Actual source code: matrix.c

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

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

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

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

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

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

 54:   Logically Collective

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

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

 68:   Level: intermediate

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

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

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

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

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

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

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

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

110:   Logically Collective

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

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

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

124:   Level: advanced

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

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

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

142:   Logically Collective

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

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

152:   Level: advanced

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

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

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

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

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

179:   Logically Collective

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

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

187:   Level: advanced

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

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

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

207:   Logically Collective

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

212:   Level: developer

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

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

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

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

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

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

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

296:   Level: intermediate

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

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

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

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

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

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

329:   Level: intermediate

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

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

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

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

362:   Not Collective

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

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

370:   Level: advanced

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

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

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

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

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

400:   Collective

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

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

408:   Level: advanced

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

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

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

429:   Logically Collective

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

434:   Level: advanced

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

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

453:   Collective

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

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

462:   Level: advanced

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

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

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

487:   Logically Collective

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

492:   Level: advanced

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

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

511:   Not Collective

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

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

520:   Level: advanced

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

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

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

545:   Not Collective

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

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

556:   Level: advanced

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

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

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

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

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

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

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

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

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

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

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

613:   Logically Collective

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

618:   Level: advanced

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

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

637:   Not Collective

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

646:   Level: advanced

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

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

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

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

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

680:   Not Collective

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

685:   Level: advanced

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

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

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

707:   Not Collective

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

712:   Level: advanced

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

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

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

735:   Logically Collective

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

741:   Level: advanced

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

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

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

761: /*@
762:   MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
763:   for matrices created with `MatGetFactor()`

765:   Logically Collective

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

771:   Level: developer

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

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

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

797: /*@
798:   MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
799:   for matrices created with `MatGetFactor()`

801:   Logically Collective

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

807:   Level: developer

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

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

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

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

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

841: /*@
842:   MatAppendOptionsPrefix - Appends to the prefix used for searching for all
843:   matrix options in the database.

845:   Logically Collective

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

851:   Level: advanced

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

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

867: /*@
868:   MatGetOptionsPrefix - Gets the prefix used for searching for all
869:   matrix options in the database.

871:   Not Collective

873:   Input Parameter:
874: . A - the matrix

876:   Output Parameter:
877: . prefix - pointer to the prefix string used

879:   Level: advanced

881:   Fortran Note:
882:   The user should pass in a string `prefix` of
883:   sufficient length to hold the prefix.

885: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
886: @*/
887: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
888: {
889:   PetscFunctionBegin;
891:   PetscAssertPointer(prefix, 2);
892:   PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
893:   PetscFunctionReturn(PETSC_SUCCESS);
894: }

896: /*@
897:   MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`

899:   Not Collective

901:   Input Parameter:
902: . A - the matrix

904:   Output Parameter:
905: . state - the object state

907:   Level: advanced

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

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

916: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
917: @*/
918: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
919: {
920:   PetscFunctionBegin;
922:   PetscAssertPointer(state, 2);
923:   PetscCall(PetscObjectStateGet((PetscObject)A, state));
924:   PetscFunctionReturn(PETSC_SUCCESS);
925: }

927: /*@
928:   MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`

930:   Collective

932:   Input Parameter:
933: . A - the matrix

935:   Level: beginner

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

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

944:   Currently only supported for  `MATAIJ` matrices.

946: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
947: @*/
948: PetscErrorCode MatResetPreallocation(Mat A)
949: {
950:   PetscFunctionBegin;
953:   PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
954:   PetscFunctionReturn(PETSC_SUCCESS);
955: }

957: /*@
958:   MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.

960:   Collective

962:   Input Parameter:
963: . A - the matrix

965:   Level: intermediate

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

970:   Currently only supported for `MATAIJ` matrices.

972: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
973: @*/
974: PetscErrorCode MatResetHash(Mat A)
975: {
976:   PetscFunctionBegin;
979:   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()");
980:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
981:   PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
982:   /* These flags are used to determine whether certain setups occur */
983:   A->was_assembled = PETSC_FALSE;
984:   A->assembled     = PETSC_FALSE;
985:   /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
986:   PetscCall(PetscObjectStateIncrease((PetscObject)A));
987:   PetscFunctionReturn(PETSC_SUCCESS);
988: }

990: /*@
991:   MatSetUp - Sets up the internal matrix data structures for later use by the matrix

993:   Collective

995:   Input Parameter:
996: . A - the matrix

998:   Level: advanced

1000:   Notes:
1001:   If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
1002:   setting values in the matrix.

1004:   This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users

1006: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1007: @*/
1008: PetscErrorCode MatSetUp(Mat A)
1009: {
1010:   PetscFunctionBegin;
1012:   if (!((PetscObject)A)->type_name) {
1013:     PetscMPIInt size;

1015:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1016:     PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1017:   }
1018:   if (!A->preallocated) PetscTryTypeMethod(A, setup);
1019:   PetscCall(PetscLayoutSetUp(A->rmap));
1020:   PetscCall(PetscLayoutSetUp(A->cmap));
1021:   A->preallocated = PETSC_TRUE;
1022:   PetscFunctionReturn(PETSC_SUCCESS);
1023: }

1025: #if defined(PETSC_HAVE_SAWS)
1026: #include <petscviewersaws.h>
1027: #endif

1029: /*
1030:    If threadsafety is on extraneous matrices may be printed

1032:    This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1033: */
1034: #if !defined(PETSC_HAVE_THREADSAFETY)
1035: static PetscInt insidematview = 0;
1036: #endif

1038: /*@
1039:   MatViewFromOptions - View properties of the matrix based on options set in the options database

1041:   Collective

1043:   Input Parameters:
1044: + A    - the matrix
1045: . obj  - optional additional object that provides the options prefix to use
1046: - name - command line option

1048:   Options Database Key:
1049: . -mat_view [viewertype]:... - the viewer and its options

1051:   Level: intermediate

1053:   Note:
1054: .vb
1055:     If no value is provided ascii:stdout is used
1056:        ascii[:[filename][:[format][:append]]]    defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
1057:                                                   for example ascii::ascii_info prints just the information about the object not all details
1058:                                                   unless :append is given filename opens in write mode, overwriting what was already there
1059:        binary[:[filename][:[format][:append]]]   defaults to the file binaryoutput
1060:        draw[:drawtype[:filename]]                for example, draw:tikz, draw:tikz:figure.tex  or draw:x
1061:        socket[:port]                             defaults to the standard output port
1062:        saws[:communicatorname]                    publishes object to the Scientific Application Webserver (SAWs)
1063: .ve

1065: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1066: @*/
1067: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1068: {
1069:   PetscFunctionBegin;
1071: #if !defined(PETSC_HAVE_THREADSAFETY)
1072:   if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1073: #endif
1074:   PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1075:   PetscFunctionReturn(PETSC_SUCCESS);
1076: }

1078: /*@
1079:   MatView - display information about a matrix in a variety ways

1081:   Collective on viewer

1083:   Input Parameters:
1084: + mat    - the matrix
1085: - viewer - visualization context

1087:   Options Database Keys:
1088: + -mat_view ::ascii_info           - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1089: . -mat_view ::ascii_info_detail    - Prints more detailed info
1090: . -mat_view                        - Prints matrix in ASCII format
1091: . -mat_view ::ascii_matlab         - Prints matrix in MATLAB format
1092: . -mat_view draw                   - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1093: . -display <name>                  - Sets display name (default is host)
1094: . -draw_pause <sec>                - Sets number of seconds to pause after display
1095: . -mat_view socket                 - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1096: . -viewer_socket_machine <machine> - -
1097: . -viewer_socket_port <port>       - -
1098: . -mat_view binary                 - save matrix to file in binary format
1099: - -viewer_binary_filename <name>   - -

1101:   Level: beginner

1103:   Notes:
1104:   The available visualization contexts include
1105: +    `PETSC_VIEWER_STDOUT_SELF`   - for sequential matrices
1106: .    `PETSC_VIEWER_STDOUT_WORLD`  - for parallel matrices created on `PETSC_COMM_WORLD`
1107: .    `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1108: -     `PETSC_VIEWER_DRAW_WORLD`   - graphical display of nonzero structure

1110:   The user can open alternative visualization contexts with
1111: +    `PetscViewerASCIIOpen()`  - Outputs matrix to a specified file
1112: .    `PetscViewerBinaryOpen()` - Outputs matrix in binary to a  specified file; corresponding input uses `MatLoad()`
1113: .    `PetscViewerDrawOpen()`   - Outputs nonzero matrix nonzero structure to an X window display
1114: -    `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.

1116:   The user can call `PetscViewerPushFormat()` to specify the output
1117:   format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1118:   `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`).  Available formats include
1119: +    `PETSC_VIEWER_DEFAULT`           - default, prints matrix contents
1120: .    `PETSC_VIEWER_ASCII_MATLAB`      - prints matrix contents in MATLAB format
1121: .    `PETSC_VIEWER_ASCII_DENSE`       - prints entire matrix including zeros
1122: .    `PETSC_VIEWER_ASCII_COMMON`      - prints matrix contents, using a sparse  format common among all matrix types
1123: .    `PETSC_VIEWER_ASCII_IMPL`        - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1124: .    `PETSC_VIEWER_ASCII_INFO`        - prints basic information about the matrix size and structure (not the matrix entries)
1125: -    `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)

1127:   The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1128:   the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

1130:   In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).

1132:   See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1133:   viewer is used.

1135:   See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1136:   viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.

1138:   One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1139:   and then use the following mouse functions.
1140: .vb
1141:   left mouse: zoom in
1142:   middle mouse: zoom out
1143:   right mouse: continue with the simulation
1144: .ve

1146: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1147:           `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1148: @*/
1149: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1150: {
1151:   PetscInt          rows, cols, rbs, cbs;
1152:   PetscBool         isascii, isstring, issaws;
1153:   PetscViewerFormat format;
1154:   PetscMPIInt       size;

1156:   PetscFunctionBegin;
1159:   if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));

1162:   PetscCall(PetscViewerGetFormat(viewer, &format));
1163:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1164:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);

1166: #if !defined(PETSC_HAVE_THREADSAFETY)
1167:   insidematview++;
1168: #endif
1169:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1170:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1171:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1172:   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");

1174:   PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1175:   if (isascii) {
1176:     if (!mat->preallocated) {
1177:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1178: #if !defined(PETSC_HAVE_THREADSAFETY)
1179:       insidematview--;
1180: #endif
1181:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1182:       PetscFunctionReturn(PETSC_SUCCESS);
1183:     }
1184:     if (!mat->assembled) {
1185:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1186: #if !defined(PETSC_HAVE_THREADSAFETY)
1187:       insidematview--;
1188: #endif
1189:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1190:       PetscFunctionReturn(PETSC_SUCCESS);
1191:     }
1192:     PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1193:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1194:       MatNullSpace nullsp, transnullsp;

1196:       PetscCall(PetscViewerASCIIPushTab(viewer));
1197:       PetscCall(MatGetSize(mat, &rows, &cols));
1198:       PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1199:       if (rbs != 1 || cbs != 1) {
1200:         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" : ""));
1201:         else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1202:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1203:       if (mat->factortype) {
1204:         MatSolverType solver;
1205:         PetscCall(MatFactorGetSolverType(mat, &solver));
1206:         PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1207:       }
1208:       if (mat->ops->getinfo) {
1209:         MatInfo info;
1210:         PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1211:         PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1212:         if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1213:       }
1214:       PetscCall(MatGetNullSpace(mat, &nullsp));
1215:       PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1216:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached null space\n"));
1217:       if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached transposed null space\n"));
1218:       PetscCall(MatGetNearNullSpace(mat, &nullsp));
1219:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached near null space\n"));
1220:       PetscCall(PetscViewerASCIIPushTab(viewer));
1221:       PetscCall(MatProductView(mat, viewer));
1222:       PetscCall(PetscViewerASCIIPopTab(viewer));
1223:       if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1224:         IS tmp;

1226:         PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1227:         PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1228:         PetscCall(PetscViewerASCIIPushTab(viewer));
1229:         PetscCall(ISView(tmp, viewer));
1230:         PetscCall(PetscViewerASCIIPopTab(viewer));
1231:         PetscCall(ISDestroy(&tmp));
1232:       }
1233:     }
1234:   } else if (issaws) {
1235: #if defined(PETSC_HAVE_SAWS)
1236:     PetscMPIInt rank;

1238:     PetscCall(PetscObjectName((PetscObject)mat));
1239:     PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1240:     if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1241: #endif
1242:   } else if (isstring) {
1243:     const char *type;
1244:     PetscCall(MatGetType(mat, &type));
1245:     PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1246:     PetscTryTypeMethod(mat, view, viewer);
1247:   }
1248:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1249:     PetscCall(PetscViewerASCIIPushTab(viewer));
1250:     PetscUseTypeMethod(mat, viewnative, viewer);
1251:     PetscCall(PetscViewerASCIIPopTab(viewer));
1252:   } else if (mat->ops->view) {
1253:     PetscCall(PetscViewerASCIIPushTab(viewer));
1254:     PetscUseTypeMethod(mat, view, viewer);
1255:     PetscCall(PetscViewerASCIIPopTab(viewer));
1256:   }
1257:   if (isascii) {
1258:     PetscCall(PetscViewerGetFormat(viewer, &format));
1259:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1260:   }
1261:   PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1262: #if !defined(PETSC_HAVE_THREADSAFETY)
1263:   insidematview--;
1264: #endif
1265:   PetscFunctionReturn(PETSC_SUCCESS);
1266: }

1268: #if defined(PETSC_USE_DEBUG)
1269: #include <../src/sys/totalview/tv_data_display.h>
1270: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1271: {
1272:   TV_add_row("Local rows", "int", &mat->rmap->n);
1273:   TV_add_row("Local columns", "int", &mat->cmap->n);
1274:   TV_add_row("Global rows", "int", &mat->rmap->N);
1275:   TV_add_row("Global columns", "int", &mat->cmap->N);
1276:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1277:   return TV_format_OK;
1278: }
1279: #endif

1281: /*@
1282:   MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1283:   with `MatView()`.  The matrix format is determined from the options database.
1284:   Generates a parallel MPI matrix if the communicator has more than one
1285:   processor.  The default matrix type is `MATAIJ`.

1287:   Collective

1289:   Input Parameters:
1290: + mat    - the newly loaded matrix, this needs to have been created with `MatCreate()`
1291:             or some related function before a call to `MatLoad()`
1292: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer

1294:   Options Database Key:
1295: . -matload_block_size <bs> - set block size

1297:   Level: beginner

1299:   Notes:
1300:   If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1301:   `Mat` before calling this routine if you wish to set it from the options database.

1303:   `MatLoad()` automatically loads into the options database any options
1304:   given in the file filename.info where filename is the name of the file
1305:   that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1306:   file will be ignored if you use the -viewer_binary_skip_info option.

1308:   If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1309:   sets the default matrix type AIJ and sets the local and global sizes.
1310:   If type and/or size is already set, then the same are used.

1312:   In parallel, each processor can load a subset of rows (or the
1313:   entire matrix).  This routine is especially useful when a large
1314:   matrix is stored on disk and only part of it is desired on each
1315:   processor.  For example, a parallel solver may access only some of
1316:   the rows from each processor.  The algorithm used here reads
1317:   relatively small blocks of data rather than reading the entire
1318:   matrix and then subsetting it.

1320:   Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1321:   Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1322:   or the sequence like
1323: .vb
1324:     `PetscViewer` v;
1325:     `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1326:     `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1327:     `PetscViewerSetFromOptions`(v);
1328:     `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1329:     `PetscViewerFileSetName`(v,"datafile");
1330: .ve
1331:   The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1332: $ -viewer_type {binary, hdf5}

1334:   See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1335:   and src/mat/tutorials/ex10.c with the second approach.

1337:   In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1338:   is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1339:   Multiple objects, both matrices and vectors, can be stored within the same file.
1340:   Their `PetscObject` name is ignored; they are loaded in the order of their storage.

1342:   Most users should not need to know the details of the binary storage
1343:   format, since `MatLoad()` and `MatView()` completely hide these details.
1344:   But for anyone who is interested, the standard binary matrix storage
1345:   format is

1347: .vb
1348:     PetscInt    MAT_FILE_CLASSID
1349:     PetscInt    number of rows
1350:     PetscInt    number of columns
1351:     PetscInt    total number of nonzeros
1352:     PetscInt    *number nonzeros in each row
1353:     PetscInt    *column indices of all nonzeros (starting index is zero)
1354:     PetscScalar *values of all nonzeros
1355: .ve
1356:   If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1357:   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
1358:   case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.

1360:   PETSc automatically does the byte swapping for
1361:   machines that store the bytes reversed. Thus if you write your own binary
1362:   read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1363:   and `PetscBinaryWrite()` to see how this may be done.

1365:   In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1366:   Each processor's chunk is loaded independently by its owning MPI process.
1367:   Multiple objects, both matrices and vectors, can be stored within the same file.
1368:   They are looked up by their PetscObject name.

1370:   As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1371:   by default the same structure and naming of the AIJ arrays and column count
1372:   within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1373: $    save example.mat A b -v7.3
1374:   can be directly read by this routine (see Reference 1 for details).

1376:   Depending on your MATLAB version, this format might be a default,
1377:   otherwise you can set it as default in Preferences.

1379:   Unless -nocompression flag is used to save the file in MATLAB,
1380:   PETSc must be configured with ZLIB package.

1382:   See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

1384:   This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`

1386:   Corresponding `MatView()` is not yet implemented.

1388:   The loaded matrix is actually a transpose of the original one in MATLAB,
1389:   unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1390:   With this format, matrix is automatically transposed by PETSc,
1391:   unless the matrix is marked as SPD or symmetric
1392:   (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).

1394:   See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>

1396: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1397:  @*/
1398: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1399: {
1400:   PetscBool flg;

1402:   PetscFunctionBegin;

1406:   if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));

1408:   flg = PETSC_FALSE;
1409:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1410:   if (flg) {
1411:     PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1412:     PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1413:   }
1414:   flg = PETSC_FALSE;
1415:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1416:   if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));

1418:   PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1419:   PetscUseTypeMethod(mat, load, viewer);
1420:   PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1421:   PetscFunctionReturn(PETSC_SUCCESS);
1422: }

1424: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1425: {
1426:   Mat_Redundant *redund = *redundant;

1428:   PetscFunctionBegin;
1429:   if (redund) {
1430:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1431:       PetscCall(ISDestroy(&redund->isrow));
1432:       PetscCall(ISDestroy(&redund->iscol));
1433:       PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1434:     } else {
1435:       PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1436:       PetscCall(PetscFree(redund->sbuf_j));
1437:       PetscCall(PetscFree(redund->sbuf_a));
1438:       for (PetscInt i = 0; i < redund->nrecvs; i++) {
1439:         PetscCall(PetscFree(redund->rbuf_j[i]));
1440:         PetscCall(PetscFree(redund->rbuf_a[i]));
1441:       }
1442:       PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1443:     }

1445:     if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1446:     PetscCall(PetscFree(redund));
1447:   }
1448:   PetscFunctionReturn(PETSC_SUCCESS);
1449: }

1451: /*@
1452:   MatDestroy - Frees space taken by a matrix.

1454:   Collective

1456:   Input Parameter:
1457: . A - the matrix

1459:   Level: beginner

1461:   Developer Note:
1462:   Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1463:   `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1464:   `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1465:   if changes are needed here.

1467: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1468: @*/
1469: PetscErrorCode MatDestroy(Mat *A)
1470: {
1471:   PetscFunctionBegin;
1472:   if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1474:   if (--((PetscObject)*A)->refct > 0) {
1475:     *A = NULL;
1476:     PetscFunctionReturn(PETSC_SUCCESS);
1477:   }

1479:   /* if memory was published with SAWs then destroy it */
1480:   PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1481:   PetscTryTypeMethod(*A, destroy);

1483:   PetscCall(PetscFree((*A)->factorprefix));
1484:   PetscCall(PetscFree((*A)->defaultvectype));
1485:   PetscCall(PetscFree((*A)->defaultrandtype));
1486:   PetscCall(PetscFree((*A)->bsizes));
1487:   PetscCall(PetscFree((*A)->solvertype));
1488:   for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1489:   if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1490:   PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1491:   PetscCall(MatProductClear(*A));
1492:   PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1493:   PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1494:   PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1495:   PetscCall(MatDestroy(&(*A)->schur));
1496:   PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1497:   PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1498:   PetscCall(PetscHeaderDestroy(A));
1499:   PetscFunctionReturn(PETSC_SUCCESS);
1500: }

1502: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1503: /*@
1504:   MatSetValues - Inserts or adds a block of values into a matrix.
1505:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1506:   MUST be called after all calls to `MatSetValues()` have been completed.

1508:   Not Collective

1510:   Input Parameters:
1511: + mat  - the matrix
1512: . v    - a logically two-dimensional array of values
1513: . m    - the number of rows
1514: . idxm - the global indices of the rows
1515: . n    - the number of columns
1516: . idxn - the global indices of the columns
1517: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1519:   Level: beginner

1521:   Notes:
1522:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1524:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1525:   options cannot be mixed without intervening calls to the assembly
1526:   routines.

1528:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1529:   as well as in C.

1531:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1532:   simply ignored. This allows easily inserting element stiffness matrices
1533:   with homogeneous Dirichlet boundary conditions that you don't want represented
1534:   in the matrix.

1536:   Efficiency Alert:
1537:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1538:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1540:   Fortran Notes:
1541:   If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1542: .vb
1543:   call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1544: .ve

1546:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1548:   Developer Note:
1549:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1550:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1552: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1553:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1554: @*/
1555: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1556: {
1557:   PetscFunctionBeginHot;
1560:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1561:   PetscAssertPointer(idxm, 3);
1562:   PetscAssertPointer(idxn, 5);
1563:   MatCheckPreallocated(mat, 1);

1565:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1566:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

1568:   if (PetscDefined(USE_DEBUG)) {
1569:     PetscInt i, j;

1571:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1572:     if (v) {
1573:       for (i = 0; i < m; i++) {
1574:         for (j = 0; j < n; j++) {
1575:           if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1576: #if defined(PETSC_USE_COMPLEX)
1577:             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]);
1578: #else
1579:             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]);
1580: #endif
1581:         }
1582:       }
1583:     }
1584:     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);
1585:     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);
1586:   }

1588:   if (mat->assembled) {
1589:     mat->was_assembled = PETSC_TRUE;
1590:     mat->assembled     = PETSC_FALSE;
1591:   }
1592:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1593:   PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1594:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1595:   PetscFunctionReturn(PETSC_SUCCESS);
1596: }

1598: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1599: /*@
1600:   MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1601:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1602:   MUST be called after all calls to `MatSetValues()` have been completed.

1604:   Not Collective

1606:   Input Parameters:
1607: + mat  - the matrix
1608: . v    - a logically two-dimensional array of values
1609: . ism  - the rows to provide
1610: . isn  - the columns to provide
1611: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1613:   Level: beginner

1615:   Notes:
1616:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1618:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1619:   options cannot be mixed without intervening calls to the assembly
1620:   routines.

1622:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1623:   as well as in C.

1625:   Negative indices may be passed in `ism` and `isn`, these rows and columns are
1626:   simply ignored. This allows easily inserting element stiffness matrices
1627:   with homogeneous Dirichlet boundary conditions that you don't want represented
1628:   in the matrix.

1630:   Efficiency Alert:
1631:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1632:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1634:   This is currently not optimized for any particular `ISType`

1636:   Developer Note:
1637:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1638:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1640: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1641:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1642: @*/
1643: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1644: {
1645:   PetscInt        m, n;
1646:   const PetscInt *rows, *cols;

1648:   PetscFunctionBeginHot;
1650:   PetscCall(ISGetIndices(ism, &rows));
1651:   PetscCall(ISGetIndices(isn, &cols));
1652:   PetscCall(ISGetLocalSize(ism, &m));
1653:   PetscCall(ISGetLocalSize(isn, &n));
1654:   PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1655:   PetscCall(ISRestoreIndices(ism, &rows));
1656:   PetscCall(ISRestoreIndices(isn, &cols));
1657:   PetscFunctionReturn(PETSC_SUCCESS);
1658: }

1660: /*@
1661:   MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1662:   values into a matrix

1664:   Not Collective

1666:   Input Parameters:
1667: + mat - the matrix
1668: . row - the (block) row to set
1669: - v   - a logically two-dimensional array of values

1671:   Level: intermediate

1673:   Notes:
1674:   The values, `v`, are column-oriented (for the block version) and sorted

1676:   All the nonzero values in `row` must be provided

1678:   The matrix must have previously had its column indices set, likely by having been assembled.

1680:   `row` must belong to this MPI process

1682: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1683:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1684: @*/
1685: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1686: {
1687:   PetscInt globalrow;

1689:   PetscFunctionBegin;
1692:   PetscAssertPointer(v, 3);
1693:   PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1694:   PetscCall(MatSetValuesRow(mat, globalrow, v));
1695:   PetscFunctionReturn(PETSC_SUCCESS);
1696: }

1698: /*@
1699:   MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1700:   values into a matrix

1702:   Not Collective

1704:   Input Parameters:
1705: + mat - the matrix
1706: . row - the (block) row to set
1707: - 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

1709:   Level: advanced

1711:   Notes:
1712:   The values, `v`, are column-oriented for the block version.

1714:   All the nonzeros in `row` must be provided

1716:   THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

1718:   `row` must belong to this process

1720: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1721:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1722: @*/
1723: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1724: {
1725:   PetscFunctionBeginHot;
1728:   MatCheckPreallocated(mat, 1);
1729:   PetscAssertPointer(v, 3);
1730:   PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1731:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1732:   mat->insertmode = INSERT_VALUES;

1734:   if (mat->assembled) {
1735:     mat->was_assembled = PETSC_TRUE;
1736:     mat->assembled     = PETSC_FALSE;
1737:   }
1738:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1739:   PetscUseTypeMethod(mat, setvaluesrow, row, v);
1740:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1741:   PetscFunctionReturn(PETSC_SUCCESS);
1742: }

1744: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1745: /*@
1746:   MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1747:   Using structured grid indexing

1749:   Not Collective

1751:   Input Parameters:
1752: + mat  - the matrix
1753: . m    - number of rows being entered
1754: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1755: . n    - number of columns being entered
1756: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1757: . v    - a logically two-dimensional array of values
1758: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

1760:   Level: beginner

1762:   Notes:
1763:   By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

1765:   Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1766:   options cannot be mixed without intervening calls to the assembly
1767:   routines.

1769:   The grid coordinates are across the entire grid, not just the local portion

1771:   `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1772:   as well as in C.

1774:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1776:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1777:   or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1779:   The columns and rows in the stencil passed in MUST be contained within the
1780:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1781:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1782:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1783:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1785:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1786:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1787:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1788:   `DM_BOUNDARY_PERIODIC` boundary type.

1790:   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
1791:   a single value per point) you can skip filling those indices.

1793:   Inspired by the structured grid interface to the HYPRE package
1794:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1796:   Efficiency Alert:
1797:   The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1798:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1800: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1801:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1802: @*/
1803: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1804: {
1805:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1806:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1807:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1809:   PetscFunctionBegin;
1810:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1813:   PetscAssertPointer(idxm, 3);
1814:   PetscAssertPointer(idxn, 5);

1816:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1817:     jdxm = buf;
1818:     jdxn = buf + m;
1819:   } else {
1820:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1821:     jdxm = bufm;
1822:     jdxn = bufn;
1823:   }
1824:   for (i = 0; i < m; i++) {
1825:     for (j = 0; j < 3 - sdim; j++) dxm++;
1826:     tmp = *dxm++ - starts[0];
1827:     for (j = 0; j < dim - 1; j++) {
1828:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1829:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1830:     }
1831:     if (mat->stencil.noc) dxm++;
1832:     jdxm[i] = tmp;
1833:   }
1834:   for (i = 0; i < n; i++) {
1835:     for (j = 0; j < 3 - sdim; j++) dxn++;
1836:     tmp = *dxn++ - starts[0];
1837:     for (j = 0; j < dim - 1; j++) {
1838:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1839:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1840:     }
1841:     if (mat->stencil.noc) dxn++;
1842:     jdxn[i] = tmp;
1843:   }
1844:   PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1845:   PetscCall(PetscFree2(bufm, bufn));
1846:   PetscFunctionReturn(PETSC_SUCCESS);
1847: }

1849: /*@
1850:   MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1851:   Using structured grid indexing

1853:   Not Collective

1855:   Input Parameters:
1856: + mat  - the matrix
1857: . m    - number of rows being entered
1858: . idxm - grid coordinates for matrix rows being entered
1859: . n    - number of columns being entered
1860: . idxn - grid coordinates for matrix columns being entered
1861: . v    - a logically two-dimensional array of values
1862: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

1864:   Level: beginner

1866:   Notes:
1867:   By default the values, `v`, are row-oriented and unsorted.
1868:   See `MatSetOption()` for other options.

1870:   Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1871:   options cannot be mixed without intervening calls to the assembly
1872:   routines.

1874:   The grid coordinates are across the entire grid, not just the local portion

1876:   `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1877:   as well as in C.

1879:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1881:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1882:   or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1884:   The columns and rows in the stencil passed in MUST be contained within the
1885:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1886:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1887:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1888:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1890:   Negative indices may be passed in idxm and idxn, these rows and columns are
1891:   simply ignored. This allows easily inserting element stiffness matrices
1892:   with homogeneous Dirichlet boundary conditions that you don't want represented
1893:   in the matrix.

1895:   Inspired by the structured grid interface to the HYPRE package
1896:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1898:   Fortran Note:
1899:   `idxm` and `idxn` should be declared as
1900: $     MatStencil idxm(4,m),idxn(4,n)
1901:   and the values inserted using
1902: .vb
1903:     idxm(MatStencil_i,1) = i
1904:     idxm(MatStencil_j,1) = j
1905:     idxm(MatStencil_k,1) = k
1906:    etc
1907: .ve

1909: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1910:           `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1911:           `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1912: @*/
1913: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1914: {
1915:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1916:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1917:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1919:   PetscFunctionBegin;
1920:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1923:   PetscAssertPointer(idxm, 3);
1924:   PetscAssertPointer(idxn, 5);
1925:   PetscAssertPointer(v, 6);

1927:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1928:     jdxm = buf;
1929:     jdxn = buf + m;
1930:   } else {
1931:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1932:     jdxm = bufm;
1933:     jdxn = bufn;
1934:   }
1935:   for (i = 0; i < m; i++) {
1936:     for (j = 0; j < 3 - sdim; j++) dxm++;
1937:     tmp = *dxm++ - starts[0];
1938:     for (j = 0; j < sdim - 1; j++) {
1939:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1940:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1941:     }
1942:     dxm++;
1943:     jdxm[i] = tmp;
1944:   }
1945:   for (i = 0; i < n; i++) {
1946:     for (j = 0; j < 3 - sdim; j++) dxn++;
1947:     tmp = *dxn++ - starts[0];
1948:     for (j = 0; j < sdim - 1; j++) {
1949:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1950:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1951:     }
1952:     dxn++;
1953:     jdxn[i] = tmp;
1954:   }
1955:   PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1956:   PetscCall(PetscFree2(bufm, bufn));
1957:   PetscFunctionReturn(PETSC_SUCCESS);
1958: }

1960: /*@
1961:   MatSetStencil - Sets the grid information for setting values into a matrix via
1962:   `MatSetValuesStencil()`

1964:   Not Collective

1966:   Input Parameters:
1967: + mat    - the matrix
1968: . dim    - dimension of the grid 1, 2, or 3
1969: . dims   - number of grid points in x, y, and z direction, including ghost points on your processor
1970: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1971: - dof    - number of degrees of freedom per node

1973:   Level: beginner

1975:   Notes:
1976:   Inspired by the structured grid interface to the HYPRE package
1977:   (www.llnl.gov/CASC/hyper)

1979:   For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1980:   user.

1982: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1983:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1984: @*/
1985: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1986: {
1987:   PetscFunctionBegin;
1989:   PetscAssertPointer(dims, 3);
1990:   PetscAssertPointer(starts, 4);

1992:   mat->stencil.dim = dim + (dof > 1);
1993:   for (PetscInt i = 0; i < dim; i++) {
1994:     mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
1995:     mat->stencil.starts[i] = starts[dim - i - 1];
1996:   }
1997:   mat->stencil.dims[dim]   = dof;
1998:   mat->stencil.starts[dim] = 0;
1999:   mat->stencil.noc         = (PetscBool)(dof == 1);
2000:   PetscFunctionReturn(PETSC_SUCCESS);
2001: }

2003: /*@
2004:   MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

2006:   Not Collective

2008:   Input Parameters:
2009: + mat  - the matrix
2010: . v    - a logically two-dimensional array of values
2011: . m    - the number of block rows
2012: . idxm - the global block indices
2013: . n    - the number of block columns
2014: . idxn - the global block indices
2015: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

2017:   Level: intermediate

2019:   Notes:
2020:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2021:   MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

2023:   The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2024:   NOT the total number of rows/columns; for example, if the block size is 2 and
2025:   you are passing in values for rows 2,3,4,5  then `m` would be 2 (not 4).
2026:   The values in `idxm` would be 1 2; that is the first index for each block divided by
2027:   the block size.

2029:   You must call `MatSetBlockSize()` when constructing this matrix (before
2030:   preallocating it).

2032:   By default the values, `v`, are row-oriented, so the layout of
2033:   `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.

2035:   Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2036:   options cannot be mixed without intervening calls to the assembly
2037:   routines.

2039:   `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2040:   as well as in C.

2042:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2043:   simply ignored. This allows easily inserting element stiffness matrices
2044:   with homogeneous Dirichlet boundary conditions that you don't want represented
2045:   in the matrix.

2047:   Each time an entry is set within a sparse matrix via `MatSetValues()`,
2048:   internal searching must be done to determine where to place the
2049:   data in the matrix storage space.  By instead inserting blocks of
2050:   entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2051:   reduced.

2053:   Example:
2054: .vb
2055:    Suppose m=n=2 and block size(bs) = 2 The array is

2057:    1  2  | 3  4
2058:    5  6  | 7  8
2059:    - - - | - - -
2060:    9  10 | 11 12
2061:    13 14 | 15 16

2063:    v[] should be passed in like
2064:    v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

2066:   If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2067:    v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2068: .ve

2070:   Fortran Notes:
2071:   If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2072: .vb
2073:   call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2074: .ve

2076:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2078: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2079: @*/
2080: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2081: {
2082:   PetscFunctionBeginHot;
2085:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2086:   PetscAssertPointer(idxm, 3);
2087:   PetscAssertPointer(idxn, 5);
2088:   MatCheckPreallocated(mat, 1);
2089:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2090:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2091:   if (PetscDefined(USE_DEBUG)) {
2092:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2093:     PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2094:   }
2095:   if (PetscDefined(USE_DEBUG)) {
2096:     PetscInt rbs, cbs, M, N, i;
2097:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2098:     PetscCall(MatGetSize(mat, &M, &N));
2099:     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);
2100:     for (i = 0; i < n; i++)
2101:       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);
2102:   }
2103:   if (mat->assembled) {
2104:     mat->was_assembled = PETSC_TRUE;
2105:     mat->assembled     = PETSC_FALSE;
2106:   }
2107:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2108:   if (mat->ops->setvaluesblocked) {
2109:     PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2110:   } else {
2111:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2112:     PetscInt i, j, bs, cbs;

2114:     PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2115:     if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2116:       iidxm = buf;
2117:       iidxn = buf + m * bs;
2118:     } else {
2119:       PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2120:       iidxm = bufr;
2121:       iidxn = bufc;
2122:     }
2123:     for (i = 0; i < m; i++) {
2124:       for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2125:     }
2126:     if (m != n || bs != cbs || idxm != idxn) {
2127:       for (i = 0; i < n; i++) {
2128:         for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2129:       }
2130:     } else iidxn = iidxm;
2131:     PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2132:     PetscCall(PetscFree2(bufr, bufc));
2133:   }
2134:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2135:   PetscFunctionReturn(PETSC_SUCCESS);
2136: }

2138: /*@
2139:   MatGetValues - Gets a block of local values from a matrix.

2141:   Not Collective; can only return values that are owned by the give process

2143:   Input Parameters:
2144: + mat  - the matrix
2145: . v    - a logically two-dimensional array for storing the values
2146: . m    - the number of rows
2147: . idxm - the  global indices of the rows
2148: . n    - the number of columns
2149: - idxn - the global indices of the columns

2151:   Level: advanced

2153:   Notes:
2154:   The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2155:   The values, `v`, are then returned in a row-oriented format,
2156:   analogous to that used by default in `MatSetValues()`.

2158:   `MatGetValues()` uses 0-based row and column numbers in
2159:   Fortran as well as in C.

2161:   `MatGetValues()` requires that the matrix has been assembled
2162:   with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
2163:   `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2164:   without intermediate matrix assembly.

2166:   Negative row or column indices will be ignored and those locations in `v` will be
2167:   left unchanged.

2169:   For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2170:   That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2171:   from `MatGetOwnershipRange`(mat,&rstart,&rend).

2173: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2174: @*/
2175: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2176: {
2177:   PetscFunctionBegin;
2180:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2181:   PetscAssertPointer(idxm, 3);
2182:   PetscAssertPointer(idxn, 5);
2183:   PetscAssertPointer(v, 6);
2184:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2185:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2186:   MatCheckPreallocated(mat, 1);

2188:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2189:   PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2190:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2191:   PetscFunctionReturn(PETSC_SUCCESS);
2192: }

2194: /*@
2195:   MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2196:   defined previously by `MatSetLocalToGlobalMapping()`

2198:   Not Collective

2200:   Input Parameters:
2201: + mat  - the matrix
2202: . nrow - number of rows
2203: . irow - the row local indices
2204: . ncol - number of columns
2205: - icol - the column local indices

2207:   Output Parameter:
2208: . y - a logically two-dimensional array of values

2210:   Level: advanced

2212:   Notes:
2213:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

2215:   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,
2216:   are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2217:   determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2218:   with `MatSetLocalToGlobalMapping()`.

2220:   Developer Note:
2221:   This is labelled with C so does not automatically generate Fortran stubs and interfaces
2222:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2224: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2225:           `MatSetValuesLocal()`, `MatGetValues()`
2226: @*/
2227: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2228: {
2229:   PetscFunctionBeginHot;
2232:   MatCheckPreallocated(mat, 1);
2233:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2234:   PetscAssertPointer(irow, 3);
2235:   PetscAssertPointer(icol, 5);
2236:   if (PetscDefined(USE_DEBUG)) {
2237:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2238:     PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2239:   }
2240:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2241:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2242:   if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2243:   else {
2244:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2245:     if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2246:       irowm = buf;
2247:       icolm = buf + nrow;
2248:     } else {
2249:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2250:       irowm = bufr;
2251:       icolm = bufc;
2252:     }
2253:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2254:     PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2255:     PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2256:     PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2257:     PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2258:     PetscCall(PetscFree2(bufr, bufc));
2259:   }
2260:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2261:   PetscFunctionReturn(PETSC_SUCCESS);
2262: }

2264: /*@
2265:   MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2266:   the same size. Currently, this can only be called once and creates the given matrix.

2268:   Not Collective

2270:   Input Parameters:
2271: + mat  - the matrix
2272: . nb   - the number of blocks
2273: . bs   - the number of rows (and columns) in each block
2274: . rows - a concatenation of the rows for each block
2275: - v    - a concatenation of logically two-dimensional arrays of values

2277:   Level: advanced

2279:   Notes:
2280:   `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values

2282:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

2284: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2285:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2286: @*/
2287: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2288: {
2289:   PetscFunctionBegin;
2292:   PetscAssertPointer(rows, 4);
2293:   PetscAssertPointer(v, 5);
2294:   PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

2296:   PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2297:   if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2298:   else {
2299:     for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2300:   }
2301:   PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2302:   PetscFunctionReturn(PETSC_SUCCESS);
2303: }

2305: /*@
2306:   MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2307:   the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2308:   using a local (per-processor) numbering.

2310:   Not Collective

2312:   Input Parameters:
2313: + x        - the matrix
2314: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2315: - cmapping - column mapping

2317:   Level: intermediate

2319:   Note:
2320:   If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix

2322: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2323: @*/
2324: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2325: {
2326:   PetscFunctionBegin;
2331:   if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2332:   else {
2333:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2334:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2335:   }
2336:   PetscFunctionReturn(PETSC_SUCCESS);
2337: }

2339: /*@
2340:   MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`

2342:   Not Collective

2344:   Input Parameter:
2345: . A - the matrix

2347:   Output Parameters:
2348: + rmapping - row mapping
2349: - cmapping - column mapping

2351:   Level: advanced

2353: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2354: @*/
2355: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2356: {
2357:   PetscFunctionBegin;
2360:   if (rmapping) {
2361:     PetscAssertPointer(rmapping, 2);
2362:     *rmapping = A->rmap->mapping;
2363:   }
2364:   if (cmapping) {
2365:     PetscAssertPointer(cmapping, 3);
2366:     *cmapping = A->cmap->mapping;
2367:   }
2368:   PetscFunctionReturn(PETSC_SUCCESS);
2369: }

2371: /*@
2372:   MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix

2374:   Logically Collective

2376:   Input Parameters:
2377: + A    - the matrix
2378: . rmap - row layout
2379: - cmap - column layout

2381:   Level: advanced

2383:   Note:
2384:   The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.

2386: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2387: @*/
2388: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2389: {
2390:   PetscFunctionBegin;
2392:   PetscCall(PetscLayoutReference(rmap, &A->rmap));
2393:   PetscCall(PetscLayoutReference(cmap, &A->cmap));
2394:   PetscFunctionReturn(PETSC_SUCCESS);
2395: }

2397: /*@
2398:   MatGetLayouts - Gets the `PetscLayout` objects for rows and columns

2400:   Not Collective

2402:   Input Parameter:
2403: . A - the matrix

2405:   Output Parameters:
2406: + rmap - row layout
2407: - cmap - column layout

2409:   Level: advanced

2411: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2412: @*/
2413: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2414: {
2415:   PetscFunctionBegin;
2418:   if (rmap) {
2419:     PetscAssertPointer(rmap, 2);
2420:     *rmap = A->rmap;
2421:   }
2422:   if (cmap) {
2423:     PetscAssertPointer(cmap, 3);
2424:     *cmap = A->cmap;
2425:   }
2426:   PetscFunctionReturn(PETSC_SUCCESS);
2427: }

2429: /*@
2430:   MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2431:   using a local numbering of the rows and columns.

2433:   Not Collective

2435:   Input Parameters:
2436: + mat  - the matrix
2437: . nrow - number of rows
2438: . irow - the row local indices
2439: . ncol - number of columns
2440: . icol - the column local indices
2441: . y    - a logically two-dimensional array of values
2442: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2444:   Level: intermediate

2446:   Notes:
2447:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

2449:   Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2450:   options cannot be mixed without intervening calls to the assembly
2451:   routines.

2453:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2454:   MUST be called after all calls to `MatSetValuesLocal()` have been completed.

2456:   Fortran Notes:
2457:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2458: .vb
2459:   call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2460: .ve

2462:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2464:   Developer Note:
2465:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2466:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2468: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2469:           `MatGetValuesLocal()`
2470: @*/
2471: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2472: {
2473:   PetscFunctionBeginHot;
2476:   MatCheckPreallocated(mat, 1);
2477:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2478:   PetscAssertPointer(irow, 3);
2479:   PetscAssertPointer(icol, 5);
2480:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2481:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2482:   if (PetscDefined(USE_DEBUG)) {
2483:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2484:     PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2485:   }

2487:   if (mat->assembled) {
2488:     mat->was_assembled = PETSC_TRUE;
2489:     mat->assembled     = PETSC_FALSE;
2490:   }
2491:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2492:   if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2493:   else {
2494:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2495:     const PetscInt *irowm, *icolm;

2497:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2498:       bufr  = buf;
2499:       bufc  = buf + nrow;
2500:       irowm = bufr;
2501:       icolm = bufc;
2502:     } else {
2503:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2504:       irowm = bufr;
2505:       icolm = bufc;
2506:     }
2507:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2508:     else irowm = irow;
2509:     if (mat->cmap->mapping) {
2510:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2511:         PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2512:       } else icolm = irowm;
2513:     } else icolm = icol;
2514:     PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2515:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2516:   }
2517:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2518:   PetscFunctionReturn(PETSC_SUCCESS);
2519: }

2521: /*@
2522:   MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2523:   using a local ordering of the nodes a block at a time.

2525:   Not Collective

2527:   Input Parameters:
2528: + mat  - the matrix
2529: . nrow - number of rows
2530: . irow - the row local indices
2531: . ncol - number of columns
2532: . icol - the column local indices
2533: . y    - a logically two-dimensional array of values
2534: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2536:   Level: intermediate

2538:   Notes:
2539:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2540:   before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

2542:   Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2543:   options cannot be mixed without intervening calls to the assembly
2544:   routines.

2546:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2547:   MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

2549:   Fortran Notes:
2550:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2551: .vb
2552:   call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2553: .ve

2555:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2557:   Developer Note:
2558:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2559:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2561: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2562:           `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2563: @*/
2564: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2565: {
2566:   PetscFunctionBeginHot;
2569:   MatCheckPreallocated(mat, 1);
2570:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2571:   PetscAssertPointer(irow, 3);
2572:   PetscAssertPointer(icol, 5);
2573:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2574:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2575:   if (PetscDefined(USE_DEBUG)) {
2576:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2577:     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);
2578:   }

2580:   if (mat->assembled) {
2581:     mat->was_assembled = PETSC_TRUE;
2582:     mat->assembled     = PETSC_FALSE;
2583:   }
2584:   if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2585:     PetscInt irbs, rbs;
2586:     PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2587:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2588:     PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2589:   }
2590:   if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2591:     PetscInt icbs, cbs;
2592:     PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2593:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2594:     PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2595:   }
2596:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2597:   if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2598:   else {
2599:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2600:     const PetscInt *irowm, *icolm;

2602:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2603:       bufr  = buf;
2604:       bufc  = buf + nrow;
2605:       irowm = bufr;
2606:       icolm = bufc;
2607:     } else {
2608:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2609:       irowm = bufr;
2610:       icolm = bufc;
2611:     }
2612:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2613:     else irowm = irow;
2614:     if (mat->cmap->mapping) {
2615:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2616:         PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2617:       } else icolm = irowm;
2618:     } else icolm = icol;
2619:     PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2620:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2621:   }
2622:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2623:   PetscFunctionReturn(PETSC_SUCCESS);
2624: }

2626: /*@
2627:   MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal

2629:   Collective

2631:   Input Parameters:
2632: + mat - the matrix
2633: - x   - the vector to be multiplied

2635:   Output Parameter:
2636: . y - the result

2638:   Level: developer

2640:   Note:
2641:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2642:   call `MatMultDiagonalBlock`(A,y,y).

2644: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2645: @*/
2646: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2647: {
2648:   PetscFunctionBegin;

2654:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2655:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2656:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2657:   MatCheckPreallocated(mat, 1);

2659:   PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2660:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2661:   PetscFunctionReturn(PETSC_SUCCESS);
2662: }

2664: /*@
2665:   MatMult - Computes the matrix-vector product, $y = Ax$.

2667:   Neighbor-wise Collective

2669:   Input Parameters:
2670: + mat - the matrix
2671: - x   - the vector to be multiplied

2673:   Output Parameter:
2674: . y - the result

2676:   Level: beginner

2678:   Note:
2679:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2680:   call `MatMult`(A,y,y).

2682: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2683: @*/
2684: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2685: {
2686:   PetscFunctionBegin;
2690:   VecCheckAssembled(x);
2692:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2693:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2694:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2695:   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);
2696:   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);
2697:   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);
2698:   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);
2699:   PetscCall(VecSetErrorIfLocked(y, 3));
2700:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2701:   MatCheckPreallocated(mat, 1);

2703:   PetscCall(VecLockReadPush(x));
2704:   PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2705:   PetscUseTypeMethod(mat, mult, x, y);
2706:   PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2707:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2708:   PetscCall(VecLockReadPop(x));
2709:   PetscFunctionReturn(PETSC_SUCCESS);
2710: }

2712: /*@
2713:   MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.

2715:   Neighbor-wise Collective

2717:   Input Parameters:
2718: + mat - the matrix
2719: - x   - the vector to be multiplied

2721:   Output Parameter:
2722: . y - the result

2724:   Level: beginner

2726:   Notes:
2727:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2728:   call `MatMultTranspose`(A,y,y).

2730:   For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2731:   use `MatMultHermitianTranspose()`

2733: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2734: @*/
2735: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2736: {
2737:   PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;

2739:   PetscFunctionBegin;
2743:   VecCheckAssembled(x);

2746:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2747:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2748:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2749:   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);
2750:   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);
2751:   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);
2752:   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);
2753:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2754:   MatCheckPreallocated(mat, 1);

2756:   if (!mat->ops->multtranspose) {
2757:     if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2758:     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);
2759:   } else op = mat->ops->multtranspose;
2760:   PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2761:   PetscCall(VecLockReadPush(x));
2762:   PetscCall((*op)(mat, x, y));
2763:   PetscCall(VecLockReadPop(x));
2764:   PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2765:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2766:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2767:   PetscFunctionReturn(PETSC_SUCCESS);
2768: }

2770: /*@
2771:   MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.

2773:   Neighbor-wise Collective

2775:   Input Parameters:
2776: + mat - the matrix
2777: - x   - the vector to be multiplied

2779:   Output Parameter:
2780: . y - the result

2782:   Level: beginner

2784:   Notes:
2785:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2786:   call `MatMultHermitianTranspose`(A,y,y).

2788:   Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2790:   For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

2792: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2793: @*/
2794: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2795: {
2796:   PetscFunctionBegin;

2802:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2803:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2804:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2805:   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);
2806:   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);
2807:   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);
2808:   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);
2809:   MatCheckPreallocated(mat, 1);

2811:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2812: #if defined(PETSC_USE_COMPLEX)
2813:   if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2814:     PetscCall(VecLockReadPush(x));
2815:     if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2816:     else PetscUseTypeMethod(mat, mult, x, y);
2817:     PetscCall(VecLockReadPop(x));
2818:   } else {
2819:     Vec w;
2820:     PetscCall(VecDuplicate(x, &w));
2821:     PetscCall(VecCopy(x, w));
2822:     PetscCall(VecConjugate(w));
2823:     PetscCall(MatMultTranspose(mat, w, y));
2824:     PetscCall(VecDestroy(&w));
2825:     PetscCall(VecConjugate(y));
2826:   }
2827:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2828: #else
2829:   PetscCall(MatMultTranspose(mat, x, y));
2830: #endif
2831:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2832:   PetscFunctionReturn(PETSC_SUCCESS);
2833: }

2835: /*@
2836:   MatMultAdd -  Computes $v3 = v2 + A * v1$.

2838:   Neighbor-wise Collective

2840:   Input Parameters:
2841: + mat - the matrix
2842: . v1  - the vector to be multiplied by `mat`
2843: - v2  - the vector to be added to the result

2845:   Output Parameter:
2846: . v3 - the result

2848:   Level: beginner

2850:   Note:
2851:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2852:   call `MatMultAdd`(A,v1,v2,v1).

2854: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2855: @*/
2856: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2857: {
2858:   PetscFunctionBegin;

2865:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2866:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2867:   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);
2868:   /* 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);
2869:      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); */
2870:   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);
2871:   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);
2872:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2873:   MatCheckPreallocated(mat, 1);

2875:   PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2876:   PetscCall(VecLockReadPush(v1));
2877:   PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2878:   PetscCall(VecLockReadPop(v1));
2879:   PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2880:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2881:   PetscFunctionReturn(PETSC_SUCCESS);
2882: }

2884: /*@
2885:   MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.

2887:   Neighbor-wise Collective

2889:   Input Parameters:
2890: + mat - the matrix
2891: . v1  - the vector to be multiplied by the transpose of the matrix
2892: - v2  - the vector to be added to the result

2894:   Output Parameter:
2895: . v3 - the result

2897:   Level: beginner

2899:   Note:
2900:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2901:   call `MatMultTransposeAdd`(A,v1,v2,v1).

2903: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2904: @*/
2905: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2906: {
2907:   PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

2909:   PetscFunctionBegin;

2916:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2917:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2918:   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);
2919:   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);
2920:   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);
2921:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2922:   PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2923:   MatCheckPreallocated(mat, 1);

2925:   PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2926:   PetscCall(VecLockReadPush(v1));
2927:   PetscCall((*op)(mat, v1, v2, v3));
2928:   PetscCall(VecLockReadPop(v1));
2929:   PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2930:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2931:   PetscFunctionReturn(PETSC_SUCCESS);
2932: }

2934: /*@
2935:   MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.

2937:   Neighbor-wise Collective

2939:   Input Parameters:
2940: + mat - the matrix
2941: . v1  - the vector to be multiplied by the Hermitian transpose
2942: - v2  - the vector to be added to the result

2944:   Output Parameter:
2945: . v3 - the result

2947:   Level: beginner

2949:   Note:
2950:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2951:   call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

2953: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2954: @*/
2955: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2956: {
2957:   PetscFunctionBegin;

2964:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2965:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2966:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2967:   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);
2968:   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);
2969:   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);
2970:   MatCheckPreallocated(mat, 1);

2972:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2973:   PetscCall(VecLockReadPush(v1));
2974:   if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2975:   else {
2976:     Vec w, z;
2977:     PetscCall(VecDuplicate(v1, &w));
2978:     PetscCall(VecCopy(v1, w));
2979:     PetscCall(VecConjugate(w));
2980:     PetscCall(VecDuplicate(v3, &z));
2981:     PetscCall(MatMultTranspose(mat, w, z));
2982:     PetscCall(VecDestroy(&w));
2983:     PetscCall(VecConjugate(z));
2984:     if (v2 != v3) {
2985:       PetscCall(VecWAXPY(v3, 1.0, v2, z));
2986:     } else {
2987:       PetscCall(VecAXPY(v3, 1.0, z));
2988:     }
2989:     PetscCall(VecDestroy(&z));
2990:   }
2991:   PetscCall(VecLockReadPop(v1));
2992:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2993:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2994:   PetscFunctionReturn(PETSC_SUCCESS);
2995: }

2997: /*@
2998:   MatGetFactorType - gets the type of factorization a matrix is

3000:   Not Collective

3002:   Input Parameter:
3003: . mat - the matrix

3005:   Output Parameter:
3006: . 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`

3008:   Level: intermediate

3010: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3011:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3012: @*/
3013: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3014: {
3015:   PetscFunctionBegin;
3018:   PetscAssertPointer(t, 2);
3019:   *t = mat->factortype;
3020:   PetscFunctionReturn(PETSC_SUCCESS);
3021: }

3023: /*@
3024:   MatSetFactorType - sets the type of factorization a matrix is

3026:   Logically Collective

3028:   Input Parameters:
3029: + mat - the matrix
3030: - 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`

3032:   Level: intermediate

3034: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3035:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3036: @*/
3037: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3038: {
3039:   PetscFunctionBegin;
3042:   mat->factortype = t;
3043:   PetscFunctionReturn(PETSC_SUCCESS);
3044: }

3046: /*@
3047:   MatGetInfo - Returns information about matrix storage (number of
3048:   nonzeros, memory, etc.).

3050:   Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

3052:   Input Parameters:
3053: + mat  - the matrix
3054: - 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)

3056:   Output Parameter:
3057: . info - matrix information context

3059:   Options Database Key:
3060: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

3062:   Level: intermediate

3064:   Notes:
3065:   The `MatInfo` context contains a variety of matrix data, including
3066:   number of nonzeros allocated and used, number of mallocs during
3067:   matrix assembly, etc.  Additional information for factored matrices
3068:   is provided (such as the fill ratio, number of mallocs during
3069:   factorization, etc.).

3071:   Example:
3072:   See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3073:   data within the `MatInfo` context.  For example,
3074: .vb
3075:       MatInfo info;
3076:       Mat     A;
3077:       double  mal, nz_a, nz_u;

3079:       MatGetInfo(A, MAT_LOCAL, &info);
3080:       mal  = info.mallocs;
3081:       nz_a = info.nz_allocated;
3082: .ve

3084: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3085: @*/
3086: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3087: {
3088:   PetscFunctionBegin;
3091:   PetscAssertPointer(info, 3);
3092:   MatCheckPreallocated(mat, 1);
3093:   PetscUseTypeMethod(mat, getinfo, flag, info);
3094:   PetscFunctionReturn(PETSC_SUCCESS);
3095: }

3097: /*
3098:    This is used by external packages where it is not easy to get the info from the actual
3099:    matrix factorization.
3100: */
3101: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3102: {
3103:   PetscFunctionBegin;
3104:   PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3105:   PetscFunctionReturn(PETSC_SUCCESS);
3106: }

3108: /*@
3109:   MatLUFactor - Performs in-place LU factorization of matrix.

3111:   Collective

3113:   Input Parameters:
3114: + mat  - the matrix
3115: . row  - row permutation
3116: . col  - column permutation
3117: - info - options for factorization, includes
3118: .vb
3119:           fill - expected fill as ratio of original fill.
3120:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3121:                    Run with the option -info to determine an optimal value to use
3122: .ve

3124:   Level: developer

3126:   Notes:
3127:   Most users should employ the `KSP` interface for linear solvers
3128:   instead of working directly with matrix algebra routines such as this.
3129:   See, e.g., `KSPCreate()`.

3131:   This changes the state of the matrix to a factored matrix; it cannot be used
3132:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3134:   This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3135:   when not using `KSP`.

3137:   Developer Note:
3138:   The Fortran interface is not autogenerated as the
3139:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3141: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3142:           `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3143: @*/
3144: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3145: {
3146:   MatFactorInfo tinfo;

3148:   PetscFunctionBegin;
3152:   if (info) PetscAssertPointer(info, 4);
3154:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3155:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3156:   MatCheckPreallocated(mat, 1);
3157:   if (!info) {
3158:     PetscCall(MatFactorInfoInitialize(&tinfo));
3159:     info = &tinfo;
3160:   }

3162:   PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3163:   PetscUseTypeMethod(mat, lufactor, row, col, info);
3164:   PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3165:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3166:   PetscFunctionReturn(PETSC_SUCCESS);
3167: }

3169: /*@
3170:   MatILUFactor - Performs in-place ILU factorization of matrix.

3172:   Collective

3174:   Input Parameters:
3175: + mat  - the matrix
3176: . row  - row permutation
3177: . col  - column permutation
3178: - info - structure containing
3179: .vb
3180:       levels - number of levels of fill.
3181:       expected fill - as ratio of original fill.
3182:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3183:                 missing diagonal entries)
3184: .ve

3186:   Level: developer

3188:   Notes:
3189:   Most users should employ the `KSP` interface for linear solvers
3190:   instead of working directly with matrix algebra routines such as this.
3191:   See, e.g., `KSPCreate()`.

3193:   Probably really in-place only when level of fill is zero, otherwise allocates
3194:   new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3195:   when not using `KSP`.

3197:   Developer Note:
3198:   The Fortran interface is not autogenerated as the
3199:   interface definition cannot be generated correctly [due to MatFactorInfo]

3201: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3202: @*/
3203: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3204: {
3205:   PetscFunctionBegin;
3209:   PetscAssertPointer(info, 4);
3211:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3212:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3213:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3214:   MatCheckPreallocated(mat, 1);

3216:   PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3217:   PetscUseTypeMethod(mat, ilufactor, row, col, info);
3218:   PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3219:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3220:   PetscFunctionReturn(PETSC_SUCCESS);
3221: }

3223: /*@
3224:   MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3225:   Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

3227:   Collective

3229:   Input Parameters:
3230: + fact - the factor matrix obtained with `MatGetFactor()`
3231: . mat  - the matrix
3232: . row  - the row permutation
3233: . col  - the column permutation
3234: - info - options for factorization, includes
3235: .vb
3236:           fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3237:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3238: .ve

3240:   Level: developer

3242:   Notes:
3243:   See [Matrix Factorization](sec_matfactor) for additional information about factorizations

3245:   Most users should employ the simplified `KSP` interface for linear solvers
3246:   instead of working directly with matrix algebra routines such as this.
3247:   See, e.g., `KSPCreate()`.

3249:   Developer Note:
3250:   The Fortran interface is not autogenerated as the
3251:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3253: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3254: @*/
3255: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3256: {
3257:   MatFactorInfo tinfo;

3259:   PetscFunctionBegin;
3264:   if (info) PetscAssertPointer(info, 5);
3267:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3268:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3269:   MatCheckPreallocated(mat, 2);
3270:   if (!info) {
3271:     PetscCall(MatFactorInfoInitialize(&tinfo));
3272:     info = &tinfo;
3273:   }

3275:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3276:   PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3277:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3278:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3279:   PetscFunctionReturn(PETSC_SUCCESS);
3280: }

3282: /*@
3283:   MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3284:   Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

3286:   Collective

3288:   Input Parameters:
3289: + fact - the factor matrix obtained with `MatGetFactor()`
3290: . mat  - the matrix
3291: - info - options for factorization

3293:   Level: developer

3295:   Notes:
3296:   See `MatLUFactor()` for in-place factorization.  See
3297:   `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

3299:   Most users should employ the `KSP` interface for linear solvers
3300:   instead of working directly with matrix algebra routines such as this.
3301:   See, e.g., `KSPCreate()`.

3303:   Developer Note:
3304:   The Fortran interface is not autogenerated as the
3305:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3307: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3308: @*/
3309: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3310: {
3311:   MatFactorInfo tinfo;

3313:   PetscFunctionBegin;
3318:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3319:   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,
3320:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3322:   MatCheckPreallocated(mat, 2);
3323:   if (!info) {
3324:     PetscCall(MatFactorInfoInitialize(&tinfo));
3325:     info = &tinfo;
3326:   }

3328:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3329:   else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3330:   PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3331:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3332:   else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3333:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3334:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3335:   PetscFunctionReturn(PETSC_SUCCESS);
3336: }

3338: /*@
3339:   MatCholeskyFactor - Performs in-place Cholesky factorization of a
3340:   symmetric matrix.

3342:   Collective

3344:   Input Parameters:
3345: + mat  - the matrix
3346: . perm - row and column permutations
3347: - info - expected fill as ratio of original fill

3349:   Level: developer

3351:   Notes:
3352:   See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
3353:   `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

3355:   Most users should employ the `KSP` interface for linear solvers
3356:   instead of working directly with matrix algebra routines such as this.
3357:   See, e.g., `KSPCreate()`.

3359:   Developer Note:
3360:   The Fortran interface is not autogenerated as the
3361:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3363: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3364:           `MatGetOrdering()`
3365: @*/
3366: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3367: {
3368:   MatFactorInfo tinfo;

3370:   PetscFunctionBegin;
3373:   if (info) PetscAssertPointer(info, 3);
3375:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3376:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3377:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3378:   MatCheckPreallocated(mat, 1);
3379:   if (!info) {
3380:     PetscCall(MatFactorInfoInitialize(&tinfo));
3381:     info = &tinfo;
3382:   }

3384:   PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3385:   PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3386:   PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3387:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3388:   PetscFunctionReturn(PETSC_SUCCESS);
3389: }

3391: /*@
3392:   MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3393:   of a symmetric matrix.

3395:   Collective

3397:   Input Parameters:
3398: + fact - the factor matrix obtained with `MatGetFactor()`
3399: . mat  - the matrix
3400: . perm - row and column permutations
3401: - info - options for factorization, includes
3402: .vb
3403:           fill - expected fill as ratio of original fill.
3404:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3405:                    Run with the option -info to determine an optimal value to use
3406: .ve

3408:   Level: developer

3410:   Notes:
3411:   See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
3412:   `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

3414:   Most users should employ the `KSP` interface for linear solvers
3415:   instead of working directly with matrix algebra routines such as this.
3416:   See, e.g., `KSPCreate()`.

3418:   Developer Note:
3419:   The Fortran interface is not autogenerated as the
3420:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3422: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3423:           `MatGetOrdering()`
3424: @*/
3425: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3426: {
3427:   MatFactorInfo tinfo;

3429:   PetscFunctionBegin;
3433:   if (info) PetscAssertPointer(info, 4);
3436:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3437:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3438:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3439:   MatCheckPreallocated(mat, 2);
3440:   if (!info) {
3441:     PetscCall(MatFactorInfoInitialize(&tinfo));
3442:     info = &tinfo;
3443:   }

3445:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3446:   PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3447:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3448:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3449:   PetscFunctionReturn(PETSC_SUCCESS);
3450: }

3452: /*@
3453:   MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3454:   of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3455:   `MatCholeskyFactorSymbolic()`.

3457:   Collective

3459:   Input Parameters:
3460: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3461: . mat  - the initial matrix that is to be factored
3462: - info - options for factorization

3464:   Level: developer

3466:   Note:
3467:   Most users should employ the `KSP` interface for linear solvers
3468:   instead of working directly with matrix algebra routines such as this.
3469:   See, e.g., `KSPCreate()`.

3471:   Developer Note:
3472:   The Fortran interface is not autogenerated as the
3473:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3475: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3476: @*/
3477: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3478: {
3479:   MatFactorInfo tinfo;

3481:   PetscFunctionBegin;
3486:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3487:   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,
3488:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3489:   MatCheckPreallocated(mat, 2);
3490:   if (!info) {
3491:     PetscCall(MatFactorInfoInitialize(&tinfo));
3492:     info = &tinfo;
3493:   }

3495:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3496:   else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3497:   PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3498:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3499:   else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3500:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3501:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3502:   PetscFunctionReturn(PETSC_SUCCESS);
3503: }

3505: /*@
3506:   MatQRFactor - Performs in-place QR factorization of matrix.

3508:   Collective

3510:   Input Parameters:
3511: + mat  - the matrix
3512: . col  - column permutation
3513: - info - options for factorization, includes
3514: .vb
3515:           fill - expected fill as ratio of original fill.
3516:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3517:                    Run with the option -info to determine an optimal value to use
3518: .ve

3520:   Level: developer

3522:   Notes:
3523:   Most users should employ the `KSP` interface for linear solvers
3524:   instead of working directly with matrix algebra routines such as this.
3525:   See, e.g., `KSPCreate()`.

3527:   This changes the state of the matrix to a factored matrix; it cannot be used
3528:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3530:   Developer Note:
3531:   The Fortran interface is not autogenerated as the
3532:   interface definition cannot be generated correctly [due to MatFactorInfo]

3534: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3535:           `MatSetUnfactored()`
3536: @*/
3537: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3538: {
3539:   PetscFunctionBegin;
3542:   if (info) PetscAssertPointer(info, 3);
3544:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3545:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3546:   MatCheckPreallocated(mat, 1);
3547:   PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3548:   PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3549:   PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3550:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3551:   PetscFunctionReturn(PETSC_SUCCESS);
3552: }

3554: /*@
3555:   MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3556:   Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

3558:   Collective

3560:   Input Parameters:
3561: + fact - the factor matrix obtained with `MatGetFactor()`
3562: . mat  - the matrix
3563: . col  - column permutation
3564: - info - options for factorization, includes
3565: .vb
3566:           fill - expected fill as ratio of original fill.
3567:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3568:                    Run with the option -info to determine an optimal value to use
3569: .ve

3571:   Level: developer

3573:   Note:
3574:   Most users should employ the `KSP` interface for linear solvers
3575:   instead of working directly with matrix algebra routines such as this.
3576:   See, e.g., `KSPCreate()`.

3578:   Developer Note:
3579:   The Fortran interface is not autogenerated as the
3580:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3582: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3583: @*/
3584: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3585: {
3586:   MatFactorInfo tinfo;

3588:   PetscFunctionBegin;
3592:   if (info) PetscAssertPointer(info, 4);
3595:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3596:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3597:   MatCheckPreallocated(mat, 2);
3598:   if (!info) {
3599:     PetscCall(MatFactorInfoInitialize(&tinfo));
3600:     info = &tinfo;
3601:   }

3603:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3604:   PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3605:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3606:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3607:   PetscFunctionReturn(PETSC_SUCCESS);
3608: }

3610: /*@
3611:   MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3612:   Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

3614:   Collective

3616:   Input Parameters:
3617: + fact - the factor matrix obtained with `MatGetFactor()`
3618: . mat  - the matrix
3619: - info - options for factorization

3621:   Level: developer

3623:   Notes:
3624:   See `MatQRFactor()` for in-place factorization.

3626:   Most users should employ the `KSP` interface for linear solvers
3627:   instead of working directly with matrix algebra routines such as this.
3628:   See, e.g., `KSPCreate()`.

3630:   Developer Note:
3631:   The Fortran interface is not autogenerated as the
3632:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

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

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

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

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

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

3668:   Neighbor-wise Collective

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

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

3677:   Level: developer

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

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

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

3705:   PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3706:   PetscCall(VecFlag(x, mat->factorerrortype));
3707:   if (mat->factorerrortype) {
3708:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3709:   } else PetscUseTypeMethod(mat, solve, b, x);
3710:   PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3711:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3712:   PetscFunctionReturn(PETSC_SUCCESS);
3713: }

3715: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3716: {
3717:   Vec      b, x;
3718:   PetscInt N, i;
3719:   PetscErrorCode (*f)(Mat, Vec, Vec);
3720:   PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

3722:   PetscFunctionBegin;
3723:   if (A->factorerrortype) {
3724:     PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3725:     PetscCall(MatSetInf(X));
3726:     PetscFunctionReturn(PETSC_SUCCESS);
3727:   }
3728:   f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3729:   PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3730:   PetscCall(MatBoundToCPU(A, &Abound));
3731:   if (!Abound) {
3732:     PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3733:     PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3734:   }
3735: #if PetscDefined(HAVE_CUDA)
3736:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3737:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3738: #elif PetscDefined(HAVE_HIP)
3739:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3740:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3741: #endif
3742:   PetscCall(MatGetSize(B, NULL, &N));
3743:   for (i = 0; i < N; i++) {
3744:     PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3745:     PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3746:     PetscCall((*f)(A, b, x));
3747:     PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3748:     PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3749:   }
3750:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3751:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3752:   PetscFunctionReturn(PETSC_SUCCESS);
3753: }

3755: /*@
3756:   MatMatSolve - Solves $A X = B$, given a factored matrix.

3758:   Neighbor-wise Collective

3760:   Input Parameters:
3761: + A - the factored matrix
3762: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

3764:   Output Parameter:
3765: . X - the result matrix (dense matrix)

3767:   Level: developer

3769:   Note:
3770:   If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3771:   otherwise, `B` and `X` cannot be the same.

3773: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3774: @*/
3775: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3776: {
3777:   PetscFunctionBegin;
3782:   PetscCheckSameComm(A, 1, B, 2);
3783:   PetscCheckSameComm(A, 1, X, 3);
3784:   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);
3785:   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);
3786:   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");
3787:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3788:   MatCheckPreallocated(A, 1);

3790:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3791:   if (!A->ops->matsolve) {
3792:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3793:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3794:   } else PetscUseTypeMethod(A, matsolve, B, X);
3795:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3796:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3797:   PetscFunctionReturn(PETSC_SUCCESS);
3798: }

3800: /*@
3801:   MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.

3803:   Neighbor-wise Collective

3805:   Input Parameters:
3806: + A - the factored matrix
3807: - B - the right-hand-side matrix  (`MATDENSE` matrix)

3809:   Output Parameter:
3810: . X - the result matrix (dense matrix)

3812:   Level: developer

3814:   Note:
3815:   The matrices `B` and `X` cannot be the same.  I.e., one cannot
3816:   call `MatMatSolveTranspose`(A,X,X).

3818: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3819: @*/
3820: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3821: {
3822:   PetscFunctionBegin;
3827:   PetscCheckSameComm(A, 1, B, 2);
3828:   PetscCheckSameComm(A, 1, X, 3);
3829:   PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3830:   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);
3831:   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);
3832:   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);
3833:   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");
3834:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3835:   MatCheckPreallocated(A, 1);

3837:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3838:   if (!A->ops->matsolvetranspose) {
3839:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3840:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3841:   } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3842:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3843:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3844:   PetscFunctionReturn(PETSC_SUCCESS);
3845: }

3847: /*@
3848:   MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.

3850:   Neighbor-wise Collective

3852:   Input Parameters:
3853: + A  - the factored matrix
3854: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`

3856:   Output Parameter:
3857: . X - the result matrix (dense matrix)

3859:   Level: developer

3861:   Note:
3862:   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
3863:   format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

3865: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3866: @*/
3867: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3868: {
3869:   PetscFunctionBegin;
3874:   PetscCheckSameComm(A, 1, Bt, 2);
3875:   PetscCheckSameComm(A, 1, X, 3);

3877:   PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3878:   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);
3879:   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);
3880:   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");
3881:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3882:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3883:   MatCheckPreallocated(A, 1);

3885:   PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3886:   PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3887:   PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3888:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3889:   PetscFunctionReturn(PETSC_SUCCESS);
3890: }

3892: /*@
3893:   MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3894:   $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3896:   Neighbor-wise Collective

3898:   Input Parameters:
3899: + mat - the factored matrix
3900: - b   - the right-hand-side vector

3902:   Output Parameter:
3903: . x - the result vector

3905:   Level: developer

3907:   Notes:
3908:   `MatSolve()` should be used for most applications, as it performs
3909:   a forward solve followed by a backward solve.

3911:   The vectors `b` and `x` cannot be the same,  i.e., one cannot
3912:   call `MatForwardSolve`(A,x,x).

3914:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3915:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3916:   `MatForwardSolve()` solves $U^T*D y = b$, and
3917:   `MatBackwardSolve()` solves $U x = y$.
3918:   Thus they do not provide a symmetric preconditioner.

3920: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3921: @*/
3922: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3923: {
3924:   PetscFunctionBegin;
3929:   PetscCheckSameComm(mat, 1, b, 2);
3930:   PetscCheckSameComm(mat, 1, x, 3);
3931:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3932:   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);
3933:   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);
3934:   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);
3935:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3936:   MatCheckPreallocated(mat, 1);

3938:   PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3939:   PetscUseTypeMethod(mat, forwardsolve, b, x);
3940:   PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3941:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3942:   PetscFunctionReturn(PETSC_SUCCESS);
3943: }

3945: /*@
3946:   MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3947:   $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3949:   Neighbor-wise Collective

3951:   Input Parameters:
3952: + mat - the factored matrix
3953: - b   - the right-hand-side vector

3955:   Output Parameter:
3956: . x - the result vector

3958:   Level: developer

3960:   Notes:
3961:   `MatSolve()` should be used for most applications, as it performs
3962:   a forward solve followed by a backward solve.

3964:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3965:   call `MatBackwardSolve`(A,x,x).

3967:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3968:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3969:   `MatForwardSolve()` solves $U^T*D y = b$, and
3970:   `MatBackwardSolve()` solves $U x = y$.
3971:   Thus they do not provide a symmetric preconditioner.

3973: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3974: @*/
3975: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3976: {
3977:   PetscFunctionBegin;
3982:   PetscCheckSameComm(mat, 1, b, 2);
3983:   PetscCheckSameComm(mat, 1, x, 3);
3984:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3985:   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);
3986:   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);
3987:   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);
3988:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3989:   MatCheckPreallocated(mat, 1);

3991:   PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3992:   PetscUseTypeMethod(mat, backwardsolve, b, x);
3993:   PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3994:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3995:   PetscFunctionReturn(PETSC_SUCCESS);
3996: }

3998: /*@
3999:   MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.

4001:   Neighbor-wise Collective

4003:   Input Parameters:
4004: + mat - the factored matrix
4005: . b   - the right-hand-side vector
4006: - y   - the vector to be added to

4008:   Output Parameter:
4009: . x - the result vector

4011:   Level: developer

4013:   Note:
4014:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4015:   call `MatSolveAdd`(A,x,y,x).

4017: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4018: @*/
4019: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4020: {
4021:   PetscScalar one = 1.0;
4022:   Vec         tmp;

4024:   PetscFunctionBegin;
4030:   PetscCheckSameComm(mat, 1, b, 2);
4031:   PetscCheckSameComm(mat, 1, y, 3);
4032:   PetscCheckSameComm(mat, 1, x, 4);
4033:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4034:   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);
4035:   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);
4036:   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);
4037:   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);
4038:   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);
4039:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4040:   MatCheckPreallocated(mat, 1);

4042:   PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4043:   PetscCall(VecFlag(x, mat->factorerrortype));
4044:   if (mat->factorerrortype) {
4045:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4046:   } else if (mat->ops->solveadd) {
4047:     PetscUseTypeMethod(mat, solveadd, b, y, x);
4048:   } else {
4049:     /* do the solve then the add manually */
4050:     if (x != y) {
4051:       PetscCall(MatSolve(mat, b, x));
4052:       PetscCall(VecAXPY(x, one, y));
4053:     } else {
4054:       PetscCall(VecDuplicate(x, &tmp));
4055:       PetscCall(VecCopy(x, tmp));
4056:       PetscCall(MatSolve(mat, b, x));
4057:       PetscCall(VecAXPY(x, one, tmp));
4058:       PetscCall(VecDestroy(&tmp));
4059:     }
4060:   }
4061:   PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4062:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4063:   PetscFunctionReturn(PETSC_SUCCESS);
4064: }

4066: /*@
4067:   MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.

4069:   Neighbor-wise Collective

4071:   Input Parameters:
4072: + mat - the factored matrix
4073: - b   - the right-hand-side vector

4075:   Output Parameter:
4076: . x - the result vector

4078:   Level: developer

4080:   Notes:
4081:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4082:   call `MatSolveTranspose`(A,x,x).

4084:   Most users should employ the `KSP` interface for linear solvers
4085:   instead of working directly with matrix algebra routines such as this.
4086:   See, e.g., `KSPCreate()`.

4088: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4089: @*/
4090: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4091: {
4092:   PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

4094:   PetscFunctionBegin;
4099:   PetscCheckSameComm(mat, 1, b, 2);
4100:   PetscCheckSameComm(mat, 1, x, 3);
4101:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4102:   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);
4103:   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);
4104:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4105:   MatCheckPreallocated(mat, 1);
4106:   PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4107:   PetscCall(VecFlag(x, mat->factorerrortype));
4108:   if (mat->factorerrortype) {
4109:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4110:   } else {
4111:     PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4112:     PetscCall((*f)(mat, b, x));
4113:   }
4114:   PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4115:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4116:   PetscFunctionReturn(PETSC_SUCCESS);
4117: }

4119: /*@
4120:   MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4121:   factored matrix.

4123:   Neighbor-wise Collective

4125:   Input Parameters:
4126: + mat - the factored matrix
4127: . b   - the right-hand-side vector
4128: - y   - the vector to be added to

4130:   Output Parameter:
4131: . x - the result vector

4133:   Level: developer

4135:   Note:
4136:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4137:   call `MatSolveTransposeAdd`(A,x,y,x).

4139: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4140: @*/
4141: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4142: {
4143:   PetscScalar one = 1.0;
4144:   Vec         tmp;
4145:   PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

4147:   PetscFunctionBegin;
4153:   PetscCheckSameComm(mat, 1, b, 2);
4154:   PetscCheckSameComm(mat, 1, y, 3);
4155:   PetscCheckSameComm(mat, 1, x, 4);
4156:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4157:   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);
4158:   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);
4159:   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);
4160:   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);
4161:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4162:   MatCheckPreallocated(mat, 1);

4164:   PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4165:   PetscCall(VecFlag(x, mat->factorerrortype));
4166:   if (mat->factorerrortype) {
4167:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4168:   } else if (f) {
4169:     PetscCall((*f)(mat, b, y, x));
4170:   } else {
4171:     /* do the solve then the add manually */
4172:     if (x != y) {
4173:       PetscCall(MatSolveTranspose(mat, b, x));
4174:       PetscCall(VecAXPY(x, one, y));
4175:     } else {
4176:       PetscCall(VecDuplicate(x, &tmp));
4177:       PetscCall(VecCopy(x, tmp));
4178:       PetscCall(MatSolveTranspose(mat, b, x));
4179:       PetscCall(VecAXPY(x, one, tmp));
4180:       PetscCall(VecDestroy(&tmp));
4181:     }
4182:   }
4183:   PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4184:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4185:   PetscFunctionReturn(PETSC_SUCCESS);
4186: }

4188: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4189: /*@
4190:   MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

4192:   Neighbor-wise Collective

4194:   Input Parameters:
4195: + mat   - the matrix
4196: . b     - the right-hand side
4197: . omega - the relaxation factor
4198: . flag  - flag indicating the type of SOR (see below)
4199: . shift - diagonal shift
4200: . its   - the number of iterations
4201: - lits  - the number of local iterations

4203:   Output Parameter:
4204: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

4206:   SOR Flags:
4207: +     `SOR_FORWARD_SWEEP` - forward SOR
4208: .     `SOR_BACKWARD_SWEEP` - backward SOR
4209: .     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4210: .     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4211: .     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4212: .     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4213: .     `SOR_EISENSTAT` - SOR with Eisenstat trick
4214: .     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4215:   upper/lower triangular part of matrix to
4216:   vector (with omega)
4217: -     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

4219:   Level: developer

4221:   Notes:
4222:   `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4223:   `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4224:   on each processor.

4226:   Application programmers will not generally use `MatSOR()` directly,
4227:   but instead will employ the `KSP`/`PC` interface.

4229:   For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

4231:   Most users should employ the `KSP` interface for linear solvers
4232:   instead of working directly with matrix algebra routines such as this.
4233:   See, e.g., `KSPCreate()`.

4235:   Vectors `x` and `b` CANNOT be the same

4237:   The flags are implemented as bitwise inclusive or operations.
4238:   For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4239:   to specify a zero initial guess for SSOR.

4241:   Developer Note:
4242:   We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

4244: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4245: @*/
4246: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4247: {
4248:   PetscFunctionBegin;
4253:   PetscCheckSameComm(mat, 1, b, 2);
4254:   PetscCheckSameComm(mat, 1, x, 8);
4255:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4256:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4257:   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);
4258:   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);
4259:   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);
4260:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4261:   PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4262:   PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

4264:   MatCheckPreallocated(mat, 1);
4265:   PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4266:   PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4267:   PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4268:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4269:   PetscFunctionReturn(PETSC_SUCCESS);
4270: }

4272: /*
4273:       Default matrix copy routine.
4274: */
4275: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4276: {
4277:   PetscInt           i, rstart = 0, rend = 0, nz;
4278:   const PetscInt    *cwork;
4279:   const PetscScalar *vwork;

4281:   PetscFunctionBegin;
4282:   if (B->assembled) PetscCall(MatZeroEntries(B));
4283:   if (str == SAME_NONZERO_PATTERN) {
4284:     PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4285:     for (i = rstart; i < rend; i++) {
4286:       PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4287:       PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4288:       PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4289:     }
4290:   } else {
4291:     PetscCall(MatAYPX(B, 0.0, A, str));
4292:   }
4293:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4294:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4295:   PetscFunctionReturn(PETSC_SUCCESS);
4296: }

4298: /*@
4299:   MatCopy - Copies a matrix to another matrix.

4301:   Collective

4303:   Input Parameters:
4304: + A   - the matrix
4305: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

4307:   Output Parameter:
4308: . B - where the copy is put

4310:   Level: intermediate

4312:   Notes:
4313:   If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.

4315:   `MatCopy()` copies the matrix entries of a matrix to another existing
4316:   matrix (after first zeroing the second matrix).  A related routine is
4317:   `MatConvert()`, which first creates a new matrix and then copies the data.

4319: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4320: @*/
4321: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4322: {
4323:   PetscInt i;

4325:   PetscFunctionBegin;
4330:   PetscCheckSameComm(A, 1, B, 2);
4331:   MatCheckPreallocated(B, 2);
4332:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4333:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4334:   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,
4335:              A->cmap->N, B->cmap->N);
4336:   MatCheckPreallocated(A, 1);
4337:   if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

4339:   PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4340:   if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4341:   else PetscCall(MatCopy_Basic(A, B, str));

4343:   B->stencil.dim = A->stencil.dim;
4344:   B->stencil.noc = A->stencil.noc;
4345:   for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4346:     B->stencil.dims[i]   = A->stencil.dims[i];
4347:     B->stencil.starts[i] = A->stencil.starts[i];
4348:   }

4350:   PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4351:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4352:   PetscFunctionReturn(PETSC_SUCCESS);
4353: }

4355: /*@
4356:   MatConvert - Converts a matrix to another matrix, either of the same
4357:   or different type.

4359:   Collective

4361:   Input Parameters:
4362: + mat     - the matrix
4363: . newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
4364:             same type as the original matrix.
4365: - reuse   - denotes if the destination matrix is to be created or reused.
4366:             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
4367:             `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).

4369:   Output Parameter:
4370: . M - pointer to place new matrix

4372:   Level: intermediate

4374:   Notes:
4375:   `MatConvert()` first creates a new matrix and then copies the data from
4376:   the first matrix.  A related routine is `MatCopy()`, which copies the matrix
4377:   entries of one matrix to another already existing matrix context.

4379:   Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4380:   the MPI communicator of the generated matrix is always the same as the communicator
4381:   of the input matrix.

4383: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4384: @*/
4385: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4386: {
4387:   PetscBool  sametype, issame, flg;
4388:   PetscBool3 issymmetric, ishermitian;
4389:   char       convname[256], mtype[256];
4390:   Mat        B;

4392:   PetscFunctionBegin;
4395:   PetscAssertPointer(M, 4);
4396:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4397:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4398:   MatCheckPreallocated(mat, 1);

4400:   PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4401:   if (flg) newtype = mtype;

4403:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4404:   PetscCall(PetscStrcmp(newtype, "same", &issame));
4405:   PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4406:   if (reuse == MAT_REUSE_MATRIX) {
4408:     PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4409:   }

4411:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4412:     PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4413:     PetscFunctionReturn(PETSC_SUCCESS);
4414:   }

4416:   /* Cache Mat options because some converters use MatHeaderReplace  */
4417:   issymmetric = mat->symmetric;
4418:   ishermitian = mat->hermitian;

4420:   if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4421:     PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4422:     PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4423:   } else {
4424:     PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4425:     const char *prefix[3]                                 = {"seq", "mpi", ""};
4426:     PetscInt    i;
4427:     /*
4428:        Order of precedence:
4429:        0) See if newtype is a superclass of the current matrix.
4430:        1) See if a specialized converter is known to the current matrix.
4431:        2) See if a specialized converter is known to the desired matrix class.
4432:        3) See if a good general converter is registered for the desired class
4433:           (as of 6/27/03 only MATMPIADJ falls into this category).
4434:        4) See if a good general converter is known for the current matrix.
4435:        5) Use a really basic converter.
4436:     */

4438:     /* 0) See if newtype is a superclass of the current matrix.
4439:           i.e mat is mpiaij and newtype is aij */
4440:     for (i = 0; i < 2; i++) {
4441:       PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4442:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4443:       PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4444:       PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4445:       if (flg) {
4446:         if (reuse == MAT_INPLACE_MATRIX) {
4447:           PetscCall(PetscInfo(mat, "Early return\n"));
4448:           PetscFunctionReturn(PETSC_SUCCESS);
4449:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4450:           PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4451:           PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4452:           PetscFunctionReturn(PETSC_SUCCESS);
4453:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4454:           PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4455:           PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4456:           PetscFunctionReturn(PETSC_SUCCESS);
4457:         }
4458:       }
4459:     }
4460:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4461:     for (i = 0; i < 3; i++) {
4462:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4463:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4464:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4465:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4466:       PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4467:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4468:       PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4469:       PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4470:       if (conv) goto foundconv;
4471:     }

4473:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4474:     PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4475:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4476:     PetscCall(MatSetType(B, newtype));
4477:     for (i = 0; i < 3; i++) {
4478:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4479:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4480:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4481:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4482:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4483:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4484:       PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4485:       PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4486:       if (conv) {
4487:         PetscCall(MatDestroy(&B));
4488:         goto foundconv;
4489:       }
4490:     }

4492:     /* 3) See if a good general converter is registered for the desired class */
4493:     conv = B->ops->convertfrom;
4494:     PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4495:     PetscCall(MatDestroy(&B));
4496:     if (conv) goto foundconv;

4498:     /* 4) See if a good general converter is known for the current matrix */
4499:     if (mat->ops->convert) conv = mat->ops->convert;
4500:     PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4501:     if (conv) goto foundconv;

4503:     /* 5) Use a really basic converter. */
4504:     PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4505:     conv = MatConvert_Basic;

4507:   foundconv:
4508:     PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4509:     PetscCall((*conv)(mat, newtype, reuse, M));
4510:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4511:       /* the block sizes must be same if the mappings are copied over */
4512:       (*M)->rmap->bs = mat->rmap->bs;
4513:       (*M)->cmap->bs = mat->cmap->bs;
4514:       PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4515:       PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4516:       (*M)->rmap->mapping = mat->rmap->mapping;
4517:       (*M)->cmap->mapping = mat->cmap->mapping;
4518:     }
4519:     (*M)->stencil.dim = mat->stencil.dim;
4520:     (*M)->stencil.noc = mat->stencil.noc;
4521:     for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4522:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4523:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4524:     }
4525:     PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4526:   }
4527:   PetscCall(PetscObjectStateIncrease((PetscObject)*M));

4529:   /* Copy Mat options */
4530:   if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4531:   else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4532:   if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4533:   else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4534:   PetscFunctionReturn(PETSC_SUCCESS);
4535: }

4537: /*@
4538:   MatFactorGetSolverType - Returns name of the package providing the factorization routines

4540:   Not Collective

4542:   Input Parameter:
4543: . mat - the matrix, must be a factored matrix

4545:   Output Parameter:
4546: . type - the string name of the package (do not free this string)

4548:   Level: intermediate

4550:   Fortran Note:
4551:   Pass in an empty string that is long enough and the package name will be copied into it.

4553: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4554: @*/
4555: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4556: {
4557:   PetscErrorCode (*conv)(Mat, MatSolverType *);

4559:   PetscFunctionBegin;
4562:   PetscAssertPointer(type, 2);
4563:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4564:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4565:   if (conv) PetscCall((*conv)(mat, type));
4566:   else *type = MATSOLVERPETSC;
4567:   PetscFunctionReturn(PETSC_SUCCESS);
4568: }

4570: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4571: struct _MatSolverTypeForSpecifcType {
4572:   MatType mtype;
4573:   /* no entry for MAT_FACTOR_NONE */
4574:   PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4575:   MatSolverTypeForSpecifcType next;
4576: };

4578: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4579: struct _MatSolverTypeHolder {
4580:   char                       *name;
4581:   MatSolverTypeForSpecifcType handlers;
4582:   MatSolverTypeHolder         next;
4583: };

4585: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4587: /*@C
4588:   MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

4590:   Logically Collective, No Fortran Support

4592:   Input Parameters:
4593: + package      - name of the package, for example petsc or superlu
4594: . mtype        - the matrix type that works with this package
4595: . ftype        - the type of factorization supported by the package
4596: - createfactor - routine that will create the factored matrix ready to be used

4598:   Level: developer

4600: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4601:   `MatGetFactor()`
4602: @*/
4603: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4604: {
4605:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
4606:   PetscBool                   flg;
4607:   MatSolverTypeForSpecifcType inext, iprev = NULL;

4609:   PetscFunctionBegin;
4610:   PetscCall(MatInitializePackage());
4611:   if (!next) {
4612:     PetscCall(PetscNew(&MatSolverTypeHolders));
4613:     PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4614:     PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4615:     PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4616:     MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4617:     PetscFunctionReturn(PETSC_SUCCESS);
4618:   }
4619:   while (next) {
4620:     PetscCall(PetscStrcasecmp(package, next->name, &flg));
4621:     if (flg) {
4622:       PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4623:       inext = next->handlers;
4624:       while (inext) {
4625:         PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4626:         if (flg) {
4627:           inext->createfactor[(int)ftype - 1] = createfactor;
4628:           PetscFunctionReturn(PETSC_SUCCESS);
4629:         }
4630:         iprev = inext;
4631:         inext = inext->next;
4632:       }
4633:       PetscCall(PetscNew(&iprev->next));
4634:       PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4635:       iprev->next->createfactor[(int)ftype - 1] = createfactor;
4636:       PetscFunctionReturn(PETSC_SUCCESS);
4637:     }
4638:     prev = next;
4639:     next = next->next;
4640:   }
4641:   PetscCall(PetscNew(&prev->next));
4642:   PetscCall(PetscStrallocpy(package, &prev->next->name));
4643:   PetscCall(PetscNew(&prev->next->handlers));
4644:   PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4645:   prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4646:   PetscFunctionReturn(PETSC_SUCCESS);
4647: }

4649: /*@C
4650:   MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

4652:   Input Parameters:
4653: + 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
4654: . ftype - the type of factorization supported by the type
4655: - mtype - the matrix type that works with this type

4657:   Output Parameters:
4658: + foundtype    - `PETSC_TRUE` if the type was registered
4659: . foundmtype   - `PETSC_TRUE` if the type supports the requested mtype
4660: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

4662:   Calling sequence of `createfactor`:
4663: + A     - the matrix providing the factor matrix
4664: . ftype - the `MatFactorType` of the factor requested
4665: - B     - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`

4667:   Level: developer

4669:   Note:
4670:   When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4671:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4672:   For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.

4674: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4675:           `MatInitializePackage()`
4676: @*/
4677: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4678: {
4679:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4680:   PetscBool                   flg;
4681:   MatSolverTypeForSpecifcType inext;

4683:   PetscFunctionBegin;
4684:   if (foundtype) *foundtype = PETSC_FALSE;
4685:   if (foundmtype) *foundmtype = PETSC_FALSE;
4686:   if (createfactor) *createfactor = NULL;

4688:   if (type) {
4689:     while (next) {
4690:       PetscCall(PetscStrcasecmp(type, next->name, &flg));
4691:       if (flg) {
4692:         if (foundtype) *foundtype = PETSC_TRUE;
4693:         inext = next->handlers;
4694:         while (inext) {
4695:           PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4696:           if (flg) {
4697:             if (foundmtype) *foundmtype = PETSC_TRUE;
4698:             if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4699:             PetscFunctionReturn(PETSC_SUCCESS);
4700:           }
4701:           inext = inext->next;
4702:         }
4703:       }
4704:       next = next->next;
4705:     }
4706:   } else {
4707:     while (next) {
4708:       inext = next->handlers;
4709:       while (inext) {
4710:         PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4711:         if (flg && inext->createfactor[(int)ftype - 1]) {
4712:           if (foundtype) *foundtype = PETSC_TRUE;
4713:           if (foundmtype) *foundmtype = PETSC_TRUE;
4714:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4715:           PetscFunctionReturn(PETSC_SUCCESS);
4716:         }
4717:         inext = inext->next;
4718:       }
4719:       next = next->next;
4720:     }
4721:     /* try with base classes inext->mtype */
4722:     next = MatSolverTypeHolders;
4723:     while (next) {
4724:       inext = next->handlers;
4725:       while (inext) {
4726:         PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4727:         if (flg && inext->createfactor[(int)ftype - 1]) {
4728:           if (foundtype) *foundtype = PETSC_TRUE;
4729:           if (foundmtype) *foundmtype = PETSC_TRUE;
4730:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4731:           PetscFunctionReturn(PETSC_SUCCESS);
4732:         }
4733:         inext = inext->next;
4734:       }
4735:       next = next->next;
4736:     }
4737:   }
4738:   PetscFunctionReturn(PETSC_SUCCESS);
4739: }

4741: PetscErrorCode MatSolverTypeDestroy(void)
4742: {
4743:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
4744:   MatSolverTypeForSpecifcType inext, iprev;

4746:   PetscFunctionBegin;
4747:   while (next) {
4748:     PetscCall(PetscFree(next->name));
4749:     inext = next->handlers;
4750:     while (inext) {
4751:       PetscCall(PetscFree(inext->mtype));
4752:       iprev = inext;
4753:       inext = inext->next;
4754:       PetscCall(PetscFree(iprev));
4755:     }
4756:     prev = next;
4757:     next = next->next;
4758:     PetscCall(PetscFree(prev));
4759:   }
4760:   MatSolverTypeHolders = NULL;
4761:   PetscFunctionReturn(PETSC_SUCCESS);
4762: }

4764: /*@
4765:   MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

4767:   Logically Collective

4769:   Input Parameter:
4770: . mat - the matrix

4772:   Output Parameter:
4773: . flg - `PETSC_TRUE` if uses the ordering

4775:   Level: developer

4777:   Note:
4778:   Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4779:   packages do not, thus we want to skip generating the ordering when it is not needed or used.

4781: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4782: @*/
4783: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4784: {
4785:   PetscFunctionBegin;
4786:   *flg = mat->canuseordering;
4787:   PetscFunctionReturn(PETSC_SUCCESS);
4788: }

4790: /*@
4791:   MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

4793:   Logically Collective

4795:   Input Parameters:
4796: + mat   - the matrix obtained with `MatGetFactor()`
4797: - ftype - the factorization type to be used

4799:   Output Parameter:
4800: . otype - the preferred ordering type

4802:   Level: developer

4804: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4805: @*/
4806: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4807: {
4808:   PetscFunctionBegin;
4809:   *otype = mat->preferredordering[ftype];
4810:   PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4811:   PetscFunctionReturn(PETSC_SUCCESS);
4812: }

4814: /*@
4815:   MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()

4817:   Collective

4819:   Input Parameters:
4820: + mat   - the matrix
4821: . 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
4822:           the other criteria is returned
4823: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4825:   Output Parameter:
4826: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.

4828:   Options Database Keys:
4829: + -pc_factor_mat_solver_type <type>    - choose the type at run time. When using `KSP` solvers
4830: . -pc_factor_mat_factor_on_host <bool> - do mat factorization on host (with device matrices). Default is doing it on device
4831: - -pc_factor_mat_solve_on_host <bool>  - do mat solve on host (with device matrices). Default is doing it on device

4833:   Level: intermediate

4835:   Notes:
4836:   The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4837:   types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.

4839:   Users usually access the factorization solvers via `KSP`

4841:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4842:   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

4844:   When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4845:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4846:   For example if one configuration had --download-mumps while a different one had --download-superlu_dist.

4848:   Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4849:   where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4850:   call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

4852:   Developer Note:
4853:   This should actually be called `MatCreateFactor()` since it creates a new factor object

4855: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4856:           `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4857:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4858: @*/
4859: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4860: {
4861:   PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4862:   PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

4864:   PetscFunctionBegin;

4868:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4869:   MatCheckPreallocated(mat, 1);

4871:   PetscCall(MatIsShell(mat, &shell));
4872:   if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4873:   if (hasop) {
4874:     PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4875:     PetscFunctionReturn(PETSC_SUCCESS);
4876:   }

4878:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4879:   if (!foundtype) {
4880:     if (type) {
4881:       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],
4882:               ((PetscObject)mat)->type_name, type);
4883:     } else {
4884:       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);
4885:     }
4886:   }
4887:   PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4888:   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);

4890:   PetscCall((*conv)(mat, ftype, f));
4891:   if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4892:   PetscFunctionReturn(PETSC_SUCCESS);
4893: }

4895: /*@
4896:   MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type

4898:   Not Collective

4900:   Input Parameters:
4901: + mat   - the matrix
4902: . type  - name of solver type, for example, superlu, petsc (to use PETSc's default)
4903: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4905:   Output Parameter:
4906: . flg - PETSC_TRUE if the factorization is available

4908:   Level: intermediate

4910:   Notes:
4911:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4912:   such as pastix, superlu, mumps etc.

4914:   PETSc must have been ./configure to use the external solver, using the option --download-package

4916:   Developer Note:
4917:   This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object

4919: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4920:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4921: @*/
4922: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4923: {
4924:   PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

4926:   PetscFunctionBegin;
4928:   PetscAssertPointer(flg, 4);

4930:   *flg = PETSC_FALSE;
4931:   if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);

4933:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4934:   MatCheckPreallocated(mat, 1);

4936:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4937:   *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4938:   PetscFunctionReturn(PETSC_SUCCESS);
4939: }

4941: /*@
4942:   MatDuplicate - Duplicates a matrix including the non-zero structure.

4944:   Collective

4946:   Input Parameters:
4947: + mat - the matrix
4948: - op  - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4949:         See the manual page for `MatDuplicateOption()` for an explanation of these options.

4951:   Output Parameter:
4952: . M - pointer to place new matrix

4954:   Level: intermediate

4956:   Notes:
4957:   You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.

4959:   If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.

4961:   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.

4963:   When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4964:   is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4965:   User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.

4967: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4968: @*/
4969: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4970: {
4971:   Mat         B;
4972:   VecType     vtype;
4973:   PetscInt    i;
4974:   PetscObject dm, container_h, container_d;
4975:   void (*viewf)(void);

4977:   PetscFunctionBegin;
4980:   PetscAssertPointer(M, 3);
4981:   PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4982:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4983:   MatCheckPreallocated(mat, 1);

4985:   PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4986:   PetscUseTypeMethod(mat, duplicate, op, M);
4987:   PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4988:   B = *M;

4990:   PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4991:   if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4992:   PetscCall(MatGetVecType(mat, &vtype));
4993:   PetscCall(MatSetVecType(B, vtype));

4995:   B->stencil.dim = mat->stencil.dim;
4996:   B->stencil.noc = mat->stencil.noc;
4997:   for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4998:     B->stencil.dims[i]   = mat->stencil.dims[i];
4999:     B->stencil.starts[i] = mat->stencil.starts[i];
5000:   }

5002:   B->nooffproczerorows = mat->nooffproczerorows;
5003:   B->nooffprocentries  = mat->nooffprocentries;

5005:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5006:   if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5007:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5008:   if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5009:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5010:   if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5011:   if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5012:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
5013:   PetscFunctionReturn(PETSC_SUCCESS);
5014: }

5016: /*@
5017:   MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`

5019:   Logically Collective

5021:   Input Parameter:
5022: . mat - the matrix

5024:   Output Parameter:
5025: . v - the diagonal of the matrix

5027:   Level: intermediate

5029:   Note:
5030:   If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5031:   of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5032:   is larger than `ndiag`, the values of the remaining entries are unspecified.

5034:   Currently only correct in parallel for square matrices.

5036: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5037: @*/
5038: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5039: {
5040:   PetscFunctionBegin;
5044:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5045:   MatCheckPreallocated(mat, 1);
5046:   if (PetscDefined(USE_DEBUG)) {
5047:     PetscInt nv, row, col, ndiag;

5049:     PetscCall(VecGetLocalSize(v, &nv));
5050:     PetscCall(MatGetLocalSize(mat, &row, &col));
5051:     ndiag = PetscMin(row, col);
5052:     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);
5053:   }

5055:   PetscUseTypeMethod(mat, getdiagonal, v);
5056:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5057:   PetscFunctionReturn(PETSC_SUCCESS);
5058: }

5060: /*@
5061:   MatGetRowMin - Gets the minimum value (of the real part) of each
5062:   row of the matrix

5064:   Logically Collective

5066:   Input Parameter:
5067: . mat - the matrix

5069:   Output Parameters:
5070: + v   - the vector for storing the maximums
5071: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)

5073:   Level: intermediate

5075:   Note:
5076:   The result of this call are the same as if one converted the matrix to dense format
5077:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5079:   This code is only implemented for a couple of matrix formats.

5081: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5082:           `MatGetRowMax()`
5083: @*/
5084: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5085: {
5086:   PetscFunctionBegin;
5090:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5092:   if (!mat->cmap->N) {
5093:     PetscCall(VecSet(v, PETSC_MAX_REAL));
5094:     if (idx) {
5095:       PetscInt i, m = mat->rmap->n;
5096:       for (i = 0; i < m; i++) idx[i] = -1;
5097:     }
5098:   } else {
5099:     MatCheckPreallocated(mat, 1);
5100:   }
5101:   PetscUseTypeMethod(mat, getrowmin, v, idx);
5102:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5103:   PetscFunctionReturn(PETSC_SUCCESS);
5104: }

5106: /*@
5107:   MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5108:   row of the matrix

5110:   Logically Collective

5112:   Input Parameter:
5113: . mat - the matrix

5115:   Output Parameters:
5116: + v   - the vector for storing the minimums
5117: - idx - the indices of the column found for each row (or `NULL` if not needed)

5119:   Level: intermediate

5121:   Notes:
5122:   if a row is completely empty or has only 0.0 values, then the `idx` value for that
5123:   row is 0 (the first column).

5125:   This code is only implemented for a couple of matrix formats.

5127: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5128: @*/
5129: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5130: {
5131:   PetscFunctionBegin;
5135:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5136:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

5138:   if (!mat->cmap->N) {
5139:     PetscCall(VecSet(v, 0.0));
5140:     if (idx) {
5141:       PetscInt i, m = mat->rmap->n;
5142:       for (i = 0; i < m; i++) idx[i] = -1;
5143:     }
5144:   } else {
5145:     MatCheckPreallocated(mat, 1);
5146:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5147:     PetscUseTypeMethod(mat, getrowminabs, v, idx);
5148:   }
5149:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5150:   PetscFunctionReturn(PETSC_SUCCESS);
5151: }

5153: /*@
5154:   MatGetRowMax - Gets the maximum value (of the real part) of each
5155:   row of the matrix

5157:   Logically Collective

5159:   Input Parameter:
5160: . mat - the matrix

5162:   Output Parameters:
5163: + v   - the vector for storing the maximums
5164: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)

5166:   Level: intermediate

5168:   Notes:
5169:   The result of this call are the same as if one converted the matrix to dense format
5170:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5172:   This code is only implemented for a couple of matrix formats.

5174: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5175: @*/
5176: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5177: {
5178:   PetscFunctionBegin;
5182:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5184:   if (!mat->cmap->N) {
5185:     PetscCall(VecSet(v, PETSC_MIN_REAL));
5186:     if (idx) {
5187:       PetscInt i, m = mat->rmap->n;
5188:       for (i = 0; i < m; i++) idx[i] = -1;
5189:     }
5190:   } else {
5191:     MatCheckPreallocated(mat, 1);
5192:     PetscUseTypeMethod(mat, getrowmax, v, idx);
5193:   }
5194:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5195:   PetscFunctionReturn(PETSC_SUCCESS);
5196: }

5198: /*@
5199:   MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5200:   row of the matrix

5202:   Logically Collective

5204:   Input Parameter:
5205: . mat - the matrix

5207:   Output Parameters:
5208: + v   - the vector for storing the maximums
5209: - idx - the indices of the column found for each row (or `NULL` if not needed)

5211:   Level: intermediate

5213:   Notes:
5214:   if a row is completely empty or has only 0.0 values, then the `idx` value for that
5215:   row is 0 (the first column).

5217:   This code is only implemented for a couple of matrix formats.

5219: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5220: @*/
5221: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5222: {
5223:   PetscFunctionBegin;
5227:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5229:   if (!mat->cmap->N) {
5230:     PetscCall(VecSet(v, 0.0));
5231:     if (idx) {
5232:       PetscInt i, m = mat->rmap->n;
5233:       for (i = 0; i < m; i++) idx[i] = -1;
5234:     }
5235:   } else {
5236:     MatCheckPreallocated(mat, 1);
5237:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5238:     PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5239:   }
5240:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5241:   PetscFunctionReturn(PETSC_SUCCESS);
5242: }

5244: /*@
5245:   MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix

5247:   Logically Collective

5249:   Input Parameter:
5250: . mat - the matrix

5252:   Output Parameter:
5253: . v - the vector for storing the sum

5255:   Level: intermediate

5257:   This code is only implemented for a couple of matrix formats.

5259: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5260: @*/
5261: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5262: {
5263:   PetscFunctionBegin;
5267:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5269:   if (!mat->cmap->N) {
5270:     PetscCall(VecSet(v, 0.0));
5271:   } else {
5272:     MatCheckPreallocated(mat, 1);
5273:     PetscUseTypeMethod(mat, getrowsumabs, v);
5274:   }
5275:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5276:   PetscFunctionReturn(PETSC_SUCCESS);
5277: }

5279: /*@
5280:   MatGetRowSum - Gets the sum of each row of the matrix

5282:   Logically or Neighborhood Collective

5284:   Input Parameter:
5285: . mat - the matrix

5287:   Output Parameter:
5288: . v - the vector for storing the sum of rows

5290:   Level: intermediate

5292:   Note:
5293:   This code is slow since it is not currently specialized for different formats

5295: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5296: @*/
5297: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5298: {
5299:   Vec ones;

5301:   PetscFunctionBegin;
5305:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5306:   MatCheckPreallocated(mat, 1);
5307:   PetscCall(MatCreateVecs(mat, &ones, NULL));
5308:   PetscCall(VecSet(ones, 1.));
5309:   PetscCall(MatMult(mat, ones, v));
5310:   PetscCall(VecDestroy(&ones));
5311:   PetscFunctionReturn(PETSC_SUCCESS);
5312: }

5314: /*@
5315:   MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5316:   when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)

5318:   Collective

5320:   Input Parameter:
5321: . mat - the matrix to provide the transpose

5323:   Output Parameter:
5324: . 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

5326:   Level: advanced

5328:   Note:
5329:   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
5330:   routine allows bypassing that call.

5332: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5333: @*/
5334: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5335: {
5336:   MatParentState *rb = NULL;

5338:   PetscFunctionBegin;
5339:   PetscCall(PetscNew(&rb));
5340:   rb->id    = ((PetscObject)mat)->id;
5341:   rb->state = 0;
5342:   PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5343:   PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5344:   PetscFunctionReturn(PETSC_SUCCESS);
5345: }

5347: /*@
5348:   MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.

5350:   Collective

5352:   Input Parameters:
5353: + mat   - the matrix to transpose
5354: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5356:   Output Parameter:
5357: . B - the transpose of the matrix

5359:   Level: intermediate

5361:   Notes:
5362:   If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`

5364:   `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
5365:   transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.

5367:   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.

5369:   Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5370:   For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.

5372:   If `mat` is unchanged from the last call this function returns immediately without recomputing the result

5374:   If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`

5376: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5377:           `MatTransposeSymbolic()`, `MatCreateTranspose()`
5378: @*/
5379: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5380: {
5381:   PetscContainer  rB = NULL;
5382:   MatParentState *rb = NULL;

5384:   PetscFunctionBegin;
5387:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5388:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5389:   PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5390:   PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5391:   MatCheckPreallocated(mat, 1);
5392:   if (reuse == MAT_REUSE_MATRIX) {
5393:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5394:     PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5395:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5396:     PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5397:     if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5398:   }

5400:   PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5401:   if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5402:     PetscUseTypeMethod(mat, transpose, reuse, B);
5403:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5404:   }
5405:   PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

5407:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5408:   if (reuse != MAT_INPLACE_MATRIX) {
5409:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5410:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5411:     rb->state        = ((PetscObject)mat)->state;
5412:     rb->nonzerostate = mat->nonzerostate;
5413:   }
5414:   PetscFunctionReturn(PETSC_SUCCESS);
5415: }

5417: /*@
5418:   MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

5420:   Collective

5422:   Input Parameter:
5423: . A - the matrix to transpose

5425:   Output Parameter:
5426: . 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
5427:       numerical portion.

5429:   Level: intermediate

5431:   Note:
5432:   This is not supported for many matrix types, use `MatTranspose()` in those cases

5434: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5435: @*/
5436: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5437: {
5438:   PetscFunctionBegin;
5441:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5442:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5443:   PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5444:   PetscUseTypeMethod(A, transposesymbolic, B);
5445:   PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

5447:   PetscCall(MatTransposeSetPrecursor(A, *B));
5448:   PetscFunctionReturn(PETSC_SUCCESS);
5449: }

5451: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5452: {
5453:   PetscContainer  rB;
5454:   MatParentState *rb;

5456:   PetscFunctionBegin;
5459:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5460:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5461:   PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5462:   PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5463:   PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5464:   PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5465:   PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5466:   PetscFunctionReturn(PETSC_SUCCESS);
5467: }

5469: /*@
5470:   MatIsTranspose - Test whether a matrix is another one's transpose,
5471:   or its own, in which case it tests symmetry.

5473:   Collective

5475:   Input Parameters:
5476: + A   - the matrix to test
5477: . B   - the matrix to test against, this can equal the first parameter
5478: - tol - tolerance, differences between entries smaller than this are counted as zero

5480:   Output Parameter:
5481: . flg - the result

5483:   Level: intermediate

5485:   Notes:
5486:   The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5487:   test involves parallel copies of the block off-diagonal parts of the matrix.

5489: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5490: @*/
5491: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5492: {
5493:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5495:   PetscFunctionBegin;
5498:   PetscAssertPointer(flg, 4);
5499:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5500:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5501:   *flg = PETSC_FALSE;
5502:   if (f && g) {
5503:     PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5504:     PetscCall((*f)(A, B, tol, flg));
5505:   } else {
5506:     MatType mattype;

5508:     PetscCall(MatGetType(f ? B : A, &mattype));
5509:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5510:   }
5511:   PetscFunctionReturn(PETSC_SUCCESS);
5512: }

5514: /*@
5515:   MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

5517:   Collective

5519:   Input Parameters:
5520: + mat   - the matrix to transpose and complex conjugate
5521: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5523:   Output Parameter:
5524: . B - the Hermitian transpose

5526:   Level: intermediate

5528: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5529: @*/
5530: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5531: {
5532:   PetscFunctionBegin;
5533:   PetscCall(MatTranspose(mat, reuse, B));
5534: #if defined(PETSC_USE_COMPLEX)
5535:   PetscCall(MatConjugate(*B));
5536: #endif
5537:   PetscFunctionReturn(PETSC_SUCCESS);
5538: }

5540: /*@
5541:   MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

5543:   Collective

5545:   Input Parameters:
5546: + A   - the matrix to test
5547: . B   - the matrix to test against, this can equal the first parameter
5548: - tol - tolerance, differences between entries smaller than this are counted as zero

5550:   Output Parameter:
5551: . flg - the result

5553:   Level: intermediate

5555:   Notes:
5556:   Only available for `MATAIJ` matrices.

5558:   The sequential algorithm
5559:   has a running time of the order of the number of nonzeros; the parallel
5560:   test involves parallel copies of the block off-diagonal parts of the matrix.

5562: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5563: @*/
5564: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5565: {
5566:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5568:   PetscFunctionBegin;
5571:   PetscAssertPointer(flg, 4);
5572:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5573:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5574:   if (f && g) {
5575:     PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5576:     PetscCall((*f)(A, B, tol, flg));
5577:   }
5578:   PetscFunctionReturn(PETSC_SUCCESS);
5579: }

5581: /*@
5582:   MatPermute - Creates a new matrix with rows and columns permuted from the
5583:   original.

5585:   Collective

5587:   Input Parameters:
5588: + mat - the matrix to permute
5589: . row - row permutation, each processor supplies only the permutation for its rows
5590: - col - column permutation, each processor supplies only the permutation for its columns

5592:   Output Parameter:
5593: . B - the permuted matrix

5595:   Level: advanced

5597:   Note:
5598:   The index sets map from row/col of permuted matrix to row/col of original matrix.
5599:   The index sets should be on the same communicator as mat and have the same local sizes.

5601:   Developer Note:
5602:   If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5603:   exploit the fact that row and col are permutations, consider implementing the
5604:   more general `MatCreateSubMatrix()` instead.

5606: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5607: @*/
5608: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5609: {
5610:   PetscFunctionBegin;
5615:   PetscAssertPointer(B, 4);
5616:   PetscCheckSameComm(mat, 1, row, 2);
5617:   if (row != col) PetscCheckSameComm(row, 2, col, 3);
5618:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5619:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5620:   PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5621:   MatCheckPreallocated(mat, 1);

5623:   if (mat->ops->permute) {
5624:     PetscUseTypeMethod(mat, permute, row, col, B);
5625:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5626:   } else {
5627:     PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5628:   }
5629:   PetscFunctionReturn(PETSC_SUCCESS);
5630: }

5632: /*@
5633:   MatEqual - Compares two matrices.

5635:   Collective

5637:   Input Parameters:
5638: + A - the first matrix
5639: - B - the second matrix

5641:   Output Parameter:
5642: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

5644:   Level: intermediate

5646:   Note:
5647:   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
5648:   using several randomly created vectors, see `MatMultEqual()`.

5650: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5651: @*/
5652: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5653: {
5654:   PetscFunctionBegin;
5659:   PetscAssertPointer(flg, 3);
5660:   PetscCheckSameComm(A, 1, B, 2);
5661:   MatCheckPreallocated(A, 1);
5662:   MatCheckPreallocated(B, 2);
5663:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5664:   PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5665:   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,
5666:              B->cmap->N);
5667:   if (A->ops->equal && A->ops->equal == B->ops->equal) {
5668:     PetscUseTypeMethod(A, equal, B, flg);
5669:   } else {
5670:     PetscCall(MatMultEqual(A, B, 10, flg));
5671:   }
5672:   PetscFunctionReturn(PETSC_SUCCESS);
5673: }

5675: /*@
5676:   MatDiagonalScale - Scales a matrix on the left and right by diagonal
5677:   matrices that are stored as vectors.  Either of the two scaling
5678:   matrices can be `NULL`.

5680:   Collective

5682:   Input Parameters:
5683: + mat - the matrix to be scaled
5684: . l   - the left scaling vector (or `NULL`)
5685: - r   - the right scaling vector (or `NULL`)

5687:   Level: intermediate

5689:   Note:
5690:   `MatDiagonalScale()` computes $A = LAR$, where
5691:   L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5692:   The L scales the rows of the matrix, the R scales the columns of the matrix.

5694: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5695: @*/
5696: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5697: {
5698:   PetscFunctionBegin;
5701:   if (l) {
5703:     PetscCheckSameComm(mat, 1, l, 2);
5704:   }
5705:   if (r) {
5707:     PetscCheckSameComm(mat, 1, r, 3);
5708:   }
5709:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5710:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5711:   MatCheckPreallocated(mat, 1);
5712:   if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

5714:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5715:   PetscUseTypeMethod(mat, diagonalscale, l, r);
5716:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5717:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5718:   if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5719:   PetscFunctionReturn(PETSC_SUCCESS);
5720: }

5722: /*@
5723:   MatScale - Scales all elements of a matrix by a given number.

5725:   Logically Collective

5727:   Input Parameters:
5728: + mat - the matrix to be scaled
5729: - a   - the scaling value

5731:   Level: intermediate

5733: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5734: @*/
5735: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5736: {
5737:   PetscFunctionBegin;
5740:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5741:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5743:   MatCheckPreallocated(mat, 1);

5745:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5746:   if (a != (PetscScalar)1.0) {
5747:     PetscUseTypeMethod(mat, scale, a);
5748:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5749:   }
5750:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5751:   PetscFunctionReturn(PETSC_SUCCESS);
5752: }

5754: /*@
5755:   MatNorm - Calculates various norms of a matrix.

5757:   Collective

5759:   Input Parameters:
5760: + mat  - the matrix
5761: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

5763:   Output Parameter:
5764: . nrm - the resulting norm

5766:   Level: intermediate

5768: .seealso: [](ch_matrices), `Mat`
5769: @*/
5770: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5771: {
5772:   PetscFunctionBegin;
5775:   PetscAssertPointer(nrm, 3);

5777:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5778:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5779:   MatCheckPreallocated(mat, 1);

5781:   PetscUseTypeMethod(mat, norm, type, nrm);
5782:   PetscFunctionReturn(PETSC_SUCCESS);
5783: }

5785: /*
5786:      This variable is used to prevent counting of MatAssemblyBegin() that
5787:    are called from within a MatAssemblyEnd().
5788: */
5789: static PetscInt MatAssemblyEnd_InUse = 0;
5790: /*@
5791:   MatAssemblyBegin - Begins assembling the matrix.  This routine should
5792:   be called after completing all calls to `MatSetValues()`.

5794:   Collective

5796:   Input Parameters:
5797: + mat  - the matrix
5798: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5800:   Level: beginner

5802:   Notes:
5803:   `MatSetValues()` generally caches the values that belong to other MPI processes.  The matrix is ready to
5804:   use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

5806:   Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5807:   in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5808:   using the matrix.

5810:   ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5811:   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
5812:   a global collective operation requiring all processes that share the matrix.

5814:   Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5815:   out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5816:   before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

5818: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5819: @*/
5820: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5821: {
5822:   PetscFunctionBegin;
5825:   MatCheckPreallocated(mat, 1);
5826:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5827:   if (mat->assembled) {
5828:     mat->was_assembled = PETSC_TRUE;
5829:     mat->assembled     = PETSC_FALSE;
5830:   }

5832:   if (!MatAssemblyEnd_InUse) {
5833:     PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5834:     PetscTryTypeMethod(mat, assemblybegin, type);
5835:     PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5836:   } else PetscTryTypeMethod(mat, assemblybegin, type);
5837:   PetscFunctionReturn(PETSC_SUCCESS);
5838: }

5840: /*@
5841:   MatAssembled - Indicates if a matrix has been assembled and is ready for
5842:   use; for example, in matrix-vector product.

5844:   Not Collective

5846:   Input Parameter:
5847: . mat - the matrix

5849:   Output Parameter:
5850: . assembled - `PETSC_TRUE` or `PETSC_FALSE`

5852:   Level: advanced

5854: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5855: @*/
5856: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5857: {
5858:   PetscFunctionBegin;
5860:   PetscAssertPointer(assembled, 2);
5861:   *assembled = mat->assembled;
5862:   PetscFunctionReturn(PETSC_SUCCESS);
5863: }

5865: /*@
5866:   MatAssemblyEnd - Completes assembling the matrix.  This routine should
5867:   be called after `MatAssemblyBegin()`.

5869:   Collective

5871:   Input Parameters:
5872: + mat  - the matrix
5873: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5875:   Options Database Keys:
5876: + -mat_view ::ascii_info             - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5877: . -mat_view ::ascii_info_detail      - Prints more detailed info
5878: . -mat_view                          - Prints matrix in ASCII format
5879: . -mat_view ::ascii_matlab           - Prints matrix in MATLAB format
5880: . -mat_view draw                     - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5881: . -display <name>                    - Sets display name (default is host)
5882: . -draw_pause <sec>                  - Sets number of seconds to pause after display
5883: . -mat_view socket                   - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5884: . -viewer_socket_machine <machine>   - Machine to use for socket
5885: . -viewer_socket_port <port>         - Port number to use for socket
5886: - -mat_view binary:filename[:append] - Save matrix to file in binary format

5888:   Level: beginner

5890: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5891: @*/
5892: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5893: {
5894:   static PetscInt inassm = 0;
5895:   PetscBool       flg    = PETSC_FALSE;

5897:   PetscFunctionBegin;

5901:   inassm++;
5902:   MatAssemblyEnd_InUse++;
5903:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5904:     PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5905:     PetscTryTypeMethod(mat, assemblyend, type);
5906:     PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5907:   } else PetscTryTypeMethod(mat, assemblyend, type);

5909:   /* Flush assembly is not a true assembly */
5910:   if (type != MAT_FLUSH_ASSEMBLY) {
5911:     if (mat->num_ass) {
5912:       if (!mat->symmetry_eternal) {
5913:         mat->symmetric = PETSC_BOOL3_UNKNOWN;
5914:         mat->hermitian = PETSC_BOOL3_UNKNOWN;
5915:       }
5916:       if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5917:       if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5918:     }
5919:     mat->num_ass++;
5920:     mat->assembled        = PETSC_TRUE;
5921:     mat->ass_nonzerostate = mat->nonzerostate;
5922:   }

5924:   mat->insertmode = NOT_SET_VALUES;
5925:   MatAssemblyEnd_InUse--;
5926:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5927:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5928:     PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

5930:     if (mat->checksymmetryonassembly) {
5931:       PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5932:       if (flg) {
5933:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5934:       } else {
5935:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5936:       }
5937:     }
5938:     if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5939:   }
5940:   inassm--;
5941:   PetscFunctionReturn(PETSC_SUCCESS);
5942: }

5944: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5945: /*@
5946:   MatSetOption - Sets a parameter option for a matrix. Some options
5947:   may be specific to certain storage formats.  Some options
5948:   determine how values will be inserted (or added). Sorted,
5949:   row-oriented input will generally assemble the fastest. The default
5950:   is row-oriented.

5952:   Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

5954:   Input Parameters:
5955: + mat - the matrix
5956: . op  - the option, one of those listed below (and possibly others),
5957: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5959:   Options Describing Matrix Structure:
5960: + `MAT_SPD`                         - symmetric positive definite
5961: . `MAT_SYMMETRIC`                   - symmetric in terms of both structure and value
5962: . `MAT_HERMITIAN`                   - transpose is the complex conjugation
5963: . `MAT_STRUCTURALLY_SYMMETRIC`      - symmetric nonzero structure
5964: . `MAT_SYMMETRY_ETERNAL`            - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5965: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5966: . `MAT_SPD_ETERNAL`                 - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

5968:    These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5969:    do not need to be computed (usually at a high cost)

5971:    Options For Use with `MatSetValues()`:
5972:    Insert a logically dense subblock, which can be
5973: . `MAT_ROW_ORIENTED`                - row-oriented (default)

5975:    These options reflect the data you pass in with `MatSetValues()`; it has
5976:    nothing to do with how the data is stored internally in the matrix
5977:    data structure.

5979:    When (re)assembling a matrix, we can restrict the input for
5980:    efficiency/debugging purposes.  These options include
5981: . `MAT_NEW_NONZERO_LOCATIONS`       - additional insertions will be allowed if they generate a new nonzero (slow)
5982: . `MAT_FORCE_DIAGONAL_ENTRIES`      - forces diagonal entries to be allocated
5983: . `MAT_IGNORE_OFF_PROC_ENTRIES`     - drops off-processor entries
5984: . `MAT_NEW_NONZERO_LOCATION_ERR`    - generates an error for new matrix entry
5985: . `MAT_USE_HASH_TABLE`              - uses a hash table to speed up matrix assembly
5986: . `MAT_NO_OFF_PROC_ENTRIES`         - you know each process will only set values for its own rows, will generate an error if
5987:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5988:         performance for very large process counts.
5989: - `MAT_SUBSET_OFF_PROC_ENTRIES`     - you know that the first assembly after setting this flag will set a superset
5990:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5991:         functions, instead sending only neighbor messages.

5993:   Level: intermediate

5995:   Notes:
5996:   Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

5998:   Some options are relevant only for particular matrix types and
5999:   are thus ignored by others.  Other options are not supported by
6000:   certain matrix types and will generate an error message if set.

6002:   If using Fortran to compute a matrix, one may need to
6003:   use the column-oriented option (or convert to the row-oriented
6004:   format).

6006:   `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6007:   that would generate a new entry in the nonzero structure is instead
6008:   ignored.  Thus, if memory has not already been allocated for this particular
6009:   data, then the insertion is ignored. For dense matrices, in which
6010:   the entire array is allocated, no entries are ever ignored.
6011:   Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

6013:   `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6014:   that would generate a new entry in the nonzero structure instead produces
6015:   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

6017:   `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6018:   that would generate a new entry that has not been preallocated will
6019:   instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6020:   only.) This is a useful flag when debugging matrix memory preallocation.
6021:   If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

6023:   `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6024:   other processors should be dropped, rather than stashed.
6025:   This is useful if you know that the "owning" processor is also
6026:   always generating the correct matrix entries, so that PETSc need
6027:   not transfer duplicate entries generated on another processor.

6029:   `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6030:   searches during matrix assembly. When this flag is set, the hash table
6031:   is created during the first matrix assembly. This hash table is
6032:   used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6033:   to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6034:   should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6035:   supported by `MATMPIBAIJ` format only.

6037:   `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6038:   are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`

6040:   `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6041:   a zero location in the matrix

6043:   `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

6045:   `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6046:   zero row routines and thus improves performance for very large process counts.

6048:   `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6049:   part of the matrix (since they should match the upper triangular part).

6051:   `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6052:   single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6053:   with finite difference schemes with non-periodic boundary conditions.

6055:   Developer Note:
6056:   `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6057:   places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6058:   to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6059:   not changed.

6061: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6062: @*/
6063: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6064: {
6065:   PetscFunctionBegin;
6067:   if (op > 0) {
6070:   }

6072:   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);

6074:   switch (op) {
6075:   case MAT_FORCE_DIAGONAL_ENTRIES:
6076:     mat->force_diagonals = flg;
6077:     PetscFunctionReturn(PETSC_SUCCESS);
6078:   case MAT_NO_OFF_PROC_ENTRIES:
6079:     mat->nooffprocentries = flg;
6080:     PetscFunctionReturn(PETSC_SUCCESS);
6081:   case MAT_SUBSET_OFF_PROC_ENTRIES:
6082:     mat->assembly_subset = flg;
6083:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6084: #if !defined(PETSC_HAVE_MPIUNI)
6085:       PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6086: #endif
6087:       mat->stash.first_assembly_done = PETSC_FALSE;
6088:     }
6089:     PetscFunctionReturn(PETSC_SUCCESS);
6090:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6091:     mat->nooffproczerorows = flg;
6092:     PetscFunctionReturn(PETSC_SUCCESS);
6093:   case MAT_SPD:
6094:     if (flg) {
6095:       mat->spd                    = PETSC_BOOL3_TRUE;
6096:       mat->symmetric              = PETSC_BOOL3_TRUE;
6097:       mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6098:     } else {
6099:       mat->spd = PETSC_BOOL3_FALSE;
6100:     }
6101:     break;
6102:   case MAT_SYMMETRIC:
6103:     mat->symmetric = PetscBoolToBool3(flg);
6104:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6105: #if !defined(PETSC_USE_COMPLEX)
6106:     mat->hermitian = PetscBoolToBool3(flg);
6107: #endif
6108:     break;
6109:   case MAT_HERMITIAN:
6110:     mat->hermitian = PetscBoolToBool3(flg);
6111:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6112: #if !defined(PETSC_USE_COMPLEX)
6113:     mat->symmetric = PetscBoolToBool3(flg);
6114: #endif
6115:     break;
6116:   case MAT_STRUCTURALLY_SYMMETRIC:
6117:     mat->structurally_symmetric = PetscBoolToBool3(flg);
6118:     break;
6119:   case MAT_SYMMETRY_ETERNAL:
6120:     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");
6121:     mat->symmetry_eternal = flg;
6122:     if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6123:     break;
6124:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6125:     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");
6126:     mat->structural_symmetry_eternal = flg;
6127:     break;
6128:   case MAT_SPD_ETERNAL:
6129:     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");
6130:     mat->spd_eternal = flg;
6131:     if (flg) {
6132:       mat->structural_symmetry_eternal = PETSC_TRUE;
6133:       mat->symmetry_eternal            = PETSC_TRUE;
6134:     }
6135:     break;
6136:   case MAT_STRUCTURE_ONLY:
6137:     mat->structure_only = flg;
6138:     break;
6139:   case MAT_SORTED_FULL:
6140:     mat->sortedfull = flg;
6141:     break;
6142:   default:
6143:     break;
6144:   }
6145:   PetscTryTypeMethod(mat, setoption, op, flg);
6146:   PetscFunctionReturn(PETSC_SUCCESS);
6147: }

6149: /*@
6150:   MatGetOption - Gets a parameter option that has been set for a matrix.

6152:   Logically Collective

6154:   Input Parameters:
6155: + mat - the matrix
6156: - op  - the option, this only responds to certain options, check the code for which ones

6158:   Output Parameter:
6159: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

6161:   Level: intermediate

6163:   Notes:
6164:   Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

6166:   Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6167:   `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

6169: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6170:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6171: @*/
6172: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6173: {
6174:   PetscFunctionBegin;

6178:   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);
6179:   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()");

6181:   switch (op) {
6182:   case MAT_NO_OFF_PROC_ENTRIES:
6183:     *flg = mat->nooffprocentries;
6184:     break;
6185:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6186:     *flg = mat->nooffproczerorows;
6187:     break;
6188:   case MAT_SYMMETRIC:
6189:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6190:     break;
6191:   case MAT_HERMITIAN:
6192:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6193:     break;
6194:   case MAT_STRUCTURALLY_SYMMETRIC:
6195:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6196:     break;
6197:   case MAT_SPD:
6198:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6199:     break;
6200:   case MAT_SYMMETRY_ETERNAL:
6201:     *flg = mat->symmetry_eternal;
6202:     break;
6203:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6204:     *flg = mat->symmetry_eternal;
6205:     break;
6206:   default:
6207:     break;
6208:   }
6209:   PetscFunctionReturn(PETSC_SUCCESS);
6210: }

6212: /*@
6213:   MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
6214:   this routine retains the old nonzero structure.

6216:   Logically Collective

6218:   Input Parameter:
6219: . mat - the matrix

6221:   Level: intermediate

6223:   Note:
6224:   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.
6225:   See the Performance chapter of the users manual for information on preallocating matrices.

6227: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6228: @*/
6229: PetscErrorCode MatZeroEntries(Mat mat)
6230: {
6231:   PetscFunctionBegin;
6234:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6235:   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");
6236:   MatCheckPreallocated(mat, 1);

6238:   PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6239:   PetscUseTypeMethod(mat, zeroentries);
6240:   PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6241:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6242:   PetscFunctionReturn(PETSC_SUCCESS);
6243: }

6245: /*@
6246:   MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6247:   of a set of rows and columns of a matrix.

6249:   Collective

6251:   Input Parameters:
6252: + mat     - the matrix
6253: . numRows - the number of rows/columns to zero
6254: . rows    - the global row indices
6255: . diag    - value put in the diagonal of the eliminated rows
6256: . x       - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6257: - b       - optional vector of the right-hand side, that will be adjusted by provided solution entries

6259:   Level: intermediate

6261:   Notes:
6262:   This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6264:   For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6265:   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

6267:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6268:   Krylov method to take advantage of the known solution on the zeroed rows.

6270:   For the parallel case, all processes that share the matrix (i.e.,
6271:   those in the communicator used for matrix creation) MUST call this
6272:   routine, regardless of whether any rows being zeroed are owned by
6273:   them.

6275:   Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6276:   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
6277:   missing.

6279:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6280:   list only rows local to itself).

6282:   The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

6284: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6285:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6286: @*/
6287: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6288: {
6289:   PetscFunctionBegin;
6292:   if (numRows) PetscAssertPointer(rows, 3);
6293:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6294:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6295:   MatCheckPreallocated(mat, 1);

6297:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6298:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6299:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6300:   PetscFunctionReturn(PETSC_SUCCESS);
6301: }

6303: /*@
6304:   MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6305:   of a set of rows and columns of a matrix.

6307:   Collective

6309:   Input Parameters:
6310: + mat  - the matrix
6311: . is   - the rows to zero
6312: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6313: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6314: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6316:   Level: intermediate

6318:   Note:
6319:   See `MatZeroRowsColumns()` for details on how this routine operates.

6321: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6322:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6323: @*/
6324: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6325: {
6326:   PetscInt        numRows;
6327:   const PetscInt *rows;

6329:   PetscFunctionBegin;
6334:   PetscCall(ISGetLocalSize(is, &numRows));
6335:   PetscCall(ISGetIndices(is, &rows));
6336:   PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6337:   PetscCall(ISRestoreIndices(is, &rows));
6338:   PetscFunctionReturn(PETSC_SUCCESS);
6339: }

6341: /*@
6342:   MatZeroRows - Zeros all entries (except possibly the main diagonal)
6343:   of a set of rows of a matrix.

6345:   Collective

6347:   Input Parameters:
6348: + mat     - the matrix
6349: . numRows - the number of rows to zero
6350: . rows    - the global row indices
6351: . diag    - value put in the diagonal of the zeroed rows
6352: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6353: - b       - optional vector of right-hand side, that will be adjusted by provided solution entries

6355:   Level: intermediate

6357:   Notes:
6358:   This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6360:   For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

6362:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6363:   Krylov method to take advantage of the known solution on the zeroed rows.

6365:   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)
6366:   from the matrix.

6368:   Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6369:   but does not release memory.  Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6370:   formats this does not alter the nonzero structure.

6372:   If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6373:   of the matrix is not changed the values are
6374:   merely zeroed.

6376:   The user can set a value in the diagonal entry (or for the `MATAIJ` format
6377:   formats can optionally remove the main diagonal entry from the
6378:   nonzero structure as well, by passing 0.0 as the final argument).

6380:   For the parallel case, all processes that share the matrix (i.e.,
6381:   those in the communicator used for matrix creation) MUST call this
6382:   routine, regardless of whether any rows being zeroed are owned by
6383:   them.

6385:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6386:   list only rows local to itself).

6388:   You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6389:   owns that are to be zeroed. This saves a global synchronization in the implementation.

6391: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6392:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6393: @*/
6394: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6395: {
6396:   PetscFunctionBegin;
6399:   if (numRows) PetscAssertPointer(rows, 3);
6400:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6401:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6402:   MatCheckPreallocated(mat, 1);

6404:   PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6405:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6406:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6407:   PetscFunctionReturn(PETSC_SUCCESS);
6408: }

6410: /*@
6411:   MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6412:   of a set of rows of a matrix indicated by an `IS`

6414:   Collective

6416:   Input Parameters:
6417: + mat  - the matrix
6418: . is   - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6419: . diag - value put in all diagonals of eliminated rows
6420: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6421: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6423:   Level: intermediate

6425:   Note:
6426:   See `MatZeroRows()` for details on how this routine operates.

6428: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6429:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6430: @*/
6431: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6432: {
6433:   PetscInt        numRows = 0;
6434:   const PetscInt *rows    = NULL;

6436:   PetscFunctionBegin;
6439:   if (is) {
6441:     PetscCall(ISGetLocalSize(is, &numRows));
6442:     PetscCall(ISGetIndices(is, &rows));
6443:   }
6444:   PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6445:   if (is) PetscCall(ISRestoreIndices(is, &rows));
6446:   PetscFunctionReturn(PETSC_SUCCESS);
6447: }

6449: /*@
6450:   MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6451:   of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.

6453:   Collective

6455:   Input Parameters:
6456: + mat     - the matrix
6457: . numRows - the number of rows to remove
6458: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6459: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6460: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6461: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6463:   Level: intermediate

6465:   Notes:
6466:   See `MatZeroRows()` for details on how this routine operates.

6468:   The grid coordinates are across the entire grid, not just the local portion

6470:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6471:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6472:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6473:   `DM_BOUNDARY_PERIODIC` boundary type.

6475:   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
6476:   a single value per point) you can skip filling those indices.

6478:   Fortran Note:
6479:   `idxm` and `idxn` should be declared as
6480: $     MatStencil idxm(4, m)
6481:   and the values inserted using
6482: .vb
6483:     idxm(MatStencil_i, 1) = i
6484:     idxm(MatStencil_j, 1) = j
6485:     idxm(MatStencil_k, 1) = k
6486:     idxm(MatStencil_c, 1) = c
6487:    etc
6488: .ve

6490: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6491:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6492: @*/
6493: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6494: {
6495:   PetscInt  dim    = mat->stencil.dim;
6496:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6497:   PetscInt *dims   = mat->stencil.dims + 1;
6498:   PetscInt *starts = mat->stencil.starts;
6499:   PetscInt *dxm    = (PetscInt *)rows;
6500:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6502:   PetscFunctionBegin;
6505:   if (numRows) PetscAssertPointer(rows, 3);

6507:   PetscCall(PetscMalloc1(numRows, &jdxm));
6508:   for (i = 0; i < numRows; ++i) {
6509:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6510:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6511:     /* Local index in X dir */
6512:     tmp = *dxm++ - starts[0];
6513:     /* Loop over remaining dimensions */
6514:     for (j = 0; j < dim - 1; ++j) {
6515:       /* If nonlocal, set index to be negative */
6516:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6517:       /* Update local index */
6518:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6519:     }
6520:     /* Skip component slot if necessary */
6521:     if (mat->stencil.noc) dxm++;
6522:     /* Local row number */
6523:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6524:   }
6525:   PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6526:   PetscCall(PetscFree(jdxm));
6527:   PetscFunctionReturn(PETSC_SUCCESS);
6528: }

6530: /*@
6531:   MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6532:   of a set of rows and columns of a matrix.

6534:   Collective

6536:   Input Parameters:
6537: + mat     - the matrix
6538: . numRows - the number of rows/columns to remove
6539: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6540: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6541: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6542: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6544:   Level: intermediate

6546:   Notes:
6547:   See `MatZeroRowsColumns()` for details on how this routine operates.

6549:   The grid coordinates are across the entire grid, not just the local portion

6551:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6552:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6553:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6554:   `DM_BOUNDARY_PERIODIC` boundary type.

6556:   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
6557:   a single value per point) you can skip filling those indices.

6559:   Fortran Note:
6560:   `idxm` and `idxn` should be declared as
6561: $     MatStencil idxm(4, m)
6562:   and the values inserted using
6563: .vb
6564:     idxm(MatStencil_i, 1) = i
6565:     idxm(MatStencil_j, 1) = j
6566:     idxm(MatStencil_k, 1) = k
6567:     idxm(MatStencil_c, 1) = c
6568:     etc
6569: .ve

6571: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6572:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6573: @*/
6574: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6575: {
6576:   PetscInt  dim    = mat->stencil.dim;
6577:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6578:   PetscInt *dims   = mat->stencil.dims + 1;
6579:   PetscInt *starts = mat->stencil.starts;
6580:   PetscInt *dxm    = (PetscInt *)rows;
6581:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6583:   PetscFunctionBegin;
6586:   if (numRows) PetscAssertPointer(rows, 3);

6588:   PetscCall(PetscMalloc1(numRows, &jdxm));
6589:   for (i = 0; i < numRows; ++i) {
6590:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6591:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6592:     /* Local index in X dir */
6593:     tmp = *dxm++ - starts[0];
6594:     /* Loop over remaining dimensions */
6595:     for (j = 0; j < dim - 1; ++j) {
6596:       /* If nonlocal, set index to be negative */
6597:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6598:       /* Update local index */
6599:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6600:     }
6601:     /* Skip component slot if necessary */
6602:     if (mat->stencil.noc) dxm++;
6603:     /* Local row number */
6604:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6605:   }
6606:   PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6607:   PetscCall(PetscFree(jdxm));
6608:   PetscFunctionReturn(PETSC_SUCCESS);
6609: }

6611: /*@
6612:   MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6613:   of a set of rows of a matrix; using local numbering of rows.

6615:   Collective

6617:   Input Parameters:
6618: + mat     - the matrix
6619: . numRows - the number of rows to remove
6620: . rows    - the local row indices
6621: . diag    - value put in all diagonals of eliminated rows
6622: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6623: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6625:   Level: intermediate

6627:   Notes:
6628:   Before calling `MatZeroRowsLocal()`, the user must first set the
6629:   local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

6631:   See `MatZeroRows()` for details on how this routine operates.

6633: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6634:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6635: @*/
6636: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6637: {
6638:   PetscFunctionBegin;
6641:   if (numRows) PetscAssertPointer(rows, 3);
6642:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6643:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6644:   MatCheckPreallocated(mat, 1);

6646:   if (mat->ops->zerorowslocal) {
6647:     PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6648:   } else {
6649:     IS              is, newis;
6650:     const PetscInt *newRows;

6652:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6653:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6654:     PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6655:     PetscCall(ISGetIndices(newis, &newRows));
6656:     PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6657:     PetscCall(ISRestoreIndices(newis, &newRows));
6658:     PetscCall(ISDestroy(&newis));
6659:     PetscCall(ISDestroy(&is));
6660:   }
6661:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6662:   PetscFunctionReturn(PETSC_SUCCESS);
6663: }

6665: /*@
6666:   MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6667:   of a set of rows of a matrix; using local numbering of rows.

6669:   Collective

6671:   Input Parameters:
6672: + mat  - the matrix
6673: . is   - index set of rows to remove
6674: . diag - value put in all diagonals of eliminated rows
6675: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6676: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6678:   Level: intermediate

6680:   Notes:
6681:   Before calling `MatZeroRowsLocalIS()`, the user must first set the
6682:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6684:   See `MatZeroRows()` for details on how this routine operates.

6686: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6687:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6688: @*/
6689: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6690: {
6691:   PetscInt        numRows;
6692:   const PetscInt *rows;

6694:   PetscFunctionBegin;
6698:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6699:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6700:   MatCheckPreallocated(mat, 1);

6702:   PetscCall(ISGetLocalSize(is, &numRows));
6703:   PetscCall(ISGetIndices(is, &rows));
6704:   PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6705:   PetscCall(ISRestoreIndices(is, &rows));
6706:   PetscFunctionReturn(PETSC_SUCCESS);
6707: }

6709: /*@
6710:   MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6711:   of a set of rows and columns of a matrix; using local numbering of rows.

6713:   Collective

6715:   Input Parameters:
6716: + mat     - the matrix
6717: . numRows - the number of rows to remove
6718: . rows    - the global row indices
6719: . diag    - value put in all diagonals of eliminated rows
6720: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6721: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6723:   Level: intermediate

6725:   Notes:
6726:   Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6727:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6729:   See `MatZeroRowsColumns()` for details on how this routine operates.

6731: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6732:           `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6733: @*/
6734: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6735: {
6736:   IS              is, newis;
6737:   const PetscInt *newRows;

6739:   PetscFunctionBegin;
6742:   if (numRows) PetscAssertPointer(rows, 3);
6743:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6744:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6745:   MatCheckPreallocated(mat, 1);

6747:   PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6748:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6749:   PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6750:   PetscCall(ISGetIndices(newis, &newRows));
6751:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6752:   PetscCall(ISRestoreIndices(newis, &newRows));
6753:   PetscCall(ISDestroy(&newis));
6754:   PetscCall(ISDestroy(&is));
6755:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6756:   PetscFunctionReturn(PETSC_SUCCESS);
6757: }

6759: /*@
6760:   MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6761:   of a set of rows and columns of a matrix; using local numbering of rows.

6763:   Collective

6765:   Input Parameters:
6766: + mat  - the matrix
6767: . is   - index set of rows to remove
6768: . diag - value put in all diagonals of eliminated rows
6769: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6770: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6772:   Level: intermediate

6774:   Notes:
6775:   Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6776:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6778:   See `MatZeroRowsColumns()` for details on how this routine operates.

6780: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6781:           `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6782: @*/
6783: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6784: {
6785:   PetscInt        numRows;
6786:   const PetscInt *rows;

6788:   PetscFunctionBegin;
6792:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6793:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6794:   MatCheckPreallocated(mat, 1);

6796:   PetscCall(ISGetLocalSize(is, &numRows));
6797:   PetscCall(ISGetIndices(is, &rows));
6798:   PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6799:   PetscCall(ISRestoreIndices(is, &rows));
6800:   PetscFunctionReturn(PETSC_SUCCESS);
6801: }

6803: /*@
6804:   MatGetSize - Returns the numbers of rows and columns in a matrix.

6806:   Not Collective

6808:   Input Parameter:
6809: . mat - the matrix

6811:   Output Parameters:
6812: + m - the number of global rows
6813: - n - the number of global columns

6815:   Level: beginner

6817:   Note:
6818:   Both output parameters can be `NULL` on input.

6820: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6821: @*/
6822: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6823: {
6824:   PetscFunctionBegin;
6826:   if (m) *m = mat->rmap->N;
6827:   if (n) *n = mat->cmap->N;
6828:   PetscFunctionReturn(PETSC_SUCCESS);
6829: }

6831: /*@
6832:   MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6833:   of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

6835:   Not Collective

6837:   Input Parameter:
6838: . mat - the matrix

6840:   Output Parameters:
6841: + m - the number of local rows, use `NULL` to not obtain this value
6842: - n - the number of local columns, use `NULL` to not obtain this value

6844:   Level: beginner

6846: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6847: @*/
6848: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6849: {
6850:   PetscFunctionBegin;
6852:   if (m) PetscAssertPointer(m, 2);
6853:   if (n) PetscAssertPointer(n, 3);
6854:   if (m) *m = mat->rmap->n;
6855:   if (n) *n = mat->cmap->n;
6856:   PetscFunctionReturn(PETSC_SUCCESS);
6857: }

6859: /*@
6860:   MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6861:   vector one multiplies this matrix by that are owned by this processor.

6863:   Not Collective, unless matrix has not been allocated, then collective

6865:   Input Parameter:
6866: . mat - the matrix

6868:   Output Parameters:
6869: + m - the global index of the first local column, use `NULL` to not obtain this value
6870: - n - one more than the global index of the last local column, use `NULL` to not obtain this value

6872:   Level: developer

6874:   Notes:
6875:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6877:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6878:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6880:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6881:   the local values in the matrix.

6883:   Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6884:   Layouts](sec_matlayout) for details on matrix layouts.

6886: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6887:           `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6888: @*/
6889: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6890: {
6891:   PetscFunctionBegin;
6894:   if (m) PetscAssertPointer(m, 2);
6895:   if (n) PetscAssertPointer(n, 3);
6896:   MatCheckPreallocated(mat, 1);
6897:   if (m) *m = mat->cmap->rstart;
6898:   if (n) *n = mat->cmap->rend;
6899:   PetscFunctionReturn(PETSC_SUCCESS);
6900: }

6902: /*@
6903:   MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6904:   this MPI process.

6906:   Not Collective

6908:   Input Parameter:
6909: . mat - the matrix

6911:   Output Parameters:
6912: + m - the global index of the first local row, use `NULL` to not obtain this value
6913: - n - one more than the global index of the last local row, use `NULL` to not obtain this value

6915:   Level: beginner

6917:   Notes:
6918:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6920:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6921:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6923:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6924:   the local values in the matrix.

6926:   The high argument is one more than the last element stored locally.

6928:   For all matrices  it returns the range of matrix rows associated with rows of a vector that
6929:   would contain the result of a matrix vector product with this matrix. See [Matrix
6930:   Layouts](sec_matlayout) for details on matrix layouts.

6932: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6933:           `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6934: @*/
6935: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6936: {
6937:   PetscFunctionBegin;
6940:   if (m) PetscAssertPointer(m, 2);
6941:   if (n) PetscAssertPointer(n, 3);
6942:   MatCheckPreallocated(mat, 1);
6943:   if (m) *m = mat->rmap->rstart;
6944:   if (n) *n = mat->rmap->rend;
6945:   PetscFunctionReturn(PETSC_SUCCESS);
6946: }

6948: /*@C
6949:   MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6950:   `MATSCALAPACK`, returns the range of matrix rows owned by each process.

6952:   Not Collective, unless matrix has not been allocated

6954:   Input Parameter:
6955: . mat - the matrix

6957:   Output Parameter:
6958: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6959:            where `size` is the number of MPI processes used by `mat`

6961:   Level: beginner

6963:   Notes:
6964:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6966:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6967:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6969:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6970:   the local values in the matrix.

6972:   For all matrices  it returns the ranges of matrix rows associated with rows of a vector that
6973:   would contain the result of a matrix vector product with this matrix. See [Matrix
6974:   Layouts](sec_matlayout) for details on matrix layouts.

6976: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6977:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6978:           `DMDAGetGhostCorners()`, `DM`
6979: @*/
6980: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6981: {
6982:   PetscFunctionBegin;
6985:   MatCheckPreallocated(mat, 1);
6986:   PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6987:   PetscFunctionReturn(PETSC_SUCCESS);
6988: }

6990: /*@C
6991:   MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6992:   vector one multiplies this vector by that are owned by each processor.

6994:   Not Collective, unless matrix has not been allocated

6996:   Input Parameter:
6997: . mat - the matrix

6999:   Output Parameter:
7000: . ranges - start of each processors portion plus one more than the total length at the end

7002:   Level: beginner

7004:   Notes:
7005:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

7007:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7008:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

7010:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7011:   the local values in the matrix.

7013:   Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7014:   Layouts](sec_matlayout) for details on matrix layouts.

7016: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7017:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7018:           `DMDAGetGhostCorners()`, `DM`
7019: @*/
7020: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7021: {
7022:   PetscFunctionBegin;
7025:   MatCheckPreallocated(mat, 1);
7026:   PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7027:   PetscFunctionReturn(PETSC_SUCCESS);
7028: }

7030: /*@
7031:   MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.

7033:   Not Collective

7035:   Input Parameter:
7036: . A - matrix

7038:   Output Parameters:
7039: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7040: - cols - columns in which this process owns elements, use `NULL` to not obtain this value

7042:   Level: intermediate

7044:   Note:
7045:   You should call `ISDestroy()` on the returned `IS`

7047:   For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7048:   returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7049:   `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7050:   details on matrix layouts.

7052: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7053: @*/
7054: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7055: {
7056:   PetscErrorCode (*f)(Mat, IS *, IS *);

7058:   PetscFunctionBegin;
7061:   MatCheckPreallocated(A, 1);
7062:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7063:   if (f) {
7064:     PetscCall((*f)(A, rows, cols));
7065:   } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7066:     if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7067:     if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7068:   }
7069:   PetscFunctionReturn(PETSC_SUCCESS);
7070: }

7072: /*@
7073:   MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7074:   Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7075:   to complete the factorization.

7077:   Collective

7079:   Input Parameters:
7080: + fact - the factorized matrix obtained with `MatGetFactor()`
7081: . mat  - the matrix
7082: . row  - row permutation
7083: . col  - column permutation
7084: - info - structure containing
7085: .vb
7086:       levels - number of levels of fill.
7087:       expected fill - as ratio of original fill.
7088:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7089:                 missing diagonal entries)
7090: .ve

7092:   Level: developer

7094:   Notes:
7095:   See [Matrix Factorization](sec_matfactor) for additional information.

7097:   Most users should employ the `KSP` interface for linear solvers
7098:   instead of working directly with matrix algebra routines such as this.
7099:   See, e.g., `KSPCreate()`.

7101:   Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`

7103:   Developer Note:
7104:   The Fortran interface is not autogenerated as the
7105:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

7107: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7108:           `MatGetOrdering()`, `MatFactorInfo`
7109: @*/
7110: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7111: {
7112:   PetscFunctionBegin;
7117:   PetscAssertPointer(info, 5);
7118:   PetscAssertPointer(fact, 1);
7119:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7120:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7121:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7122:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7123:   MatCheckPreallocated(mat, 2);

7125:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7126:   PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7127:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7128:   PetscFunctionReturn(PETSC_SUCCESS);
7129: }

7131: /*@
7132:   MatICCFactorSymbolic - Performs symbolic incomplete
7133:   Cholesky factorization for a symmetric matrix.  Use
7134:   `MatCholeskyFactorNumeric()` to complete the factorization.

7136:   Collective

7138:   Input Parameters:
7139: + fact - the factorized matrix obtained with `MatGetFactor()`
7140: . mat  - the matrix to be factored
7141: . perm - row and column permutation
7142: - info - structure containing
7143: .vb
7144:       levels - number of levels of fill.
7145:       expected fill - as ratio of original fill.
7146: .ve

7148:   Level: developer

7150:   Notes:
7151:   Most users should employ the `KSP` interface for linear solvers
7152:   instead of working directly with matrix algebra routines such as this.
7153:   See, e.g., `KSPCreate()`.

7155:   This uses the definition of level of fill as in Y. Saad {cite}`saad2003`

7157:   Developer Note:
7158:   The Fortran interface is not autogenerated as the
7159:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

7161: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7162: @*/
7163: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7164: {
7165:   PetscFunctionBegin;
7169:   PetscAssertPointer(info, 4);
7170:   PetscAssertPointer(fact, 1);
7171:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7172:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7173:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7174:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7175:   MatCheckPreallocated(mat, 2);

7177:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7178:   PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7179:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7180:   PetscFunctionReturn(PETSC_SUCCESS);
7181: }

7183: /*@C
7184:   MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7185:   points to an array of valid matrices, they may be reused to store the new
7186:   submatrices.

7188:   Collective

7190:   Input Parameters:
7191: + mat   - the matrix
7192: . n     - the number of submatrixes to be extracted (on this processor, may be zero)
7193: . irow  - index set of rows to extract
7194: . icol  - index set of columns to extract
7195: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7197:   Output Parameter:
7198: . submat - the array of submatrices

7200:   Level: advanced

7202:   Notes:
7203:   `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7204:   (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7205:   to extract a parallel submatrix.

7207:   Some matrix types place restrictions on the row and column
7208:   indices, such as that they be sorted or that they be equal to each other.

7210:   The index sets may not have duplicate entries.

7212:   When extracting submatrices from a parallel matrix, each processor can
7213:   form a different submatrix by setting the rows and columns of its
7214:   individual index sets according to the local submatrix desired.

7216:   When finished using the submatrices, the user should destroy
7217:   them with `MatDestroySubMatrices()`.

7219:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7220:   original matrix has not changed from that last call to `MatCreateSubMatrices()`.

7222:   This routine creates the matrices in submat; you should NOT create them before
7223:   calling it. It also allocates the array of matrix pointers submat.

7225:   For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7226:   request one row/column in a block, they must request all rows/columns that are in
7227:   that block. For example, if the block size is 2 you cannot request just row 0 and
7228:   column 0.

7230:   Fortran Note:
7231: .vb
7232:   Mat, pointer :: submat(:)
7233: .ve

7235: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7236: @*/
7237: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7238: {
7239:   PetscInt  i;
7240:   PetscBool eq;

7242:   PetscFunctionBegin;
7245:   if (n) {
7246:     PetscAssertPointer(irow, 3);
7248:     PetscAssertPointer(icol, 4);
7250:   }
7251:   PetscAssertPointer(submat, 6);
7252:   if (n && scall == MAT_REUSE_MATRIX) {
7253:     PetscAssertPointer(*submat, 6);
7255:   }
7256:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7257:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7258:   MatCheckPreallocated(mat, 1);
7259:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7260:   PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7261:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7262:   for (i = 0; i < n; i++) {
7263:     (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7264:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7265:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7266: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7267:     if (mat->boundtocpu && mat->bindingpropagates) {
7268:       PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7269:       PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7270:     }
7271: #endif
7272:   }
7273:   PetscFunctionReturn(PETSC_SUCCESS);
7274: }

7276: /*@C
7277:   MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).

7279:   Collective

7281:   Input Parameters:
7282: + mat   - the matrix
7283: . n     - the number of submatrixes to be extracted
7284: . irow  - index set of rows to extract
7285: . icol  - index set of columns to extract
7286: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7288:   Output Parameter:
7289: . submat - the array of submatrices

7291:   Level: advanced

7293:   Note:
7294:   This is used by `PCGASM`

7296: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7297: @*/
7298: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7299: {
7300:   PetscInt  i;
7301:   PetscBool eq;

7303:   PetscFunctionBegin;
7306:   if (n) {
7307:     PetscAssertPointer(irow, 3);
7309:     PetscAssertPointer(icol, 4);
7311:   }
7312:   PetscAssertPointer(submat, 6);
7313:   if (n && scall == MAT_REUSE_MATRIX) {
7314:     PetscAssertPointer(*submat, 6);
7316:   }
7317:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7318:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7319:   MatCheckPreallocated(mat, 1);

7321:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7322:   PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7323:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7324:   for (i = 0; i < n; i++) {
7325:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7326:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7327:   }
7328:   PetscFunctionReturn(PETSC_SUCCESS);
7329: }

7331: /*@C
7332:   MatDestroyMatrices - Destroys an array of matrices

7334:   Collective

7336:   Input Parameters:
7337: + n   - the number of local matrices
7338: - mat - the matrices (this is a pointer to the array of matrices)

7340:   Level: advanced

7342:   Notes:
7343:   Frees not only the matrices, but also the array that contains the matrices

7345:   For matrices obtained with  `MatCreateSubMatrices()` use `MatDestroySubMatrices()`

7347:   Fortran Note:
7348:   Does not free the `mat` array.

7350: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7351: @*/
7352: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7353: {
7354:   PetscInt i;

7356:   PetscFunctionBegin;
7357:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7358:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7359:   PetscAssertPointer(mat, 2);

7361:   for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

7363:   /* memory is allocated even if n = 0 */
7364:   PetscCall(PetscFree(*mat));
7365:   PetscFunctionReturn(PETSC_SUCCESS);
7366: }

7368: /*@C
7369:   MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

7371:   Collective

7373:   Input Parameters:
7374: + n   - the number of local matrices
7375: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)

7377:   Level: advanced

7379:   Note:
7380:   Frees not only the matrices, but also the array that contains the matrices

7382: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7383: @*/
7384: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7385: {
7386:   Mat mat0;

7388:   PetscFunctionBegin;
7389:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7390:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7391:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7392:   PetscAssertPointer(mat, 2);

7394:   mat0 = (*mat)[0];
7395:   if (mat0 && mat0->ops->destroysubmatrices) {
7396:     PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7397:   } else {
7398:     PetscCall(MatDestroyMatrices(n, mat));
7399:   }
7400:   PetscFunctionReturn(PETSC_SUCCESS);
7401: }

7403: /*@
7404:   MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

7406:   Collective

7408:   Input Parameter:
7409: . mat - the matrix

7411:   Output Parameter:
7412: . matstruct - the sequential matrix with the nonzero structure of `mat`

7414:   Level: developer

7416: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7417: @*/
7418: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7419: {
7420:   PetscFunctionBegin;
7422:   PetscAssertPointer(matstruct, 2);

7425:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7426:   MatCheckPreallocated(mat, 1);

7428:   PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7429:   PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7430:   PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7431:   PetscFunctionReturn(PETSC_SUCCESS);
7432: }

7434: /*@C
7435:   MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

7437:   Collective

7439:   Input Parameter:
7440: . mat - the matrix

7442:   Level: advanced

7444:   Note:
7445:   This is not needed, one can just call `MatDestroy()`

7447: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7448: @*/
7449: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7450: {
7451:   PetscFunctionBegin;
7452:   PetscAssertPointer(mat, 1);
7453:   PetscCall(MatDestroy(mat));
7454:   PetscFunctionReturn(PETSC_SUCCESS);
7455: }

7457: /*@
7458:   MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7459:   replaces the index sets by larger ones that represent submatrices with
7460:   additional overlap.

7462:   Collective

7464:   Input Parameters:
7465: + mat - the matrix
7466: . n   - the number of index sets
7467: . is  - the array of index sets (these index sets will changed during the call)
7468: - ov  - the additional overlap requested

7470:   Options Database Key:
7471: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7473:   Level: developer

7475:   Note:
7476:   The computed overlap preserves the matrix block sizes when the blocks are square.
7477:   That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7478:   that block are included in the overlap regardless of whether each specific column would increase the overlap.

7480: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7481: @*/
7482: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7483: {
7484:   PetscInt i, bs, cbs;

7486:   PetscFunctionBegin;
7490:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7491:   if (n) {
7492:     PetscAssertPointer(is, 3);
7494:   }
7495:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7496:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7497:   MatCheckPreallocated(mat, 1);

7499:   if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7500:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7501:   PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7502:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7503:   PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7504:   if (bs == cbs) {
7505:     for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7506:   }
7507:   PetscFunctionReturn(PETSC_SUCCESS);
7508: }

7510: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

7512: /*@
7513:   MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7514:   a sub communicator, replaces the index sets by larger ones that represent submatrices with
7515:   additional overlap.

7517:   Collective

7519:   Input Parameters:
7520: + mat - the matrix
7521: . n   - the number of index sets
7522: . is  - the array of index sets (these index sets will changed during the call)
7523: - ov  - the additional overlap requested

7525:   `   Options Database Key:
7526: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7528:   Level: developer

7530: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7531: @*/
7532: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7533: {
7534:   PetscInt i;

7536:   PetscFunctionBegin;
7539:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7540:   if (n) {
7541:     PetscAssertPointer(is, 3);
7543:   }
7544:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7545:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7546:   MatCheckPreallocated(mat, 1);
7547:   if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7548:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7549:   for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7550:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7551:   PetscFunctionReturn(PETSC_SUCCESS);
7552: }

7554: /*@
7555:   MatGetBlockSize - Returns the matrix block size.

7557:   Not Collective

7559:   Input Parameter:
7560: . mat - the matrix

7562:   Output Parameter:
7563: . bs - block size

7565:   Level: intermediate

7567:   Notes:
7568:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

7570:   If the block size has not been set yet this routine returns 1.

7572: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7573: @*/
7574: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7575: {
7576:   PetscFunctionBegin;
7578:   PetscAssertPointer(bs, 2);
7579:   *bs = mat->rmap->bs;
7580:   PetscFunctionReturn(PETSC_SUCCESS);
7581: }

7583: /*@
7584:   MatGetBlockSizes - Returns the matrix block row and column sizes.

7586:   Not Collective

7588:   Input Parameter:
7589: . mat - the matrix

7591:   Output Parameters:
7592: + rbs - row block size
7593: - cbs - column block size

7595:   Level: intermediate

7597:   Notes:
7598:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7599:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7601:   If a block size has not been set yet this routine returns 1.

7603: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7604: @*/
7605: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7606: {
7607:   PetscFunctionBegin;
7609:   if (rbs) PetscAssertPointer(rbs, 2);
7610:   if (cbs) PetscAssertPointer(cbs, 3);
7611:   if (rbs) *rbs = mat->rmap->bs;
7612:   if (cbs) *cbs = mat->cmap->bs;
7613:   PetscFunctionReturn(PETSC_SUCCESS);
7614: }

7616: /*@
7617:   MatSetBlockSize - Sets the matrix block size.

7619:   Logically Collective

7621:   Input Parameters:
7622: + mat - the matrix
7623: - bs  - block size

7625:   Level: intermediate

7627:   Notes:
7628:   Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7629:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7631:   For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7632:   is compatible with the matrix local sizes.

7634: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7635: @*/
7636: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7637: {
7638:   PetscFunctionBegin;
7641:   PetscCall(MatSetBlockSizes(mat, bs, bs));
7642:   PetscFunctionReturn(PETSC_SUCCESS);
7643: }

7645: typedef struct {
7646:   PetscInt         n;
7647:   IS              *is;
7648:   Mat             *mat;
7649:   PetscObjectState nonzerostate;
7650:   Mat              C;
7651: } EnvelopeData;

7653: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7654: {
7655:   EnvelopeData *edata = (EnvelopeData *)*ptr;

7657:   PetscFunctionBegin;
7658:   for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7659:   PetscCall(PetscFree(edata->is));
7660:   PetscCall(PetscFree(edata));
7661:   PetscFunctionReturn(PETSC_SUCCESS);
7662: }

7664: /*@
7665:   MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7666:   the sizes of these blocks in the matrix. An individual block may lie over several processes.

7668:   Collective

7670:   Input Parameter:
7671: . mat - the matrix

7673:   Level: intermediate

7675:   Notes:
7676:   There can be zeros within the blocks

7678:   The blocks can overlap between processes, including laying on more than two processes

7680: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7681: @*/
7682: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7683: {
7684:   PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7685:   PetscInt          *diag, *odiag, sc;
7686:   VecScatter         scatter;
7687:   PetscScalar       *seqv;
7688:   const PetscScalar *parv;
7689:   const PetscInt    *ia, *ja;
7690:   PetscBool          set, flag, done;
7691:   Mat                AA = mat, A;
7692:   MPI_Comm           comm;
7693:   PetscMPIInt        rank, size, tag;
7694:   MPI_Status         status;
7695:   PetscContainer     container;
7696:   EnvelopeData      *edata;
7697:   Vec                seq, par;
7698:   IS                 isglobal;

7700:   PetscFunctionBegin;
7702:   PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7703:   if (!set || !flag) {
7704:     /* TODO: only needs nonzero structure of transpose */
7705:     PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7706:     PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7707:   }
7708:   PetscCall(MatAIJGetLocalMat(AA, &A));
7709:   PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7710:   PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

7712:   PetscCall(MatGetLocalSize(mat, &n, NULL));
7713:   PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7714:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7715:   PetscCallMPI(MPI_Comm_size(comm, &size));
7716:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

7718:   PetscCall(PetscMalloc2(n, &sizes, n, &starts));

7720:   if (rank > 0) {
7721:     PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7722:     PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7723:   }
7724:   PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7725:   for (i = 0; i < n; i++) {
7726:     env = PetscMax(env, ja[ia[i + 1] - 1]);
7727:     II  = rstart + i;
7728:     if (env == II) {
7729:       starts[lblocks]  = tbs;
7730:       sizes[lblocks++] = 1 + II - tbs;
7731:       tbs              = 1 + II;
7732:     }
7733:   }
7734:   if (rank < size - 1) {
7735:     PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7736:     PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7737:   }

7739:   PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7740:   if (!set || !flag) PetscCall(MatDestroy(&AA));
7741:   PetscCall(MatDestroy(&A));

7743:   PetscCall(PetscNew(&edata));
7744:   PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7745:   edata->n = lblocks;
7746:   /* create IS needed for extracting blocks from the original matrix */
7747:   PetscCall(PetscMalloc1(lblocks, &edata->is));
7748:   for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

7750:   /* Create the resulting inverse matrix nonzero structure with preallocation information */
7751:   PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7752:   PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7753:   PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7754:   PetscCall(MatSetType(edata->C, MATAIJ));

7756:   /* Communicate the start and end of each row, from each block to the correct rank */
7757:   /* TODO: Use PetscSF instead of VecScatter */
7758:   for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7759:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7760:   PetscCall(VecGetArrayWrite(seq, &seqv));
7761:   for (PetscInt i = 0; i < lblocks; i++) {
7762:     for (PetscInt j = 0; j < sizes[i]; j++) {
7763:       seqv[cnt]     = starts[i];
7764:       seqv[cnt + 1] = starts[i] + sizes[i];
7765:       cnt += 2;
7766:     }
7767:   }
7768:   PetscCall(VecRestoreArrayWrite(seq, &seqv));
7769:   PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7770:   sc -= cnt;
7771:   PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7772:   PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7773:   PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7774:   PetscCall(ISDestroy(&isglobal));
7775:   PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7776:   PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7777:   PetscCall(VecScatterDestroy(&scatter));
7778:   PetscCall(VecDestroy(&seq));
7779:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7780:   PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7781:   PetscCall(VecGetArrayRead(par, &parv));
7782:   cnt = 0;
7783:   PetscCall(MatGetSize(mat, NULL, &n));
7784:   for (PetscInt i = 0; i < mat->rmap->n; i++) {
7785:     PetscInt start, end, d = 0, od = 0;

7787:     start = (PetscInt)PetscRealPart(parv[cnt]);
7788:     end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
7789:     cnt += 2;

7791:     if (start < cstart) {
7792:       od += cstart - start + n - cend;
7793:       d += cend - cstart;
7794:     } else if (start < cend) {
7795:       od += n - cend;
7796:       d += cend - start;
7797:     } else od += n - start;
7798:     if (end <= cstart) {
7799:       od -= cstart - end + n - cend;
7800:       d -= cend - cstart;
7801:     } else if (end < cend) {
7802:       od -= n - cend;
7803:       d -= cend - end;
7804:     } else od -= n - end;

7806:     odiag[i] = od;
7807:     diag[i]  = d;
7808:   }
7809:   PetscCall(VecRestoreArrayRead(par, &parv));
7810:   PetscCall(VecDestroy(&par));
7811:   PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7812:   PetscCall(PetscFree2(diag, odiag));
7813:   PetscCall(PetscFree2(sizes, starts));

7815:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7816:   PetscCall(PetscContainerSetPointer(container, edata));
7817:   PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7818:   PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7819:   PetscCall(PetscObjectDereference((PetscObject)container));
7820:   PetscFunctionReturn(PETSC_SUCCESS);
7821: }

7823: /*@
7824:   MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

7826:   Collective

7828:   Input Parameters:
7829: + A     - the matrix
7830: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine

7832:   Output Parameter:
7833: . C - matrix with inverted block diagonal of `A`

7835:   Level: advanced

7837:   Note:
7838:   For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

7840: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7841: @*/
7842: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7843: {
7844:   PetscContainer   container;
7845:   EnvelopeData    *edata;
7846:   PetscObjectState nonzerostate;

7848:   PetscFunctionBegin;
7849:   PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7850:   if (!container) {
7851:     PetscCall(MatComputeVariableBlockEnvelope(A));
7852:     PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7853:   }
7854:   PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7855:   PetscCall(MatGetNonzeroState(A, &nonzerostate));
7856:   PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7857:   PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

7859:   PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7860:   *C = edata->C;

7862:   for (PetscInt i = 0; i < edata->n; i++) {
7863:     Mat          D;
7864:     PetscScalar *dvalues;

7866:     PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7867:     PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7868:     PetscCall(MatSeqDenseInvert(D));
7869:     PetscCall(MatDenseGetArray(D, &dvalues));
7870:     PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7871:     PetscCall(MatDestroy(&D));
7872:   }
7873:   PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7874:   PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7875:   PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7876:   PetscFunctionReturn(PETSC_SUCCESS);
7877: }

7879: /*@
7880:   MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

7882:   Not Collective

7884:   Input Parameters:
7885: + mat     - the matrix
7886: . nblocks - the number of blocks on this process, each block can only exist on a single process
7887: - bsizes  - the block sizes

7889:   Level: intermediate

7891:   Notes:
7892:   Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

7894:   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.

7896: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7897:           `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7898: @*/
7899: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7900: {
7901:   PetscInt ncnt = 0, nlocal;

7903:   PetscFunctionBegin;
7905:   PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7906:   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);
7907:   for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7908:   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);
7909:   PetscCall(PetscFree(mat->bsizes));
7910:   mat->nblocks = nblocks;
7911:   PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7912:   PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7913:   PetscFunctionReturn(PETSC_SUCCESS);
7914: }

7916: /*@C
7917:   MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7919:   Not Collective; No Fortran Support

7921:   Input Parameter:
7922: . mat - the matrix

7924:   Output Parameters:
7925: + nblocks - the number of blocks on this process
7926: - bsizes  - the block sizes

7928:   Level: intermediate

7930: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7931: @*/
7932: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7933: {
7934:   PetscFunctionBegin;
7936:   if (nblocks) *nblocks = mat->nblocks;
7937:   if (bsizes) *bsizes = mat->bsizes;
7938:   PetscFunctionReturn(PETSC_SUCCESS);
7939: }

7941: /*@
7942:   MatSetBlockSizes - Sets the matrix block row and column sizes.

7944:   Logically Collective

7946:   Input Parameters:
7947: + mat - the matrix
7948: . rbs - row block size
7949: - cbs - column block size

7951:   Level: intermediate

7953:   Notes:
7954:   Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7955:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7956:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7958:   For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7959:   are compatible with the matrix local sizes.

7961:   The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

7963: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7964: @*/
7965: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7966: {
7967:   PetscFunctionBegin;
7971:   PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7972:   if (mat->rmap->refcnt) {
7973:     ISLocalToGlobalMapping l2g  = NULL;
7974:     PetscLayout            nmap = NULL;

7976:     PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7977:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7978:     PetscCall(PetscLayoutDestroy(&mat->rmap));
7979:     mat->rmap          = nmap;
7980:     mat->rmap->mapping = l2g;
7981:   }
7982:   if (mat->cmap->refcnt) {
7983:     ISLocalToGlobalMapping l2g  = NULL;
7984:     PetscLayout            nmap = NULL;

7986:     PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7987:     if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7988:     PetscCall(PetscLayoutDestroy(&mat->cmap));
7989:     mat->cmap          = nmap;
7990:     mat->cmap->mapping = l2g;
7991:   }
7992:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7993:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7994:   PetscFunctionReturn(PETSC_SUCCESS);
7995: }

7997: /*@
7998:   MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

8000:   Logically Collective

8002:   Input Parameters:
8003: + mat     - the matrix
8004: . fromRow - matrix from which to copy row block size
8005: - fromCol - matrix from which to copy column block size (can be same as fromRow)

8007:   Level: developer

8009: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8010: @*/
8011: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8012: {
8013:   PetscFunctionBegin;
8017:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8018:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8019:   PetscFunctionReturn(PETSC_SUCCESS);
8020: }

8022: /*@
8023:   MatResidual - Default routine to calculate the residual r = b - Ax

8025:   Collective

8027:   Input Parameters:
8028: + mat - the matrix
8029: . b   - the right-hand-side
8030: - x   - the approximate solution

8032:   Output Parameter:
8033: . r - location to store the residual

8035:   Level: developer

8037: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8038: @*/
8039: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8040: {
8041:   PetscFunctionBegin;
8047:   MatCheckPreallocated(mat, 1);
8048:   PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8049:   if (!mat->ops->residual) {
8050:     PetscCall(MatMult(mat, x, r));
8051:     PetscCall(VecAYPX(r, -1.0, b));
8052:   } else {
8053:     PetscUseTypeMethod(mat, residual, b, x, r);
8054:   }
8055:   PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8056:   PetscFunctionReturn(PETSC_SUCCESS);
8057: }

8059: /*@C
8060:   MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

8062:   Collective

8064:   Input Parameters:
8065: + mat             - the matrix
8066: . shift           - 0 or 1 indicating we want the indices starting at 0 or 1
8067: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8068: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8069:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8070:                  always used.

8072:   Output Parameters:
8073: + n    - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8074: . 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
8075: . ja   - the column indices, use `NULL` if not needed
8076: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8077:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8079:   Level: developer

8081:   Notes:
8082:   You CANNOT change any of the ia[] or ja[] values.

8084:   Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

8086:   Fortran Notes:
8087:   Use
8088: .vb
8089:     PetscInt, pointer :: ia(:),ja(:)
8090:     call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8091:     ! Access the ith and jth entries via ia(i) and ja(j)
8092: .ve

8094: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8095: @*/
8096: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8097: {
8098:   PetscFunctionBegin;
8101:   if (n) PetscAssertPointer(n, 5);
8102:   if (ia) PetscAssertPointer(ia, 6);
8103:   if (ja) PetscAssertPointer(ja, 7);
8104:   if (done) PetscAssertPointer(done, 8);
8105:   MatCheckPreallocated(mat, 1);
8106:   if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8107:   else {
8108:     if (done) *done = PETSC_TRUE;
8109:     PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8110:     PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8111:     PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8112:   }
8113:   PetscFunctionReturn(PETSC_SUCCESS);
8114: }

8116: /*@C
8117:   MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

8119:   Collective

8121:   Input Parameters:
8122: + mat             - the matrix
8123: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8124: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8125:                 symmetrized
8126: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8127:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8128:                  always used.
8129: . n               - number of columns in the (possibly compressed) matrix
8130: . ia              - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8131: - ja              - the row indices

8133:   Output Parameter:
8134: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

8136:   Level: developer

8138: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8139: @*/
8140: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8141: {
8142:   PetscFunctionBegin;
8145:   PetscAssertPointer(n, 5);
8146:   if (ia) PetscAssertPointer(ia, 6);
8147:   if (ja) PetscAssertPointer(ja, 7);
8148:   PetscAssertPointer(done, 8);
8149:   MatCheckPreallocated(mat, 1);
8150:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8151:   else {
8152:     *done = PETSC_TRUE;
8153:     PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8154:   }
8155:   PetscFunctionReturn(PETSC_SUCCESS);
8156: }

8158: /*@C
8159:   MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

8161:   Collective

8163:   Input Parameters:
8164: + mat             - the matrix
8165: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8166: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8167: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8168:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8169:                     always used.
8170: . n               - size of (possibly compressed) matrix
8171: . ia              - the row pointers
8172: - ja              - the column indices

8174:   Output Parameter:
8175: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8177:   Level: developer

8179:   Note:
8180:   This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8181:   us of the array after it has been restored. If you pass `NULL`, it will
8182:   not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

8184: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8185: @*/
8186: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8187: {
8188:   PetscFunctionBegin;
8191:   if (ia) PetscAssertPointer(ia, 6);
8192:   if (ja) PetscAssertPointer(ja, 7);
8193:   if (done) PetscAssertPointer(done, 8);
8194:   MatCheckPreallocated(mat, 1);

8196:   if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8197:   else {
8198:     if (done) *done = PETSC_TRUE;
8199:     PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8200:     if (n) *n = 0;
8201:     if (ia) *ia = NULL;
8202:     if (ja) *ja = NULL;
8203:   }
8204:   PetscFunctionReturn(PETSC_SUCCESS);
8205: }

8207: /*@C
8208:   MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

8210:   Collective

8212:   Input Parameters:
8213: + mat             - the matrix
8214: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8215: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8216: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8217:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8218:                     always used.

8220:   Output Parameters:
8221: + n    - size of (possibly compressed) matrix
8222: . ia   - the column pointers
8223: . ja   - the row indices
8224: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8226:   Level: developer

8228: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8229: @*/
8230: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8231: {
8232:   PetscFunctionBegin;
8235:   if (ia) PetscAssertPointer(ia, 6);
8236:   if (ja) PetscAssertPointer(ja, 7);
8237:   PetscAssertPointer(done, 8);
8238:   MatCheckPreallocated(mat, 1);

8240:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8241:   else {
8242:     *done = PETSC_TRUE;
8243:     PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8244:     if (n) *n = 0;
8245:     if (ia) *ia = NULL;
8246:     if (ja) *ja = NULL;
8247:   }
8248:   PetscFunctionReturn(PETSC_SUCCESS);
8249: }

8251: /*@
8252:   MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8253:   `MatGetColumnIJ()`.

8255:   Collective

8257:   Input Parameters:
8258: + mat        - the matrix
8259: . ncolors    - maximum color value
8260: . n          - number of entries in colorarray
8261: - colorarray - array indicating color for each column

8263:   Output Parameter:
8264: . iscoloring - coloring generated using colorarray information

8266:   Level: developer

8268: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8269: @*/
8270: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8271: {
8272:   PetscFunctionBegin;
8275:   PetscAssertPointer(colorarray, 4);
8276:   PetscAssertPointer(iscoloring, 5);
8277:   MatCheckPreallocated(mat, 1);

8279:   if (!mat->ops->coloringpatch) {
8280:     PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8281:   } else {
8282:     PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8283:   }
8284:   PetscFunctionReturn(PETSC_SUCCESS);
8285: }

8287: /*@
8288:   MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

8290:   Logically Collective

8292:   Input Parameter:
8293: . mat - the factored matrix to be reset

8295:   Level: developer

8297:   Notes:
8298:   This routine should be used only with factored matrices formed by in-place
8299:   factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8300:   format).  This option can save memory, for example, when solving nonlinear
8301:   systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8302:   ILU(0) preconditioner.

8304:   One can specify in-place ILU(0) factorization by calling
8305: .vb
8306:      PCType(pc,PCILU);
8307:      PCFactorSeUseInPlace(pc);
8308: .ve
8309:   or by using the options -pc_type ilu -pc_factor_in_place

8311:   In-place factorization ILU(0) can also be used as a local
8312:   solver for the blocks within the block Jacobi or additive Schwarz
8313:   methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
8314:   for details on setting local solver options.

8316:   Most users should employ the `KSP` interface for linear solvers
8317:   instead of working directly with matrix algebra routines such as this.
8318:   See, e.g., `KSPCreate()`.

8320: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8321: @*/
8322: PetscErrorCode MatSetUnfactored(Mat mat)
8323: {
8324:   PetscFunctionBegin;
8327:   MatCheckPreallocated(mat, 1);
8328:   mat->factortype = MAT_FACTOR_NONE;
8329:   if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8330:   PetscUseTypeMethod(mat, setunfactored);
8331:   PetscFunctionReturn(PETSC_SUCCESS);
8332: }

8334: /*@
8335:   MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8336:   as the original matrix.

8338:   Collective

8340:   Input Parameters:
8341: + mat   - the original matrix
8342: . isrow - parallel `IS` containing the rows this processor should obtain
8343: . 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.
8344: - cll   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

8346:   Output Parameter:
8347: . newmat - the new submatrix, of the same type as the original matrix

8349:   Level: advanced

8351:   Notes:
8352:   The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

8354:   Some matrix types place restrictions on the row and column indices, such
8355:   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;
8356:   for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

8358:   The index sets may not have duplicate entries.

8360:   The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8361:   the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8362:   to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8363:   will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
8364:   you are finished using it.

8366:   The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8367:   the input matrix.

8369:   If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

8371:   If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8372:   is used by `PCFIELDSPLIT` to allow easy nesting of its use.

8374:   Example usage:
8375:   Consider the following 8x8 matrix with 34 non-zero values, that is
8376:   assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8377:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8378:   as follows
8379: .vb
8380:             1  2  0  |  0  3  0  |  0  4
8381:     Proc0   0  5  6  |  7  0  0  |  8  0
8382:             9  0 10  | 11  0  0  | 12  0
8383:     -------------------------------------
8384:            13  0 14  | 15 16 17  |  0  0
8385:     Proc1   0 18  0  | 19 20 21  |  0  0
8386:             0  0  0  | 22 23  0  | 24  0
8387:     -------------------------------------
8388:     Proc2  25 26 27  |  0  0 28  | 29  0
8389:            30  0  0  | 31 32 33  |  0 34
8390: .ve

8392:   Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

8394: .vb
8395:             2  0  |  0  3  0  |  0
8396:     Proc0   5  6  |  7  0  0  |  8
8397:     -------------------------------
8398:     Proc1  18  0  | 19 20 21  |  0
8399:     -------------------------------
8400:     Proc2  26 27  |  0  0 28  | 29
8401:             0  0  | 31 32 33  |  0
8402: .ve

8404: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8405: @*/
8406: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8407: {
8408:   PetscMPIInt size;
8409:   Mat        *local;
8410:   IS          iscoltmp;
8411:   PetscBool   flg;

8413:   PetscFunctionBegin;
8417:   PetscAssertPointer(newmat, 5);
8420:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8421:   PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");

8423:   MatCheckPreallocated(mat, 1);
8424:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));

8426:   if (!iscol || isrow == iscol) {
8427:     PetscBool   stride;
8428:     PetscMPIInt grabentirematrix = 0, grab;
8429:     PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8430:     if (stride) {
8431:       PetscInt first, step, n, rstart, rend;
8432:       PetscCall(ISStrideGetInfo(isrow, &first, &step));
8433:       if (step == 1) {
8434:         PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8435:         if (rstart == first) {
8436:           PetscCall(ISGetLocalSize(isrow, &n));
8437:           if (n == rend - rstart) grabentirematrix = 1;
8438:         }
8439:       }
8440:     }
8441:     PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8442:     if (grab) {
8443:       PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8444:       if (cll == MAT_INITIAL_MATRIX) {
8445:         *newmat = mat;
8446:         PetscCall(PetscObjectReference((PetscObject)mat));
8447:       }
8448:       PetscFunctionReturn(PETSC_SUCCESS);
8449:     }
8450:   }

8452:   if (!iscol) {
8453:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8454:   } else {
8455:     iscoltmp = iscol;
8456:   }

8458:   /* if original matrix is on just one processor then use submatrix generated */
8459:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8460:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8461:     goto setproperties;
8462:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8463:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8464:     *newmat = *local;
8465:     PetscCall(PetscFree(local));
8466:     goto setproperties;
8467:   } else if (!mat->ops->createsubmatrix) {
8468:     /* Create a new matrix type that implements the operation using the full matrix */
8469:     PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8470:     switch (cll) {
8471:     case MAT_INITIAL_MATRIX:
8472:       PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8473:       break;
8474:     case MAT_REUSE_MATRIX:
8475:       PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8476:       break;
8477:     default:
8478:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8479:     }
8480:     PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8481:     goto setproperties;
8482:   }

8484:   PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8485:   PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8486:   PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));

8488: setproperties:
8489:   if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8490:     PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8491:     if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8492:   }
8493:   if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8494:   if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8495:   PetscFunctionReturn(PETSC_SUCCESS);
8496: }

8498: /*@
8499:   MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

8501:   Not Collective

8503:   Input Parameters:
8504: + A - the matrix we wish to propagate options from
8505: - B - the matrix we wish to propagate options to

8507:   Level: beginner

8509:   Note:
8510:   Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`

8512: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8513: @*/
8514: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8515: {
8516:   PetscFunctionBegin;
8519:   B->symmetry_eternal            = A->symmetry_eternal;
8520:   B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8521:   B->symmetric                   = A->symmetric;
8522:   B->structurally_symmetric      = A->structurally_symmetric;
8523:   B->spd                         = A->spd;
8524:   B->hermitian                   = A->hermitian;
8525:   PetscFunctionReturn(PETSC_SUCCESS);
8526: }

8528: /*@
8529:   MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8530:   used during the assembly process to store values that belong to
8531:   other processors.

8533:   Not Collective

8535:   Input Parameters:
8536: + mat   - the matrix
8537: . size  - the initial size of the stash.
8538: - bsize - the initial size of the block-stash(if used).

8540:   Options Database Keys:
8541: + -matstash_initial_size <size> or <size0,size1,...sizep-1>            - set initial size
8542: - -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1> - set initial block size

8544:   Level: intermediate

8546:   Notes:
8547:   The block-stash is used for values set with `MatSetValuesBlocked()` while
8548:   the stash is used for values set with `MatSetValues()`

8550:   Run with the option -info and look for output of the form
8551:   MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8552:   to determine the appropriate value, MM, to use for size and
8553:   MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8554:   to determine the value, BMM to use for bsize

8556: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8557: @*/
8558: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8559: {
8560:   PetscFunctionBegin;
8563:   PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8564:   PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8565:   PetscFunctionReturn(PETSC_SUCCESS);
8566: }

8568: /*@
8569:   MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8570:   the matrix

8572:   Neighbor-wise Collective

8574:   Input Parameters:
8575: + A - the matrix
8576: . x - the vector to be multiplied by the interpolation operator
8577: - y - the vector to be added to the result

8579:   Output Parameter:
8580: . w - the resulting vector

8582:   Level: intermediate

8584:   Notes:
8585:   `w` may be the same vector as `y`.

8587:   This allows one to use either the restriction or interpolation (its transpose)
8588:   matrix to do the interpolation

8590: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8591: @*/
8592: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8593: {
8594:   PetscInt M, N, Ny;

8596:   PetscFunctionBegin;
8601:   PetscCall(MatGetSize(A, &M, &N));
8602:   PetscCall(VecGetSize(y, &Ny));
8603:   if (M == Ny) {
8604:     PetscCall(MatMultAdd(A, x, y, w));
8605:   } else {
8606:     PetscCall(MatMultTransposeAdd(A, x, y, w));
8607:   }
8608:   PetscFunctionReturn(PETSC_SUCCESS);
8609: }

8611: /*@
8612:   MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8613:   the matrix

8615:   Neighbor-wise Collective

8617:   Input Parameters:
8618: + A - the matrix
8619: - x - the vector to be interpolated

8621:   Output Parameter:
8622: . y - the resulting vector

8624:   Level: intermediate

8626:   Note:
8627:   This allows one to use either the restriction or interpolation (its transpose)
8628:   matrix to do the interpolation

8630: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8631: @*/
8632: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8633: {
8634:   PetscInt M, N, Ny;

8636:   PetscFunctionBegin;
8640:   PetscCall(MatGetSize(A, &M, &N));
8641:   PetscCall(VecGetSize(y, &Ny));
8642:   if (M == Ny) {
8643:     PetscCall(MatMult(A, x, y));
8644:   } else {
8645:     PetscCall(MatMultTranspose(A, x, y));
8646:   }
8647:   PetscFunctionReturn(PETSC_SUCCESS);
8648: }

8650: /*@
8651:   MatRestrict - $y = A*x$ or $A^T*x$

8653:   Neighbor-wise Collective

8655:   Input Parameters:
8656: + A - the matrix
8657: - x - the vector to be restricted

8659:   Output Parameter:
8660: . y - the resulting vector

8662:   Level: intermediate

8664:   Note:
8665:   This allows one to use either the restriction or interpolation (its transpose)
8666:   matrix to do the restriction

8668: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8669: @*/
8670: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8671: {
8672:   PetscInt M, N, Nx;

8674:   PetscFunctionBegin;
8678:   PetscCall(MatGetSize(A, &M, &N));
8679:   PetscCall(VecGetSize(x, &Nx));
8680:   if (M == Nx) {
8681:     PetscCall(MatMultTranspose(A, x, y));
8682:   } else {
8683:     PetscCall(MatMult(A, x, y));
8684:   }
8685:   PetscFunctionReturn(PETSC_SUCCESS);
8686: }

8688: /*@
8689:   MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`

8691:   Neighbor-wise Collective

8693:   Input Parameters:
8694: + A - the matrix
8695: . x - the input dense matrix to be multiplied
8696: - w - the input dense matrix to be added to the result

8698:   Output Parameter:
8699: . y - the output dense matrix

8701:   Level: intermediate

8703:   Note:
8704:   This allows one to use either the restriction or interpolation (its transpose)
8705:   matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8706:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8708: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8709: @*/
8710: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8711: {
8712:   PetscInt  M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8713:   PetscBool trans = PETSC_TRUE;
8714:   MatReuse  reuse = MAT_INITIAL_MATRIX;

8716:   PetscFunctionBegin;
8722:   PetscCall(MatGetSize(A, &M, &N));
8723:   PetscCall(MatGetSize(x, &Mx, &Nx));
8724:   if (N == Mx) trans = PETSC_FALSE;
8725:   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);
8726:   Mo = trans ? N : M;
8727:   if (*y) {
8728:     PetscCall(MatGetSize(*y, &My, &Ny));
8729:     if (Mo == My && Nx == Ny) {
8730:       reuse = MAT_REUSE_MATRIX;
8731:     } else {
8732:       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);
8733:       PetscCall(MatDestroy(y));
8734:     }
8735:   }

8737:   if (w && *y == w) { /* this is to minimize changes in PCMG */
8738:     PetscBool flg;

8740:     PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8741:     if (w) {
8742:       PetscInt My, Ny, Mw, Nw;

8744:       PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8745:       PetscCall(MatGetSize(*y, &My, &Ny));
8746:       PetscCall(MatGetSize(w, &Mw, &Nw));
8747:       if (!flg || My != Mw || Ny != Nw) w = NULL;
8748:     }
8749:     if (!w) {
8750:       PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8751:       PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8752:       PetscCall(PetscObjectDereference((PetscObject)w));
8753:     } else {
8754:       PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8755:     }
8756:   }
8757:   if (!trans) {
8758:     PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8759:   } else {
8760:     PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8761:   }
8762:   if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8763:   PetscFunctionReturn(PETSC_SUCCESS);
8764: }

8766: /*@
8767:   MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

8769:   Neighbor-wise Collective

8771:   Input Parameters:
8772: + A - the matrix
8773: - x - the input dense matrix

8775:   Output Parameter:
8776: . y - the output dense matrix

8778:   Level: intermediate

8780:   Note:
8781:   This allows one to use either the restriction or interpolation (its transpose)
8782:   matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8783:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8785: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8786: @*/
8787: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8788: {
8789:   PetscFunctionBegin;
8790:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8791:   PetscFunctionReturn(PETSC_SUCCESS);
8792: }

8794: /*@
8795:   MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

8797:   Neighbor-wise Collective

8799:   Input Parameters:
8800: + A - the matrix
8801: - x - the input dense matrix

8803:   Output Parameter:
8804: . y - the output dense matrix

8806:   Level: intermediate

8808:   Note:
8809:   This allows one to use either the restriction or interpolation (its transpose)
8810:   matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8811:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8813: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8814: @*/
8815: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8816: {
8817:   PetscFunctionBegin;
8818:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8819:   PetscFunctionReturn(PETSC_SUCCESS);
8820: }

8822: /*@
8823:   MatGetNullSpace - retrieves the null space of a matrix.

8825:   Logically Collective

8827:   Input Parameters:
8828: + mat    - the matrix
8829: - nullsp - the null space object

8831:   Level: developer

8833: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8834: @*/
8835: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8836: {
8837:   PetscFunctionBegin;
8839:   PetscAssertPointer(nullsp, 2);
8840:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8841:   PetscFunctionReturn(PETSC_SUCCESS);
8842: }

8844: /*@C
8845:   MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices

8847:   Logically Collective

8849:   Input Parameters:
8850: + n   - the number of matrices
8851: - mat - the array of matrices

8853:   Output Parameters:
8854: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`

8856:   Level: developer

8858:   Note:
8859:   Call `MatRestoreNullspaces()` to provide these to another array of matrices

8861: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8862:           `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8863: @*/
8864: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8865: {
8866:   PetscFunctionBegin;
8867:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8868:   PetscAssertPointer(mat, 2);
8869:   PetscAssertPointer(nullsp, 3);

8871:   PetscCall(PetscCalloc1(3 * n, nullsp));
8872:   for (PetscInt i = 0; i < n; i++) {
8874:     (*nullsp)[i] = mat[i]->nullsp;
8875:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8876:     (*nullsp)[n + i] = mat[i]->nearnullsp;
8877:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8878:     (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8879:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8880:   }
8881:   PetscFunctionReturn(PETSC_SUCCESS);
8882: }

8884: /*@C
8885:   MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices

8887:   Logically Collective

8889:   Input Parameters:
8890: + n      - the number of matrices
8891: . mat    - the array of matrices
8892: - nullsp - an array of null spaces

8894:   Level: developer

8896:   Note:
8897:   Call `MatGetNullSpaces()` to create `nullsp`

8899: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8900:           `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8901: @*/
8902: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8903: {
8904:   PetscFunctionBegin;
8905:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8906:   PetscAssertPointer(mat, 2);
8907:   PetscAssertPointer(nullsp, 3);
8908:   PetscAssertPointer(*nullsp, 3);

8910:   for (PetscInt i = 0; i < n; i++) {
8912:     PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8913:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
8914:     PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
8915:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
8916:     PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
8917:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
8918:   }
8919:   PetscCall(PetscFree(*nullsp));
8920:   PetscFunctionReturn(PETSC_SUCCESS);
8921: }

8923: /*@
8924:   MatSetNullSpace - attaches a null space to a matrix.

8926:   Logically Collective

8928:   Input Parameters:
8929: + mat    - the matrix
8930: - nullsp - the null space object

8932:   Level: advanced

8934:   Notes:
8935:   This null space is used by the `KSP` linear solvers to solve singular systems.

8937:   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`

8939:   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
8940:   to zero but the linear system will still be solved in a least squares sense.

8942:   The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8943:   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 $A^T$, $R(A^T)$.
8944:   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
8945:   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
8946:   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($A^T$).
8947:   This  \hat{b} can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.

8949:   If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one as called
8950:   `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
8951:   routine also automatically calls `MatSetTransposeNullSpace()`.

8953:   The user should call `MatNullSpaceDestroy()`.

8955: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
8956:           `KSPSetPCSide()`
8957: @*/
8958: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
8959: {
8960:   PetscFunctionBegin;
8963:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8964:   PetscCall(MatNullSpaceDestroy(&mat->nullsp));
8965:   mat->nullsp = nullsp;
8966:   if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
8967:   PetscFunctionReturn(PETSC_SUCCESS);
8968: }

8970: /*@
8971:   MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

8973:   Logically Collective

8975:   Input Parameters:
8976: + mat    - the matrix
8977: - nullsp - the null space object

8979:   Level: developer

8981: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
8982: @*/
8983: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8984: {
8985:   PetscFunctionBegin;
8988:   PetscAssertPointer(nullsp, 2);
8989:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
8990:   PetscFunctionReturn(PETSC_SUCCESS);
8991: }

8993: /*@
8994:   MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix

8996:   Logically Collective

8998:   Input Parameters:
8999: + mat    - the matrix
9000: - nullsp - the null space object

9002:   Level: advanced

9004:   Notes:
9005:   This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.

9007:   See `MatSetNullSpace()`

9009: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9010: @*/
9011: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9012: {
9013:   PetscFunctionBegin;
9016:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9017:   PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9018:   mat->transnullsp = nullsp;
9019:   PetscFunctionReturn(PETSC_SUCCESS);
9020: }

9022: /*@
9023:   MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9024:   This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

9026:   Logically Collective

9028:   Input Parameters:
9029: + mat    - the matrix
9030: - nullsp - the null space object

9032:   Level: advanced

9034:   Notes:
9035:   Overwrites any previous near null space that may have been attached

9037:   You can remove the null space by calling this routine with an `nullsp` of `NULL`

9039: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9040: @*/
9041: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9042: {
9043:   PetscFunctionBegin;
9047:   MatCheckPreallocated(mat, 1);
9048:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9049:   PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9050:   mat->nearnullsp = nullsp;
9051:   PetscFunctionReturn(PETSC_SUCCESS);
9052: }

9054: /*@
9055:   MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`

9057:   Not Collective

9059:   Input Parameter:
9060: . mat - the matrix

9062:   Output Parameter:
9063: . nullsp - the null space object, `NULL` if not set

9065:   Level: advanced

9067: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9068: @*/
9069: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9070: {
9071:   PetscFunctionBegin;
9074:   PetscAssertPointer(nullsp, 2);
9075:   MatCheckPreallocated(mat, 1);
9076:   *nullsp = mat->nearnullsp;
9077:   PetscFunctionReturn(PETSC_SUCCESS);
9078: }

9080: /*@
9081:   MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

9083:   Collective

9085:   Input Parameters:
9086: + mat  - the matrix
9087: . row  - row/column permutation
9088: - info - information on desired factorization process

9090:   Level: developer

9092:   Notes:
9093:   Probably really in-place only when level of fill is zero, otherwise allocates
9094:   new space to store factored matrix and deletes previous memory.

9096:   Most users should employ the `KSP` interface for linear solvers
9097:   instead of working directly with matrix algebra routines such as this.
9098:   See, e.g., `KSPCreate()`.

9100:   Developer Note:
9101:   The Fortran interface is not autogenerated as the
9102:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

9104: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9105: @*/
9106: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9107: {
9108:   PetscFunctionBegin;
9112:   PetscAssertPointer(info, 3);
9113:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9114:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9115:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9116:   MatCheckPreallocated(mat, 1);
9117:   PetscUseTypeMethod(mat, iccfactor, row, info);
9118:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9119:   PetscFunctionReturn(PETSC_SUCCESS);
9120: }

9122: /*@
9123:   MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9124:   ghosted ones.

9126:   Not Collective

9128:   Input Parameters:
9129: + mat  - the matrix
9130: - diag - the diagonal values, including ghost ones

9132:   Level: developer

9134:   Notes:
9135:   Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices

9137:   This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`

9139: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9140: @*/
9141: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9142: {
9143:   PetscMPIInt size;

9145:   PetscFunctionBegin;

9150:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9151:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9152:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9153:   if (size == 1) {
9154:     PetscInt n, m;
9155:     PetscCall(VecGetSize(diag, &n));
9156:     PetscCall(MatGetSize(mat, NULL, &m));
9157:     PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9158:     PetscCall(MatDiagonalScale(mat, NULL, diag));
9159:   } else {
9160:     PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9161:   }
9162:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9163:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9164:   PetscFunctionReturn(PETSC_SUCCESS);
9165: }

9167: /*@
9168:   MatGetInertia - Gets the inertia from a factored matrix

9170:   Collective

9172:   Input Parameter:
9173: . mat - the matrix

9175:   Output Parameters:
9176: + nneg  - number of negative eigenvalues
9177: . nzero - number of zero eigenvalues
9178: - npos  - number of positive eigenvalues

9180:   Level: advanced

9182:   Note:
9183:   Matrix must have been factored by `MatCholeskyFactor()`

9185: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9186: @*/
9187: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9188: {
9189:   PetscFunctionBegin;
9192:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9193:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9194:   PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9195:   PetscFunctionReturn(PETSC_SUCCESS);
9196: }

9198: /*@C
9199:   MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors

9201:   Neighbor-wise Collective

9203:   Input Parameters:
9204: + mat - the factored matrix obtained with `MatGetFactor()`
9205: - b   - the right-hand-side vectors

9207:   Output Parameter:
9208: . x - the result vectors

9210:   Level: developer

9212:   Note:
9213:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
9214:   call `MatSolves`(A,x,x).

9216: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9217: @*/
9218: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9219: {
9220:   PetscFunctionBegin;
9223:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9224:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9225:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);

9227:   MatCheckPreallocated(mat, 1);
9228:   PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9229:   PetscUseTypeMethod(mat, solves, b, x);
9230:   PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9231:   PetscFunctionReturn(PETSC_SUCCESS);
9232: }

9234: /*@
9235:   MatIsSymmetric - Test whether a matrix is symmetric

9237:   Collective

9239:   Input Parameters:
9240: + A   - the matrix to test
9241: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

9243:   Output Parameter:
9244: . flg - the result

9246:   Level: intermediate

9248:   Notes:
9249:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9251:   If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`

9253:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9254:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9256: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9257:           `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9258: @*/
9259: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9260: {
9261:   PetscFunctionBegin;
9263:   PetscAssertPointer(flg, 3);
9264:   if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9265:   else {
9266:     if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9267:     else PetscCall(MatIsTranspose(A, A, tol, flg));
9268:     if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9269:   }
9270:   PetscFunctionReturn(PETSC_SUCCESS);
9271: }

9273: /*@
9274:   MatIsHermitian - Test whether a matrix is Hermitian

9276:   Collective

9278:   Input Parameters:
9279: + A   - the matrix to test
9280: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

9282:   Output Parameter:
9283: . flg - the result

9285:   Level: intermediate

9287:   Notes:
9288:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9290:   If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`

9292:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9293:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)

9295: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9296:           `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9297: @*/
9298: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9299: {
9300:   PetscFunctionBegin;
9302:   PetscAssertPointer(flg, 3);
9303:   if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9304:   else {
9305:     if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9306:     else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9307:     if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9308:   }
9309:   PetscFunctionReturn(PETSC_SUCCESS);
9310: }

9312: /*@
9313:   MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state

9315:   Not Collective

9317:   Input Parameter:
9318: . A - the matrix to check

9320:   Output Parameters:
9321: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9322: - flg - the result (only valid if set is `PETSC_TRUE`)

9324:   Level: advanced

9326:   Notes:
9327:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9328:   if you want it explicitly checked

9330:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9331:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9333: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9334: @*/
9335: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9336: {
9337:   PetscFunctionBegin;
9339:   PetscAssertPointer(set, 2);
9340:   PetscAssertPointer(flg, 3);
9341:   if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9342:     *set = PETSC_TRUE;
9343:     *flg = PetscBool3ToBool(A->symmetric);
9344:   } else {
9345:     *set = PETSC_FALSE;
9346:   }
9347:   PetscFunctionReturn(PETSC_SUCCESS);
9348: }

9350: /*@
9351:   MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state

9353:   Not Collective

9355:   Input Parameter:
9356: . A - the matrix to check

9358:   Output Parameters:
9359: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9360: - flg - the result (only valid if set is `PETSC_TRUE`)

9362:   Level: advanced

9364:   Notes:
9365:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).

9367:   One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9368:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)

9370: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9371: @*/
9372: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9373: {
9374:   PetscFunctionBegin;
9376:   PetscAssertPointer(set, 2);
9377:   PetscAssertPointer(flg, 3);
9378:   if (A->spd != PETSC_BOOL3_UNKNOWN) {
9379:     *set = PETSC_TRUE;
9380:     *flg = PetscBool3ToBool(A->spd);
9381:   } else {
9382:     *set = PETSC_FALSE;
9383:   }
9384:   PetscFunctionReturn(PETSC_SUCCESS);
9385: }

9387: /*@
9388:   MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state

9390:   Not Collective

9392:   Input Parameter:
9393: . A - the matrix to check

9395:   Output Parameters:
9396: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9397: - flg - the result (only valid if set is `PETSC_TRUE`)

9399:   Level: advanced

9401:   Notes:
9402:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9403:   if you want it explicitly checked

9405:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9406:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9408: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9409: @*/
9410: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9411: {
9412:   PetscFunctionBegin;
9414:   PetscAssertPointer(set, 2);
9415:   PetscAssertPointer(flg, 3);
9416:   if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9417:     *set = PETSC_TRUE;
9418:     *flg = PetscBool3ToBool(A->hermitian);
9419:   } else {
9420:     *set = PETSC_FALSE;
9421:   }
9422:   PetscFunctionReturn(PETSC_SUCCESS);
9423: }

9425: /*@
9426:   MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

9428:   Collective

9430:   Input Parameter:
9431: . A - the matrix to test

9433:   Output Parameter:
9434: . flg - the result

9436:   Level: intermediate

9438:   Notes:
9439:   If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`

9441:   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
9442:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9444: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9445: @*/
9446: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9447: {
9448:   PetscFunctionBegin;
9450:   PetscAssertPointer(flg, 2);
9451:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9452:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9453:   } else {
9454:     PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9455:     PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9456:   }
9457:   PetscFunctionReturn(PETSC_SUCCESS);
9458: }

9460: /*@
9461:   MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state

9463:   Not Collective

9465:   Input Parameter:
9466: . A - the matrix to check

9468:   Output Parameters:
9469: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9470: - flg - the result (only valid if set is PETSC_TRUE)

9472:   Level: advanced

9474:   Notes:
9475:   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
9476:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9478:   Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)

9480: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9481: @*/
9482: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9483: {
9484:   PetscFunctionBegin;
9486:   PetscAssertPointer(set, 2);
9487:   PetscAssertPointer(flg, 3);
9488:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9489:     *set = PETSC_TRUE;
9490:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9491:   } else {
9492:     *set = PETSC_FALSE;
9493:   }
9494:   PetscFunctionReturn(PETSC_SUCCESS);
9495: }

9497: /*@
9498:   MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9499:   to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process

9501:   Not Collective

9503:   Input Parameter:
9504: . mat - the matrix

9506:   Output Parameters:
9507: + nstash    - the size of the stash
9508: . reallocs  - the number of additional mallocs incurred.
9509: . bnstash   - the size of the block stash
9510: - breallocs - the number of additional mallocs incurred.in the block stash

9512:   Level: advanced

9514: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9515: @*/
9516: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9517: {
9518:   PetscFunctionBegin;
9519:   PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9520:   PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9521:   PetscFunctionReturn(PETSC_SUCCESS);
9522: }

9524: /*@
9525:   MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9526:   parallel layout, `PetscLayout` for rows and columns

9528:   Collective

9530:   Input Parameter:
9531: . mat - the matrix

9533:   Output Parameters:
9534: + right - (optional) vector that the matrix can be multiplied against
9535: - left  - (optional) vector that the matrix vector product can be stored in

9537:   Level: advanced

9539:   Notes:
9540:   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()`.

9542:   These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed

9544: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9545: @*/
9546: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9547: {
9548:   PetscFunctionBegin;
9551:   if (mat->ops->getvecs) {
9552:     PetscUseTypeMethod(mat, getvecs, right, left);
9553:   } else {
9554:     if (right) {
9555:       PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9556:       PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9557:       PetscCall(VecSetType(*right, mat->defaultvectype));
9558: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9559:       if (mat->boundtocpu && mat->bindingpropagates) {
9560:         PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9561:         PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9562:       }
9563: #endif
9564:     }
9565:     if (left) {
9566:       PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9567:       PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9568:       PetscCall(VecSetType(*left, mat->defaultvectype));
9569: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9570:       if (mat->boundtocpu && mat->bindingpropagates) {
9571:         PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9572:         PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9573:       }
9574: #endif
9575:     }
9576:   }
9577:   PetscFunctionReturn(PETSC_SUCCESS);
9578: }

9580: /*@
9581:   MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9582:   with default values.

9584:   Not Collective

9586:   Input Parameter:
9587: . info - the `MatFactorInfo` data structure

9589:   Level: developer

9591:   Notes:
9592:   The solvers are generally used through the `KSP` and `PC` objects, for example
9593:   `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`

9595:   Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed

9597: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9598: @*/
9599: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9600: {
9601:   PetscFunctionBegin;
9602:   PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9603:   PetscFunctionReturn(PETSC_SUCCESS);
9604: }

9606: /*@
9607:   MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

9609:   Collective

9611:   Input Parameters:
9612: + mat - the factored matrix
9613: - is  - the index set defining the Schur indices (0-based)

9615:   Level: advanced

9617:   Notes:
9618:   Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.

9620:   You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.

9622:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9624: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9625:           `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9626: @*/
9627: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9628: {
9629:   PetscErrorCode (*f)(Mat, IS);

9631:   PetscFunctionBegin;
9636:   PetscCheckSameComm(mat, 1, is, 2);
9637:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9638:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9639:   PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9640:   PetscCall(MatDestroy(&mat->schur));
9641:   PetscCall((*f)(mat, is));
9642:   PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9643:   PetscFunctionReturn(PETSC_SUCCESS);
9644: }

9646: /*@
9647:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

9649:   Logically Collective

9651:   Input Parameters:
9652: + F      - the factored matrix obtained by calling `MatGetFactor()`
9653: . S      - location where to return the Schur complement, can be `NULL`
9654: - status - the status of the Schur complement matrix, can be `NULL`

9656:   Level: advanced

9658:   Notes:
9659:   You must call `MatFactorSetSchurIS()` before calling this routine.

9661:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9663:   The routine provides a copy of the Schur matrix stored within the solver data structures.
9664:   The caller must destroy the object when it is no longer needed.
9665:   If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.

9667:   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)

9669:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9671:   Developer Note:
9672:   The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9673:   matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

9675: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9676: @*/
9677: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9678: {
9679:   PetscFunctionBegin;
9681:   if (S) PetscAssertPointer(S, 2);
9682:   if (status) PetscAssertPointer(status, 3);
9683:   if (S) {
9684:     PetscErrorCode (*f)(Mat, Mat *);

9686:     PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9687:     if (f) {
9688:       PetscCall((*f)(F, S));
9689:     } else {
9690:       PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9691:     }
9692:   }
9693:   if (status) *status = F->schur_status;
9694:   PetscFunctionReturn(PETSC_SUCCESS);
9695: }

9697: /*@
9698:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

9700:   Logically Collective

9702:   Input Parameters:
9703: + F      - the factored matrix obtained by calling `MatGetFactor()`
9704: . S      - location where to return the Schur complement, can be `NULL`
9705: - status - the status of the Schur complement matrix, can be `NULL`

9707:   Level: advanced

9709:   Notes:
9710:   You must call `MatFactorSetSchurIS()` before calling this routine.

9712:   Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`

9714:   The routine returns a the Schur Complement stored within the data structures of the solver.

9716:   If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.

9718:   The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.

9720:   Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix

9722:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9724: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9725: @*/
9726: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9727: {
9728:   PetscFunctionBegin;
9730:   if (S) {
9731:     PetscAssertPointer(S, 2);
9732:     *S = F->schur;
9733:   }
9734:   if (status) {
9735:     PetscAssertPointer(status, 3);
9736:     *status = F->schur_status;
9737:   }
9738:   PetscFunctionReturn(PETSC_SUCCESS);
9739: }

9741: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9742: {
9743:   Mat S = F->schur;

9745:   PetscFunctionBegin;
9746:   switch (F->schur_status) {
9747:   case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9748:   case MAT_FACTOR_SCHUR_INVERTED:
9749:     if (S) {
9750:       S->ops->solve             = NULL;
9751:       S->ops->matsolve          = NULL;
9752:       S->ops->solvetranspose    = NULL;
9753:       S->ops->matsolvetranspose = NULL;
9754:       S->ops->solveadd          = NULL;
9755:       S->ops->solvetransposeadd = NULL;
9756:       S->factortype             = MAT_FACTOR_NONE;
9757:       PetscCall(PetscFree(S->solvertype));
9758:     }
9759:   case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9760:     break;
9761:   default:
9762:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9763:   }
9764:   PetscFunctionReturn(PETSC_SUCCESS);
9765: }

9767: /*@
9768:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`

9770:   Logically Collective

9772:   Input Parameters:
9773: + F      - the factored matrix obtained by calling `MatGetFactor()`
9774: . S      - location where the Schur complement is stored
9775: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)

9777:   Level: advanced

9779: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9780: @*/
9781: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9782: {
9783:   PetscFunctionBegin;
9785:   if (S) {
9787:     *S = NULL;
9788:   }
9789:   F->schur_status = status;
9790:   PetscCall(MatFactorUpdateSchurStatus_Private(F));
9791:   PetscFunctionReturn(PETSC_SUCCESS);
9792: }

9794: /*@
9795:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

9797:   Logically Collective

9799:   Input Parameters:
9800: + F   - the factored matrix obtained by calling `MatGetFactor()`
9801: . rhs - location where the right-hand side of the Schur complement system is stored
9802: - sol - location where the solution of the Schur complement system has to be returned

9804:   Level: advanced

9806:   Notes:
9807:   The sizes of the vectors should match the size of the Schur complement

9809:   Must be called after `MatFactorSetSchurIS()`

9811: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9812: @*/
9813: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9814: {
9815:   PetscFunctionBegin;
9822:   PetscCheckSameComm(F, 1, rhs, 2);
9823:   PetscCheckSameComm(F, 1, sol, 3);
9824:   PetscCall(MatFactorFactorizeSchurComplement(F));
9825:   switch (F->schur_status) {
9826:   case MAT_FACTOR_SCHUR_FACTORED:
9827:     PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9828:     break;
9829:   case MAT_FACTOR_SCHUR_INVERTED:
9830:     PetscCall(MatMultTranspose(F->schur, rhs, sol));
9831:     break;
9832:   default:
9833:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9834:   }
9835:   PetscFunctionReturn(PETSC_SUCCESS);
9836: }

9838: /*@
9839:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

9841:   Logically Collective

9843:   Input Parameters:
9844: + F   - the factored matrix obtained by calling `MatGetFactor()`
9845: . rhs - location where the right-hand side of the Schur complement system is stored
9846: - sol - location where the solution of the Schur complement system has to be returned

9848:   Level: advanced

9850:   Notes:
9851:   The sizes of the vectors should match the size of the Schur complement

9853:   Must be called after `MatFactorSetSchurIS()`

9855: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9856: @*/
9857: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9858: {
9859:   PetscFunctionBegin;
9866:   PetscCheckSameComm(F, 1, rhs, 2);
9867:   PetscCheckSameComm(F, 1, sol, 3);
9868:   PetscCall(MatFactorFactorizeSchurComplement(F));
9869:   switch (F->schur_status) {
9870:   case MAT_FACTOR_SCHUR_FACTORED:
9871:     PetscCall(MatSolve(F->schur, rhs, sol));
9872:     break;
9873:   case MAT_FACTOR_SCHUR_INVERTED:
9874:     PetscCall(MatMult(F->schur, rhs, sol));
9875:     break;
9876:   default:
9877:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9878:   }
9879:   PetscFunctionReturn(PETSC_SUCCESS);
9880: }

9882: PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9883: #if PetscDefined(HAVE_CUDA)
9884: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9885: #endif

9887: /* Schur status updated in the interface */
9888: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9889: {
9890:   Mat S = F->schur;

9892:   PetscFunctionBegin;
9893:   if (S) {
9894:     PetscMPIInt size;
9895:     PetscBool   isdense, isdensecuda;

9897:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9898:     PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9899:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9900:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9901:     PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9902:     PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9903:     if (isdense) {
9904:       PetscCall(MatSeqDenseInvertFactors_Private(S));
9905:     } else if (isdensecuda) {
9906: #if defined(PETSC_HAVE_CUDA)
9907:       PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9908: #endif
9909:     }
9910:     // HIP??????????????
9911:     PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9912:   }
9913:   PetscFunctionReturn(PETSC_SUCCESS);
9914: }

9916: /*@
9917:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

9919:   Logically Collective

9921:   Input Parameter:
9922: . F - the factored matrix obtained by calling `MatGetFactor()`

9924:   Level: advanced

9926:   Notes:
9927:   Must be called after `MatFactorSetSchurIS()`.

9929:   Call `MatFactorGetSchurComplement()` or  `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.

9931: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9932: @*/
9933: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9934: {
9935:   PetscFunctionBegin;
9938:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9939:   PetscCall(MatFactorFactorizeSchurComplement(F));
9940:   PetscCall(MatFactorInvertSchurComplement_Private(F));
9941:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9942:   PetscFunctionReturn(PETSC_SUCCESS);
9943: }

9945: /*@
9946:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

9948:   Logically Collective

9950:   Input Parameter:
9951: . F - the factored matrix obtained by calling `MatGetFactor()`

9953:   Level: advanced

9955:   Note:
9956:   Must be called after `MatFactorSetSchurIS()`

9958: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
9959: @*/
9960: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9961: {
9962:   MatFactorInfo info;

9964:   PetscFunctionBegin;
9967:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
9968:   PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
9969:   PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
9970:   if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
9971:     PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
9972:   } else {
9973:     PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
9974:   }
9975:   PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
9976:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9977:   PetscFunctionReturn(PETSC_SUCCESS);
9978: }

9980: /*@
9981:   MatPtAP - Creates the matrix product $C = P^T * A * P$

9983:   Neighbor-wise Collective

9985:   Input Parameters:
9986: + A     - the matrix
9987: . P     - the projection matrix
9988: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
9989: - 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
9990:           if the result is a dense matrix this is irrelevant

9992:   Output Parameter:
9993: . C - the product matrix

9995:   Level: intermediate

9997:   Notes:
9998:   C will be created and must be destroyed by the user with `MatDestroy()`.

10000:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

10002:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10004:   Developer Note:
10005:   For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.

10007: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10008: @*/
10009: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10010: {
10011:   PetscFunctionBegin;
10012:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10013:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10015:   if (scall == MAT_INITIAL_MATRIX) {
10016:     PetscCall(MatProductCreate(A, P, NULL, C));
10017:     PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10018:     PetscCall(MatProductSetAlgorithm(*C, "default"));
10019:     PetscCall(MatProductSetFill(*C, fill));

10021:     (*C)->product->api_user = PETSC_TRUE;
10022:     PetscCall(MatProductSetFromOptions(*C));
10023:     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);
10024:     PetscCall(MatProductSymbolic(*C));
10025:   } else { /* scall == MAT_REUSE_MATRIX */
10026:     PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10027:   }

10029:   PetscCall(MatProductNumeric(*C));
10030:   (*C)->symmetric = A->symmetric;
10031:   (*C)->spd       = A->spd;
10032:   PetscFunctionReturn(PETSC_SUCCESS);
10033: }

10035: /*@
10036:   MatRARt - Creates the matrix product $C = R * A * R^T$

10038:   Neighbor-wise Collective

10040:   Input Parameters:
10041: + A     - the matrix
10042: . R     - the projection matrix
10043: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10044: - fill  - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10045:           if the result is a dense matrix this is irrelevant

10047:   Output Parameter:
10048: . C - the product matrix

10050:   Level: intermediate

10052:   Notes:
10053:   `C` will be created and must be destroyed by the user with `MatDestroy()`.

10055:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

10057:   This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10058:   which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10059:   the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10060:   We recommend using `MatPtAP()` when possible.

10062:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10064: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10065: @*/
10066: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10067: {
10068:   PetscFunctionBegin;
10069:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10070:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10072:   if (scall == MAT_INITIAL_MATRIX) {
10073:     PetscCall(MatProductCreate(A, R, NULL, C));
10074:     PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10075:     PetscCall(MatProductSetAlgorithm(*C, "default"));
10076:     PetscCall(MatProductSetFill(*C, fill));

10078:     (*C)->product->api_user = PETSC_TRUE;
10079:     PetscCall(MatProductSetFromOptions(*C));
10080:     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);
10081:     PetscCall(MatProductSymbolic(*C));
10082:   } else { /* scall == MAT_REUSE_MATRIX */
10083:     PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10084:   }

10086:   PetscCall(MatProductNumeric(*C));
10087:   if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10088:   PetscFunctionReturn(PETSC_SUCCESS);
10089: }

10091: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10092: {
10093:   PetscBool flg = PETSC_TRUE;

10095:   PetscFunctionBegin;
10096:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10097:   if (scall == MAT_INITIAL_MATRIX) {
10098:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10099:     PetscCall(MatProductCreate(A, B, NULL, C));
10100:     PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10101:     PetscCall(MatProductSetFill(*C, fill));
10102:   } else { /* scall == MAT_REUSE_MATRIX */
10103:     Mat_Product *product = (*C)->product;

10105:     PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10106:     if (flg && product && product->type != ptype) {
10107:       PetscCall(MatProductClear(*C));
10108:       product = NULL;
10109:     }
10110:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10111:     if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10112:       PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10113:       PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10114:       product        = (*C)->product;
10115:       product->fill  = fill;
10116:       product->clear = PETSC_TRUE;
10117:     } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10118:       flg = PETSC_FALSE;
10119:       PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10120:     }
10121:   }
10122:   if (flg) {
10123:     (*C)->product->api_user = PETSC_TRUE;
10124:     PetscCall(MatProductSetType(*C, ptype));
10125:     PetscCall(MatProductSetFromOptions(*C));
10126:     PetscCall(MatProductSymbolic(*C));
10127:   }
10128:   PetscCall(MatProductNumeric(*C));
10129:   PetscFunctionReturn(PETSC_SUCCESS);
10130: }

10132: /*@
10133:   MatMatMult - Performs matrix-matrix multiplication C=A*B.

10135:   Neighbor-wise Collective

10137:   Input Parameters:
10138: + A     - the left matrix
10139: . B     - the right matrix
10140: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10141: - 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
10142:           if the result is a dense matrix this is irrelevant

10144:   Output Parameter:
10145: . C - the product matrix

10147:   Notes:
10148:   Unless scall is `MAT_REUSE_MATRIX` C will be created.

10150:   `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
10151:   call to this function with `MAT_INITIAL_MATRIX`.

10153:   To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.

10155:   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`,
10156:   rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.

10158:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10160:   Example of Usage:
10161: .vb
10162:      MatProductCreate(A,B,NULL,&C);
10163:      MatProductSetType(C,MATPRODUCT_AB);
10164:      MatProductSymbolic(C);
10165:      MatProductNumeric(C); // compute C=A * B
10166:      MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10167:      MatProductNumeric(C);
10168:      MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10169:      MatProductNumeric(C);
10170: .ve

10172:   Level: intermediate

10174: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10175: @*/
10176: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10177: {
10178:   PetscFunctionBegin;
10179:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10180:   PetscFunctionReturn(PETSC_SUCCESS);
10181: }

10183: /*@
10184:   MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.

10186:   Neighbor-wise Collective

10188:   Input Parameters:
10189: + A     - the left matrix
10190: . B     - the right matrix
10191: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10192: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

10194:   Output Parameter:
10195: . C - the product matrix

10197:   Options Database Key:
10198: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10199:               first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10200:               the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.

10202:   Level: intermediate

10204:   Notes:
10205:   C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10207:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call

10209:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10210:   actually needed.

10212:   This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10213:   and for pairs of `MATMPIDENSE` matrices.

10215:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`

10217:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10219: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10220: @*/
10221: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10222: {
10223:   PetscFunctionBegin;
10224:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10225:   if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10226:   PetscFunctionReturn(PETSC_SUCCESS);
10227: }

10229: /*@
10230:   MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.

10232:   Neighbor-wise Collective

10234:   Input Parameters:
10235: + A     - the left matrix
10236: . B     - the right matrix
10237: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10238: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

10240:   Output Parameter:
10241: . C - the product matrix

10243:   Level: intermediate

10245:   Notes:
10246:   `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10248:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.

10250:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`

10252:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10253:   actually needed.

10255:   This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10256:   which inherit from `MATSEQAIJ`.  `C` will be of the same type as the input matrices.

10258:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10260: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10261: @*/
10262: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10263: {
10264:   PetscFunctionBegin;
10265:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10266:   PetscFunctionReturn(PETSC_SUCCESS);
10267: }

10269: /*@
10270:   MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.

10272:   Neighbor-wise Collective

10274:   Input Parameters:
10275: + A     - the left matrix
10276: . B     - the middle matrix
10277: . C     - the right matrix
10278: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10279: - 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
10280:           if the result is a dense matrix this is irrelevant

10282:   Output Parameter:
10283: . D - the product matrix

10285:   Level: intermediate

10287:   Notes:
10288:   Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.

10290:   `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call

10292:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`

10294:   To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10295:   actually needed.

10297:   If you have many matrices with the same non-zero structure to multiply, you
10298:   should use `MAT_REUSE_MATRIX` in all calls but the first

10300:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10302: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10303: @*/
10304: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10305: {
10306:   PetscFunctionBegin;
10307:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10308:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10310:   if (scall == MAT_INITIAL_MATRIX) {
10311:     PetscCall(MatProductCreate(A, B, C, D));
10312:     PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10313:     PetscCall(MatProductSetAlgorithm(*D, "default"));
10314:     PetscCall(MatProductSetFill(*D, fill));

10316:     (*D)->product->api_user = PETSC_TRUE;
10317:     PetscCall(MatProductSetFromOptions(*D));
10318:     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,
10319:                ((PetscObject)C)->type_name);
10320:     PetscCall(MatProductSymbolic(*D));
10321:   } else { /* user may change input matrices when REUSE */
10322:     PetscCall(MatProductReplaceMats(A, B, C, *D));
10323:   }
10324:   PetscCall(MatProductNumeric(*D));
10325:   PetscFunctionReturn(PETSC_SUCCESS);
10326: }

10328: /*@
10329:   MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.

10331:   Collective

10333:   Input Parameters:
10334: + mat      - the matrix
10335: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10336: . subcomm  - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10337: - reuse    - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10339:   Output Parameter:
10340: . matredundant - redundant matrix

10342:   Level: advanced

10344:   Notes:
10345:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10346:   original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.

10348:   This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10349:   calling it.

10351:   `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.

10353: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10354: @*/
10355: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10356: {
10357:   MPI_Comm       comm;
10358:   PetscMPIInt    size;
10359:   PetscInt       mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10360:   Mat_Redundant *redund     = NULL;
10361:   PetscSubcomm   psubcomm   = NULL;
10362:   MPI_Comm       subcomm_in = subcomm;
10363:   Mat           *matseq;
10364:   IS             isrow, iscol;
10365:   PetscBool      newsubcomm = PETSC_FALSE;

10367:   PetscFunctionBegin;
10369:   if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10370:     PetscAssertPointer(*matredundant, 5);
10372:   }

10374:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10375:   if (size == 1 || nsubcomm == 1) {
10376:     if (reuse == MAT_INITIAL_MATRIX) {
10377:       PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10378:     } else {
10379:       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");
10380:       PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10381:     }
10382:     PetscFunctionReturn(PETSC_SUCCESS);
10383:   }

10385:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10386:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10387:   MatCheckPreallocated(mat, 1);

10389:   PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10390:   if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10391:     /* create psubcomm, then get subcomm */
10392:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10393:     PetscCallMPI(MPI_Comm_size(comm, &size));
10394:     PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);

10396:     PetscCall(PetscSubcommCreate(comm, &psubcomm));
10397:     PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10398:     PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10399:     PetscCall(PetscSubcommSetFromOptions(psubcomm));
10400:     PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10401:     newsubcomm = PETSC_TRUE;
10402:     PetscCall(PetscSubcommDestroy(&psubcomm));
10403:   }

10405:   /* get isrow, iscol and a local sequential matrix matseq[0] */
10406:   if (reuse == MAT_INITIAL_MATRIX) {
10407:     mloc_sub = PETSC_DECIDE;
10408:     nloc_sub = PETSC_DECIDE;
10409:     if (bs < 1) {
10410:       PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10411:       PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10412:     } else {
10413:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10414:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10415:     }
10416:     PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10417:     rstart = rend - mloc_sub;
10418:     PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10419:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10420:     PetscCall(ISSetIdentity(iscol));
10421:   } else { /* reuse == MAT_REUSE_MATRIX */
10422:     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");
10423:     /* retrieve subcomm */
10424:     PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10425:     redund = (*matredundant)->redundant;
10426:     isrow  = redund->isrow;
10427:     iscol  = redund->iscol;
10428:     matseq = redund->matseq;
10429:   }
10430:   PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));

10432:   /* get matredundant over subcomm */
10433:   if (reuse == MAT_INITIAL_MATRIX) {
10434:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));

10436:     /* create a supporting struct and attach it to C for reuse */
10437:     PetscCall(PetscNew(&redund));
10438:     (*matredundant)->redundant = redund;
10439:     redund->isrow              = isrow;
10440:     redund->iscol              = iscol;
10441:     redund->matseq             = matseq;
10442:     if (newsubcomm) {
10443:       redund->subcomm = subcomm;
10444:     } else {
10445:       redund->subcomm = MPI_COMM_NULL;
10446:     }
10447:   } else {
10448:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10449:   }
10450: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10451:   if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10452:     PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10453:     PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10454:   }
10455: #endif
10456:   PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10457:   PetscFunctionReturn(PETSC_SUCCESS);
10458: }

10460: /*@C
10461:   MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10462:   a given `Mat`. Each submatrix can span multiple procs.

10464:   Collective

10466:   Input Parameters:
10467: + mat     - the matrix
10468: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10469: - scall   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10471:   Output Parameter:
10472: . subMat - parallel sub-matrices each spanning a given `subcomm`

10474:   Level: advanced

10476:   Notes:
10477:   The submatrix partition across processors is dictated by `subComm` a
10478:   communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10479:   is not restricted to be grouped with consecutive original MPI processes.

10481:   Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10482:   map directly to the layout of the original matrix [wrt the local
10483:   row,col partitioning]. So the original 'DiagonalMat' naturally maps
10484:   into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10485:   the `subMat`. However the offDiagMat looses some columns - and this is
10486:   reconstructed with `MatSetValues()`

10488:   This is used by `PCBJACOBI` when a single block spans multiple MPI processes.

10490: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10491: @*/
10492: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10493: {
10494:   PetscMPIInt commsize, subCommSize;

10496:   PetscFunctionBegin;
10497:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10498:   PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10499:   PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);

10501:   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");
10502:   PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10503:   PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10504:   PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10505:   PetscFunctionReturn(PETSC_SUCCESS);
10506: }

10508: /*@
10509:   MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering

10511:   Not Collective

10513:   Input Parameters:
10514: + mat   - matrix to extract local submatrix from
10515: . isrow - local row indices for submatrix
10516: - iscol - local column indices for submatrix

10518:   Output Parameter:
10519: . submat - the submatrix

10521:   Level: intermediate

10523:   Notes:
10524:   `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.

10526:   Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`.  Its communicator may be
10527:   the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.

10529:   `submat` always implements `MatSetValuesLocal()`.  If `isrow` and `iscol` have the same block size, then
10530:   `MatSetValuesBlockedLocal()` will also be implemented.

10532:   `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10533:   Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.

10535: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10536: @*/
10537: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10538: {
10539:   PetscFunctionBegin;
10543:   PetscCheckSameComm(isrow, 2, iscol, 3);
10544:   PetscAssertPointer(submat, 4);
10545:   PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");

10547:   if (mat->ops->getlocalsubmatrix) {
10548:     PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10549:   } else {
10550:     PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10551:   }
10552:   PetscFunctionReturn(PETSC_SUCCESS);
10553: }

10555: /*@
10556:   MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`

10558:   Not Collective

10560:   Input Parameters:
10561: + mat    - matrix to extract local submatrix from
10562: . isrow  - local row indices for submatrix
10563: . iscol  - local column indices for submatrix
10564: - submat - the submatrix

10566:   Level: intermediate

10568: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10569: @*/
10570: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10571: {
10572:   PetscFunctionBegin;
10576:   PetscCheckSameComm(isrow, 2, iscol, 3);
10577:   PetscAssertPointer(submat, 4);

10580:   if (mat->ops->restorelocalsubmatrix) {
10581:     PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10582:   } else {
10583:     PetscCall(MatDestroy(submat));
10584:   }
10585:   *submat = NULL;
10586:   PetscFunctionReturn(PETSC_SUCCESS);
10587: }

10589: /*@
10590:   MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix

10592:   Collective

10594:   Input Parameter:
10595: . mat - the matrix

10597:   Output Parameter:
10598: . is - if any rows have zero diagonals this contains the list of them

10600:   Level: developer

10602: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10603: @*/
10604: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10605: {
10606:   PetscFunctionBegin;
10609:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10610:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10612:   if (!mat->ops->findzerodiagonals) {
10613:     Vec                diag;
10614:     const PetscScalar *a;
10615:     PetscInt          *rows;
10616:     PetscInt           rStart, rEnd, r, nrow = 0;

10618:     PetscCall(MatCreateVecs(mat, &diag, NULL));
10619:     PetscCall(MatGetDiagonal(mat, diag));
10620:     PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10621:     PetscCall(VecGetArrayRead(diag, &a));
10622:     for (r = 0; r < rEnd - rStart; ++r)
10623:       if (a[r] == 0.0) ++nrow;
10624:     PetscCall(PetscMalloc1(nrow, &rows));
10625:     nrow = 0;
10626:     for (r = 0; r < rEnd - rStart; ++r)
10627:       if (a[r] == 0.0) rows[nrow++] = r + rStart;
10628:     PetscCall(VecRestoreArrayRead(diag, &a));
10629:     PetscCall(VecDestroy(&diag));
10630:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10631:   } else {
10632:     PetscUseTypeMethod(mat, findzerodiagonals, is);
10633:   }
10634:   PetscFunctionReturn(PETSC_SUCCESS);
10635: }

10637: /*@
10638:   MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)

10640:   Collective

10642:   Input Parameter:
10643: . mat - the matrix

10645:   Output Parameter:
10646: . is - contains the list of rows with off block diagonal entries

10648:   Level: developer

10650: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10651: @*/
10652: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10653: {
10654:   PetscFunctionBegin;
10657:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10658:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10660:   PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10661:   PetscFunctionReturn(PETSC_SUCCESS);
10662: }

10664: /*@C
10665:   MatInvertBlockDiagonal - Inverts the block diagonal entries.

10667:   Collective; No Fortran Support

10669:   Input Parameter:
10670: . mat - the matrix

10672:   Output Parameter:
10673: . values - the block inverses in column major order (FORTRAN-like)

10675:   Level: advanced

10677:   Notes:
10678:   The size of the blocks is determined by the block size of the matrix.

10680:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10682:   The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size

10684: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10685: @*/
10686: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10687: {
10688:   PetscFunctionBegin;
10690:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10691:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10692:   PetscUseTypeMethod(mat, invertblockdiagonal, values);
10693:   PetscFunctionReturn(PETSC_SUCCESS);
10694: }

10696: /*@
10697:   MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.

10699:   Collective; No Fortran Support

10701:   Input Parameters:
10702: + mat     - the matrix
10703: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10704: - bsizes  - the size of each block on the process, set with `MatSetVariableBlockSizes()`

10706:   Output Parameter:
10707: . values - the block inverses in column major order (FORTRAN-like)

10709:   Level: advanced

10711:   Notes:
10712:   Use `MatInvertBlockDiagonal()` if all blocks have the same size

10714:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10716: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10717: @*/
10718: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10719: {
10720:   PetscFunctionBegin;
10722:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10723:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10724:   PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10725:   PetscFunctionReturn(PETSC_SUCCESS);
10726: }

10728: /*@
10729:   MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A

10731:   Collective

10733:   Input Parameters:
10734: + A - the matrix
10735: - C - matrix with inverted block diagonal of `A`.  This matrix should be created and may have its type set.

10737:   Level: advanced

10739:   Note:
10740:   The blocksize of the matrix is used to determine the blocks on the diagonal of `C`

10742: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10743: @*/
10744: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10745: {
10746:   const PetscScalar *vals;
10747:   PetscInt          *dnnz;
10748:   PetscInt           m, rstart, rend, bs, i, j;

10750:   PetscFunctionBegin;
10751:   PetscCall(MatInvertBlockDiagonal(A, &vals));
10752:   PetscCall(MatGetBlockSize(A, &bs));
10753:   PetscCall(MatGetLocalSize(A, &m, NULL));
10754:   PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10755:   PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10756:   PetscCall(PetscMalloc1(m / bs, &dnnz));
10757:   for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10758:   PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10759:   PetscCall(PetscFree(dnnz));
10760:   PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10761:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10762:   for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10763:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10764:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10765:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10766:   PetscFunctionReturn(PETSC_SUCCESS);
10767: }

10769: /*@
10770:   MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10771:   via `MatTransposeColoringCreate()`.

10773:   Collective

10775:   Input Parameter:
10776: . c - coloring context

10778:   Level: intermediate

10780: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10781: @*/
10782: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10783: {
10784:   MatTransposeColoring matcolor = *c;

10786:   PetscFunctionBegin;
10787:   if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10788:   if (--((PetscObject)matcolor)->refct > 0) {
10789:     matcolor = NULL;
10790:     PetscFunctionReturn(PETSC_SUCCESS);
10791:   }

10793:   PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10794:   PetscCall(PetscFree(matcolor->rows));
10795:   PetscCall(PetscFree(matcolor->den2sp));
10796:   PetscCall(PetscFree(matcolor->colorforcol));
10797:   PetscCall(PetscFree(matcolor->columns));
10798:   if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10799:   PetscCall(PetscHeaderDestroy(c));
10800:   PetscFunctionReturn(PETSC_SUCCESS);
10801: }

10803: /*@
10804:   MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10805:   a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10806:   `MatTransposeColoring` to sparse `B`.

10808:   Collective

10810:   Input Parameters:
10811: + coloring - coloring context created with `MatTransposeColoringCreate()`
10812: - B        - sparse matrix

10814:   Output Parameter:
10815: . Btdense - dense matrix $B^T$

10817:   Level: developer

10819:   Note:
10820:   These are used internally for some implementations of `MatRARt()`

10822: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10823: @*/
10824: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10825: {
10826:   PetscFunctionBegin;

10831:   PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10832:   PetscFunctionReturn(PETSC_SUCCESS);
10833: }

10835: /*@
10836:   MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10837:   a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10838:   in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10839:   $C_{sp}$ from $C_{den}$.

10841:   Collective

10843:   Input Parameters:
10844: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10845: - Cden        - matrix product of a sparse matrix and a dense matrix Btdense

10847:   Output Parameter:
10848: . Csp - sparse matrix

10850:   Level: developer

10852:   Note:
10853:   These are used internally for some implementations of `MatRARt()`

10855: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10856: @*/
10857: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10858: {
10859:   PetscFunctionBegin;

10864:   PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10865:   PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10866:   PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10867:   PetscFunctionReturn(PETSC_SUCCESS);
10868: }

10870: /*@
10871:   MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.

10873:   Collective

10875:   Input Parameters:
10876: + mat        - the matrix product C
10877: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`

10879:   Output Parameter:
10880: . color - the new coloring context

10882:   Level: intermediate

10884: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10885:           `MatTransColoringApplyDenToSp()`
10886: @*/
10887: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10888: {
10889:   MatTransposeColoring c;
10890:   MPI_Comm             comm;

10892:   PetscFunctionBegin;
10893:   PetscAssertPointer(color, 3);

10895:   PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10896:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10897:   PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10898:   c->ctype = iscoloring->ctype;
10899:   PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10900:   *color = c;
10901:   PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10902:   PetscFunctionReturn(PETSC_SUCCESS);
10903: }

10905: /*@
10906:   MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10907:   matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.

10909:   Not Collective

10911:   Input Parameter:
10912: . mat - the matrix

10914:   Output Parameter:
10915: . state - the current state

10917:   Level: intermediate

10919:   Notes:
10920:   You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10921:   different matrices

10923:   Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix

10925:   Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.

10927: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10928: @*/
10929: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10930: {
10931:   PetscFunctionBegin;
10933:   *state = mat->nonzerostate;
10934:   PetscFunctionReturn(PETSC_SUCCESS);
10935: }

10937: /*@
10938:   MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10939:   matrices from each processor

10941:   Collective

10943:   Input Parameters:
10944: + comm   - the communicators the parallel matrix will live on
10945: . seqmat - the input sequential matrices
10946: . n      - number of local columns (or `PETSC_DECIDE`)
10947: - reuse  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10949:   Output Parameter:
10950: . mpimat - the parallel matrix generated

10952:   Level: developer

10954:   Note:
10955:   The number of columns of the matrix in EACH processor MUST be the same.

10957: .seealso: [](ch_matrices), `Mat`
10958: @*/
10959: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
10960: {
10961:   PetscMPIInt size;

10963:   PetscFunctionBegin;
10964:   PetscCallMPI(MPI_Comm_size(comm, &size));
10965:   if (size == 1) {
10966:     if (reuse == MAT_INITIAL_MATRIX) {
10967:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
10968:     } else {
10969:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
10970:     }
10971:     PetscFunctionReturn(PETSC_SUCCESS);
10972:   }

10974:   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");

10976:   PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
10977:   PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
10978:   PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
10979:   PetscFunctionReturn(PETSC_SUCCESS);
10980: }

10982: /*@
10983:   MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.

10985:   Collective

10987:   Input Parameters:
10988: + A - the matrix to create subdomains from
10989: - N - requested number of subdomains

10991:   Output Parameters:
10992: + n   - number of subdomains resulting on this MPI process
10993: - iss - `IS` list with indices of subdomains on this MPI process

10995:   Level: advanced

10997:   Note:
10998:   The number of subdomains must be smaller than the communicator size

11000: .seealso: [](ch_matrices), `Mat`, `IS`
11001: @*/
11002: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11003: {
11004:   MPI_Comm    comm, subcomm;
11005:   PetscMPIInt size, rank, color;
11006:   PetscInt    rstart, rend, k;

11008:   PetscFunctionBegin;
11009:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11010:   PetscCallMPI(MPI_Comm_size(comm, &size));
11011:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
11012:   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);
11013:   *n    = 1;
11014:   k     = size / N + (size % N > 0); /* There are up to k ranks to a color */
11015:   color = rank / k;
11016:   PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11017:   PetscCall(PetscMalloc1(1, iss));
11018:   PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11019:   PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11020:   PetscCallMPI(MPI_Comm_free(&subcomm));
11021:   PetscFunctionReturn(PETSC_SUCCESS);
11022: }

11024: /*@
11025:   MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.

11027:   If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11028:   If they are not the same, uses `MatMatMatMult()`.

11030:   Once the coarse grid problem is constructed, correct for interpolation operators
11031:   that are not of full rank, which can legitimately happen in the case of non-nested
11032:   geometric multigrid.

11034:   Input Parameters:
11035: + restrct     - restriction operator
11036: . dA          - fine grid matrix
11037: . interpolate - interpolation operator
11038: . reuse       - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11039: - fill        - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate

11041:   Output Parameter:
11042: . A - the Galerkin coarse matrix

11044:   Options Database Key:
11045: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used

11047:   Level: developer

11049:   Note:
11050:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

11052: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11053: @*/
11054: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11055: {
11056:   IS  zerorows;
11057:   Vec diag;

11059:   PetscFunctionBegin;
11060:   PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11061:   /* Construct the coarse grid matrix */
11062:   if (interpolate == restrct) {
11063:     PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11064:   } else {
11065:     PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11066:   }

11068:   /* If the interpolation matrix is not of full rank, A will have zero rows.
11069:      This can legitimately happen in the case of non-nested geometric multigrid.
11070:      In that event, we set the rows of the matrix to the rows of the identity,
11071:      ignoring the equations (as the RHS will also be zero). */

11073:   PetscCall(MatFindZeroRows(*A, &zerorows));

11075:   if (zerorows != NULL) { /* if there are any zero rows */
11076:     PetscCall(MatCreateVecs(*A, &diag, NULL));
11077:     PetscCall(MatGetDiagonal(*A, diag));
11078:     PetscCall(VecISSet(diag, zerorows, 1.0));
11079:     PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11080:     PetscCall(VecDestroy(&diag));
11081:     PetscCall(ISDestroy(&zerorows));
11082:   }
11083:   PetscFunctionReturn(PETSC_SUCCESS);
11084: }

11086: /*@C
11087:   MatSetOperation - Allows user to set a matrix operation for any matrix type

11089:   Logically Collective

11091:   Input Parameters:
11092: + mat - the matrix
11093: . op  - the name of the operation
11094: - f   - the function that provides the operation

11096:   Level: developer

11098:   Example Usage:
11099: .vb
11100:   extern PetscErrorCode usermult(Mat, Vec, Vec);

11102:   PetscCall(MatCreateXXX(comm, ..., &A));
11103:   PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
11104: .ve

11106:   Notes:
11107:   See the file `include/petscmat.h` for a complete list of matrix
11108:   operations, which all have the form MATOP_<OPERATION>, where
11109:   <OPERATION> is the name (in all capital letters) of the
11110:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11112:   All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11113:   sequence as the usual matrix interface routines, since they
11114:   are intended to be accessed via the usual matrix interface
11115:   routines, e.g.,
11116: .vb
11117:   MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11118: .ve

11120:   In particular each function MUST return `PETSC_SUCCESS` on success and
11121:   nonzero on failure.

11123:   This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.

11125: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11126: @*/
11127: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11128: {
11129:   PetscFunctionBegin;
11131:   if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
11132:   (((void (**)(void))mat->ops)[op]) = f;
11133:   PetscFunctionReturn(PETSC_SUCCESS);
11134: }

11136: /*@C
11137:   MatGetOperation - Gets a matrix operation for any matrix type.

11139:   Not Collective

11141:   Input Parameters:
11142: + mat - the matrix
11143: - op  - the name of the operation

11145:   Output Parameter:
11146: . f - the function that provides the operation

11148:   Level: developer

11150:   Example Usage:
11151: .vb
11152:   PetscErrorCode (*usermult)(Mat, Vec, Vec);

11154:   MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11155: .ve

11157:   Notes:
11158:   See the file include/petscmat.h for a complete list of matrix
11159:   operations, which all have the form MATOP_<OPERATION>, where
11160:   <OPERATION> is the name (in all capital letters) of the
11161:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11163:   This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.

11165: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11166: @*/
11167: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11168: {
11169:   PetscFunctionBegin;
11171:   *f = (((void (**)(void))mat->ops)[op]);
11172:   PetscFunctionReturn(PETSC_SUCCESS);
11173: }

11175: /*@
11176:   MatHasOperation - Determines whether the given matrix supports the particular operation.

11178:   Not Collective

11180:   Input Parameters:
11181: + mat - the matrix
11182: - op  - the operation, for example, `MATOP_GET_DIAGONAL`

11184:   Output Parameter:
11185: . has - either `PETSC_TRUE` or `PETSC_FALSE`

11187:   Level: advanced

11189:   Note:
11190:   See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.

11192: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11193: @*/
11194: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11195: {
11196:   PetscFunctionBegin;
11198:   PetscAssertPointer(has, 3);
11199:   if (mat->ops->hasoperation) {
11200:     PetscUseTypeMethod(mat, hasoperation, op, has);
11201:   } else {
11202:     if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11203:     else {
11204:       *has = PETSC_FALSE;
11205:       if (op == MATOP_CREATE_SUBMATRIX) {
11206:         PetscMPIInt size;

11208:         PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11209:         if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11210:       }
11211:     }
11212:   }
11213:   PetscFunctionReturn(PETSC_SUCCESS);
11214: }

11216: /*@
11217:   MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent

11219:   Collective

11221:   Input Parameter:
11222: . mat - the matrix

11224:   Output Parameter:
11225: . cong - either `PETSC_TRUE` or `PETSC_FALSE`

11227:   Level: beginner

11229: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11230: @*/
11231: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11232: {
11233:   PetscFunctionBegin;
11236:   PetscAssertPointer(cong, 2);
11237:   if (!mat->rmap || !mat->cmap) {
11238:     *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11239:     PetscFunctionReturn(PETSC_SUCCESS);
11240:   }
11241:   if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11242:     PetscCall(PetscLayoutSetUp(mat->rmap));
11243:     PetscCall(PetscLayoutSetUp(mat->cmap));
11244:     PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11245:     if (*cong) mat->congruentlayouts = 1;
11246:     else mat->congruentlayouts = 0;
11247:   } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11248:   PetscFunctionReturn(PETSC_SUCCESS);
11249: }

11251: PetscErrorCode MatSetInf(Mat A)
11252: {
11253:   PetscFunctionBegin;
11254:   PetscUseTypeMethod(A, setinf);
11255:   PetscFunctionReturn(PETSC_SUCCESS);
11256: }

11258: /*@
11259:   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
11260:   and possibly removes small values from the graph structure.

11262:   Collective

11264:   Input Parameters:
11265: + A       - the matrix
11266: . sym     - `PETSC_TRUE` indicates that the graph should be symmetrized
11267: . scale   - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11268: . filter  - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11269: . num_idx - size of 'index' array
11270: - index   - array of block indices to use for graph strength of connection weight

11272:   Output Parameter:
11273: . graph - the resulting graph

11275:   Level: advanced

11277: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11278: @*/
11279: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11280: {
11281:   PetscFunctionBegin;
11285:   PetscAssertPointer(graph, 7);
11286:   PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11287:   PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11288:   PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11289:   PetscFunctionReturn(PETSC_SUCCESS);
11290: }

11292: /*@
11293:   MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11294:   meaning the same memory is used for the matrix, and no new memory is allocated.

11296:   Collective

11298:   Input Parameters:
11299: + A    - the matrix
11300: - 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

11302:   Level: intermediate

11304:   Developer Note:
11305:   The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11306:   of the arrays in the data structure are unneeded.

11308: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11309: @*/
11310: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11311: {
11312:   PetscFunctionBegin;
11314:   PetscUseTypeMethod(A, eliminatezeros, keep);
11315:   PetscFunctionReturn(PETSC_SUCCESS);
11316: }