10955:   Note:
10956:   The blocksize of the matrix is used to determine the blocks on the diagonal of `C`

10958: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10959: @*/
10960: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10961: {
10962:   const PetscScalar *vals;
10963:   PetscInt          *dnnz;
10964:   PetscInt           m, rstart, rend, bs, i, j;

10966:   PetscFunctionBegin;
10967:   PetscCall(MatInvertBlockDiagonal(A, &vals));
10968:   PetscCall(MatGetBlockSize(A, &bs));
10969:   PetscCall(MatGetLocalSize(A, &m, NULL));
10970:   PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10971:   PetscCall(PetscMalloc1(m / bs, &dnnz));
10972:   for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10973:   PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10974:   PetscCall(PetscFree(dnnz));
10975:   PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10976:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10977:   for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10978:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10979:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10980:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10981:   PetscFunctionReturn(PETSC_SUCCESS);
10982: }

10984: /*@
10985:   MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10986:   via `MatTransposeColoringCreate()`.

10988:   Collective

10990:   Input Parameter:
10991: . c - coloring context

10993:   Level: intermediate

10995: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10996: @*/
10997: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10998: {
10999:   MatTransposeColoring matcolor = *c;

11001:   PetscFunctionBegin;
11002:   if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
11003:   if (--((PetscObject)matcolor)->refct > 0) {
11004:     matcolor = NULL;
11005:     PetscFunctionReturn(PETSC_SUCCESS);
11006:   }

11008:   PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
11009:   PetscCall(PetscFree(matcolor->rows));
11010:   PetscCall(PetscFree(matcolor->den2sp));
11011:   PetscCall(PetscFree(matcolor->colorforcol));
11012:   PetscCall(PetscFree(matcolor->columns));
11013:   if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
11014:   PetscCall(PetscHeaderDestroy(c));
11015:   PetscFunctionReturn(PETSC_SUCCESS);
11016: }

11018: /*@
11019:   MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
11020:   a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
11021:   `MatTransposeColoring` to sparse `B`.

11023:   Collective

11025:   Input Parameters:
11026: + coloring - coloring context created with `MatTransposeColoringCreate()`
11027: - B        - sparse matrix

11029:   Output Parameter:
11030: . Btdense - dense matrix $B^T$

11032:   Level: developer

11034:   Note:
11035:   These are used internally for some implementations of `MatRARt()`

11037: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
11038: @*/
11039: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
11040: {
11041:   PetscFunctionBegin;

11046:   PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
11047:   PetscFunctionReturn(PETSC_SUCCESS);
11048: }

11050: /*@
11051:   MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
11052:   a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
11053:   in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
11054:   $C_{sp}$ from $C_{den}$.

11056:   Collective

11058:   Input Parameters:
11059: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
11060: - Cden        - matrix product of a sparse matrix and a dense matrix Btdense

11062:   Output Parameter:
11063: . Csp - sparse matrix

11065:   Level: developer

11067:   Note:
11068:   These are used internally for some implementations of `MatRARt()`

11070: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
11071: @*/
11072: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
11073: {
11074:   PetscFunctionBegin;

11079:   PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
11080:   PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
11081:   PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
11082:   PetscFunctionReturn(PETSC_SUCCESS);
11083: }

11085: /*@
11086:   MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.

11088:   Collective

11090:   Input Parameters:
11091: + mat        - the matrix product C
11092: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`

11094:   Output Parameter:
11095: . color - the new coloring context

11097:   Level: intermediate

11099: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
11100:           `MatTransColoringApplyDenToSp()`
11101: @*/
11102: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
11103: {
11104:   MatTransposeColoring c;
11105:   MPI_Comm             comm;

11107:   PetscFunctionBegin;
11108:   PetscAssertPointer(color, 3);

11110:   PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11111:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
11112:   PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
11113:   c->ctype = iscoloring->ctype;
11114:   PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
11115:   *color = c;
11116:   PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11117:   PetscFunctionReturn(PETSC_SUCCESS);
11118: }

11120: /*@
11121:   MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
11122:   matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.

11124:   Not Collective

11126:   Input Parameter:
11127: . mat - the matrix

11129:   Output Parameter:
11130: . state - the current state

11132:   Level: intermediate

11134:   Notes:
11135:   You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11136:   different matrices

11138:   Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix

11140:   Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.

11142: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11143: @*/
11144: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11145: {
11146:   PetscFunctionBegin;
11148:   *state = mat->nonzerostate;
11149:   PetscFunctionReturn(PETSC_SUCCESS);
11150: }

11152: /*@
11153:   MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11154:   matrices from each processor

11156:   Collective

11158:   Input Parameters:
11159: + comm   - the communicators the parallel matrix will live on
11160: . seqmat - the input sequential matrices
11161: . n      - number of local columns (or `PETSC_DECIDE`)
11162: - reuse  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

11164:   Output Parameter:
11165: . mpimat - the parallel matrix generated

11167:   Level: developer

11169:   Note:
11170:   The number of columns of the matrix in EACH processor MUST be the same.

11172: .seealso: [](ch_matrices), `Mat`
11173: @*/
11174: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11175: {
11176:   PetscMPIInt size;

11178:   PetscFunctionBegin;
11179:   PetscCallMPI(MPI_Comm_size(comm, &size));
11180:   if (size == 1) {
11181:     if (reuse == MAT_INITIAL_MATRIX) {
11182:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11183:     } else {
11184:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11185:     }
11186:     PetscFunctionReturn(PETSC_SUCCESS);
11187:   }

11189:   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");

11191:   PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11192:   PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11193:   PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11194:   PetscFunctionReturn(PETSC_SUCCESS);
11195: }

11197: /*@
11198:   MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.

11200:   Collective

11202:   Input Parameters:
11203: + A - the matrix to create subdomains from
11204: - N - requested number of subdomains

11206:   Output Parameters:
11207: + n   - number of subdomains resulting on this MPI process
11208: - iss - `IS` list with indices of subdomains on this MPI process

11210:   Level: advanced

11212:   Note:
11213:   The number of subdomains must be smaller than the communicator size

11215: .seealso: [](ch_matrices), `Mat`, `IS`
11216: @*/
11217: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11218: {
11219:   MPI_Comm    comm, subcomm;
11220:   PetscMPIInt size, rank, color;
11221:   PetscInt    rstart, rend, k;

11223:   PetscFunctionBegin;
11224:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11225:   PetscCallMPI(MPI_Comm_size(comm, &size));
11226:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
11227:   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);
11228:   *n    = 1;
11229:   k     = size / N + (size % N > 0); /* There are up to k ranks to a color */
11230:   color = rank / k;
11231:   PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11232:   PetscCall(PetscMalloc1(1, iss));
11233:   PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11234:   PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11235:   PetscCallMPI(MPI_Comm_free(&subcomm));
11236:   PetscFunctionReturn(PETSC_SUCCESS);
11237: }

11239: /*@
11240:   MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.

11242:   If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11243:   If they are not the same, uses `MatMatMatMult()`.

11245:   Once the coarse grid problem is constructed, correct for interpolation operators
11246:   that are not of full rank, which can legitimately happen in the case of non-nested
11247:   geometric multigrid.

11249:   Input Parameters:
11250: + restrct     - restriction operator
11251: . dA          - fine grid matrix
11252: . interpolate - interpolation operator
11253: . reuse       - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11254: - fill        - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate

11256:   Output Parameter:
11257: . A - the Galerkin coarse matrix

11259:   Options Database Key:
11260: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used

11262:   Level: developer

11264:   Note:
11265:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

11267: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11268: @*/
11269: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11270: {
11271:   IS  zerorows;
11272:   Vec diag;

11274:   PetscFunctionBegin;
11275:   PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11276:   /* Construct the coarse grid matrix */
11277:   if (interpolate == restrct) {
11278:     PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11279:   } else {
11280:     PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11281:   }

11283:   /* If the interpolation matrix is not of full rank, A will have zero rows.
11284:      This can legitimately happen in the case of non-nested geometric multigrid.
11285:      In that event, we set the rows of the matrix to the rows of the identity,
11286:      ignoring the equations (as the RHS will also be zero). */

11288:   PetscCall(MatFindZeroRows(*A, &zerorows));

11290:   if (zerorows != NULL) { /* if there are any zero rows */
11291:     PetscCall(MatCreateVecs(*A, &diag, NULL));
11292:     PetscCall(MatGetDiagonal(*A, diag));
11293:     PetscCall(VecISSet(diag, zerorows, 1.0));
11294:     PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11295:     PetscCall(VecDestroy(&diag));
11296:     PetscCall(ISDestroy(&zerorows));
11297:   }
11298:   PetscFunctionReturn(PETSC_SUCCESS);
11299: }

11301: /*@C
11302:   MatSetOperation - Allows user to set a matrix operation for any matrix type

11304:   Logically Collective

11306:   Input Parameters:
11307: + mat - the matrix
11308: . op  - the name of the operation
11309: - f   - the function that provides the operation

11311:   Level: developer

11313:   Example Usage:
11314: .vb
11315:   extern PetscErrorCode usermult(Mat, Vec, Vec);

11317:   PetscCall(MatCreateXXX(comm, ..., &A));
11318:   PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
11319: .ve

11321:   Notes:
11322:   See the file `include/petscmat.h` for a complete list of matrix
11323:   operations, which all have the form MATOP_<OPERATION>, where
11324:   <OPERATION> is the name (in all capital letters) of the
11325:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11327:   All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11328:   sequence as the usual matrix interface routines, since they
11329:   are intended to be accessed via the usual matrix interface
11330:   routines, e.g.,
11331: .vb
11332:   MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11333: .ve

11335:   In particular each function MUST return `PETSC_SUCCESS` on success and
11336:   nonzero on failure.

11338:   This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.

11340: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11341: @*/
11342: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11343: {
11344:   PetscFunctionBegin;
11346:   if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
11347:   (((void (**)(void))mat->ops)[op]) = f;
11348:   PetscFunctionReturn(PETSC_SUCCESS);
11349: }

11351: /*@C
11352:   MatGetOperation - Gets a matrix operation for any matrix type.

11354:   Not Collective

11356:   Input Parameters:
11357: + mat - the matrix
11358: - op  - the name of the operation

11360:   Output Parameter:
11361: . f - the function that provides the operation

11363:   Level: developer

11365:   Example Usage:
11366: .vb
11367:   PetscErrorCode (*usermult)(Mat, Vec, Vec);

11369:   MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11370: .ve

11372:   Notes:
11373:   See the file include/petscmat.h for a complete list of matrix
11374:   operations, which all have the form MATOP_<OPERATION>, where
11375:   <OPERATION> is the name (in all capital letters) of the
11376:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11378:   This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.

11380: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11381: @*/
11382: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11383: {
11384:   PetscFunctionBegin;
11386:   *f = (((void (**)(void))mat->ops)[op]);
11387:   PetscFunctionReturn(PETSC_SUCCESS);
11388: }

11390: /*@
11391:   MatHasOperation - Determines whether the given matrix supports the particular operation.

11393:   Not Collective

11395:   Input Parameters:
11396: + mat - the matrix
11397: - op  - the operation, for example, `MATOP_GET_DIAGONAL`

11399:   Output Parameter:
11400: . has - either `PETSC_TRUE` or `PETSC_FALSE`

11402:   Level: advanced

11404:   Note:
11405:   See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.

11407: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11408: @*/
11409: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11410: {
11411:   PetscFunctionBegin;
11413:   PetscAssertPointer(has, 3);
11414:   if (mat->ops->hasoperation) {
11415:     PetscUseTypeMethod(mat, hasoperation, op, has);
11416:   } else {
11417:     if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11418:     else {
11419:       *has = PETSC_FALSE;
11420:       if (op == MATOP_CREATE_SUBMATRIX) {
11421:         PetscMPIInt size;

11423:         PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11424:         if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11425:       }
11426:     }
11427:   }
11428:   PetscFunctionReturn(PETSC_SUCCESS);
11429: }

11431: /*@
11432:   MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent

11434:   Collective

11436:   Input Parameter:
11437: . mat - the matrix

11439:   Output Parameter:
11440: . cong - either `PETSC_TRUE` or `PETSC_FALSE`

11442:   Level: beginner

11444: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11445: @*/
11446: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11447: {
11448:   PetscFunctionBegin;
11451:   PetscAssertPointer(cong, 2);
11452:   if (!mat->rmap || !mat->cmap) {
11453:     *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11454:     PetscFunctionReturn(PETSC_SUCCESS);
11455:   }
11456:   if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11457:     PetscCall(PetscLayoutSetUp(mat->rmap));
11458:     PetscCall(PetscLayoutSetUp(mat->cmap));
11459:     PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11460:     if (*cong) mat->congruentlayouts = 1;
11461:     else mat->congruentlayouts = 0;
11462:   } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11463:   PetscFunctionReturn(PETSC_SUCCESS);
11464: }

11466: PetscErrorCode MatSetInf(Mat A)
11467: {
11468:   PetscFunctionBegin;
11469:   PetscUseTypeMethod(A, setinf);
11470:   PetscFunctionReturn(PETSC_SUCCESS);
11471: }

11473: /*@
11474:   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
11475:   and possibly removes small values from the graph structure.

11477:   Collective

11479:   Input Parameters:
11480: + A       - the matrix
11481: . sym     - `PETSC_TRUE` indicates that the graph should be symmetrized
11482: . scale   - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11483: . filter  - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11484: . num_idx - size of 'index' array
11485: - index   - array of block indices to use for graph strength of connection weight

11487:   Output Parameter:
11488: . graph - the resulting graph

11490:   Level: advanced

11492: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11493: @*/
11494: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11495: {
11496:   PetscFunctionBegin;
11500:   PetscAssertPointer(graph, 7);
11501:   PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11502:   PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11503:   PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11504:   PetscFunctionReturn(PETSC_SUCCESS);
11505: }

11507: /*@
11508:   MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11509:   meaning the same memory is used for the matrix, and no new memory is allocated.

11511:   Collective

11513:   Input Parameters:
11514: + A    - the matrix
11515: - 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

11517:   Level: intermediate

11519:   Developer Note:
11520:   The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11521:   of the arrays in the data structure are unneeded.

11523: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11524: @*/
11525: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11526: {
11527:   PetscFunctionBegin;
11529:   PetscUseTypeMethod(A, eliminatezeros, keep);
11530:   PetscFunctionReturn(PETSC_SUCCESS);
11531: }