Actual source code: matrix.c

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

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

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

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

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

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

 54:   Logically Collective

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

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

 68:   Level: intermediate

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

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

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

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

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

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

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

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

110:   Logically Collective

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

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

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

124:   Level: advanced

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

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

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

142:   Logically Collective

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

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

152:   Level: advanced

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

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

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

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

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

179:   Logically Collective

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

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

187:   Level: advanced

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

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

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

207:   Logically Collective

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

212:   Level: developer

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

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

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

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

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

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

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

296:   Level: intermediate

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

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

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

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

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

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

329:   Level: intermediate

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

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

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

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

362:   Not Collective

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

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

370:   Level: advanced

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

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

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

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

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

400:   Collective

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

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

408:   Level: advanced

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

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

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

429:   Logically Collective

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

434:   Level: advanced

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

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

453:   Collective

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

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

462:   Level: advanced

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

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

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

487:   Logically Collective

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

492:   Level: advanced

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

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

511:   Not Collective

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

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

520:   Level: advanced

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

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

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

545:   Not Collective

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

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

556:   Level: advanced

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

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

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

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

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

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

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

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

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

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

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

613:   Logically Collective

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

618:   Level: advanced

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

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

637:   Not Collective

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

646:   Level: advanced

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

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

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

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

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

680:   Not Collective

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

685:   Level: advanced

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

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

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

707:   Not Collective

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

712:   Level: advanced

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

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

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

735:   Logically Collective

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

741:   Level: advanced

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

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

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

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

766:   Logically Collective

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

772:   Level: developer

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

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

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

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

802:   Logically Collective

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

808:   Level: developer

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

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

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

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

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

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

846:   Logically Collective

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

852:   Level: advanced

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

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

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

873:   Not Collective

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

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

881:   Level: advanced

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

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

897:   Not Collective

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

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

905:   Level: advanced

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

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

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

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

928:   Collective

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

933:   Level: beginner

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

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

942:   Currently only supported for  `MATAIJ` matrices.

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

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

958:   Collective

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

963:   Level: intermediate

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

968:   Currently only supported for `MATAIJ` matrices.

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

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

991:   Collective

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

996:   Level: advanced

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

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

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

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

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

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

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

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

1039:   Collective

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

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

1049:   Level: intermediate

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

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

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

1079:   Collective on viewer

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

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

1099:   Level: beginner

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1292:   Collective

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

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

1302:   Level: beginner

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

1411:   PetscFunctionBegin;

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

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

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

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

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

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

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

1463:   Collective

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

1468:   Level: beginner

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

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

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

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

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

1517:   Not Collective

1519:   Input Parameters:
1520: + mat  - the matrix
1521: . m    - the number of rows
1522: . idxm - the global indices of the rows
1523: . n    - the number of columns
1524: . idxn - the global indices of the columns
1525: . v    - a logically two-dimensional array of values
1526: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1528:   Level: beginner

1530:   Notes:
1531:   By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.

1533:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1534:   options cannot be mixed without intervening calls to the assembly
1535:   routines.

1537:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1538:   as well as in C.

1540:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1541:   simply ignored. This allows easily inserting element stiffness matrices
1542:   with homogeneous Dirichlet boundary conditions that you don't want represented
1543:   in the matrix.

1545:   Efficiency Alert:
1546:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1547:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1549:   Fortran Notes:
1550:   If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1551: .vb
1552:   call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1553: .ve

1555:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1557:   Developer Note:
1558:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1559:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1561: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1562:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1563: @*/
1564: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1565: {
1566:   PetscFunctionBeginHot;
1569:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1570:   PetscAssertPointer(idxm, 3);
1571:   PetscAssertPointer(idxn, 5);
1572:   MatCheckPreallocated(mat, 1);

1574:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1575:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

1577:   if (PetscDefined(USE_DEBUG)) {
1578:     PetscInt i, j;

1580:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1581:     if (v) {
1582:       for (i = 0; i < m; i++) {
1583:         for (j = 0; j < n; j++) {
1584:           if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1585: #if defined(PETSC_USE_COMPLEX)
1586:             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]);
1587: #else
1588:             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]);
1589: #endif
1590:         }
1591:       }
1592:     }
1593:     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);
1594:     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);
1595:   }

1597:   if (mat->assembled) {
1598:     mat->was_assembled = PETSC_TRUE;
1599:     mat->assembled     = PETSC_FALSE;
1600:   }
1601:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1602:   PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1603:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1604:   PetscFunctionReturn(PETSC_SUCCESS);
1605: }

1607: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1608: /*@
1609:   MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1610:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1611:   MUST be called after all calls to `MatSetValues()` have been completed.

1613:   Not Collective

1615:   Input Parameters:
1616: + mat  - the matrix
1617: . ism  - the rows to provide
1618: . isn  - the columns to provide
1619: . v    - a logically two-dimensional array of values
1620: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1622:   Level: beginner

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

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

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

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

1639:   Efficiency Alert:
1640:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1641:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1643:   This is currently not optimized for any particular `ISType`

1645: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1646:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1647: @*/
1648: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1649: {
1650:   PetscInt        m, n;
1651:   const PetscInt *rows, *cols;

1653:   PetscFunctionBeginHot;
1655:   PetscCall(ISGetIndices(ism, &rows));
1656:   PetscCall(ISGetIndices(isn, &cols));
1657:   PetscCall(ISGetLocalSize(ism, &m));
1658:   PetscCall(ISGetLocalSize(isn, &n));
1659:   PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1660:   PetscCall(ISRestoreIndices(ism, &rows));
1661:   PetscCall(ISRestoreIndices(isn, &cols));
1662:   PetscFunctionReturn(PETSC_SUCCESS);
1663: }

1665: /*@
1666:   MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1667:   values into a matrix

1669:   Not Collective

1671:   Input Parameters:
1672: + mat - the matrix
1673: . row - the (block) row to set
1674: - v   - a logically two-dimensional array of values

1676:   Level: intermediate

1678:   Notes:
1679:   The values, `v`, are column-oriented (for the block version) and sorted

1681:   All the nonzero values in `row` must be provided

1683:   The matrix must have previously had its column indices set, likely by having been assembled.

1685:   `row` must belong to this MPI process

1687: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1688:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1689: @*/
1690: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1691: {
1692:   PetscInt globalrow;

1694:   PetscFunctionBegin;
1697:   PetscAssertPointer(v, 3);
1698:   PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1699:   PetscCall(MatSetValuesRow(mat, globalrow, v));
1700:   PetscFunctionReturn(PETSC_SUCCESS);
1701: }

1703: /*@
1704:   MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1705:   values into a matrix

1707:   Not Collective

1709:   Input Parameters:
1710: + mat - the matrix
1711: . row - the (block) row to set
1712: - 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

1714:   Level: advanced

1716:   Notes:
1717:   The values, `v`, are column-oriented for the block version.

1719:   All the nonzeros in `row` must be provided

1721:   THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

1723:   `row` must belong to this process

1725: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1726:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1727: @*/
1728: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1729: {
1730:   PetscFunctionBeginHot;
1733:   MatCheckPreallocated(mat, 1);
1734:   PetscAssertPointer(v, 3);
1735:   PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1736:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1737:   mat->insertmode = INSERT_VALUES;

1739:   if (mat->assembled) {
1740:     mat->was_assembled = PETSC_TRUE;
1741:     mat->assembled     = PETSC_FALSE;
1742:   }
1743:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1744:   PetscUseTypeMethod(mat, setvaluesrow, row, v);
1745:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1746:   PetscFunctionReturn(PETSC_SUCCESS);
1747: }

1749: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1750: /*@
1751:   MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1752:   Using structured grid indexing

1754:   Not Collective

1756:   Input Parameters:
1757: + mat  - the matrix
1758: . m    - number of rows being entered
1759: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1760: . n    - number of columns being entered
1761: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1762: . v    - a logically two-dimensional array of values
1763: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

1765:   Level: beginner

1767:   Notes:
1768:   By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

1770:   Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1771:   options cannot be mixed without intervening calls to the assembly
1772:   routines.

1774:   The grid coordinates are across the entire grid, not just the local portion

1776:   `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1777:   as well as in C.

1779:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1781:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1782:   or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1784:   The columns and rows in the stencil passed in MUST be contained within the
1785:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1786:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1787:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1788:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1790:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1791:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1792:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1793:   `DM_BOUNDARY_PERIODIC` boundary type.

1795:   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
1796:   a single value per point) you can skip filling those indices.

1798:   Inspired by the structured grid interface to the HYPRE package
1799:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1801:   Efficiency Alert:
1802:   The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1803:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1805: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1806:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1807: @*/
1808: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1809: {
1810:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1811:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1812:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1814:   PetscFunctionBegin;
1815:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1818:   PetscAssertPointer(idxm, 3);
1819:   PetscAssertPointer(idxn, 5);

1821:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1822:     jdxm = buf;
1823:     jdxn = buf + m;
1824:   } else {
1825:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1826:     jdxm = bufm;
1827:     jdxn = bufn;
1828:   }
1829:   for (i = 0; i < m; i++) {
1830:     for (j = 0; j < 3 - sdim; j++) dxm++;
1831:     tmp = *dxm++ - starts[0];
1832:     for (j = 0; j < dim - 1; j++) {
1833:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1834:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1835:     }
1836:     if (mat->stencil.noc) dxm++;
1837:     jdxm[i] = tmp;
1838:   }
1839:   for (i = 0; i < n; i++) {
1840:     for (j = 0; j < 3 - sdim; j++) dxn++;
1841:     tmp = *dxn++ - starts[0];
1842:     for (j = 0; j < dim - 1; j++) {
1843:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1844:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1845:     }
1846:     if (mat->stencil.noc) dxn++;
1847:     jdxn[i] = tmp;
1848:   }
1849:   PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1850:   PetscCall(PetscFree2(bufm, bufn));
1851:   PetscFunctionReturn(PETSC_SUCCESS);
1852: }

1854: /*@
1855:   MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1856:   Using structured grid indexing

1858:   Not Collective

1860:   Input Parameters:
1861: + mat  - the matrix
1862: . m    - number of rows being entered
1863: . idxm - grid coordinates for matrix rows being entered
1864: . n    - number of columns being entered
1865: . idxn - grid coordinates for matrix columns being entered
1866: . v    - a logically two-dimensional array of values
1867: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

1869:   Level: beginner

1871:   Notes:
1872:   By default the values, `v`, are row-oriented and unsorted.
1873:   See `MatSetOption()` for other options.

1875:   Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1876:   options cannot be mixed without intervening calls to the assembly
1877:   routines.

1879:   The grid coordinates are across the entire grid, not just the local portion

1881:   `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1882:   as well as in C.

1884:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1886:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1887:   or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1889:   The columns and rows in the stencil passed in MUST be contained within the
1890:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1891:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1892:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1893:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1895:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1896:   simply ignored. This allows easily inserting element stiffness matrices
1897:   with homogeneous Dirichlet boundary conditions that you don't want represented
1898:   in the matrix.

1900:   Inspired by the structured grid interface to the HYPRE package
1901:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1903:   Fortran Note:
1904:   `idxm` and `idxn` should be declared as
1905: .vb
1906:     MatStencil idxm(4,m),idxn(4,n)
1907: .ve
1908:   and the values inserted using
1909: .vb
1910:     idxm(MatStencil_i,1) = i
1911:     idxm(MatStencil_j,1) = j
1912:     idxm(MatStencil_k,1) = k
1913:    etc
1914: .ve

1916: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1917:           `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1918:           `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1919: @*/
1920: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1921: {
1922:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1923:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1924:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1926:   PetscFunctionBegin;
1927:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1930:   PetscAssertPointer(idxm, 3);
1931:   PetscAssertPointer(idxn, 5);
1932:   PetscAssertPointer(v, 6);

1934:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1935:     jdxm = buf;
1936:     jdxn = buf + m;
1937:   } else {
1938:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1939:     jdxm = bufm;
1940:     jdxn = bufn;
1941:   }
1942:   for (i = 0; i < m; i++) {
1943:     for (j = 0; j < 3 - sdim; j++) dxm++;
1944:     tmp = *dxm++ - starts[0];
1945:     for (j = 0; j < sdim - 1; j++) {
1946:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1947:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1948:     }
1949:     dxm++;
1950:     jdxm[i] = tmp;
1951:   }
1952:   for (i = 0; i < n; i++) {
1953:     for (j = 0; j < 3 - sdim; j++) dxn++;
1954:     tmp = *dxn++ - starts[0];
1955:     for (j = 0; j < sdim - 1; j++) {
1956:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1957:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1958:     }
1959:     dxn++;
1960:     jdxn[i] = tmp;
1961:   }
1962:   PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1963:   PetscCall(PetscFree2(bufm, bufn));
1964:   PetscFunctionReturn(PETSC_SUCCESS);
1965: }

1967: /*@
1968:   MatSetStencil - Sets the grid information for setting values into a matrix via
1969:   `MatSetValuesStencil()`

1971:   Not Collective

1973:   Input Parameters:
1974: + mat    - the matrix
1975: . dim    - dimension of the grid 1, 2, or 3
1976: . dims   - number of grid points in x, y, and z direction, including ghost points on your processor
1977: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1978: - dof    - number of degrees of freedom per node

1980:   Level: beginner

1982:   Notes:
1983:   Inspired by the structured grid interface to the HYPRE package
1984:   (www.llnl.gov/CASC/hyper)

1986:   For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1987:   user.

1989: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1990:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1991: @*/
1992: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1993: {
1994:   PetscFunctionBegin;
1996:   PetscAssertPointer(dims, 3);
1997:   PetscAssertPointer(starts, 4);

1999:   mat->stencil.dim = dim + (dof > 1);
2000:   for (PetscInt i = 0; i < dim; i++) {
2001:     mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
2002:     mat->stencil.starts[i] = starts[dim - i - 1];
2003:   }
2004:   mat->stencil.dims[dim]   = dof;
2005:   mat->stencil.starts[dim] = 0;
2006:   mat->stencil.noc         = (PetscBool)(dof == 1);
2007:   PetscFunctionReturn(PETSC_SUCCESS);
2008: }

2010: /*@
2011:   MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

2013:   Not Collective

2015:   Input Parameters:
2016: + mat  - the matrix
2017: . m    - the number of block rows
2018: . idxm - the global block indices
2019: . n    - the number of block columns
2020: . idxn - the global block indices
2021: . v    - a logically two-dimensional array of values
2022: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

2024:   Level: intermediate

2026:   Notes:
2027:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2028:   MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

2030:   The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2031:   NOT the total number of rows/columns; for example, if the block size is 2 and
2032:   you are passing in values for rows 2,3,4,5  then `m` would be 2 (not 4).
2033:   The values in `idxm` would be 1 2; that is the first index for each block divided by
2034:   the block size.

2036:   You must call `MatSetBlockSize()` when constructing this matrix (before
2037:   preallocating it).

2039:   By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.

2041:   Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2042:   options cannot be mixed without intervening calls to the assembly
2043:   routines.

2045:   `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2046:   as well as in C.

2048:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2049:   simply ignored. This allows easily inserting element stiffness matrices
2050:   with homogeneous Dirichlet boundary conditions that you don't want represented
2051:   in the matrix.

2053:   Each time an entry is set within a sparse matrix via `MatSetValues()`,
2054:   internal searching must be done to determine where to place the
2055:   data in the matrix storage space.  By instead inserting blocks of
2056:   entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2057:   reduced.

2059:   Example:
2060: .vb
2061:    Suppose m=n=2 and block size(bs) = 2 The array is

2063:    1  2  | 3  4
2064:    5  6  | 7  8
2065:    - - - | - - -
2066:    9  10 | 11 12
2067:    13 14 | 15 16

2069:    v[] should be passed in like
2070:    v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

2072:   If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2073:    v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2074: .ve

2076:   Fortran Notes:
2077:   If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2078: .vb
2079:   call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2080: .ve

2082:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2084: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2085: @*/
2086: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2087: {
2088:   PetscFunctionBeginHot;
2091:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2092:   PetscAssertPointer(idxm, 3);
2093:   PetscAssertPointer(idxn, 5);
2094:   MatCheckPreallocated(mat, 1);
2095:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2096:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2097:   if (PetscDefined(USE_DEBUG)) {
2098:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2099:     PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2100:   }
2101:   if (PetscDefined(USE_DEBUG)) {
2102:     PetscInt rbs, cbs, M, N, i;
2103:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2104:     PetscCall(MatGetSize(mat, &M, &N));
2105:     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);
2106:     for (i = 0; i < n; i++)
2107:       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);
2108:   }
2109:   if (mat->assembled) {
2110:     mat->was_assembled = PETSC_TRUE;
2111:     mat->assembled     = PETSC_FALSE;
2112:   }
2113:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2114:   if (mat->ops->setvaluesblocked) {
2115:     PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2116:   } else {
2117:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2118:     PetscInt i, j, bs, cbs;

2120:     PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2121:     if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2122:       iidxm = buf;
2123:       iidxn = buf + m * bs;
2124:     } else {
2125:       PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2126:       iidxm = bufr;
2127:       iidxn = bufc;
2128:     }
2129:     for (i = 0; i < m; i++) {
2130:       for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2131:     }
2132:     if (m != n || bs != cbs || idxm != idxn) {
2133:       for (i = 0; i < n; i++) {
2134:         for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2135:       }
2136:     } else iidxn = iidxm;
2137:     PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2138:     PetscCall(PetscFree2(bufr, bufc));
2139:   }
2140:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2141:   PetscFunctionReturn(PETSC_SUCCESS);
2142: }

2144: /*@
2145:   MatGetValues - Gets a block of local values from a matrix.

2147:   Not Collective; can only return values that are owned by the give process

2149:   Input Parameters:
2150: + mat  - the matrix
2151: . v    - a logically two-dimensional array for storing the values
2152: . m    - the number of rows
2153: . idxm - the  global indices of the rows
2154: . n    - the number of columns
2155: - idxn - the global indices of the columns

2157:   Level: advanced

2159:   Notes:
2160:   The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2161:   The values, `v`, are then returned in a row-oriented format,
2162:   analogous to that used by default in `MatSetValues()`.

2164:   `MatGetValues()` uses 0-based row and column numbers in
2165:   Fortran as well as in C.

2167:   `MatGetValues()` requires that the matrix has been assembled
2168:   with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
2169:   `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2170:   without intermediate matrix assembly.

2172:   Negative row or column indices will be ignored and those locations in `v` will be
2173:   left unchanged.

2175:   For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2176:   That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2177:   from `MatGetOwnershipRange`(mat,&rstart,&rend).

2179: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2180: @*/
2181: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2182: {
2183:   PetscFunctionBegin;
2186:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2187:   PetscAssertPointer(idxm, 3);
2188:   PetscAssertPointer(idxn, 5);
2189:   PetscAssertPointer(v, 6);
2190:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2191:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2192:   MatCheckPreallocated(mat, 1);

2194:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2195:   PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2196:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2197:   PetscFunctionReturn(PETSC_SUCCESS);
2198: }

2200: /*@
2201:   MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2202:   defined previously by `MatSetLocalToGlobalMapping()`

2204:   Not Collective

2206:   Input Parameters:
2207: + mat  - the matrix
2208: . nrow - number of rows
2209: . irow - the row local indices
2210: . ncol - number of columns
2211: - icol - the column local indices

2213:   Output Parameter:
2214: . y - a logically two-dimensional array of values

2216:   Level: advanced

2218:   Notes:
2219:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

2221:   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,
2222:   are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2223:   determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2224:   with `MatSetLocalToGlobalMapping()`.

2226: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2227:           `MatSetValuesLocal()`, `MatGetValues()`
2228: @*/
2229: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2230: {
2231:   PetscFunctionBeginHot;
2234:   MatCheckPreallocated(mat, 1);
2235:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2236:   PetscAssertPointer(irow, 3);
2237:   PetscAssertPointer(icol, 5);
2238:   if (PetscDefined(USE_DEBUG)) {
2239:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2240:     PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2241:   }
2242:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2243:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2244:   if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2245:   else {
2246:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2247:     if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2248:       irowm = buf;
2249:       icolm = buf + nrow;
2250:     } else {
2251:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2252:       irowm = bufr;
2253:       icolm = bufc;
2254:     }
2255:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2256:     PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2257:     PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2258:     PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2259:     PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2260:     PetscCall(PetscFree2(bufr, bufc));
2261:   }
2262:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2263:   PetscFunctionReturn(PETSC_SUCCESS);
2264: }

2266: /*@
2267:   MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2268:   the same size. Currently, this can only be called once and creates the given matrix.

2270:   Not Collective

2272:   Input Parameters:
2273: + mat  - the matrix
2274: . nb   - the number of blocks
2275: . bs   - the number of rows (and columns) in each block
2276: . rows - a concatenation of the rows for each block
2277: - v    - a concatenation of logically two-dimensional arrays of values

2279:   Level: advanced

2281:   Notes:
2282:   `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values

2284:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

2286: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2287:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2288: @*/
2289: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2290: {
2291:   PetscFunctionBegin;
2294:   PetscAssertPointer(rows, 4);
2295:   PetscAssertPointer(v, 5);
2296:   PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

2298:   PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2299:   if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2300:   else {
2301:     for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2302:   }
2303:   PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2304:   PetscFunctionReturn(PETSC_SUCCESS);
2305: }

2307: /*@
2308:   MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2309:   the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2310:   using a local (per-processor) numbering.

2312:   Not Collective

2314:   Input Parameters:
2315: + x        - the matrix
2316: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2317: - cmapping - column mapping

2319:   Level: intermediate

2321:   Note:
2322:   If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix

2324: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2325: @*/
2326: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2327: {
2328:   PetscFunctionBegin;
2333:   if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2334:   else {
2335:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2336:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2337:   }
2338:   PetscFunctionReturn(PETSC_SUCCESS);
2339: }

2341: /*@
2342:   MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`

2344:   Not Collective

2346:   Input Parameter:
2347: . A - the matrix

2349:   Output Parameters:
2350: + rmapping - row mapping
2351: - cmapping - column mapping

2353:   Level: advanced

2355: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2356: @*/
2357: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2358: {
2359:   PetscFunctionBegin;
2362:   if (rmapping) {
2363:     PetscAssertPointer(rmapping, 2);
2364:     *rmapping = A->rmap->mapping;
2365:   }
2366:   if (cmapping) {
2367:     PetscAssertPointer(cmapping, 3);
2368:     *cmapping = A->cmap->mapping;
2369:   }
2370:   PetscFunctionReturn(PETSC_SUCCESS);
2371: }

2373: /*@
2374:   MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix

2376:   Logically Collective

2378:   Input Parameters:
2379: + A    - the matrix
2380: . rmap - row layout
2381: - cmap - column layout

2383:   Level: advanced

2385:   Note:
2386:   The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.

2388: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2389: @*/
2390: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2391: {
2392:   PetscFunctionBegin;
2394:   PetscCall(PetscLayoutReference(rmap, &A->rmap));
2395:   PetscCall(PetscLayoutReference(cmap, &A->cmap));
2396:   PetscFunctionReturn(PETSC_SUCCESS);
2397: }

2399: /*@
2400:   MatGetLayouts - Gets the `PetscLayout` objects for rows and columns

2402:   Not Collective

2404:   Input Parameter:
2405: . A - the matrix

2407:   Output Parameters:
2408: + rmap - row layout
2409: - cmap - column layout

2411:   Level: advanced

2413: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2414: @*/
2415: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2416: {
2417:   PetscFunctionBegin;
2420:   if (rmap) {
2421:     PetscAssertPointer(rmap, 2);
2422:     *rmap = A->rmap;
2423:   }
2424:   if (cmap) {
2425:     PetscAssertPointer(cmap, 3);
2426:     *cmap = A->cmap;
2427:   }
2428:   PetscFunctionReturn(PETSC_SUCCESS);
2429: }

2431: /*@
2432:   MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2433:   using a local numbering of the rows and columns.

2435:   Not Collective

2437:   Input Parameters:
2438: + mat  - the matrix
2439: . nrow - number of rows
2440: . irow - the row local indices
2441: . ncol - number of columns
2442: . icol - the column local indices
2443: . y    - a logically two-dimensional array of values
2444: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2446:   Level: intermediate

2448:   Notes:
2449:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

2451:   Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2452:   options cannot be mixed without intervening calls to the assembly
2453:   routines.

2455:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2456:   MUST be called after all calls to `MatSetValuesLocal()` have been completed.

2458:   Fortran Notes:
2459:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2460: .vb
2461:   call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2462: .ve

2464:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2466: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2467:           `MatGetValuesLocal()`
2468: @*/
2469: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2470: {
2471:   PetscFunctionBeginHot;
2474:   MatCheckPreallocated(mat, 1);
2475:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2476:   PetscAssertPointer(irow, 3);
2477:   PetscAssertPointer(icol, 5);
2478:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2479:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2480:   if (PetscDefined(USE_DEBUG)) {
2481:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2482:     PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2483:   }

2485:   if (mat->assembled) {
2486:     mat->was_assembled = PETSC_TRUE;
2487:     mat->assembled     = PETSC_FALSE;
2488:   }
2489:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2490:   if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2491:   else {
2492:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2493:     const PetscInt *irowm, *icolm;

2495:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2496:       bufr  = buf;
2497:       bufc  = buf + nrow;
2498:       irowm = bufr;
2499:       icolm = bufc;
2500:     } else {
2501:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2502:       irowm = bufr;
2503:       icolm = bufc;
2504:     }
2505:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2506:     else irowm = irow;
2507:     if (mat->cmap->mapping) {
2508:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2509:       else icolm = irowm;
2510:     } else icolm = icol;
2511:     PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2512:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2513:   }
2514:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2515:   PetscFunctionReturn(PETSC_SUCCESS);
2516: }

2518: /*@
2519:   MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2520:   using a local ordering of the nodes a block at a time.

2522:   Not Collective

2524:   Input Parameters:
2525: + mat  - the matrix
2526: . nrow - number of rows
2527: . irow - the row local indices
2528: . ncol - number of columns
2529: . icol - the column local indices
2530: . y    - a logically two-dimensional array of values
2531: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2533:   Level: intermediate

2535:   Notes:
2536:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2537:   before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

2539:   Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2540:   options cannot be mixed without intervening calls to the assembly
2541:   routines.

2543:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2544:   MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

2546:   Fortran Notes:
2547:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2548: .vb
2549:   call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2550: .ve

2552:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2554: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2555:           `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2556: @*/
2557: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2558: {
2559:   PetscFunctionBeginHot;
2562:   MatCheckPreallocated(mat, 1);
2563:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2564:   PetscAssertPointer(irow, 3);
2565:   PetscAssertPointer(icol, 5);
2566:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2567:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2568:   if (PetscDefined(USE_DEBUG)) {
2569:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2570:     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);
2571:   }

2573:   if (mat->assembled) {
2574:     mat->was_assembled = PETSC_TRUE;
2575:     mat->assembled     = PETSC_FALSE;
2576:   }
2577:   if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2578:     PetscInt irbs, rbs;
2579:     PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2580:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2581:     PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2582:   }
2583:   if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2584:     PetscInt icbs, cbs;
2585:     PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2586:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2587:     PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2588:   }
2589:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2590:   if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2591:   else {
2592:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2593:     const PetscInt *irowm, *icolm;

2595:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2596:       bufr  = buf;
2597:       bufc  = buf + nrow;
2598:       irowm = bufr;
2599:       icolm = bufc;
2600:     } else {
2601:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2602:       irowm = bufr;
2603:       icolm = bufc;
2604:     }
2605:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2606:     else irowm = irow;
2607:     if (mat->cmap->mapping) {
2608:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2609:       else icolm = irowm;
2610:     } else icolm = icol;
2611:     PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2612:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2613:   }
2614:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2615:   PetscFunctionReturn(PETSC_SUCCESS);
2616: }

2618: /*@
2619:   MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal

2621:   Collective

2623:   Input Parameters:
2624: + mat - the matrix
2625: - x   - the vector to be multiplied

2627:   Output Parameter:
2628: . y - the result

2630:   Level: developer

2632:   Note:
2633:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2634:   call `MatMultDiagonalBlock`(A,y,y).

2636: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2637: @*/
2638: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2639: {
2640:   PetscFunctionBegin;

2646:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2647:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2648:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2649:   MatCheckPreallocated(mat, 1);

2651:   PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2652:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2653:   PetscFunctionReturn(PETSC_SUCCESS);
2654: }

2656: /*@
2657:   MatMult - Computes the matrix-vector product, $y = Ax$.

2659:   Neighbor-wise Collective

2661:   Input Parameters:
2662: + mat - the matrix
2663: - x   - the vector to be multiplied

2665:   Output Parameter:
2666: . y - the result

2668:   Level: beginner

2670:   Note:
2671:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2672:   call `MatMult`(A,y,y).

2674: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2675: @*/
2676: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2677: {
2678:   PetscFunctionBegin;
2682:   VecCheckAssembled(x);
2684:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2685:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2686:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2687:   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);
2688:   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);
2689:   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);
2690:   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);
2691:   PetscCall(VecSetErrorIfLocked(y, 3));
2692:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2693:   MatCheckPreallocated(mat, 1);

2695:   PetscCall(VecLockReadPush(x));
2696:   PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2697:   PetscUseTypeMethod(mat, mult, x, y);
2698:   PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2699:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2700:   PetscCall(VecLockReadPop(x));
2701:   PetscFunctionReturn(PETSC_SUCCESS);
2702: }

2704: /*@
2705:   MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.

2707:   Neighbor-wise Collective

2709:   Input Parameters:
2710: + mat - the matrix
2711: - x   - the vector to be multiplied

2713:   Output Parameter:
2714: . y - the result

2716:   Level: beginner

2718:   Notes:
2719:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2720:   call `MatMultTranspose`(A,y,y).

2722:   For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2723:   use `MatMultHermitianTranspose()`

2725: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2726: @*/
2727: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2728: {
2729:   PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;

2731:   PetscFunctionBegin;
2735:   VecCheckAssembled(x);

2738:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2739:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2740:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2741:   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);
2742:   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);
2743:   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);
2744:   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);
2745:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2746:   MatCheckPreallocated(mat, 1);

2748:   if (!mat->ops->multtranspose) {
2749:     if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2750:     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);
2751:   } else op = mat->ops->multtranspose;
2752:   PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2753:   PetscCall(VecLockReadPush(x));
2754:   PetscCall((*op)(mat, x, y));
2755:   PetscCall(VecLockReadPop(x));
2756:   PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2757:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2758:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2759:   PetscFunctionReturn(PETSC_SUCCESS);
2760: }

2762: /*@
2763:   MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.

2765:   Neighbor-wise Collective

2767:   Input Parameters:
2768: + mat - the matrix
2769: - x   - the vector to be multiplied

2771:   Output Parameter:
2772: . y - the result

2774:   Level: beginner

2776:   Notes:
2777:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2778:   call `MatMultHermitianTranspose`(A,y,y).

2780:   Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2782:   For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

2784: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2785: @*/
2786: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2787: {
2788:   PetscFunctionBegin;

2794:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2795:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2796:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2797:   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);
2798:   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);
2799:   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);
2800:   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);
2801:   MatCheckPreallocated(mat, 1);

2803:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2804: #if defined(PETSC_USE_COMPLEX)
2805:   if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2806:     PetscCall(VecLockReadPush(x));
2807:     if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2808:     else PetscUseTypeMethod(mat, mult, x, y);
2809:     PetscCall(VecLockReadPop(x));
2810:   } else {
2811:     Vec w;
2812:     PetscCall(VecDuplicate(x, &w));
2813:     PetscCall(VecCopy(x, w));
2814:     PetscCall(VecConjugate(w));
2815:     PetscCall(MatMultTranspose(mat, w, y));
2816:     PetscCall(VecDestroy(&w));
2817:     PetscCall(VecConjugate(y));
2818:   }
2819:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2820: #else
2821:   PetscCall(MatMultTranspose(mat, x, y));
2822: #endif
2823:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2824:   PetscFunctionReturn(PETSC_SUCCESS);
2825: }

2827: /*@
2828:   MatMultAdd -  Computes $v3 = v2 + A * v1$.

2830:   Neighbor-wise Collective

2832:   Input Parameters:
2833: + mat - the matrix
2834: . v1  - the vector to be multiplied by `mat`
2835: - v2  - the vector to be added to the result

2837:   Output Parameter:
2838: . v3 - the result

2840:   Level: beginner

2842:   Note:
2843:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2844:   call `MatMultAdd`(A,v1,v2,v1).

2846: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2847: @*/
2848: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2849: {
2850:   PetscFunctionBegin;

2857:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2858:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2859:   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);
2860:   /* 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);
2861:      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); */
2862:   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);
2863:   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);
2864:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2865:   MatCheckPreallocated(mat, 1);

2867:   PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2868:   PetscCall(VecLockReadPush(v1));
2869:   PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2870:   PetscCall(VecLockReadPop(v1));
2871:   PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2872:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2873:   PetscFunctionReturn(PETSC_SUCCESS);
2874: }

2876: /*@
2877:   MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.

2879:   Neighbor-wise Collective

2881:   Input Parameters:
2882: + mat - the matrix
2883: . v1  - the vector to be multiplied by the transpose of the matrix
2884: - v2  - the vector to be added to the result

2886:   Output Parameter:
2887: . v3 - the result

2889:   Level: beginner

2891:   Note:
2892:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2893:   call `MatMultTransposeAdd`(A,v1,v2,v1).

2895: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2896: @*/
2897: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2898: {
2899:   PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

2901:   PetscFunctionBegin;

2908:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2909:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2910:   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);
2911:   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);
2912:   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);
2913:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2914:   PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2915:   MatCheckPreallocated(mat, 1);

2917:   PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2918:   PetscCall(VecLockReadPush(v1));
2919:   PetscCall((*op)(mat, v1, v2, v3));
2920:   PetscCall(VecLockReadPop(v1));
2921:   PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2922:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2923:   PetscFunctionReturn(PETSC_SUCCESS);
2924: }

2926: /*@
2927:   MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.

2929:   Neighbor-wise Collective

2931:   Input Parameters:
2932: + mat - the matrix
2933: . v1  - the vector to be multiplied by the Hermitian transpose
2934: - v2  - the vector to be added to the result

2936:   Output Parameter:
2937: . v3 - the result

2939:   Level: beginner

2941:   Note:
2942:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2943:   call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

2945: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2946: @*/
2947: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2948: {
2949:   PetscFunctionBegin;

2956:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2957:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2958:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2959:   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);
2960:   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);
2961:   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);
2962:   MatCheckPreallocated(mat, 1);

2964:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2965:   PetscCall(VecLockReadPush(v1));
2966:   if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2967:   else {
2968:     Vec w, z;
2969:     PetscCall(VecDuplicate(v1, &w));
2970:     PetscCall(VecCopy(v1, w));
2971:     PetscCall(VecConjugate(w));
2972:     PetscCall(VecDuplicate(v3, &z));
2973:     PetscCall(MatMultTranspose(mat, w, z));
2974:     PetscCall(VecDestroy(&w));
2975:     PetscCall(VecConjugate(z));
2976:     if (v2 != v3) {
2977:       PetscCall(VecWAXPY(v3, 1.0, v2, z));
2978:     } else {
2979:       PetscCall(VecAXPY(v3, 1.0, z));
2980:     }
2981:     PetscCall(VecDestroy(&z));
2982:   }
2983:   PetscCall(VecLockReadPop(v1));
2984:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2985:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2986:   PetscFunctionReturn(PETSC_SUCCESS);
2987: }

2989: /*@
2990:   MatGetFactorType - gets the type of factorization a matrix is

2992:   Not Collective

2994:   Input Parameter:
2995: . mat - the matrix

2997:   Output Parameter:
2998: . 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`

3000:   Level: intermediate

3002: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3003:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3004: @*/
3005: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3006: {
3007:   PetscFunctionBegin;
3010:   PetscAssertPointer(t, 2);
3011:   *t = mat->factortype;
3012:   PetscFunctionReturn(PETSC_SUCCESS);
3013: }

3015: /*@
3016:   MatSetFactorType - sets the type of factorization a matrix is

3018:   Logically Collective

3020:   Input Parameters:
3021: + mat - the matrix
3022: - 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`

3024:   Level: intermediate

3026: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3027:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3028: @*/
3029: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3030: {
3031:   PetscFunctionBegin;
3034:   mat->factortype = t;
3035:   PetscFunctionReturn(PETSC_SUCCESS);
3036: }

3038: /*@
3039:   MatGetInfo - Returns information about matrix storage (number of
3040:   nonzeros, memory, etc.).

3042:   Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

3044:   Input Parameters:
3045: + mat  - the matrix
3046: - 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)

3048:   Output Parameter:
3049: . info - matrix information context

3051:   Options Database Key:
3052: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

3054:   Level: intermediate

3056:   Notes:
3057:   The `MatInfo` context contains a variety of matrix data, including
3058:   number of nonzeros allocated and used, number of mallocs during
3059:   matrix assembly, etc.  Additional information for factored matrices
3060:   is provided (such as the fill ratio, number of mallocs during
3061:   factorization, etc.).

3063:   Example:
3064:   See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3065:   data within the `MatInfo` context.  For example,
3066: .vb
3067:       MatInfo info;
3068:       Mat     A;
3069:       double  mal, nz_a, nz_u;

3071:       MatGetInfo(A, MAT_LOCAL, &info);
3072:       mal  = info.mallocs;
3073:       nz_a = info.nz_allocated;
3074: .ve

3076: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3077: @*/
3078: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3079: {
3080:   PetscFunctionBegin;
3083:   PetscAssertPointer(info, 3);
3084:   MatCheckPreallocated(mat, 1);
3085:   PetscUseTypeMethod(mat, getinfo, flag, info);
3086:   PetscFunctionReturn(PETSC_SUCCESS);
3087: }

3089: /*
3090:    This is used by external packages where it is not easy to get the info from the actual
3091:    matrix factorization.
3092: */
3093: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3094: {
3095:   PetscFunctionBegin;
3096:   PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3097:   PetscFunctionReturn(PETSC_SUCCESS);
3098: }

3100: /*@
3101:   MatLUFactor - Performs in-place LU factorization of matrix.

3103:   Collective

3105:   Input Parameters:
3106: + mat  - the matrix
3107: . row  - row permutation
3108: . col  - column permutation
3109: - info - options for factorization, includes
3110: .vb
3111:           fill - expected fill as ratio of original fill.
3112:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3113:                    Run with the option -info to determine an optimal value to use
3114: .ve

3116:   Level: developer

3118:   Notes:
3119:   Most users should employ the `KSP` interface for linear solvers
3120:   instead of working directly with matrix algebra routines such as this.
3121:   See, e.g., `KSPCreate()`.

3123:   This changes the state of the matrix to a factored matrix; it cannot be used
3124:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3126:   This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3127:   when not using `KSP`.

3129:   Fortran Note:
3130:   A valid (non-null) `info` argument must be provided

3132: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3133:           `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3134: @*/
3135: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3136: {
3137:   MatFactorInfo tinfo;

3139:   PetscFunctionBegin;
3143:   if (info) PetscAssertPointer(info, 4);
3145:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3146:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3147:   MatCheckPreallocated(mat, 1);
3148:   if (!info) {
3149:     PetscCall(MatFactorInfoInitialize(&tinfo));
3150:     info = &tinfo;
3151:   }

3153:   PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3154:   PetscUseTypeMethod(mat, lufactor, row, col, info);
3155:   PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3156:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3157:   PetscFunctionReturn(PETSC_SUCCESS);
3158: }

3160: /*@
3161:   MatILUFactor - Performs in-place ILU factorization of matrix.

3163:   Collective

3165:   Input Parameters:
3166: + mat  - the matrix
3167: . row  - row permutation
3168: . col  - column permutation
3169: - info - structure containing
3170: .vb
3171:       levels - number of levels of fill.
3172:       expected fill - as ratio of original fill.
3173:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3174:                 missing diagonal entries)
3175: .ve

3177:   Level: developer

3179:   Notes:
3180:   Most users should employ the `KSP` interface for linear solvers
3181:   instead of working directly with matrix algebra routines such as this.
3182:   See, e.g., `KSPCreate()`.

3184:   Probably really in-place only when level of fill is zero, otherwise allocates
3185:   new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3186:   when not using `KSP`.

3188:   Fortran Note:
3189:   A valid (non-null) `info` argument must be provided

3191: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3192: @*/
3193: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3194: {
3195:   PetscFunctionBegin;
3199:   PetscAssertPointer(info, 4);
3201:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3202:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3203:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3204:   MatCheckPreallocated(mat, 1);

3206:   PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3207:   PetscUseTypeMethod(mat, ilufactor, row, col, info);
3208:   PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3209:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3210:   PetscFunctionReturn(PETSC_SUCCESS);
3211: }

3213: /*@
3214:   MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3215:   Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

3217:   Collective

3219:   Input Parameters:
3220: + fact - the factor matrix obtained with `MatGetFactor()`
3221: . mat  - the matrix
3222: . row  - the row permutation
3223: . col  - the column permutation
3224: - info - options for factorization, includes
3225: .vb
3226:           fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3227:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3228: .ve

3230:   Level: developer

3232:   Notes:
3233:   See [Matrix Factorization](sec_matfactor) for additional information about factorizations

3235:   Most users should employ the simplified `KSP` interface for linear solvers
3236:   instead of working directly with matrix algebra routines such as this.
3237:   See, e.g., `KSPCreate()`.

3239:   Fortran Note:
3240:   A valid (non-null) `info` argument must be provided

3242: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3243: @*/
3244: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3245: {
3246:   MatFactorInfo tinfo;

3248:   PetscFunctionBegin;
3253:   if (info) PetscAssertPointer(info, 5);
3256:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3257:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3258:   MatCheckPreallocated(mat, 2);
3259:   if (!info) {
3260:     PetscCall(MatFactorInfoInitialize(&tinfo));
3261:     info = &tinfo;
3262:   }

3264:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3265:   PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3266:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3267:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3268:   PetscFunctionReturn(PETSC_SUCCESS);
3269: }

3271: /*@
3272:   MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3273:   Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

3275:   Collective

3277:   Input Parameters:
3278: + fact - the factor matrix obtained with `MatGetFactor()`
3279: . mat  - the matrix
3280: - info - options for factorization

3282:   Level: developer

3284:   Notes:
3285:   See `MatLUFactor()` for in-place factorization.  See
3286:   `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

3288:   Most users should employ the `KSP` interface for linear solvers
3289:   instead of working directly with matrix algebra routines such as this.
3290:   See, e.g., `KSPCreate()`.

3292:   Fortran Note:
3293:   A valid (non-null) `info` argument must be provided

3295: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3296: @*/
3297: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3298: {
3299:   MatFactorInfo tinfo;

3301:   PetscFunctionBegin;
3306:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3307:   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,
3308:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3310:   MatCheckPreallocated(mat, 2);
3311:   if (!info) {
3312:     PetscCall(MatFactorInfoInitialize(&tinfo));
3313:     info = &tinfo;
3314:   }

3316:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3317:   else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3318:   PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3319:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3320:   else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3321:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3322:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3323:   PetscFunctionReturn(PETSC_SUCCESS);
3324: }

3326: /*@
3327:   MatCholeskyFactor - Performs in-place Cholesky factorization of a
3328:   symmetric matrix.

3330:   Collective

3332:   Input Parameters:
3333: + mat  - the matrix
3334: . perm - row and column permutations
3335: - info - expected fill as ratio of original fill

3337:   Level: developer

3339:   Notes:
3340:   See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
3341:   `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

3343:   Most users should employ the `KSP` interface for linear solvers
3344:   instead of working directly with matrix algebra routines such as this.
3345:   See, e.g., `KSPCreate()`.

3347:   Fortran Note:
3348:   A valid (non-null) `info` argument must be provided

3350: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3351:           `MatGetOrdering()`
3352: @*/
3353: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3354: {
3355:   MatFactorInfo tinfo;

3357:   PetscFunctionBegin;
3360:   if (info) PetscAssertPointer(info, 3);
3362:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3363:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3364:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3365:   MatCheckPreallocated(mat, 1);
3366:   if (!info) {
3367:     PetscCall(MatFactorInfoInitialize(&tinfo));
3368:     info = &tinfo;
3369:   }

3371:   PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3372:   PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3373:   PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3374:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3375:   PetscFunctionReturn(PETSC_SUCCESS);
3376: }

3378: /*@
3379:   MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3380:   of a symmetric matrix.

3382:   Collective

3384:   Input Parameters:
3385: + fact - the factor matrix obtained with `MatGetFactor()`
3386: . mat  - the matrix
3387: . perm - row and column permutations
3388: - info - options for factorization, includes
3389: .vb
3390:           fill - expected fill as ratio of original fill.
3391:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3392:                    Run with the option -info to determine an optimal value to use
3393: .ve

3395:   Level: developer

3397:   Notes:
3398:   See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
3399:   `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

3401:   Most users should employ the `KSP` interface for linear solvers
3402:   instead of working directly with matrix algebra routines such as this.
3403:   See, e.g., `KSPCreate()`.

3405:   Fortran Note:
3406:   A valid (non-null) `info` argument must be provided

3408: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3409:           `MatGetOrdering()`
3410: @*/
3411: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3412: {
3413:   MatFactorInfo tinfo;

3415:   PetscFunctionBegin;
3419:   if (info) PetscAssertPointer(info, 4);
3422:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3423:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3424:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3425:   MatCheckPreallocated(mat, 2);
3426:   if (!info) {
3427:     PetscCall(MatFactorInfoInitialize(&tinfo));
3428:     info = &tinfo;
3429:   }

3431:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3432:   PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3433:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3434:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3435:   PetscFunctionReturn(PETSC_SUCCESS);
3436: }

3438: /*@
3439:   MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3440:   of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3441:   `MatCholeskyFactorSymbolic()`.

3443:   Collective

3445:   Input Parameters:
3446: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3447: . mat  - the initial matrix that is to be factored
3448: - info - options for factorization

3450:   Level: developer

3452:   Note:
3453:   Most users should employ the `KSP` interface for linear solvers
3454:   instead of working directly with matrix algebra routines such as this.
3455:   See, e.g., `KSPCreate()`.

3457:   Fortran Note:
3458:   A valid (non-null) `info` argument must be provided

3460: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3461: @*/
3462: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3463: {
3464:   MatFactorInfo tinfo;

3466:   PetscFunctionBegin;
3471:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3472:   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,
3473:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3474:   MatCheckPreallocated(mat, 2);
3475:   if (!info) {
3476:     PetscCall(MatFactorInfoInitialize(&tinfo));
3477:     info = &tinfo;
3478:   }

3480:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3481:   else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3482:   PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3483:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3484:   else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3485:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3486:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3487:   PetscFunctionReturn(PETSC_SUCCESS);
3488: }

3490: /*@
3491:   MatQRFactor - Performs in-place QR factorization of matrix.

3493:   Collective

3495:   Input Parameters:
3496: + mat  - the matrix
3497: . col  - column permutation
3498: - info - options for factorization, includes
3499: .vb
3500:           fill - expected fill as ratio of original fill.
3501:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3502:                    Run with the option -info to determine an optimal value to use
3503: .ve

3505:   Level: developer

3507:   Notes:
3508:   Most users should employ the `KSP` interface for linear solvers
3509:   instead of working directly with matrix algebra routines such as this.
3510:   See, e.g., `KSPCreate()`.

3512:   This changes the state of the matrix to a factored matrix; it cannot be used
3513:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3515:   Fortran Note:
3516:   A valid (non-null) `info` argument must be provided

3518: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3519:           `MatSetUnfactored()`
3520: @*/
3521: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3522: {
3523:   PetscFunctionBegin;
3526:   if (info) PetscAssertPointer(info, 3);
3528:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3529:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3530:   MatCheckPreallocated(mat, 1);
3531:   PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3532:   PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3533:   PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3534:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3535:   PetscFunctionReturn(PETSC_SUCCESS);
3536: }

3538: /*@
3539:   MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3540:   Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

3542:   Collective

3544:   Input Parameters:
3545: + fact - the factor matrix obtained with `MatGetFactor()`
3546: . mat  - the matrix
3547: . col  - column permutation
3548: - info - options for factorization, includes
3549: .vb
3550:           fill - expected fill as ratio of original fill.
3551:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3552:                    Run with the option -info to determine an optimal value to use
3553: .ve

3555:   Level: developer

3557:   Note:
3558:   Most users should employ the `KSP` interface for linear solvers
3559:   instead of working directly with matrix algebra routines such as this.
3560:   See, e.g., `KSPCreate()`.

3562:   Fortran Note:
3563:   A valid (non-null) `info` argument must be provided

3565: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3566: @*/
3567: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3568: {
3569:   MatFactorInfo tinfo;

3571:   PetscFunctionBegin;
3575:   if (info) PetscAssertPointer(info, 4);
3578:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3579:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3580:   MatCheckPreallocated(mat, 2);
3581:   if (!info) {
3582:     PetscCall(MatFactorInfoInitialize(&tinfo));
3583:     info = &tinfo;
3584:   }

3586:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3587:   PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3588:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3589:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3590:   PetscFunctionReturn(PETSC_SUCCESS);
3591: }

3593: /*@
3594:   MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3595:   Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

3597:   Collective

3599:   Input Parameters:
3600: + fact - the factor matrix obtained with `MatGetFactor()`
3601: . mat  - the matrix
3602: - info - options for factorization

3604:   Level: developer

3606:   Notes:
3607:   See `MatQRFactor()` for in-place factorization.

3609:   Most users should employ the `KSP` interface for linear solvers
3610:   instead of working directly with matrix algebra routines such as this.
3611:   See, e.g., `KSPCreate()`.

3613:   Fortran Note:
3614:   A valid (non-null) `info` argument must be provided

3616: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3617: @*/
3618: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3619: {
3620:   MatFactorInfo tinfo;

3622:   PetscFunctionBegin;
3627:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3628:   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,
3629:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3631:   MatCheckPreallocated(mat, 2);
3632:   if (!info) {
3633:     PetscCall(MatFactorInfoInitialize(&tinfo));
3634:     info = &tinfo;
3635:   }

3637:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3638:   else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3639:   PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3640:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3641:   else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3642:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3643:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3644:   PetscFunctionReturn(PETSC_SUCCESS);
3645: }

3647: /*@
3648:   MatSolve - Solves $A x = b$, given a factored matrix.

3650:   Neighbor-wise Collective

3652:   Input Parameters:
3653: + mat - the factored matrix
3654: - b   - the right-hand-side vector

3656:   Output Parameter:
3657: . x - the result vector

3659:   Level: developer

3661:   Notes:
3662:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3663:   call `MatSolve`(A,x,x).

3665:   Most users should employ the `KSP` interface for linear solvers
3666:   instead of working directly with matrix algebra routines such as this.
3667:   See, e.g., `KSPCreate()`.

3669: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3670: @*/
3671: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3672: {
3673:   PetscFunctionBegin;
3678:   PetscCheckSameComm(mat, 1, b, 2);
3679:   PetscCheckSameComm(mat, 1, x, 3);
3680:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3681:   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);
3682:   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);
3683:   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);
3684:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3685:   MatCheckPreallocated(mat, 1);

3687:   PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3688:   PetscCall(VecFlag(x, mat->factorerrortype));
3689:   if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3690:   else PetscUseTypeMethod(mat, solve, b, x);
3691:   PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3692:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3693:   PetscFunctionReturn(PETSC_SUCCESS);
3694: }

3696: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3697: {
3698:   Vec      b, x;
3699:   PetscInt N, i;
3700:   PetscErrorCode (*f)(Mat, Vec, Vec);
3701:   PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

3703:   PetscFunctionBegin;
3704:   if (A->factorerrortype) {
3705:     PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3706:     PetscCall(MatSetInf(X));
3707:     PetscFunctionReturn(PETSC_SUCCESS);
3708:   }
3709:   f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3710:   PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3711:   PetscCall(MatBoundToCPU(A, &Abound));
3712:   if (!Abound) {
3713:     PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3714:     PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3715:   }
3716: #if PetscDefined(HAVE_CUDA)
3717:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3718:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3719: #elif PetscDefined(HAVE_HIP)
3720:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3721:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3722: #endif
3723:   PetscCall(MatGetSize(B, NULL, &N));
3724:   for (i = 0; i < N; i++) {
3725:     PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3726:     PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3727:     PetscCall((*f)(A, b, x));
3728:     PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3729:     PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3730:   }
3731:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3732:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3733:   PetscFunctionReturn(PETSC_SUCCESS);
3734: }

3736: /*@
3737:   MatMatSolve - Solves $A X = B$, given a factored matrix.

3739:   Neighbor-wise Collective

3741:   Input Parameters:
3742: + A - the factored matrix
3743: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

3745:   Output Parameter:
3746: . X - the result matrix (dense matrix)

3748:   Level: developer

3750:   Note:
3751:   If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3752:   otherwise, `B` and `X` cannot be the same.

3754: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3755: @*/
3756: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3757: {
3758:   PetscFunctionBegin;
3763:   PetscCheckSameComm(A, 1, B, 2);
3764:   PetscCheckSameComm(A, 1, X, 3);
3765:   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);
3766:   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);
3767:   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");
3768:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3769:   MatCheckPreallocated(A, 1);

3771:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3772:   if (!A->ops->matsolve) {
3773:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3774:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3775:   } else PetscUseTypeMethod(A, matsolve, B, X);
3776:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3777:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3778:   PetscFunctionReturn(PETSC_SUCCESS);
3779: }

3781: /*@
3782:   MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.

3784:   Neighbor-wise Collective

3786:   Input Parameters:
3787: + A - the factored matrix
3788: - B - the right-hand-side matrix  (`MATDENSE` matrix)

3790:   Output Parameter:
3791: . X - the result matrix (dense matrix)

3793:   Level: developer

3795:   Note:
3796:   The matrices `B` and `X` cannot be the same.  I.e., one cannot
3797:   call `MatMatSolveTranspose`(A,X,X).

3799: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3800: @*/
3801: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3802: {
3803:   PetscFunctionBegin;
3808:   PetscCheckSameComm(A, 1, B, 2);
3809:   PetscCheckSameComm(A, 1, X, 3);
3810:   PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3811:   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);
3812:   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);
3813:   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);
3814:   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");
3815:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3816:   MatCheckPreallocated(A, 1);

3818:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3819:   if (!A->ops->matsolvetranspose) {
3820:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3821:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3822:   } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3823:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3824:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3825:   PetscFunctionReturn(PETSC_SUCCESS);
3826: }

3828: /*@
3829:   MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.

3831:   Neighbor-wise Collective

3833:   Input Parameters:
3834: + A  - the factored matrix
3835: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`

3837:   Output Parameter:
3838: . X - the result matrix (dense matrix)

3840:   Level: developer

3842:   Note:
3843:   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
3844:   format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

3846: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3847: @*/
3848: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3849: {
3850:   PetscFunctionBegin;
3855:   PetscCheckSameComm(A, 1, Bt, 2);
3856:   PetscCheckSameComm(A, 1, X, 3);

3858:   PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3859:   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);
3860:   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);
3861:   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");
3862:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3863:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3864:   MatCheckPreallocated(A, 1);

3866:   PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3867:   PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3868:   PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3869:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3870:   PetscFunctionReturn(PETSC_SUCCESS);
3871: }

3873: /*@
3874:   MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3875:   $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3877:   Neighbor-wise Collective

3879:   Input Parameters:
3880: + mat - the factored matrix
3881: - b   - the right-hand-side vector

3883:   Output Parameter:
3884: . x - the result vector

3886:   Level: developer

3888:   Notes:
3889:   `MatSolve()` should be used for most applications, as it performs
3890:   a forward solve followed by a backward solve.

3892:   The vectors `b` and `x` cannot be the same,  i.e., one cannot
3893:   call `MatForwardSolve`(A,x,x).

3895:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3896:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3897:   `MatForwardSolve()` solves $U^T*D y = b$, and
3898:   `MatBackwardSolve()` solves $U x = y$.
3899:   Thus they do not provide a symmetric preconditioner.

3901: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3902: @*/
3903: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3904: {
3905:   PetscFunctionBegin;
3910:   PetscCheckSameComm(mat, 1, b, 2);
3911:   PetscCheckSameComm(mat, 1, x, 3);
3912:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3913:   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);
3914:   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);
3915:   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);
3916:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3917:   MatCheckPreallocated(mat, 1);

3919:   PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3920:   PetscUseTypeMethod(mat, forwardsolve, b, x);
3921:   PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3922:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3923:   PetscFunctionReturn(PETSC_SUCCESS);
3924: }

3926: /*@
3927:   MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3928:   $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3930:   Neighbor-wise Collective

3932:   Input Parameters:
3933: + mat - the factored matrix
3934: - b   - the right-hand-side vector

3936:   Output Parameter:
3937: . x - the result vector

3939:   Level: developer

3941:   Notes:
3942:   `MatSolve()` should be used for most applications, as it performs
3943:   a forward solve followed by a backward solve.

3945:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3946:   call `MatBackwardSolve`(A,x,x).

3948:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3949:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3950:   `MatForwardSolve()` solves $U^T*D y = b$, and
3951:   `MatBackwardSolve()` solves $U x = y$.
3952:   Thus they do not provide a symmetric preconditioner.

3954: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3955: @*/
3956: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3957: {
3958:   PetscFunctionBegin;
3963:   PetscCheckSameComm(mat, 1, b, 2);
3964:   PetscCheckSameComm(mat, 1, x, 3);
3965:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3966:   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);
3967:   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);
3968:   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);
3969:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3970:   MatCheckPreallocated(mat, 1);

3972:   PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3973:   PetscUseTypeMethod(mat, backwardsolve, b, x);
3974:   PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3975:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3976:   PetscFunctionReturn(PETSC_SUCCESS);
3977: }

3979: /*@
3980:   MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.

3982:   Neighbor-wise Collective

3984:   Input Parameters:
3985: + mat - the factored matrix
3986: . b   - the right-hand-side vector
3987: - y   - the vector to be added to

3989:   Output Parameter:
3990: . x - the result vector

3992:   Level: developer

3994:   Note:
3995:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3996:   call `MatSolveAdd`(A,x,y,x).

3998: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3999: @*/
4000: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4001: {
4002:   PetscScalar one = 1.0;
4003:   Vec         tmp;

4005:   PetscFunctionBegin;
4011:   PetscCheckSameComm(mat, 1, b, 2);
4012:   PetscCheckSameComm(mat, 1, y, 3);
4013:   PetscCheckSameComm(mat, 1, x, 4);
4014:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4015:   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);
4016:   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);
4017:   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);
4018:   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);
4019:   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);
4020:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4021:   MatCheckPreallocated(mat, 1);

4023:   PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4024:   PetscCall(VecFlag(x, mat->factorerrortype));
4025:   if (mat->factorerrortype) {
4026:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4027:   } else if (mat->ops->solveadd) {
4028:     PetscUseTypeMethod(mat, solveadd, b, y, x);
4029:   } else {
4030:     /* do the solve then the add manually */
4031:     if (x != y) {
4032:       PetscCall(MatSolve(mat, b, x));
4033:       PetscCall(VecAXPY(x, one, y));
4034:     } else {
4035:       PetscCall(VecDuplicate(x, &tmp));
4036:       PetscCall(VecCopy(x, tmp));
4037:       PetscCall(MatSolve(mat, b, x));
4038:       PetscCall(VecAXPY(x, one, tmp));
4039:       PetscCall(VecDestroy(&tmp));
4040:     }
4041:   }
4042:   PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4043:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4044:   PetscFunctionReturn(PETSC_SUCCESS);
4045: }

4047: /*@
4048:   MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.

4050:   Neighbor-wise Collective

4052:   Input Parameters:
4053: + mat - the factored matrix
4054: - b   - the right-hand-side vector

4056:   Output Parameter:
4057: . x - the result vector

4059:   Level: developer

4061:   Notes:
4062:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4063:   call `MatSolveTranspose`(A,x,x).

4065:   Most users should employ the `KSP` interface for linear solvers
4066:   instead of working directly with matrix algebra routines such as this.
4067:   See, e.g., `KSPCreate()`.

4069: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4070: @*/
4071: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4072: {
4073:   PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

4075:   PetscFunctionBegin;
4080:   PetscCheckSameComm(mat, 1, b, 2);
4081:   PetscCheckSameComm(mat, 1, x, 3);
4082:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4083:   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);
4084:   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);
4085:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4086:   MatCheckPreallocated(mat, 1);
4087:   PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4088:   PetscCall(VecFlag(x, mat->factorerrortype));
4089:   if (mat->factorerrortype) {
4090:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4091:   } else {
4092:     PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4093:     PetscCall((*f)(mat, b, x));
4094:   }
4095:   PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4096:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4097:   PetscFunctionReturn(PETSC_SUCCESS);
4098: }

4100: /*@
4101:   MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4102:   factored matrix.

4104:   Neighbor-wise Collective

4106:   Input Parameters:
4107: + mat - the factored matrix
4108: . b   - the right-hand-side vector
4109: - y   - the vector to be added to

4111:   Output Parameter:
4112: . x - the result vector

4114:   Level: developer

4116:   Note:
4117:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4118:   call `MatSolveTransposeAdd`(A,x,y,x).

4120: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4121: @*/
4122: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4123: {
4124:   PetscScalar one = 1.0;
4125:   Vec         tmp;
4126:   PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

4128:   PetscFunctionBegin;
4134:   PetscCheckSameComm(mat, 1, b, 2);
4135:   PetscCheckSameComm(mat, 1, y, 3);
4136:   PetscCheckSameComm(mat, 1, x, 4);
4137:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4138:   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);
4139:   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);
4140:   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);
4141:   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);
4142:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4143:   MatCheckPreallocated(mat, 1);

4145:   PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4146:   PetscCall(VecFlag(x, mat->factorerrortype));
4147:   if (mat->factorerrortype) {
4148:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4149:   } else if (f) {
4150:     PetscCall((*f)(mat, b, y, x));
4151:   } else {
4152:     /* do the solve then the add manually */
4153:     if (x != y) {
4154:       PetscCall(MatSolveTranspose(mat, b, x));
4155:       PetscCall(VecAXPY(x, one, y));
4156:     } else {
4157:       PetscCall(VecDuplicate(x, &tmp));
4158:       PetscCall(VecCopy(x, tmp));
4159:       PetscCall(MatSolveTranspose(mat, b, x));
4160:       PetscCall(VecAXPY(x, one, tmp));
4161:       PetscCall(VecDestroy(&tmp));
4162:     }
4163:   }
4164:   PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4165:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4166:   PetscFunctionReturn(PETSC_SUCCESS);
4167: }

4169: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4170: /*@
4171:   MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

4173:   Neighbor-wise Collective

4175:   Input Parameters:
4176: + mat   - the matrix
4177: . b     - the right-hand side
4178: . omega - the relaxation factor
4179: . flag  - flag indicating the type of SOR (see below)
4180: . shift - diagonal shift
4181: . its   - the number of iterations
4182: - lits  - the number of local iterations

4184:   Output Parameter:
4185: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

4187:   SOR Flags:
4188: +     `SOR_FORWARD_SWEEP` - forward SOR
4189: .     `SOR_BACKWARD_SWEEP` - backward SOR
4190: .     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4191: .     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4192: .     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4193: .     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4194: .     `SOR_EISENSTAT` - SOR with Eisenstat trick
4195: .     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4196:   upper/lower triangular part of matrix to
4197:   vector (with omega)
4198: -     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

4200:   Level: developer

4202:   Notes:
4203:   `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4204:   `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4205:   on each processor.

4207:   Application programmers will not generally use `MatSOR()` directly,
4208:   but instead will employ the `KSP`/`PC` interface.

4210:   For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

4212:   Most users should employ the `KSP` interface for linear solvers
4213:   instead of working directly with matrix algebra routines such as this.
4214:   See, e.g., `KSPCreate()`.

4216:   Vectors `x` and `b` CANNOT be the same

4218:   The flags are implemented as bitwise inclusive or operations.
4219:   For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4220:   to specify a zero initial guess for SSOR.

4222:   Developer Note:
4223:   We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

4225: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4226: @*/
4227: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4228: {
4229:   PetscFunctionBegin;
4234:   PetscCheckSameComm(mat, 1, b, 2);
4235:   PetscCheckSameComm(mat, 1, x, 8);
4236:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4237:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4238:   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);
4239:   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);
4240:   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);
4241:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4242:   PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4243:   PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

4245:   MatCheckPreallocated(mat, 1);
4246:   PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4247:   PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4248:   PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4249:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4250:   PetscFunctionReturn(PETSC_SUCCESS);
4251: }

4253: /*
4254:       Default matrix copy routine.
4255: */
4256: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4257: {
4258:   PetscInt           i, rstart = 0, rend = 0, nz;
4259:   const PetscInt    *cwork;
4260:   const PetscScalar *vwork;

4262:   PetscFunctionBegin;
4263:   if (B->assembled) PetscCall(MatZeroEntries(B));
4264:   if (str == SAME_NONZERO_PATTERN) {
4265:     PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4266:     for (i = rstart; i < rend; i++) {
4267:       PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4268:       PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4269:       PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4270:     }
4271:   } else {
4272:     PetscCall(MatAYPX(B, 0.0, A, str));
4273:   }
4274:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4275:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4276:   PetscFunctionReturn(PETSC_SUCCESS);
4277: }

4279: /*@
4280:   MatCopy - Copies a matrix to another matrix.

4282:   Collective

4284:   Input Parameters:
4285: + A   - the matrix
4286: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

4288:   Output Parameter:
4289: . B - where the copy is put

4291:   Level: intermediate

4293:   Notes:
4294:   If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.

4296:   `MatCopy()` copies the matrix entries of a matrix to another existing
4297:   matrix (after first zeroing the second matrix).  A related routine is
4298:   `MatConvert()`, which first creates a new matrix and then copies the data.

4300: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4301: @*/
4302: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4303: {
4304:   PetscInt i;

4306:   PetscFunctionBegin;
4311:   PetscCheckSameComm(A, 1, B, 2);
4312:   MatCheckPreallocated(B, 2);
4313:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4314:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4315:   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,
4316:              A->cmap->N, B->cmap->N);
4317:   MatCheckPreallocated(A, 1);
4318:   if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

4320:   PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4321:   if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4322:   else PetscCall(MatCopy_Basic(A, B, str));

4324:   B->stencil.dim = A->stencil.dim;
4325:   B->stencil.noc = A->stencil.noc;
4326:   for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4327:     B->stencil.dims[i]   = A->stencil.dims[i];
4328:     B->stencil.starts[i] = A->stencil.starts[i];
4329:   }

4331:   PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4332:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4333:   PetscFunctionReturn(PETSC_SUCCESS);
4334: }

4336: /*@
4337:   MatConvert - Converts a matrix to another matrix, either of the same
4338:   or different type.

4340:   Collective

4342:   Input Parameters:
4343: + mat     - the matrix
4344: . newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
4345:             same type as the original matrix.
4346: - reuse   - denotes if the destination matrix is to be created or reused.
4347:             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
4348:             `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).

4350:   Output Parameter:
4351: . M - pointer to place new matrix

4353:   Level: intermediate

4355:   Notes:
4356:   `MatConvert()` first creates a new matrix and then copies the data from
4357:   the first matrix.  A related routine is `MatCopy()`, which copies the matrix
4358:   entries of one matrix to another already existing matrix context.

4360:   Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4361:   the MPI communicator of the generated matrix is always the same as the communicator
4362:   of the input matrix.

4364: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4365: @*/
4366: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4367: {
4368:   PetscBool  sametype, issame, flg;
4369:   PetscBool3 issymmetric, ishermitian;
4370:   char       convname[256], mtype[256];
4371:   Mat        B;

4373:   PetscFunctionBegin;
4376:   PetscAssertPointer(M, 4);
4377:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4378:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4379:   MatCheckPreallocated(mat, 1);

4381:   PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4382:   if (flg) newtype = mtype;

4384:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4385:   PetscCall(PetscStrcmp(newtype, "same", &issame));
4386:   PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4387:   if (reuse == MAT_REUSE_MATRIX) {
4389:     PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4390:   }

4392:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4393:     PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4394:     PetscFunctionReturn(PETSC_SUCCESS);
4395:   }

4397:   /* Cache Mat options because some converters use MatHeaderReplace  */
4398:   issymmetric = mat->symmetric;
4399:   ishermitian = mat->hermitian;

4401:   if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4402:     PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4403:     PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4404:   } else {
4405:     PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4406:     const char *prefix[3]                                 = {"seq", "mpi", ""};
4407:     PetscInt    i;
4408:     /*
4409:        Order of precedence:
4410:        0) See if newtype is a superclass of the current matrix.
4411:        1) See if a specialized converter is known to the current matrix.
4412:        2) See if a specialized converter is known to the desired matrix class.
4413:        3) See if a good general converter is registered for the desired class
4414:           (as of 6/27/03 only MATMPIADJ falls into this category).
4415:        4) See if a good general converter is known for the current matrix.
4416:        5) Use a really basic converter.
4417:     */

4419:     /* 0) See if newtype is a superclass of the current matrix.
4420:           i.e mat is mpiaij and newtype is aij */
4421:     for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4422:       PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4423:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4424:       PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4425:       PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4426:       if (flg) {
4427:         if (reuse == MAT_INPLACE_MATRIX) {
4428:           PetscCall(PetscInfo(mat, "Early return\n"));
4429:           PetscFunctionReturn(PETSC_SUCCESS);
4430:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4431:           PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4432:           PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4433:           PetscFunctionReturn(PETSC_SUCCESS);
4434:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4435:           PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4436:           PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4437:           PetscFunctionReturn(PETSC_SUCCESS);
4438:         }
4439:       }
4440:     }
4441:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4442:     for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4443:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4444:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4445:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4446:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4447:       PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4448:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4449:       PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4450:       PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4451:       if (conv) goto foundconv;
4452:     }

4454:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4455:     PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4456:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4457:     PetscCall(MatSetType(B, newtype));
4458:     for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4459:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4460:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4461:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4462:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4463:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4464:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4465:       PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4466:       PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4467:       if (conv) {
4468:         PetscCall(MatDestroy(&B));
4469:         goto foundconv;
4470:       }
4471:     }

4473:     /* 3) See if a good general converter is registered for the desired class */
4474:     conv = B->ops->convertfrom;
4475:     PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4476:     PetscCall(MatDestroy(&B));
4477:     if (conv) goto foundconv;

4479:     /* 4) See if a good general converter is known for the current matrix */
4480:     if (mat->ops->convert) conv = mat->ops->convert;
4481:     PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4482:     if (conv) goto foundconv;

4484:     /* 5) Use a really basic converter. */
4485:     PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4486:     conv = MatConvert_Basic;

4488:   foundconv:
4489:     PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4490:     PetscCall((*conv)(mat, newtype, reuse, M));
4491:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4492:       /* the block sizes must be same if the mappings are copied over */
4493:       (*M)->rmap->bs = mat->rmap->bs;
4494:       (*M)->cmap->bs = mat->cmap->bs;
4495:       PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4496:       PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4497:       (*M)->rmap->mapping = mat->rmap->mapping;
4498:       (*M)->cmap->mapping = mat->cmap->mapping;
4499:     }
4500:     (*M)->stencil.dim = mat->stencil.dim;
4501:     (*M)->stencil.noc = mat->stencil.noc;
4502:     for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4503:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4504:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4505:     }
4506:     PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4507:   }
4508:   PetscCall(PetscObjectStateIncrease((PetscObject)*M));

4510:   /* Copy Mat options */
4511:   if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4512:   else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4513:   if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4514:   else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4515:   PetscFunctionReturn(PETSC_SUCCESS);
4516: }

4518: /*@
4519:   MatFactorGetSolverType - Returns name of the package providing the factorization routines

4521:   Not Collective

4523:   Input Parameter:
4524: . mat - the matrix, must be a factored matrix

4526:   Output Parameter:
4527: . type - the string name of the package (do not free this string)

4529:   Level: intermediate

4531: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4532: @*/
4533: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4534: {
4535:   PetscErrorCode (*conv)(Mat, MatSolverType *);

4537:   PetscFunctionBegin;
4540:   PetscAssertPointer(type, 2);
4541:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4542:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4543:   if (conv) PetscCall((*conv)(mat, type));
4544:   else *type = MATSOLVERPETSC;
4545:   PetscFunctionReturn(PETSC_SUCCESS);
4546: }

4548: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4549: struct _MatSolverTypeForSpecifcType {
4550:   MatType mtype;
4551:   /* no entry for MAT_FACTOR_NONE */
4552:   PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4553:   MatSolverTypeForSpecifcType next;
4554: };

4556: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4557: struct _MatSolverTypeHolder {
4558:   char                       *name;
4559:   MatSolverTypeForSpecifcType handlers;
4560:   MatSolverTypeHolder         next;
4561: };

4563: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4565: /*@C
4566:   MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

4568:   Logically Collective, No Fortran Support

4570:   Input Parameters:
4571: + package      - name of the package, for example `petsc` or `superlu`
4572: . mtype        - the matrix type that works with this package
4573: . ftype        - the type of factorization supported by the package
4574: - createfactor - routine that will create the factored matrix ready to be used

4576:   Level: developer

4578: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4579:   `MatGetFactor()`
4580: @*/
4581: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4582: {
4583:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
4584:   PetscBool                   flg;
4585:   MatSolverTypeForSpecifcType inext, iprev = NULL;

4587:   PetscFunctionBegin;
4588:   PetscCall(MatInitializePackage());
4589:   if (!next) {
4590:     PetscCall(PetscNew(&MatSolverTypeHolders));
4591:     PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4592:     PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4593:     PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4594:     MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4595:     PetscFunctionReturn(PETSC_SUCCESS);
4596:   }
4597:   while (next) {
4598:     PetscCall(PetscStrcasecmp(package, next->name, &flg));
4599:     if (flg) {
4600:       PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4601:       inext = next->handlers;
4602:       while (inext) {
4603:         PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4604:         if (flg) {
4605:           inext->createfactor[(int)ftype - 1] = createfactor;
4606:           PetscFunctionReturn(PETSC_SUCCESS);
4607:         }
4608:         iprev = inext;
4609:         inext = inext->next;
4610:       }
4611:       PetscCall(PetscNew(&iprev->next));
4612:       PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4613:       iprev->next->createfactor[(int)ftype - 1] = createfactor;
4614:       PetscFunctionReturn(PETSC_SUCCESS);
4615:     }
4616:     prev = next;
4617:     next = next->next;
4618:   }
4619:   PetscCall(PetscNew(&prev->next));
4620:   PetscCall(PetscStrallocpy(package, &prev->next->name));
4621:   PetscCall(PetscNew(&prev->next->handlers));
4622:   PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4623:   prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4624:   PetscFunctionReturn(PETSC_SUCCESS);
4625: }

4627: /*@C
4628:   MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

4630:   Input Parameters:
4631: + 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
4632: . ftype - the type of factorization supported by the type
4633: - mtype - the matrix type that works with this type

4635:   Output Parameters:
4636: + foundtype    - `PETSC_TRUE` if the type was registered
4637: . foundmtype   - `PETSC_TRUE` if the type supports the requested mtype
4638: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

4640:   Calling sequence of `createfactor`:
4641: + A     - the matrix providing the factor matrix
4642: . ftype - the `MatFactorType` of the factor requested
4643: - B     - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`

4645:   Level: developer

4647:   Note:
4648:   When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4649:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4650:   For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.

4652: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4653:           `MatInitializePackage()`
4654: @*/
4655: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4656: {
4657:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4658:   PetscBool                   flg;
4659:   MatSolverTypeForSpecifcType inext;

4661:   PetscFunctionBegin;
4662:   if (foundtype) *foundtype = PETSC_FALSE;
4663:   if (foundmtype) *foundmtype = PETSC_FALSE;
4664:   if (createfactor) *createfactor = NULL;

4666:   if (type) {
4667:     while (next) {
4668:       PetscCall(PetscStrcasecmp(type, next->name, &flg));
4669:       if (flg) {
4670:         if (foundtype) *foundtype = PETSC_TRUE;
4671:         inext = next->handlers;
4672:         while (inext) {
4673:           PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4674:           if (flg) {
4675:             if (foundmtype) *foundmtype = PETSC_TRUE;
4676:             if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4677:             PetscFunctionReturn(PETSC_SUCCESS);
4678:           }
4679:           inext = inext->next;
4680:         }
4681:       }
4682:       next = next->next;
4683:     }
4684:   } else {
4685:     while (next) {
4686:       inext = next->handlers;
4687:       while (inext) {
4688:         PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4689:         if (flg && inext->createfactor[(int)ftype - 1]) {
4690:           if (foundtype) *foundtype = PETSC_TRUE;
4691:           if (foundmtype) *foundmtype = PETSC_TRUE;
4692:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4693:           PetscFunctionReturn(PETSC_SUCCESS);
4694:         }
4695:         inext = inext->next;
4696:       }
4697:       next = next->next;
4698:     }
4699:     /* try with base classes inext->mtype */
4700:     next = MatSolverTypeHolders;
4701:     while (next) {
4702:       inext = next->handlers;
4703:       while (inext) {
4704:         PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4705:         if (flg && inext->createfactor[(int)ftype - 1]) {
4706:           if (foundtype) *foundtype = PETSC_TRUE;
4707:           if (foundmtype) *foundmtype = PETSC_TRUE;
4708:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4709:           PetscFunctionReturn(PETSC_SUCCESS);
4710:         }
4711:         inext = inext->next;
4712:       }
4713:       next = next->next;
4714:     }
4715:   }
4716:   PetscFunctionReturn(PETSC_SUCCESS);
4717: }

4719: PetscErrorCode MatSolverTypeDestroy(void)
4720: {
4721:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
4722:   MatSolverTypeForSpecifcType inext, iprev;

4724:   PetscFunctionBegin;
4725:   while (next) {
4726:     PetscCall(PetscFree(next->name));
4727:     inext = next->handlers;
4728:     while (inext) {
4729:       PetscCall(PetscFree(inext->mtype));
4730:       iprev = inext;
4731:       inext = inext->next;
4732:       PetscCall(PetscFree(iprev));
4733:     }
4734:     prev = next;
4735:     next = next->next;
4736:     PetscCall(PetscFree(prev));
4737:   }
4738:   MatSolverTypeHolders = NULL;
4739:   PetscFunctionReturn(PETSC_SUCCESS);
4740: }

4742: /*@
4743:   MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

4745:   Logically Collective

4747:   Input Parameter:
4748: . mat - the matrix

4750:   Output Parameter:
4751: . flg - `PETSC_TRUE` if uses the ordering

4753:   Level: developer

4755:   Note:
4756:   Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4757:   packages do not, thus we want to skip generating the ordering when it is not needed or used.

4759: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4760: @*/
4761: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4762: {
4763:   PetscFunctionBegin;
4764:   *flg = mat->canuseordering;
4765:   PetscFunctionReturn(PETSC_SUCCESS);
4766: }

4768: /*@
4769:   MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

4771:   Logically Collective

4773:   Input Parameters:
4774: + mat   - the matrix obtained with `MatGetFactor()`
4775: - ftype - the factorization type to be used

4777:   Output Parameter:
4778: . otype - the preferred ordering type

4780:   Level: developer

4782: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4783: @*/
4784: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4785: {
4786:   PetscFunctionBegin;
4787:   *otype = mat->preferredordering[ftype];
4788:   PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4789:   PetscFunctionReturn(PETSC_SUCCESS);
4790: }

4792: /*@
4793:   MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()

4795:   Collective

4797:   Input Parameters:
4798: + mat   - the matrix
4799: . 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
4800:           the other criteria is returned
4801: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4803:   Output Parameter:
4804: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.

4806:   Options Database Keys:
4807: + -pc_factor_mat_solver_type <type>    - choose the type at run time. When using `KSP` solvers
4808: . -pc_factor_mat_factor_on_host <bool> - do mat factorization on host (with device matrices). Default is doing it on device
4809: - -pc_factor_mat_solve_on_host <bool>  - do mat solve on host (with device matrices). Default is doing it on device

4811:   Level: intermediate

4813:   Notes:
4814:   The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4815:   types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.

4817:   Users usually access the factorization solvers via `KSP`

4819:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4820:   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

4822:   When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4823:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4824:   For example if one configuration had --download-mumps while a different one had --download-superlu_dist.

4826:   Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4827:   where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4828:   call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

4830:   Developer Note:
4831:   This should actually be called `MatCreateFactor()` since it creates a new factor object

4833: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4834:           `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4835:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4836: @*/
4837: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4838: {
4839:   PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4840:   PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

4842:   PetscFunctionBegin;

4846:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4847:   MatCheckPreallocated(mat, 1);

4849:   PetscCall(MatIsShell(mat, &shell));
4850:   if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4851:   if (hasop) {
4852:     PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4853:     PetscFunctionReturn(PETSC_SUCCESS);
4854:   }

4856:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4857:   if (!foundtype) {
4858:     if (type) {
4859:       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],
4860:               ((PetscObject)mat)->type_name, type);
4861:     } else {
4862:       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);
4863:     }
4864:   }
4865:   PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4866:   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);

4868:   PetscCall((*conv)(mat, ftype, f));
4869:   if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4870:   PetscFunctionReturn(PETSC_SUCCESS);
4871: }

4873: /*@
4874:   MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type

4876:   Not Collective

4878:   Input Parameters:
4879: + mat   - the matrix
4880: . type  - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4881: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4883:   Output Parameter:
4884: . flg - PETSC_TRUE if the factorization is available

4886:   Level: intermediate

4888:   Notes:
4889:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4890:   such as pastix, superlu, mumps etc.

4892:   PETSc must have been ./configure to use the external solver, using the option --download-package

4894:   Developer Note:
4895:   This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object

4897: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4898:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4899: @*/
4900: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4901: {
4902:   PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

4904:   PetscFunctionBegin;
4906:   PetscAssertPointer(flg, 4);

4908:   *flg = PETSC_FALSE;
4909:   if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);

4911:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4912:   MatCheckPreallocated(mat, 1);

4914:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4915:   *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4916:   PetscFunctionReturn(PETSC_SUCCESS);
4917: }

4919: /*@
4920:   MatDuplicate - Duplicates a matrix including the non-zero structure.

4922:   Collective

4924:   Input Parameters:
4925: + mat - the matrix
4926: - op  - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4927:         See the manual page for `MatDuplicateOption()` for an explanation of these options.

4929:   Output Parameter:
4930: . M - pointer to place new matrix

4932:   Level: intermediate

4934:   Notes:
4935:   You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.

4937:   If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.

4939:   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.

4941:   When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4942:   is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4943:   User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.

4945: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4946: @*/
4947: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4948: {
4949:   Mat         B;
4950:   VecType     vtype;
4951:   PetscInt    i;
4952:   PetscObject dm, container_h, container_d;
4953:   void (*viewf)(void);

4955:   PetscFunctionBegin;
4958:   PetscAssertPointer(M, 3);
4959:   PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4960:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4961:   MatCheckPreallocated(mat, 1);

4963:   PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4964:   PetscUseTypeMethod(mat, duplicate, op, M);
4965:   PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4966:   B = *M;

4968:   PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4969:   if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4970:   PetscCall(MatGetVecType(mat, &vtype));
4971:   PetscCall(MatSetVecType(B, vtype));

4973:   B->stencil.dim = mat->stencil.dim;
4974:   B->stencil.noc = mat->stencil.noc;
4975:   for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4976:     B->stencil.dims[i]   = mat->stencil.dims[i];
4977:     B->stencil.starts[i] = mat->stencil.starts[i];
4978:   }

4980:   B->nooffproczerorows = mat->nooffproczerorows;
4981:   B->nooffprocentries  = mat->nooffprocentries;

4983:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4984:   if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4985:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
4986:   if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
4987:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
4988:   if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
4989:   if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
4990:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4991:   PetscFunctionReturn(PETSC_SUCCESS);
4992: }

4994: /*@
4995:   MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`

4997:   Logically Collective

4999:   Input Parameter:
5000: . mat - the matrix

5002:   Output Parameter:
5003: . v - the diagonal of the matrix

5005:   Level: intermediate

5007:   Note:
5008:   If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5009:   of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5010:   is larger than `ndiag`, the values of the remaining entries are unspecified.

5012:   Currently only correct in parallel for square matrices.

5014: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5015: @*/
5016: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5017: {
5018:   PetscFunctionBegin;
5022:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5023:   MatCheckPreallocated(mat, 1);
5024:   if (PetscDefined(USE_DEBUG)) {
5025:     PetscInt nv, row, col, ndiag;

5027:     PetscCall(VecGetLocalSize(v, &nv));
5028:     PetscCall(MatGetLocalSize(mat, &row, &col));
5029:     ndiag = PetscMin(row, col);
5030:     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);
5031:   }

5033:   PetscUseTypeMethod(mat, getdiagonal, v);
5034:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5035:   PetscFunctionReturn(PETSC_SUCCESS);
5036: }

5038: /*@
5039:   MatGetRowMin - Gets the minimum value (of the real part) of each
5040:   row of the matrix

5042:   Logically Collective

5044:   Input Parameter:
5045: . mat - the matrix

5047:   Output Parameters:
5048: + v   - the vector for storing the maximums
5049: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)

5051:   Level: intermediate

5053:   Note:
5054:   The result of this call are the same as if one converted the matrix to dense format
5055:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5057:   This code is only implemented for a couple of matrix formats.

5059: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5060:           `MatGetRowMax()`
5061: @*/
5062: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5063: {
5064:   PetscFunctionBegin;
5068:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5070:   if (!mat->cmap->N) {
5071:     PetscCall(VecSet(v, PETSC_MAX_REAL));
5072:     if (idx) {
5073:       PetscInt i, m = mat->rmap->n;
5074:       for (i = 0; i < m; i++) idx[i] = -1;
5075:     }
5076:   } else {
5077:     MatCheckPreallocated(mat, 1);
5078:   }
5079:   PetscUseTypeMethod(mat, getrowmin, v, idx);
5080:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5081:   PetscFunctionReturn(PETSC_SUCCESS);
5082: }

5084: /*@
5085:   MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5086:   row of the matrix

5088:   Logically Collective

5090:   Input Parameter:
5091: . mat - the matrix

5093:   Output Parameters:
5094: + v   - the vector for storing the minimums
5095: - idx - the indices of the column found for each row (or `NULL` if not needed)

5097:   Level: intermediate

5099:   Notes:
5100:   if a row is completely empty or has only 0.0 values, then the `idx` value for that
5101:   row is 0 (the first column).

5103:   This code is only implemented for a couple of matrix formats.

5105: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5106: @*/
5107: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5108: {
5109:   PetscFunctionBegin;
5113:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5114:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

5116:   if (!mat->cmap->N) {
5117:     PetscCall(VecSet(v, 0.0));
5118:     if (idx) {
5119:       PetscInt i, m = mat->rmap->n;
5120:       for (i = 0; i < m; i++) idx[i] = -1;
5121:     }
5122:   } else {
5123:     MatCheckPreallocated(mat, 1);
5124:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5125:     PetscUseTypeMethod(mat, getrowminabs, v, idx);
5126:   }
5127:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5128:   PetscFunctionReturn(PETSC_SUCCESS);
5129: }

5131: /*@
5132:   MatGetRowMax - Gets the maximum value (of the real part) of each
5133:   row of the matrix

5135:   Logically Collective

5137:   Input Parameter:
5138: . mat - the matrix

5140:   Output Parameters:
5141: + v   - the vector for storing the maximums
5142: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)

5144:   Level: intermediate

5146:   Notes:
5147:   The result of this call are the same as if one converted the matrix to dense format
5148:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5150:   This code is only implemented for a couple of matrix formats.

5152: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5153: @*/
5154: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5155: {
5156:   PetscFunctionBegin;
5160:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5162:   if (!mat->cmap->N) {
5163:     PetscCall(VecSet(v, PETSC_MIN_REAL));
5164:     if (idx) {
5165:       PetscInt i, m = mat->rmap->n;
5166:       for (i = 0; i < m; i++) idx[i] = -1;
5167:     }
5168:   } else {
5169:     MatCheckPreallocated(mat, 1);
5170:     PetscUseTypeMethod(mat, getrowmax, v, idx);
5171:   }
5172:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5173:   PetscFunctionReturn(PETSC_SUCCESS);
5174: }

5176: /*@
5177:   MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5178:   row of the matrix

5180:   Logically Collective

5182:   Input Parameter:
5183: . mat - the matrix

5185:   Output Parameters:
5186: + v   - the vector for storing the maximums
5187: - idx - the indices of the column found for each row (or `NULL` if not needed)

5189:   Level: intermediate

5191:   Notes:
5192:   if a row is completely empty or has only 0.0 values, then the `idx` value for that
5193:   row is 0 (the first column).

5195:   This code is only implemented for a couple of matrix formats.

5197: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5198: @*/
5199: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5200: {
5201:   PetscFunctionBegin;
5205:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5207:   if (!mat->cmap->N) {
5208:     PetscCall(VecSet(v, 0.0));
5209:     if (idx) {
5210:       PetscInt i, m = mat->rmap->n;
5211:       for (i = 0; i < m; i++) idx[i] = -1;
5212:     }
5213:   } else {
5214:     MatCheckPreallocated(mat, 1);
5215:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5216:     PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5217:   }
5218:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5219:   PetscFunctionReturn(PETSC_SUCCESS);
5220: }

5222: /*@
5223:   MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix

5225:   Logically Collective

5227:   Input Parameter:
5228: . mat - the matrix

5230:   Output Parameter:
5231: . v - the vector for storing the sum

5233:   Level: intermediate

5235:   This code is only implemented for a couple of matrix formats.

5237: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5238: @*/
5239: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5240: {
5241:   PetscFunctionBegin;
5245:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5247:   if (!mat->cmap->N) {
5248:     PetscCall(VecSet(v, 0.0));
5249:   } else {
5250:     MatCheckPreallocated(mat, 1);
5251:     PetscUseTypeMethod(mat, getrowsumabs, v);
5252:   }
5253:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5254:   PetscFunctionReturn(PETSC_SUCCESS);
5255: }

5257: /*@
5258:   MatGetRowSum - Gets the sum of each row of the matrix

5260:   Logically or Neighborhood Collective

5262:   Input Parameter:
5263: . mat - the matrix

5265:   Output Parameter:
5266: . v - the vector for storing the sum of rows

5268:   Level: intermediate

5270:   Note:
5271:   This code is slow since it is not currently specialized for different formats

5273: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5274: @*/
5275: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5276: {
5277:   Vec ones;

5279:   PetscFunctionBegin;
5283:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5284:   MatCheckPreallocated(mat, 1);
5285:   PetscCall(MatCreateVecs(mat, &ones, NULL));
5286:   PetscCall(VecSet(ones, 1.));
5287:   PetscCall(MatMult(mat, ones, v));
5288:   PetscCall(VecDestroy(&ones));
5289:   PetscFunctionReturn(PETSC_SUCCESS);
5290: }

5292: /*@
5293:   MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5294:   when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)

5296:   Collective

5298:   Input Parameter:
5299: . mat - the matrix to provide the transpose

5301:   Output Parameter:
5302: . 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

5304:   Level: advanced

5306:   Note:
5307:   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
5308:   routine allows bypassing that call.

5310: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5311: @*/
5312: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5313: {
5314:   MatParentState *rb = NULL;

5316:   PetscFunctionBegin;
5317:   PetscCall(PetscNew(&rb));
5318:   rb->id    = ((PetscObject)mat)->id;
5319:   rb->state = 0;
5320:   PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5321:   PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5322:   PetscFunctionReturn(PETSC_SUCCESS);
5323: }

5325: /*@
5326:   MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.

5328:   Collective

5330:   Input Parameters:
5331: + mat   - the matrix to transpose
5332: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5334:   Output Parameter:
5335: . B - the transpose of the matrix

5337:   Level: intermediate

5339:   Notes:
5340:   If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`

5342:   `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
5343:   transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.

5345:   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.

5347:   Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5348:   For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.

5350:   If `mat` is unchanged from the last call this function returns immediately without recomputing the result

5352:   If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`

5354: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5355:           `MatTransposeSymbolic()`, `MatCreateTranspose()`
5356: @*/
5357: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5358: {
5359:   PetscContainer  rB = NULL;
5360:   MatParentState *rb = NULL;

5362:   PetscFunctionBegin;
5365:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5366:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5367:   PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5368:   PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5369:   MatCheckPreallocated(mat, 1);
5370:   if (reuse == MAT_REUSE_MATRIX) {
5371:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5372:     PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5373:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5374:     PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5375:     if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5376:   }

5378:   PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5379:   if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5380:     PetscUseTypeMethod(mat, transpose, reuse, B);
5381:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5382:   }
5383:   PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

5385:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5386:   if (reuse != MAT_INPLACE_MATRIX) {
5387:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5388:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5389:     rb->state        = ((PetscObject)mat)->state;
5390:     rb->nonzerostate = mat->nonzerostate;
5391:   }
5392:   PetscFunctionReturn(PETSC_SUCCESS);
5393: }

5395: /*@
5396:   MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

5398:   Collective

5400:   Input Parameter:
5401: . A - the matrix to transpose

5403:   Output Parameter:
5404: . 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
5405:       numerical portion.

5407:   Level: intermediate

5409:   Note:
5410:   This is not supported for many matrix types, use `MatTranspose()` in those cases

5412: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5413: @*/
5414: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5415: {
5416:   PetscFunctionBegin;
5419:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5420:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5421:   PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5422:   PetscUseTypeMethod(A, transposesymbolic, B);
5423:   PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

5425:   PetscCall(MatTransposeSetPrecursor(A, *B));
5426:   PetscFunctionReturn(PETSC_SUCCESS);
5427: }

5429: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5430: {
5431:   PetscContainer  rB;
5432:   MatParentState *rb;

5434:   PetscFunctionBegin;
5437:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5438:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5439:   PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5440:   PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5441:   PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5442:   PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5443:   PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5444:   PetscFunctionReturn(PETSC_SUCCESS);
5445: }

5447: /*@
5448:   MatIsTranspose - Test whether a matrix is another one's transpose,
5449:   or its own, in which case it tests symmetry.

5451:   Collective

5453:   Input Parameters:
5454: + A   - the matrix to test
5455: . B   - the matrix to test against, this can equal the first parameter
5456: - tol - tolerance, differences between entries smaller than this are counted as zero

5458:   Output Parameter:
5459: . flg - the result

5461:   Level: intermediate

5463:   Notes:
5464:   The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5465:   test involves parallel copies of the block off-diagonal parts of the matrix.

5467: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5468: @*/
5469: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5470: {
5471:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5473:   PetscFunctionBegin;
5476:   PetscAssertPointer(flg, 4);
5477:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5478:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5479:   *flg = PETSC_FALSE;
5480:   if (f && g) {
5481:     PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5482:     PetscCall((*f)(A, B, tol, flg));
5483:   } else {
5484:     MatType mattype;

5486:     PetscCall(MatGetType(f ? B : A, &mattype));
5487:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5488:   }
5489:   PetscFunctionReturn(PETSC_SUCCESS);
5490: }

5492: /*@
5493:   MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

5495:   Collective

5497:   Input Parameters:
5498: + mat   - the matrix to transpose and complex conjugate
5499: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5501:   Output Parameter:
5502: . B - the Hermitian transpose

5504:   Level: intermediate

5506: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5507: @*/
5508: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5509: {
5510:   PetscFunctionBegin;
5511:   PetscCall(MatTranspose(mat, reuse, B));
5512: #if defined(PETSC_USE_COMPLEX)
5513:   PetscCall(MatConjugate(*B));
5514: #endif
5515:   PetscFunctionReturn(PETSC_SUCCESS);
5516: }

5518: /*@
5519:   MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

5521:   Collective

5523:   Input Parameters:
5524: + A   - the matrix to test
5525: . B   - the matrix to test against, this can equal the first parameter
5526: - tol - tolerance, differences between entries smaller than this are counted as zero

5528:   Output Parameter:
5529: . flg - the result

5531:   Level: intermediate

5533:   Notes:
5534:   Only available for `MATAIJ` matrices.

5536:   The sequential algorithm
5537:   has a running time of the order of the number of nonzeros; the parallel
5538:   test involves parallel copies of the block off-diagonal parts of the matrix.

5540: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5541: @*/
5542: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5543: {
5544:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5546:   PetscFunctionBegin;
5549:   PetscAssertPointer(flg, 4);
5550:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5551:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5552:   if (f && g) {
5553:     PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5554:     PetscCall((*f)(A, B, tol, flg));
5555:   }
5556:   PetscFunctionReturn(PETSC_SUCCESS);
5557: }

5559: /*@
5560:   MatPermute - Creates a new matrix with rows and columns permuted from the
5561:   original.

5563:   Collective

5565:   Input Parameters:
5566: + mat - the matrix to permute
5567: . row - row permutation, each processor supplies only the permutation for its rows
5568: - col - column permutation, each processor supplies only the permutation for its columns

5570:   Output Parameter:
5571: . B - the permuted matrix

5573:   Level: advanced

5575:   Note:
5576:   The index sets map from row/col of permuted matrix to row/col of original matrix.
5577:   The index sets should be on the same communicator as mat and have the same local sizes.

5579:   Developer Note:
5580:   If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5581:   exploit the fact that row and col are permutations, consider implementing the
5582:   more general `MatCreateSubMatrix()` instead.

5584: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5585: @*/
5586: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5587: {
5588:   PetscFunctionBegin;
5593:   PetscAssertPointer(B, 4);
5594:   PetscCheckSameComm(mat, 1, row, 2);
5595:   if (row != col) PetscCheckSameComm(row, 2, col, 3);
5596:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5597:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5598:   PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5599:   MatCheckPreallocated(mat, 1);

5601:   if (mat->ops->permute) {
5602:     PetscUseTypeMethod(mat, permute, row, col, B);
5603:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5604:   } else {
5605:     PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5606:   }
5607:   PetscFunctionReturn(PETSC_SUCCESS);
5608: }

5610: /*@
5611:   MatEqual - Compares two matrices.

5613:   Collective

5615:   Input Parameters:
5616: + A - the first matrix
5617: - B - the second matrix

5619:   Output Parameter:
5620: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

5622:   Level: intermediate

5624:   Note:
5625:   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
5626:   using several randomly created vectors, see `MatMultEqual()`.

5628: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5629: @*/
5630: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5631: {
5632:   PetscFunctionBegin;
5637:   PetscAssertPointer(flg, 3);
5638:   PetscCheckSameComm(A, 1, B, 2);
5639:   MatCheckPreallocated(A, 1);
5640:   MatCheckPreallocated(B, 2);
5641:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5642:   PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5643:   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,
5644:              B->cmap->N);
5645:   if (A->ops->equal && A->ops->equal == B->ops->equal) {
5646:     PetscUseTypeMethod(A, equal, B, flg);
5647:   } else {
5648:     PetscCall(MatMultEqual(A, B, 10, flg));
5649:   }
5650:   PetscFunctionReturn(PETSC_SUCCESS);
5651: }

5653: /*@
5654:   MatDiagonalScale - Scales a matrix on the left and right by diagonal
5655:   matrices that are stored as vectors.  Either of the two scaling
5656:   matrices can be `NULL`.

5658:   Collective

5660:   Input Parameters:
5661: + mat - the matrix to be scaled
5662: . l   - the left scaling vector (or `NULL`)
5663: - r   - the right scaling vector (or `NULL`)

5665:   Level: intermediate

5667:   Note:
5668:   `MatDiagonalScale()` computes $A = LAR$, where
5669:   L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5670:   The L scales the rows of the matrix, the R scales the columns of the matrix.

5672: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5673: @*/
5674: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5675: {
5676:   PetscFunctionBegin;
5679:   if (l) {
5681:     PetscCheckSameComm(mat, 1, l, 2);
5682:   }
5683:   if (r) {
5685:     PetscCheckSameComm(mat, 1, r, 3);
5686:   }
5687:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5688:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5689:   MatCheckPreallocated(mat, 1);
5690:   if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

5692:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5693:   PetscUseTypeMethod(mat, diagonalscale, l, r);
5694:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5695:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5696:   if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5697:   PetscFunctionReturn(PETSC_SUCCESS);
5698: }

5700: /*@
5701:   MatScale - Scales all elements of a matrix by a given number.

5703:   Logically Collective

5705:   Input Parameters:
5706: + mat - the matrix to be scaled
5707: - a   - the scaling value

5709:   Level: intermediate

5711: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5712: @*/
5713: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5714: {
5715:   PetscFunctionBegin;
5718:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5719:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5721:   MatCheckPreallocated(mat, 1);

5723:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5724:   if (a != (PetscScalar)1.0) {
5725:     PetscUseTypeMethod(mat, scale, a);
5726:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5727:   }
5728:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5729:   PetscFunctionReturn(PETSC_SUCCESS);
5730: }

5732: /*@
5733:   MatNorm - Calculates various norms of a matrix.

5735:   Collective

5737:   Input Parameters:
5738: + mat  - the matrix
5739: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

5741:   Output Parameter:
5742: . nrm - the resulting norm

5744:   Level: intermediate

5746: .seealso: [](ch_matrices), `Mat`
5747: @*/
5748: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5749: {
5750:   PetscFunctionBegin;
5753:   PetscAssertPointer(nrm, 3);

5755:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5756:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5757:   MatCheckPreallocated(mat, 1);

5759:   PetscUseTypeMethod(mat, norm, type, nrm);
5760:   PetscFunctionReturn(PETSC_SUCCESS);
5761: }

5763: /*
5764:      This variable is used to prevent counting of MatAssemblyBegin() that
5765:    are called from within a MatAssemblyEnd().
5766: */
5767: static PetscInt MatAssemblyEnd_InUse = 0;
5768: /*@
5769:   MatAssemblyBegin - Begins assembling the matrix.  This routine should
5770:   be called after completing all calls to `MatSetValues()`.

5772:   Collective

5774:   Input Parameters:
5775: + mat  - the matrix
5776: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5778:   Level: beginner

5780:   Notes:
5781:   `MatSetValues()` generally caches the values that belong to other MPI processes.  The matrix is ready to
5782:   use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

5784:   Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5785:   in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5786:   using the matrix.

5788:   ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5789:   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
5790:   a global collective operation requiring all processes that share the matrix.

5792:   Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5793:   out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5794:   before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

5796: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5797: @*/
5798: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5799: {
5800:   PetscFunctionBegin;
5803:   MatCheckPreallocated(mat, 1);
5804:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5805:   if (mat->assembled) {
5806:     mat->was_assembled = PETSC_TRUE;
5807:     mat->assembled     = PETSC_FALSE;
5808:   }

5810:   if (!MatAssemblyEnd_InUse) {
5811:     PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5812:     PetscTryTypeMethod(mat, assemblybegin, type);
5813:     PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5814:   } else PetscTryTypeMethod(mat, assemblybegin, type);
5815:   PetscFunctionReturn(PETSC_SUCCESS);
5816: }

5818: /*@
5819:   MatAssembled - Indicates if a matrix has been assembled and is ready for
5820:   use; for example, in matrix-vector product.

5822:   Not Collective

5824:   Input Parameter:
5825: . mat - the matrix

5827:   Output Parameter:
5828: . assembled - `PETSC_TRUE` or `PETSC_FALSE`

5830:   Level: advanced

5832: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5833: @*/
5834: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5835: {
5836:   PetscFunctionBegin;
5838:   PetscAssertPointer(assembled, 2);
5839:   *assembled = mat->assembled;
5840:   PetscFunctionReturn(PETSC_SUCCESS);
5841: }

5843: /*@
5844:   MatAssemblyEnd - Completes assembling the matrix.  This routine should
5845:   be called after `MatAssemblyBegin()`.

5847:   Collective

5849:   Input Parameters:
5850: + mat  - the matrix
5851: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5853:   Options Database Keys:
5854: + -mat_view ::ascii_info             - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5855: . -mat_view ::ascii_info_detail      - Prints more detailed info
5856: . -mat_view                          - Prints matrix in ASCII format
5857: . -mat_view ::ascii_matlab           - Prints matrix in MATLAB format
5858: . -mat_view draw                     - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5859: . -display <name>                    - Sets display name (default is host)
5860: . -draw_pause <sec>                  - Sets number of seconds to pause after display
5861: . -mat_view socket                   - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5862: . -viewer_socket_machine <machine>   - Machine to use for socket
5863: . -viewer_socket_port <port>         - Port number to use for socket
5864: - -mat_view binary:filename[:append] - Save matrix to file in binary format

5866:   Level: beginner

5868: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5869: @*/
5870: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5871: {
5872:   static PetscInt inassm = 0;
5873:   PetscBool       flg    = PETSC_FALSE;

5875:   PetscFunctionBegin;

5879:   inassm++;
5880:   MatAssemblyEnd_InUse++;
5881:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5882:     PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5883:     PetscTryTypeMethod(mat, assemblyend, type);
5884:     PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5885:   } else PetscTryTypeMethod(mat, assemblyend, type);

5887:   /* Flush assembly is not a true assembly */
5888:   if (type != MAT_FLUSH_ASSEMBLY) {
5889:     if (mat->num_ass) {
5890:       if (!mat->symmetry_eternal) {
5891:         mat->symmetric = PETSC_BOOL3_UNKNOWN;
5892:         mat->hermitian = PETSC_BOOL3_UNKNOWN;
5893:       }
5894:       if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5895:       if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5896:     }
5897:     mat->num_ass++;
5898:     mat->assembled        = PETSC_TRUE;
5899:     mat->ass_nonzerostate = mat->nonzerostate;
5900:   }

5902:   mat->insertmode = NOT_SET_VALUES;
5903:   MatAssemblyEnd_InUse--;
5904:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5905:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5906:     PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

5908:     if (mat->checksymmetryonassembly) {
5909:       PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5910:       if (flg) {
5911:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5912:       } else {
5913:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5914:       }
5915:     }
5916:     if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5917:   }
5918:   inassm--;
5919:   PetscFunctionReturn(PETSC_SUCCESS);
5920: }

5922: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5923: /*@
5924:   MatSetOption - Sets a parameter option for a matrix. Some options
5925:   may be specific to certain storage formats.  Some options
5926:   determine how values will be inserted (or added). Sorted,
5927:   row-oriented input will generally assemble the fastest. The default
5928:   is row-oriented.

5930:   Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

5932:   Input Parameters:
5933: + mat - the matrix
5934: . op  - the option, one of those listed below (and possibly others),
5935: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5937:   Options Describing Matrix Structure:
5938: + `MAT_SPD`                         - symmetric positive definite
5939: . `MAT_SYMMETRIC`                   - symmetric in terms of both structure and value
5940: . `MAT_HERMITIAN`                   - transpose is the complex conjugation
5941: . `MAT_STRUCTURALLY_SYMMETRIC`      - symmetric nonzero structure
5942: . `MAT_SYMMETRY_ETERNAL`            - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5943: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5944: . `MAT_SPD_ETERNAL`                 - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

5946:    These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5947:    do not need to be computed (usually at a high cost)

5949:    Options For Use with `MatSetValues()`:
5950:    Insert a logically dense subblock, which can be
5951: . `MAT_ROW_ORIENTED`                - row-oriented (default)

5953:    These options reflect the data you pass in with `MatSetValues()`; it has
5954:    nothing to do with how the data is stored internally in the matrix
5955:    data structure.

5957:    When (re)assembling a matrix, we can restrict the input for
5958:    efficiency/debugging purposes.  These options include
5959: . `MAT_NEW_NONZERO_LOCATIONS`       - additional insertions will be allowed if they generate a new nonzero (slow)
5960: . `MAT_FORCE_DIAGONAL_ENTRIES`      - forces diagonal entries to be allocated
5961: . `MAT_IGNORE_OFF_PROC_ENTRIES`     - drops off-processor entries
5962: . `MAT_NEW_NONZERO_LOCATION_ERR`    - generates an error for new matrix entry
5963: . `MAT_USE_HASH_TABLE`              - uses a hash table to speed up matrix assembly
5964: . `MAT_NO_OFF_PROC_ENTRIES`         - you know each process will only set values for its own rows, will generate an error if
5965:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5966:         performance for very large process counts.
5967: - `MAT_SUBSET_OFF_PROC_ENTRIES`     - you know that the first assembly after setting this flag will set a superset
5968:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5969:         functions, instead sending only neighbor messages.

5971:   Level: intermediate

5973:   Notes:
5974:   Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

5976:   Some options are relevant only for particular matrix types and
5977:   are thus ignored by others.  Other options are not supported by
5978:   certain matrix types and will generate an error message if set.

5980:   If using Fortran to compute a matrix, one may need to
5981:   use the column-oriented option (or convert to the row-oriented
5982:   format).

5984:   `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5985:   that would generate a new entry in the nonzero structure is instead
5986:   ignored.  Thus, if memory has not already been allocated for this particular
5987:   data, then the insertion is ignored. For dense matrices, in which
5988:   the entire array is allocated, no entries are ever ignored.
5989:   Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

5991:   `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
5992:   that would generate a new entry in the nonzero structure instead produces
5993:   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

5995:   `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
5996:   that would generate a new entry that has not been preallocated will
5997:   instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
5998:   only.) This is a useful flag when debugging matrix memory preallocation.
5999:   If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

6001:   `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6002:   other processors should be dropped, rather than stashed.
6003:   This is useful if you know that the "owning" processor is also
6004:   always generating the correct matrix entries, so that PETSc need
6005:   not transfer duplicate entries generated on another processor.

6007:   `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6008:   searches during matrix assembly. When this flag is set, the hash table
6009:   is created during the first matrix assembly. This hash table is
6010:   used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6011:   to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6012:   should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6013:   supported by `MATMPIBAIJ` format only.

6015:   `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6016:   are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`

6018:   `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6019:   a zero location in the matrix

6021:   `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

6023:   `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6024:   zero row routines and thus improves performance for very large process counts.

6026:   `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6027:   part of the matrix (since they should match the upper triangular part).

6029:   `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6030:   single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6031:   with finite difference schemes with non-periodic boundary conditions.

6033:   Developer Note:
6034:   `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6035:   places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6036:   to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6037:   not changed.

6039: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6040: @*/
6041: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6042: {
6043:   PetscFunctionBegin;
6045:   if (op > 0) {
6048:   }

6050:   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);

6052:   switch (op) {
6053:   case MAT_FORCE_DIAGONAL_ENTRIES:
6054:     mat->force_diagonals = flg;
6055:     PetscFunctionReturn(PETSC_SUCCESS);
6056:   case MAT_NO_OFF_PROC_ENTRIES:
6057:     mat->nooffprocentries = flg;
6058:     PetscFunctionReturn(PETSC_SUCCESS);
6059:   case MAT_SUBSET_OFF_PROC_ENTRIES:
6060:     mat->assembly_subset = flg;
6061:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6062: #if !defined(PETSC_HAVE_MPIUNI)
6063:       PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6064: #endif
6065:       mat->stash.first_assembly_done = PETSC_FALSE;
6066:     }
6067:     PetscFunctionReturn(PETSC_SUCCESS);
6068:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6069:     mat->nooffproczerorows = flg;
6070:     PetscFunctionReturn(PETSC_SUCCESS);
6071:   case MAT_SPD:
6072:     if (flg) {
6073:       mat->spd                    = PETSC_BOOL3_TRUE;
6074:       mat->symmetric              = PETSC_BOOL3_TRUE;
6075:       mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6076:     } else {
6077:       mat->spd = PETSC_BOOL3_FALSE;
6078:     }
6079:     break;
6080:   case MAT_SYMMETRIC:
6081:     mat->symmetric = PetscBoolToBool3(flg);
6082:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6083: #if !defined(PETSC_USE_COMPLEX)
6084:     mat->hermitian = PetscBoolToBool3(flg);
6085: #endif
6086:     break;
6087:   case MAT_HERMITIAN:
6088:     mat->hermitian = PetscBoolToBool3(flg);
6089:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6090: #if !defined(PETSC_USE_COMPLEX)
6091:     mat->symmetric = PetscBoolToBool3(flg);
6092: #endif
6093:     break;
6094:   case MAT_STRUCTURALLY_SYMMETRIC:
6095:     mat->structurally_symmetric = PetscBoolToBool3(flg);
6096:     break;
6097:   case MAT_SYMMETRY_ETERNAL:
6098:     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");
6099:     mat->symmetry_eternal = flg;
6100:     if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6101:     break;
6102:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6103:     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");
6104:     mat->structural_symmetry_eternal = flg;
6105:     break;
6106:   case MAT_SPD_ETERNAL:
6107:     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");
6108:     mat->spd_eternal = flg;
6109:     if (flg) {
6110:       mat->structural_symmetry_eternal = PETSC_TRUE;
6111:       mat->symmetry_eternal            = PETSC_TRUE;
6112:     }
6113:     break;
6114:   case MAT_STRUCTURE_ONLY:
6115:     mat->structure_only = flg;
6116:     break;
6117:   case MAT_SORTED_FULL:
6118:     mat->sortedfull = flg;
6119:     break;
6120:   default:
6121:     break;
6122:   }
6123:   PetscTryTypeMethod(mat, setoption, op, flg);
6124:   PetscFunctionReturn(PETSC_SUCCESS);
6125: }

6127: /*@
6128:   MatGetOption - Gets a parameter option that has been set for a matrix.

6130:   Logically Collective

6132:   Input Parameters:
6133: + mat - the matrix
6134: - op  - the option, this only responds to certain options, check the code for which ones

6136:   Output Parameter:
6137: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

6139:   Level: intermediate

6141:   Notes:
6142:   Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

6144:   Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6145:   `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

6147: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6148:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6149: @*/
6150: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6151: {
6152:   PetscFunctionBegin;

6156:   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);
6157:   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()");

6159:   switch (op) {
6160:   case MAT_NO_OFF_PROC_ENTRIES:
6161:     *flg = mat->nooffprocentries;
6162:     break;
6163:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6164:     *flg = mat->nooffproczerorows;
6165:     break;
6166:   case MAT_SYMMETRIC:
6167:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6168:     break;
6169:   case MAT_HERMITIAN:
6170:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6171:     break;
6172:   case MAT_STRUCTURALLY_SYMMETRIC:
6173:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6174:     break;
6175:   case MAT_SPD:
6176:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6177:     break;
6178:   case MAT_SYMMETRY_ETERNAL:
6179:     *flg = mat->symmetry_eternal;
6180:     break;
6181:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6182:     *flg = mat->symmetry_eternal;
6183:     break;
6184:   default:
6185:     break;
6186:   }
6187:   PetscFunctionReturn(PETSC_SUCCESS);
6188: }

6190: /*@
6191:   MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
6192:   this routine retains the old nonzero structure.

6194:   Logically Collective

6196:   Input Parameter:
6197: . mat - the matrix

6199:   Level: intermediate

6201:   Note:
6202:   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.
6203:   See the Performance chapter of the users manual for information on preallocating matrices.

6205: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6206: @*/
6207: PetscErrorCode MatZeroEntries(Mat mat)
6208: {
6209:   PetscFunctionBegin;
6212:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6213:   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");
6214:   MatCheckPreallocated(mat, 1);

6216:   PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6217:   PetscUseTypeMethod(mat, zeroentries);
6218:   PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6219:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6220:   PetscFunctionReturn(PETSC_SUCCESS);
6221: }

6223: /*@
6224:   MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6225:   of a set of rows and columns of a matrix.

6227:   Collective

6229:   Input Parameters:
6230: + mat     - the matrix
6231: . numRows - the number of rows/columns to zero
6232: . rows    - the global row indices
6233: . diag    - value put in the diagonal of the eliminated rows
6234: . x       - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6235: - b       - optional vector of the right-hand side, that will be adjusted by provided solution entries

6237:   Level: intermediate

6239:   Notes:
6240:   This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6242:   For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6243:   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

6245:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6246:   Krylov method to take advantage of the known solution on the zeroed rows.

6248:   For the parallel case, all processes that share the matrix (i.e.,
6249:   those in the communicator used for matrix creation) MUST call this
6250:   routine, regardless of whether any rows being zeroed are owned by
6251:   them.

6253:   Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6254:   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
6255:   missing.

6257:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6258:   list only rows local to itself).

6260:   The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

6262: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6263:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6264: @*/
6265: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6266: {
6267:   PetscFunctionBegin;
6270:   if (numRows) PetscAssertPointer(rows, 3);
6271:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6272:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6273:   MatCheckPreallocated(mat, 1);

6275:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6276:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6277:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6278:   PetscFunctionReturn(PETSC_SUCCESS);
6279: }

6281: /*@
6282:   MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6283:   of a set of rows and columns of a matrix.

6285:   Collective

6287:   Input Parameters:
6288: + mat  - the matrix
6289: . is   - the rows to zero
6290: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6291: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6292: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6294:   Level: intermediate

6296:   Note:
6297:   See `MatZeroRowsColumns()` for details on how this routine operates.

6299: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6300:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6301: @*/
6302: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6303: {
6304:   PetscInt        numRows;
6305:   const PetscInt *rows;

6307:   PetscFunctionBegin;
6312:   PetscCall(ISGetLocalSize(is, &numRows));
6313:   PetscCall(ISGetIndices(is, &rows));
6314:   PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6315:   PetscCall(ISRestoreIndices(is, &rows));
6316:   PetscFunctionReturn(PETSC_SUCCESS);
6317: }

6319: /*@
6320:   MatZeroRows - Zeros all entries (except possibly the main diagonal)
6321:   of a set of rows of a matrix.

6323:   Collective

6325:   Input Parameters:
6326: + mat     - the matrix
6327: . numRows - the number of rows to zero
6328: . rows    - the global row indices
6329: . diag    - value put in the diagonal of the zeroed rows
6330: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6331: - b       - optional vector of right-hand side, that will be adjusted by provided solution entries

6333:   Level: intermediate

6335:   Notes:
6336:   This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6338:   For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

6340:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6341:   Krylov method to take advantage of the known solution on the zeroed rows.

6343:   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)
6344:   from the matrix.

6346:   Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6347:   but does not release memory.  Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6348:   formats this does not alter the nonzero structure.

6350:   If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6351:   of the matrix is not changed the values are
6352:   merely zeroed.

6354:   The user can set a value in the diagonal entry (or for the `MATAIJ` format
6355:   formats can optionally remove the main diagonal entry from the
6356:   nonzero structure as well, by passing 0.0 as the final argument).

6358:   For the parallel case, all processes that share the matrix (i.e.,
6359:   those in the communicator used for matrix creation) MUST call this
6360:   routine, regardless of whether any rows being zeroed are owned by
6361:   them.

6363:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6364:   list only rows local to itself).

6366:   You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6367:   owns that are to be zeroed. This saves a global synchronization in the implementation.

6369: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6370:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6371: @*/
6372: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6373: {
6374:   PetscFunctionBegin;
6377:   if (numRows) PetscAssertPointer(rows, 3);
6378:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6379:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6380:   MatCheckPreallocated(mat, 1);

6382:   PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6383:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6384:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6385:   PetscFunctionReturn(PETSC_SUCCESS);
6386: }

6388: /*@
6389:   MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6390:   of a set of rows of a matrix indicated by an `IS`

6392:   Collective

6394:   Input Parameters:
6395: + mat  - the matrix
6396: . is   - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6397: . diag - value put in all diagonals of eliminated rows
6398: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6399: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6401:   Level: intermediate

6403:   Note:
6404:   See `MatZeroRows()` for details on how this routine operates.

6406: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6407:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6408: @*/
6409: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6410: {
6411:   PetscInt        numRows = 0;
6412:   const PetscInt *rows    = NULL;

6414:   PetscFunctionBegin;
6417:   if (is) {
6419:     PetscCall(ISGetLocalSize(is, &numRows));
6420:     PetscCall(ISGetIndices(is, &rows));
6421:   }
6422:   PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6423:   if (is) PetscCall(ISRestoreIndices(is, &rows));
6424:   PetscFunctionReturn(PETSC_SUCCESS);
6425: }

6427: /*@
6428:   MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6429:   of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.

6431:   Collective

6433:   Input Parameters:
6434: + mat     - the matrix
6435: . numRows - the number of rows to remove
6436: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6437: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6438: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6439: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6441:   Level: intermediate

6443:   Notes:
6444:   See `MatZeroRows()` for details on how this routine operates.

6446:   The grid coordinates are across the entire grid, not just the local portion

6448:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6449:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6450:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6451:   `DM_BOUNDARY_PERIODIC` boundary type.

6453:   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
6454:   a single value per point) you can skip filling those indices.

6456:   Fortran Note:
6457:   `idxm` and `idxn` should be declared as
6458: .vb
6459:     MatStencil idxm(4, m)
6460: .ve
6461:   and the values inserted using
6462: .vb
6463:     idxm(MatStencil_i, 1) = i
6464:     idxm(MatStencil_j, 1) = j
6465:     idxm(MatStencil_k, 1) = k
6466:     idxm(MatStencil_c, 1) = c
6467:    etc
6468: .ve

6470: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6471:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6472: @*/
6473: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6474: {
6475:   PetscInt  dim    = mat->stencil.dim;
6476:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6477:   PetscInt *dims   = mat->stencil.dims + 1;
6478:   PetscInt *starts = mat->stencil.starts;
6479:   PetscInt *dxm    = (PetscInt *)rows;
6480:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6482:   PetscFunctionBegin;
6485:   if (numRows) PetscAssertPointer(rows, 3);

6487:   PetscCall(PetscMalloc1(numRows, &jdxm));
6488:   for (i = 0; i < numRows; ++i) {
6489:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6490:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6491:     /* Local index in X dir */
6492:     tmp = *dxm++ - starts[0];
6493:     /* Loop over remaining dimensions */
6494:     for (j = 0; j < dim - 1; ++j) {
6495:       /* If nonlocal, set index to be negative */
6496:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6497:       /* Update local index */
6498:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6499:     }
6500:     /* Skip component slot if necessary */
6501:     if (mat->stencil.noc) dxm++;
6502:     /* Local row number */
6503:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6504:   }
6505:   PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6506:   PetscCall(PetscFree(jdxm));
6507:   PetscFunctionReturn(PETSC_SUCCESS);
6508: }

6510: /*@
6511:   MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6512:   of a set of rows and columns of a matrix.

6514:   Collective

6516:   Input Parameters:
6517: + mat     - the matrix
6518: . numRows - the number of rows/columns to remove
6519: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6520: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6521: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6522: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6524:   Level: intermediate

6526:   Notes:
6527:   See `MatZeroRowsColumns()` for details on how this routine operates.

6529:   The grid coordinates are across the entire grid, not just the local portion

6531:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6532:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6533:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6534:   `DM_BOUNDARY_PERIODIC` boundary type.

6536:   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
6537:   a single value per point) you can skip filling those indices.

6539:   Fortran Note:
6540:   `idxm` and `idxn` should be declared as
6541: .vb
6542:     MatStencil idxm(4, m)
6543: .ve
6544:   and the values inserted using
6545: .vb
6546:     idxm(MatStencil_i, 1) = i
6547:     idxm(MatStencil_j, 1) = j
6548:     idxm(MatStencil_k, 1) = k
6549:     idxm(MatStencil_c, 1) = c
6550:     etc
6551: .ve

6553: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6554:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6555: @*/
6556: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6557: {
6558:   PetscInt  dim    = mat->stencil.dim;
6559:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6560:   PetscInt *dims   = mat->stencil.dims + 1;
6561:   PetscInt *starts = mat->stencil.starts;
6562:   PetscInt *dxm    = (PetscInt *)rows;
6563:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6565:   PetscFunctionBegin;
6568:   if (numRows) PetscAssertPointer(rows, 3);

6570:   PetscCall(PetscMalloc1(numRows, &jdxm));
6571:   for (i = 0; i < numRows; ++i) {
6572:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6573:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6574:     /* Local index in X dir */
6575:     tmp = *dxm++ - starts[0];
6576:     /* Loop over remaining dimensions */
6577:     for (j = 0; j < dim - 1; ++j) {
6578:       /* If nonlocal, set index to be negative */
6579:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6580:       /* Update local index */
6581:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6582:     }
6583:     /* Skip component slot if necessary */
6584:     if (mat->stencil.noc) dxm++;
6585:     /* Local row number */
6586:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6587:   }
6588:   PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6589:   PetscCall(PetscFree(jdxm));
6590:   PetscFunctionReturn(PETSC_SUCCESS);
6591: }

6593: /*@
6594:   MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6595:   of a set of rows of a matrix; using local numbering of rows.

6597:   Collective

6599:   Input Parameters:
6600: + mat     - the matrix
6601: . numRows - the number of rows to remove
6602: . rows    - the local row indices
6603: . diag    - value put in all diagonals of eliminated rows
6604: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6605: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6607:   Level: intermediate

6609:   Notes:
6610:   Before calling `MatZeroRowsLocal()`, the user must first set the
6611:   local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

6613:   See `MatZeroRows()` for details on how this routine operates.

6615: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6616:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6617: @*/
6618: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6619: {
6620:   PetscFunctionBegin;
6623:   if (numRows) PetscAssertPointer(rows, 3);
6624:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6625:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6626:   MatCheckPreallocated(mat, 1);

6628:   if (mat->ops->zerorowslocal) {
6629:     PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6630:   } else {
6631:     IS              is, newis;
6632:     const PetscInt *newRows;

6634:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6635:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6636:     PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6637:     PetscCall(ISGetIndices(newis, &newRows));
6638:     PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6639:     PetscCall(ISRestoreIndices(newis, &newRows));
6640:     PetscCall(ISDestroy(&newis));
6641:     PetscCall(ISDestroy(&is));
6642:   }
6643:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6644:   PetscFunctionReturn(PETSC_SUCCESS);
6645: }

6647: /*@
6648:   MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6649:   of a set of rows of a matrix; using local numbering of rows.

6651:   Collective

6653:   Input Parameters:
6654: + mat  - the matrix
6655: . is   - index set of rows to remove
6656: . diag - value put in all diagonals of eliminated rows
6657: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6658: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6660:   Level: intermediate

6662:   Notes:
6663:   Before calling `MatZeroRowsLocalIS()`, the user must first set the
6664:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6666:   See `MatZeroRows()` for details on how this routine operates.

6668: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6669:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6670: @*/
6671: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6672: {
6673:   PetscInt        numRows;
6674:   const PetscInt *rows;

6676:   PetscFunctionBegin;
6680:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6681:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6682:   MatCheckPreallocated(mat, 1);

6684:   PetscCall(ISGetLocalSize(is, &numRows));
6685:   PetscCall(ISGetIndices(is, &rows));
6686:   PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6687:   PetscCall(ISRestoreIndices(is, &rows));
6688:   PetscFunctionReturn(PETSC_SUCCESS);
6689: }

6691: /*@
6692:   MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6693:   of a set of rows and columns of a matrix; using local numbering of rows.

6695:   Collective

6697:   Input Parameters:
6698: + mat     - the matrix
6699: . numRows - the number of rows to remove
6700: . rows    - the global row indices
6701: . diag    - value put in all diagonals of eliminated rows
6702: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6703: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6705:   Level: intermediate

6707:   Notes:
6708:   Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6709:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6711:   See `MatZeroRowsColumns()` for details on how this routine operates.

6713: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6714:           `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6715: @*/
6716: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6717: {
6718:   IS              is, newis;
6719:   const PetscInt *newRows;

6721:   PetscFunctionBegin;
6724:   if (numRows) PetscAssertPointer(rows, 3);
6725:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6726:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6727:   MatCheckPreallocated(mat, 1);

6729:   PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6730:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6731:   PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6732:   PetscCall(ISGetIndices(newis, &newRows));
6733:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6734:   PetscCall(ISRestoreIndices(newis, &newRows));
6735:   PetscCall(ISDestroy(&newis));
6736:   PetscCall(ISDestroy(&is));
6737:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6738:   PetscFunctionReturn(PETSC_SUCCESS);
6739: }

6741: /*@
6742:   MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6743:   of a set of rows and columns of a matrix; using local numbering of rows.

6745:   Collective

6747:   Input Parameters:
6748: + mat  - the matrix
6749: . is   - index set of rows to remove
6750: . diag - value put in all diagonals of eliminated rows
6751: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6752: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6754:   Level: intermediate

6756:   Notes:
6757:   Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6758:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6760:   See `MatZeroRowsColumns()` for details on how this routine operates.

6762: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6763:           `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6764: @*/
6765: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6766: {
6767:   PetscInt        numRows;
6768:   const PetscInt *rows;

6770:   PetscFunctionBegin;
6774:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6775:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6776:   MatCheckPreallocated(mat, 1);

6778:   PetscCall(ISGetLocalSize(is, &numRows));
6779:   PetscCall(ISGetIndices(is, &rows));
6780:   PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6781:   PetscCall(ISRestoreIndices(is, &rows));
6782:   PetscFunctionReturn(PETSC_SUCCESS);
6783: }

6785: /*@
6786:   MatGetSize - Returns the numbers of rows and columns in a matrix.

6788:   Not Collective

6790:   Input Parameter:
6791: . mat - the matrix

6793:   Output Parameters:
6794: + m - the number of global rows
6795: - n - the number of global columns

6797:   Level: beginner

6799:   Note:
6800:   Both output parameters can be `NULL` on input.

6802: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6803: @*/
6804: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6805: {
6806:   PetscFunctionBegin;
6808:   if (m) *m = mat->rmap->N;
6809:   if (n) *n = mat->cmap->N;
6810:   PetscFunctionReturn(PETSC_SUCCESS);
6811: }

6813: /*@
6814:   MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6815:   of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

6817:   Not Collective

6819:   Input Parameter:
6820: . mat - the matrix

6822:   Output Parameters:
6823: + m - the number of local rows, use `NULL` to not obtain this value
6824: - n - the number of local columns, use `NULL` to not obtain this value

6826:   Level: beginner

6828: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6829: @*/
6830: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6831: {
6832:   PetscFunctionBegin;
6834:   if (m) PetscAssertPointer(m, 2);
6835:   if (n) PetscAssertPointer(n, 3);
6836:   if (m) *m = mat->rmap->n;
6837:   if (n) *n = mat->cmap->n;
6838:   PetscFunctionReturn(PETSC_SUCCESS);
6839: }

6841: /*@
6842:   MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6843:   vector one multiplies this matrix by that are owned by this processor.

6845:   Not Collective, unless matrix has not been allocated, then collective

6847:   Input Parameter:
6848: . mat - the matrix

6850:   Output Parameters:
6851: + m - the global index of the first local column, use `NULL` to not obtain this value
6852: - n - one more than the global index of the last local column, use `NULL` to not obtain this value

6854:   Level: developer

6856:   Notes:
6857:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6859:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6860:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6862:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6863:   the local values in the matrix.

6865:   Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6866:   Layouts](sec_matlayout) for details on matrix layouts.

6868: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6869:           `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6870: @*/
6871: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6872: {
6873:   PetscFunctionBegin;
6876:   if (m) PetscAssertPointer(m, 2);
6877:   if (n) PetscAssertPointer(n, 3);
6878:   MatCheckPreallocated(mat, 1);
6879:   if (m) *m = mat->cmap->rstart;
6880:   if (n) *n = mat->cmap->rend;
6881:   PetscFunctionReturn(PETSC_SUCCESS);
6882: }

6884: /*@
6885:   MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6886:   this MPI process.

6888:   Not Collective

6890:   Input Parameter:
6891: . mat - the matrix

6893:   Output Parameters:
6894: + m - the global index of the first local row, use `NULL` to not obtain this value
6895: - n - one more than the global index of the last local row, use `NULL` to not obtain this value

6897:   Level: beginner

6899:   Notes:
6900:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6902:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6903:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6905:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6906:   the local values in the matrix.

6908:   The high argument is one more than the last element stored locally.

6910:   For all matrices  it returns the range of matrix rows associated with rows of a vector that
6911:   would contain the result of a matrix vector product with this matrix. See [Matrix
6912:   Layouts](sec_matlayout) for details on matrix layouts.

6914: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6915:           `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6916: @*/
6917: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6918: {
6919:   PetscFunctionBegin;
6922:   if (m) PetscAssertPointer(m, 2);
6923:   if (n) PetscAssertPointer(n, 3);
6924:   MatCheckPreallocated(mat, 1);
6925:   if (m) *m = mat->rmap->rstart;
6926:   if (n) *n = mat->rmap->rend;
6927:   PetscFunctionReturn(PETSC_SUCCESS);
6928: }

6930: /*@C
6931:   MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6932:   `MATSCALAPACK`, returns the range of matrix rows owned by each process.

6934:   Not Collective, unless matrix has not been allocated

6936:   Input Parameter:
6937: . mat - the matrix

6939:   Output Parameter:
6940: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6941:            where `size` is the number of MPI processes used by `mat`

6943:   Level: beginner

6945:   Notes:
6946:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6948:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6949:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6951:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6952:   the local values in the matrix.

6954:   For all matrices  it returns the ranges of matrix rows associated with rows of a vector that
6955:   would contain the result of a matrix vector product with this matrix. See [Matrix
6956:   Layouts](sec_matlayout) for details on matrix layouts.

6958: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6959:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6960:           `DMDAGetGhostCorners()`, `DM`
6961: @*/
6962: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6963: {
6964:   PetscFunctionBegin;
6967:   MatCheckPreallocated(mat, 1);
6968:   PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6969:   PetscFunctionReturn(PETSC_SUCCESS);
6970: }

6972: /*@C
6973:   MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6974:   vector one multiplies this vector by that are owned by each processor.

6976:   Not Collective, unless matrix has not been allocated

6978:   Input Parameter:
6979: . mat - the matrix

6981:   Output Parameter:
6982: . ranges - start of each processors portion plus one more than the total length at the end

6984:   Level: beginner

6986:   Notes:
6987:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6989:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6990:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6992:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6993:   the local values in the matrix.

6995:   Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
6996:   Layouts](sec_matlayout) for details on matrix layouts.

6998: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
6999:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7000:           `DMDAGetGhostCorners()`, `DM`
7001: @*/
7002: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7003: {
7004:   PetscFunctionBegin;
7007:   MatCheckPreallocated(mat, 1);
7008:   PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7009:   PetscFunctionReturn(PETSC_SUCCESS);
7010: }

7012: /*@
7013:   MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.

7015:   Not Collective

7017:   Input Parameter:
7018: . A - matrix

7020:   Output Parameters:
7021: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7022: - cols - columns in which this process owns elements, use `NULL` to not obtain this value

7024:   Level: intermediate

7026:   Note:
7027:   You should call `ISDestroy()` on the returned `IS`

7029:   For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7030:   returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7031:   `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7032:   details on matrix layouts.

7034: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7035: @*/
7036: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7037: {
7038:   PetscErrorCode (*f)(Mat, IS *, IS *);

7040:   PetscFunctionBegin;
7043:   MatCheckPreallocated(A, 1);
7044:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7045:   if (f) {
7046:     PetscCall((*f)(A, rows, cols));
7047:   } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7048:     if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7049:     if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7050:   }
7051:   PetscFunctionReturn(PETSC_SUCCESS);
7052: }

7054: /*@
7055:   MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7056:   Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7057:   to complete the factorization.

7059:   Collective

7061:   Input Parameters:
7062: + fact - the factorized matrix obtained with `MatGetFactor()`
7063: . mat  - the matrix
7064: . row  - row permutation
7065: . col  - column permutation
7066: - info - structure containing
7067: .vb
7068:       levels - number of levels of fill.
7069:       expected fill - as ratio of original fill.
7070:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7071:                 missing diagonal entries)
7072: .ve

7074:   Level: developer

7076:   Notes:
7077:   See [Matrix Factorization](sec_matfactor) for additional information.

7079:   Most users should employ the `KSP` interface for linear solvers
7080:   instead of working directly with matrix algebra routines such as this.
7081:   See, e.g., `KSPCreate()`.

7083:   Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`

7085:   Fortran Note:
7086:   A valid (non-null) `info` argument must be provided

7088: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7089:           `MatGetOrdering()`, `MatFactorInfo`
7090: @*/
7091: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7092: {
7093:   PetscFunctionBegin;
7098:   PetscAssertPointer(info, 5);
7099:   PetscAssertPointer(fact, 1);
7100:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7101:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7102:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7103:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7104:   MatCheckPreallocated(mat, 2);

7106:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7107:   PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7108:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7109:   PetscFunctionReturn(PETSC_SUCCESS);
7110: }

7112: /*@
7113:   MatICCFactorSymbolic - Performs symbolic incomplete
7114:   Cholesky factorization for a symmetric matrix.  Use
7115:   `MatCholeskyFactorNumeric()` to complete the factorization.

7117:   Collective

7119:   Input Parameters:
7120: + fact - the factorized matrix obtained with `MatGetFactor()`
7121: . mat  - the matrix to be factored
7122: . perm - row and column permutation
7123: - info - structure containing
7124: .vb
7125:       levels - number of levels of fill.
7126:       expected fill - as ratio of original fill.
7127: .ve

7129:   Level: developer

7131:   Notes:
7132:   Most users should employ the `KSP` interface for linear solvers
7133:   instead of working directly with matrix algebra routines such as this.
7134:   See, e.g., `KSPCreate()`.

7136:   This uses the definition of level of fill as in Y. Saad {cite}`saad2003`

7138:   Fortran Note:
7139:   A valid (non-null) `info` argument must be provided

7141: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7142: @*/
7143: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7144: {
7145:   PetscFunctionBegin;
7149:   PetscAssertPointer(info, 4);
7150:   PetscAssertPointer(fact, 1);
7151:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7152:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7153:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7154:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7155:   MatCheckPreallocated(mat, 2);

7157:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7158:   PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7159:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7160:   PetscFunctionReturn(PETSC_SUCCESS);
7161: }

7163: /*@C
7164:   MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7165:   points to an array of valid matrices, they may be reused to store the new
7166:   submatrices.

7168:   Collective

7170:   Input Parameters:
7171: + mat   - the matrix
7172: . n     - the number of submatrixes to be extracted (on this processor, may be zero)
7173: . irow  - index set of rows to extract
7174: . icol  - index set of columns to extract
7175: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7177:   Output Parameter:
7178: . submat - the array of submatrices

7180:   Level: advanced

7182:   Notes:
7183:   `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7184:   (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7185:   to extract a parallel submatrix.

7187:   Some matrix types place restrictions on the row and column
7188:   indices, such as that they be sorted or that they be equal to each other.

7190:   The index sets may not have duplicate entries.

7192:   When extracting submatrices from a parallel matrix, each processor can
7193:   form a different submatrix by setting the rows and columns of its
7194:   individual index sets according to the local submatrix desired.

7196:   When finished using the submatrices, the user should destroy
7197:   them with `MatDestroySubMatrices()`.

7199:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7200:   original matrix has not changed from that last call to `MatCreateSubMatrices()`.

7202:   This routine creates the matrices in submat; you should NOT create them before
7203:   calling it. It also allocates the array of matrix pointers submat.

7205:   For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7206:   request one row/column in a block, they must request all rows/columns that are in
7207:   that block. For example, if the block size is 2 you cannot request just row 0 and
7208:   column 0.

7210:   Fortran Note:
7211: .vb
7212:   Mat, pointer :: submat(:)
7213: .ve

7215: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7216: @*/
7217: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7218: {
7219:   PetscInt  i;
7220:   PetscBool eq;

7222:   PetscFunctionBegin;
7225:   if (n) {
7226:     PetscAssertPointer(irow, 3);
7228:     PetscAssertPointer(icol, 4);
7230:   }
7231:   PetscAssertPointer(submat, 6);
7232:   if (n && scall == MAT_REUSE_MATRIX) {
7233:     PetscAssertPointer(*submat, 6);
7235:   }
7236:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7237:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7238:   MatCheckPreallocated(mat, 1);
7239:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7240:   PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7241:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7242:   for (i = 0; i < n; i++) {
7243:     (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7244:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7245:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7246: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7247:     if (mat->boundtocpu && mat->bindingpropagates) {
7248:       PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7249:       PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7250:     }
7251: #endif
7252:   }
7253:   PetscFunctionReturn(PETSC_SUCCESS);
7254: }

7256: /*@C
7257:   MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).

7259:   Collective

7261:   Input Parameters:
7262: + mat   - the matrix
7263: . n     - the number of submatrixes to be extracted
7264: . irow  - index set of rows to extract
7265: . icol  - index set of columns to extract
7266: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7268:   Output Parameter:
7269: . submat - the array of submatrices

7271:   Level: advanced

7273:   Note:
7274:   This is used by `PCGASM`

7276: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7277: @*/
7278: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7279: {
7280:   PetscInt  i;
7281:   PetscBool eq;

7283:   PetscFunctionBegin;
7286:   if (n) {
7287:     PetscAssertPointer(irow, 3);
7289:     PetscAssertPointer(icol, 4);
7291:   }
7292:   PetscAssertPointer(submat, 6);
7293:   if (n && scall == MAT_REUSE_MATRIX) {
7294:     PetscAssertPointer(*submat, 6);
7296:   }
7297:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7298:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7299:   MatCheckPreallocated(mat, 1);

7301:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7302:   PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7303:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7304:   for (i = 0; i < n; i++) {
7305:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7306:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7307:   }
7308:   PetscFunctionReturn(PETSC_SUCCESS);
7309: }

7311: /*@C
7312:   MatDestroyMatrices - Destroys an array of matrices

7314:   Collective

7316:   Input Parameters:
7317: + n   - the number of local matrices
7318: - mat - the matrices (this is a pointer to the array of matrices)

7320:   Level: advanced

7322:   Notes:
7323:   Frees not only the matrices, but also the array that contains the matrices

7325:   For matrices obtained with  `MatCreateSubMatrices()` use `MatDestroySubMatrices()`

7327: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7328: @*/
7329: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7330: {
7331:   PetscInt i;

7333:   PetscFunctionBegin;
7334:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7335:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7336:   PetscAssertPointer(mat, 2);

7338:   for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

7340:   /* memory is allocated even if n = 0 */
7341:   PetscCall(PetscFree(*mat));
7342:   PetscFunctionReturn(PETSC_SUCCESS);
7343: }

7345: /*@C
7346:   MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

7348:   Collective

7350:   Input Parameters:
7351: + n   - the number of local matrices
7352: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)

7354:   Level: advanced

7356:   Note:
7357:   Frees not only the matrices, but also the array that contains the matrices

7359: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7360: @*/
7361: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7362: {
7363:   Mat mat0;

7365:   PetscFunctionBegin;
7366:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7367:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7368:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7369:   PetscAssertPointer(mat, 2);

7371:   mat0 = (*mat)[0];
7372:   if (mat0 && mat0->ops->destroysubmatrices) {
7373:     PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7374:   } else {
7375:     PetscCall(MatDestroyMatrices(n, mat));
7376:   }
7377:   PetscFunctionReturn(PETSC_SUCCESS);
7378: }

7380: /*@
7381:   MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

7383:   Collective

7385:   Input Parameter:
7386: . mat - the matrix

7388:   Output Parameter:
7389: . matstruct - the sequential matrix with the nonzero structure of `mat`

7391:   Level: developer

7393: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7394: @*/
7395: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7396: {
7397:   PetscFunctionBegin;
7399:   PetscAssertPointer(matstruct, 2);

7402:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7403:   MatCheckPreallocated(mat, 1);

7405:   PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7406:   PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7407:   PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7408:   PetscFunctionReturn(PETSC_SUCCESS);
7409: }

7411: /*@C
7412:   MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

7414:   Collective

7416:   Input Parameter:
7417: . mat - the matrix

7419:   Level: advanced

7421:   Note:
7422:   This is not needed, one can just call `MatDestroy()`

7424: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7425: @*/
7426: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7427: {
7428:   PetscFunctionBegin;
7429:   PetscAssertPointer(mat, 1);
7430:   PetscCall(MatDestroy(mat));
7431:   PetscFunctionReturn(PETSC_SUCCESS);
7432: }

7434: /*@
7435:   MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7436:   replaces the index sets by larger ones that represent submatrices with
7437:   additional overlap.

7439:   Collective

7441:   Input Parameters:
7442: + mat - the matrix
7443: . n   - the number of index sets
7444: . is  - the array of index sets (these index sets will changed during the call)
7445: - ov  - the additional overlap requested

7447:   Options Database Key:
7448: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7450:   Level: developer

7452:   Note:
7453:   The computed overlap preserves the matrix block sizes when the blocks are square.
7454:   That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7455:   that block are included in the overlap regardless of whether each specific column would increase the overlap.

7457: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7458: @*/
7459: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7460: {
7461:   PetscInt i, bs, cbs;

7463:   PetscFunctionBegin;
7467:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7468:   if (n) {
7469:     PetscAssertPointer(is, 3);
7471:   }
7472:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7473:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7474:   MatCheckPreallocated(mat, 1);

7476:   if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7477:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7478:   PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7479:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7480:   PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7481:   if (bs == cbs) {
7482:     for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7483:   }
7484:   PetscFunctionReturn(PETSC_SUCCESS);
7485: }

7487: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

7489: /*@
7490:   MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7491:   a sub communicator, replaces the index sets by larger ones that represent submatrices with
7492:   additional overlap.

7494:   Collective

7496:   Input Parameters:
7497: + mat - the matrix
7498: . n   - the number of index sets
7499: . is  - the array of index sets (these index sets will changed during the call)
7500: - ov  - the additional overlap requested

7502:   `   Options Database Key:
7503: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7505:   Level: developer

7507: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7508: @*/
7509: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7510: {
7511:   PetscInt i;

7513:   PetscFunctionBegin;
7516:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7517:   if (n) {
7518:     PetscAssertPointer(is, 3);
7520:   }
7521:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7522:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7523:   MatCheckPreallocated(mat, 1);
7524:   if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7525:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7526:   for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7527:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7528:   PetscFunctionReturn(PETSC_SUCCESS);
7529: }

7531: /*@
7532:   MatGetBlockSize - Returns the matrix block size.

7534:   Not Collective

7536:   Input Parameter:
7537: . mat - the matrix

7539:   Output Parameter:
7540: . bs - block size

7542:   Level: intermediate

7544:   Notes:
7545:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

7547:   If the block size has not been set yet this routine returns 1.

7549: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7550: @*/
7551: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7552: {
7553:   PetscFunctionBegin;
7555:   PetscAssertPointer(bs, 2);
7556:   *bs = mat->rmap->bs;
7557:   PetscFunctionReturn(PETSC_SUCCESS);
7558: }

7560: /*@
7561:   MatGetBlockSizes - Returns the matrix block row and column sizes.

7563:   Not Collective

7565:   Input Parameter:
7566: . mat - the matrix

7568:   Output Parameters:
7569: + rbs - row block size
7570: - cbs - column block size

7572:   Level: intermediate

7574:   Notes:
7575:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7576:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7578:   If a block size has not been set yet this routine returns 1.

7580: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7581: @*/
7582: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7583: {
7584:   PetscFunctionBegin;
7586:   if (rbs) PetscAssertPointer(rbs, 2);
7587:   if (cbs) PetscAssertPointer(cbs, 3);
7588:   if (rbs) *rbs = mat->rmap->bs;
7589:   if (cbs) *cbs = mat->cmap->bs;
7590:   PetscFunctionReturn(PETSC_SUCCESS);
7591: }

7593: /*@
7594:   MatSetBlockSize - Sets the matrix block size.

7596:   Logically Collective

7598:   Input Parameters:
7599: + mat - the matrix
7600: - bs  - block size

7602:   Level: intermediate

7604:   Notes:
7605:   Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7606:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7608:   For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7609:   is compatible with the matrix local sizes.

7611: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7612: @*/
7613: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7614: {
7615:   PetscFunctionBegin;
7618:   PetscCall(MatSetBlockSizes(mat, bs, bs));
7619:   PetscFunctionReturn(PETSC_SUCCESS);
7620: }

7622: typedef struct {
7623:   PetscInt         n;
7624:   IS              *is;
7625:   Mat             *mat;
7626:   PetscObjectState nonzerostate;
7627:   Mat              C;
7628: } EnvelopeData;

7630: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7631: {
7632:   EnvelopeData *edata = (EnvelopeData *)*ptr;

7634:   PetscFunctionBegin;
7635:   for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7636:   PetscCall(PetscFree(edata->is));
7637:   PetscCall(PetscFree(edata));
7638:   PetscFunctionReturn(PETSC_SUCCESS);
7639: }

7641: /*@
7642:   MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7643:   the sizes of these blocks in the matrix. An individual block may lie over several processes.

7645:   Collective

7647:   Input Parameter:
7648: . mat - the matrix

7650:   Level: intermediate

7652:   Notes:
7653:   There can be zeros within the blocks

7655:   The blocks can overlap between processes, including laying on more than two processes

7657: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7658: @*/
7659: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7660: {
7661:   PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7662:   PetscInt          *diag, *odiag, sc;
7663:   VecScatter         scatter;
7664:   PetscScalar       *seqv;
7665:   const PetscScalar *parv;
7666:   const PetscInt    *ia, *ja;
7667:   PetscBool          set, flag, done;
7668:   Mat                AA = mat, A;
7669:   MPI_Comm           comm;
7670:   PetscMPIInt        rank, size, tag;
7671:   MPI_Status         status;
7672:   PetscContainer     container;
7673:   EnvelopeData      *edata;
7674:   Vec                seq, par;
7675:   IS                 isglobal;

7677:   PetscFunctionBegin;
7679:   PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7680:   if (!set || !flag) {
7681:     /* TODO: only needs nonzero structure of transpose */
7682:     PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7683:     PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7684:   }
7685:   PetscCall(MatAIJGetLocalMat(AA, &A));
7686:   PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7687:   PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

7689:   PetscCall(MatGetLocalSize(mat, &n, NULL));
7690:   PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7691:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7692:   PetscCallMPI(MPI_Comm_size(comm, &size));
7693:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

7695:   PetscCall(PetscMalloc2(n, &sizes, n, &starts));

7697:   if (rank > 0) {
7698:     PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7699:     PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7700:   }
7701:   PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7702:   for (i = 0; i < n; i++) {
7703:     env = PetscMax(env, ja[ia[i + 1] - 1]);
7704:     II  = rstart + i;
7705:     if (env == II) {
7706:       starts[lblocks]  = tbs;
7707:       sizes[lblocks++] = 1 + II - tbs;
7708:       tbs              = 1 + II;
7709:     }
7710:   }
7711:   if (rank < size - 1) {
7712:     PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7713:     PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7714:   }

7716:   PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7717:   if (!set || !flag) PetscCall(MatDestroy(&AA));
7718:   PetscCall(MatDestroy(&A));

7720:   PetscCall(PetscNew(&edata));
7721:   PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7722:   edata->n = lblocks;
7723:   /* create IS needed for extracting blocks from the original matrix */
7724:   PetscCall(PetscMalloc1(lblocks, &edata->is));
7725:   for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

7727:   /* Create the resulting inverse matrix nonzero structure with preallocation information */
7728:   PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7729:   PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7730:   PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7731:   PetscCall(MatSetType(edata->C, MATAIJ));

7733:   /* Communicate the start and end of each row, from each block to the correct rank */
7734:   /* TODO: Use PetscSF instead of VecScatter */
7735:   for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7736:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7737:   PetscCall(VecGetArrayWrite(seq, &seqv));
7738:   for (PetscInt i = 0; i < lblocks; i++) {
7739:     for (PetscInt j = 0; j < sizes[i]; j++) {
7740:       seqv[cnt]     = starts[i];
7741:       seqv[cnt + 1] = starts[i] + sizes[i];
7742:       cnt += 2;
7743:     }
7744:   }
7745:   PetscCall(VecRestoreArrayWrite(seq, &seqv));
7746:   PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7747:   sc -= cnt;
7748:   PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7749:   PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7750:   PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7751:   PetscCall(ISDestroy(&isglobal));
7752:   PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7753:   PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7754:   PetscCall(VecScatterDestroy(&scatter));
7755:   PetscCall(VecDestroy(&seq));
7756:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7757:   PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7758:   PetscCall(VecGetArrayRead(par, &parv));
7759:   cnt = 0;
7760:   PetscCall(MatGetSize(mat, NULL, &n));
7761:   for (PetscInt i = 0; i < mat->rmap->n; i++) {
7762:     PetscInt start, end, d = 0, od = 0;

7764:     start = (PetscInt)PetscRealPart(parv[cnt]);
7765:     end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
7766:     cnt += 2;

7768:     if (start < cstart) {
7769:       od += cstart - start + n - cend;
7770:       d += cend - cstart;
7771:     } else if (start < cend) {
7772:       od += n - cend;
7773:       d += cend - start;
7774:     } else od += n - start;
7775:     if (end <= cstart) {
7776:       od -= cstart - end + n - cend;
7777:       d -= cend - cstart;
7778:     } else if (end < cend) {
7779:       od -= n - cend;
7780:       d -= cend - end;
7781:     } else od -= n - end;

7783:     odiag[i] = od;
7784:     diag[i]  = d;
7785:   }
7786:   PetscCall(VecRestoreArrayRead(par, &parv));
7787:   PetscCall(VecDestroy(&par));
7788:   PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7789:   PetscCall(PetscFree2(diag, odiag));
7790:   PetscCall(PetscFree2(sizes, starts));

7792:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7793:   PetscCall(PetscContainerSetPointer(container, edata));
7794:   PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7795:   PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7796:   PetscCall(PetscObjectDereference((PetscObject)container));
7797:   PetscFunctionReturn(PETSC_SUCCESS);
7798: }

7800: /*@
7801:   MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

7803:   Collective

7805:   Input Parameters:
7806: + A     - the matrix
7807: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine

7809:   Output Parameter:
7810: . C - matrix with inverted block diagonal of `A`

7812:   Level: advanced

7814:   Note:
7815:   For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

7817: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7818: @*/
7819: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7820: {
7821:   PetscContainer   container;
7822:   EnvelopeData    *edata;
7823:   PetscObjectState nonzerostate;

7825:   PetscFunctionBegin;
7826:   PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7827:   if (!container) {
7828:     PetscCall(MatComputeVariableBlockEnvelope(A));
7829:     PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7830:   }
7831:   PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7832:   PetscCall(MatGetNonzeroState(A, &nonzerostate));
7833:   PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7834:   PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

7836:   PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7837:   *C = edata->C;

7839:   for (PetscInt i = 0; i < edata->n; i++) {
7840:     Mat          D;
7841:     PetscScalar *dvalues;

7843:     PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7844:     PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7845:     PetscCall(MatSeqDenseInvert(D));
7846:     PetscCall(MatDenseGetArray(D, &dvalues));
7847:     PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7848:     PetscCall(MatDestroy(&D));
7849:   }
7850:   PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7851:   PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7852:   PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7853:   PetscFunctionReturn(PETSC_SUCCESS);
7854: }

7856: /*@
7857:   MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

7859:   Not Collective

7861:   Input Parameters:
7862: + mat     - the matrix
7863: . nblocks - the number of blocks on this process, each block can only exist on a single process
7864: - bsizes  - the block sizes

7866:   Level: intermediate

7868:   Notes:
7869:   Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

7871:   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.

7873: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7874:           `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7875: @*/
7876: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7877: {
7878:   PetscInt ncnt = 0, nlocal;

7880:   PetscFunctionBegin;
7882:   PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7883:   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);
7884:   for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7885:   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);
7886:   PetscCall(PetscFree(mat->bsizes));
7887:   mat->nblocks = nblocks;
7888:   PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7889:   PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7890:   PetscFunctionReturn(PETSC_SUCCESS);
7891: }

7893: /*@C
7894:   MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7896:   Not Collective; No Fortran Support

7898:   Input Parameter:
7899: . mat - the matrix

7901:   Output Parameters:
7902: + nblocks - the number of blocks on this process
7903: - bsizes  - the block sizes

7905:   Level: intermediate

7907: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7908: @*/
7909: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7910: {
7911:   PetscFunctionBegin;
7913:   if (nblocks) *nblocks = mat->nblocks;
7914:   if (bsizes) *bsizes = mat->bsizes;
7915:   PetscFunctionReturn(PETSC_SUCCESS);
7916: }

7918: /*
7919:   MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes

7921:   Not Collective

7923:   Input Parameter:
7924: + subA  - the submatrix
7925: . A     - the original matrix
7926: - isrow - The `IS` of selected rows for the submatrix

7928:   Level: developer

7930: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7931: */
7932: static PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
7933: {
7934:   const PetscInt *rows;
7935:   PetscInt        n, rStart, rEnd, Nb = 0;

7937:   PetscFunctionBegin;
7938:   if (!A->bsizes) PetscFunctionReturn(PETSC_SUCCESS);
7939:   // The IS contains global row numbers, we cannot preserve blocks if it contains off-process entries
7940:   PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
7941:   PetscCall(ISGetIndices(isrow, &rows));
7942:   PetscCall(ISGetLocalSize(isrow, &n));
7943:   for (PetscInt i = 0; i < n; ++i) {
7944:     if (rows[i] < rStart || rows[i] >= rEnd) {
7945:       PetscCall(ISRestoreIndices(isrow, &rows));
7946:       PetscFunctionReturn(PETSC_SUCCESS);
7947:     }
7948:   }
7949:   for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
7950:     PetscBool occupied = PETSC_FALSE;

7952:     for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
7953:       const PetscInt row = gr + br;

7955:       if (i == n) break;
7956:       if (rows[i] == row) {
7957:         occupied = PETSC_TRUE;
7958:         ++i;
7959:       }
7960:       while (i < n && rows[i] < row) ++i;
7961:     }
7962:     gr += A->bsizes[b];
7963:     if (occupied) ++Nb;
7964:   }
7965:   subA->nblocks = Nb;
7966:   PetscCall(PetscFree(subA->bsizes));
7967:   PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
7968:   PetscInt sb = 0;
7969:   for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
7970:     if (sb < subA->nblocks) subA->bsizes[sb] = 0;
7971:     for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
7972:       const PetscInt row = gr + br;

7974:       if (i == n) break;
7975:       if (rows[i] == row) {
7976:         ++subA->bsizes[sb];
7977:         ++i;
7978:       }
7979:       while (i < n && rows[i] < row) ++i;
7980:     }
7981:     gr += A->bsizes[b];
7982:     if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
7983:   }
7984:   PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
7985:   PetscInt nlocal, ncnt = 0;
7986:   PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
7987:   PetscCheck(subA->nblocks >= 0 && subA->nblocks <= nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks %" PetscInt_FMT " is not in [0, %" PetscInt_FMT "]", subA->nblocks, nlocal);
7988:   for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
7989:   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);
7990:   PetscCall(ISRestoreIndices(isrow, &rows));
7991:   PetscFunctionReturn(PETSC_SUCCESS);
7992: }

7994: /*@
7995:   MatSetBlockSizes - Sets the matrix block row and column sizes.

7997:   Logically Collective

7999:   Input Parameters:
8000: + mat - the matrix
8001: . rbs - row block size
8002: - cbs - column block size

8004:   Level: intermediate

8006:   Notes:
8007:   Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8008:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8009:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

8011:   For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8012:   are compatible with the matrix local sizes.

8014:   The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

8016: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8017: @*/
8018: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8019: {
8020:   PetscFunctionBegin;
8024:   PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8025:   if (mat->rmap->refcnt) {
8026:     ISLocalToGlobalMapping l2g  = NULL;
8027:     PetscLayout            nmap = NULL;

8029:     PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8030:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8031:     PetscCall(PetscLayoutDestroy(&mat->rmap));
8032:     mat->rmap          = nmap;
8033:     mat->rmap->mapping = l2g;
8034:   }
8035:   if (mat->cmap->refcnt) {
8036:     ISLocalToGlobalMapping l2g  = NULL;
8037:     PetscLayout            nmap = NULL;

8039:     PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8040:     if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8041:     PetscCall(PetscLayoutDestroy(&mat->cmap));
8042:     mat->cmap          = nmap;
8043:     mat->cmap->mapping = l2g;
8044:   }
8045:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8046:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8047:   PetscFunctionReturn(PETSC_SUCCESS);
8048: }

8050: /*@
8051:   MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

8053:   Logically Collective

8055:   Input Parameters:
8056: + mat     - the matrix
8057: . fromRow - matrix from which to copy row block size
8058: - fromCol - matrix from which to copy column block size (can be same as fromRow)

8060:   Level: developer

8062: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8063: @*/
8064: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8065: {
8066:   PetscFunctionBegin;
8070:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8071:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8072:   PetscFunctionReturn(PETSC_SUCCESS);
8073: }

8075: /*@
8076:   MatResidual - Default routine to calculate the residual r = b - Ax

8078:   Collective

8080:   Input Parameters:
8081: + mat - the matrix
8082: . b   - the right-hand-side
8083: - x   - the approximate solution

8085:   Output Parameter:
8086: . r - location to store the residual

8088:   Level: developer

8090: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8091: @*/
8092: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8093: {
8094:   PetscFunctionBegin;
8100:   MatCheckPreallocated(mat, 1);
8101:   PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8102:   if (!mat->ops->residual) {
8103:     PetscCall(MatMult(mat, x, r));
8104:     PetscCall(VecAYPX(r, -1.0, b));
8105:   } else {
8106:     PetscUseTypeMethod(mat, residual, b, x, r);
8107:   }
8108:   PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8109:   PetscFunctionReturn(PETSC_SUCCESS);
8110: }

8112: /*@C
8113:   MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

8115:   Collective

8117:   Input Parameters:
8118: + mat             - the matrix
8119: . shift           - 0 or 1 indicating we want the indices starting at 0 or 1
8120: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8121: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8122:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8123:                  always used.

8125:   Output Parameters:
8126: + n    - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8127: . 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
8128: . ja   - the column indices, use `NULL` if not needed
8129: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8130:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8132:   Level: developer

8134:   Notes:
8135:   You CANNOT change any of the ia[] or ja[] values.

8137:   Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

8139:   Fortran Notes:
8140:   Use
8141: .vb
8142:     PetscInt, pointer :: ia(:),ja(:)
8143:     call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8144:     ! Access the ith and jth entries via ia(i) and ja(j)
8145: .ve

8147: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8148: @*/
8149: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8150: {
8151:   PetscFunctionBegin;
8154:   if (n) PetscAssertPointer(n, 5);
8155:   if (ia) PetscAssertPointer(ia, 6);
8156:   if (ja) PetscAssertPointer(ja, 7);
8157:   if (done) PetscAssertPointer(done, 8);
8158:   MatCheckPreallocated(mat, 1);
8159:   if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8160:   else {
8161:     if (done) *done = PETSC_TRUE;
8162:     PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8163:     PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8164:     PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8165:   }
8166:   PetscFunctionReturn(PETSC_SUCCESS);
8167: }

8169: /*@C
8170:   MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

8172:   Collective

8174:   Input Parameters:
8175: + mat             - the matrix
8176: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8177: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8178:                 symmetrized
8179: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8180:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8181:                  always used.
8182: . n               - number of columns in the (possibly compressed) matrix
8183: . ia              - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8184: - ja              - the row indices

8186:   Output Parameter:
8187: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

8189:   Level: developer

8191: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8192: @*/
8193: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8194: {
8195:   PetscFunctionBegin;
8198:   PetscAssertPointer(n, 5);
8199:   if (ia) PetscAssertPointer(ia, 6);
8200:   if (ja) PetscAssertPointer(ja, 7);
8201:   PetscAssertPointer(done, 8);
8202:   MatCheckPreallocated(mat, 1);
8203:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8204:   else {
8205:     *done = PETSC_TRUE;
8206:     PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8207:   }
8208:   PetscFunctionReturn(PETSC_SUCCESS);
8209: }

8211: /*@C
8212:   MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

8214:   Collective

8216:   Input Parameters:
8217: + mat             - the matrix
8218: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8219: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8220: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8221:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8222:                     always used.
8223: . n               - size of (possibly compressed) matrix
8224: . ia              - the row pointers
8225: - ja              - the column indices

8227:   Output Parameter:
8228: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8230:   Level: developer

8232:   Note:
8233:   This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8234:   us of the array after it has been restored. If you pass `NULL`, it will
8235:   not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

8237: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8238: @*/
8239: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8240: {
8241:   PetscFunctionBegin;
8244:   if (ia) PetscAssertPointer(ia, 6);
8245:   if (ja) PetscAssertPointer(ja, 7);
8246:   if (done) PetscAssertPointer(done, 8);
8247:   MatCheckPreallocated(mat, 1);

8249:   if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8250:   else {
8251:     if (done) *done = PETSC_TRUE;
8252:     PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8253:     if (n) *n = 0;
8254:     if (ia) *ia = NULL;
8255:     if (ja) *ja = NULL;
8256:   }
8257:   PetscFunctionReturn(PETSC_SUCCESS);
8258: }

8260: /*@C
8261:   MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

8263:   Collective

8265:   Input Parameters:
8266: + mat             - the matrix
8267: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8268: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8269: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8270:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8271:                     always used.

8273:   Output Parameters:
8274: + n    - size of (possibly compressed) matrix
8275: . ia   - the column pointers
8276: . ja   - the row indices
8277: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8279:   Level: developer

8281: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8282: @*/
8283: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8284: {
8285:   PetscFunctionBegin;
8288:   if (ia) PetscAssertPointer(ia, 6);
8289:   if (ja) PetscAssertPointer(ja, 7);
8290:   PetscAssertPointer(done, 8);
8291:   MatCheckPreallocated(mat, 1);

8293:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8294:   else {
8295:     *done = PETSC_TRUE;
8296:     PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8297:     if (n) *n = 0;
8298:     if (ia) *ia = NULL;
8299:     if (ja) *ja = NULL;
8300:   }
8301:   PetscFunctionReturn(PETSC_SUCCESS);
8302: }

8304: /*@
8305:   MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8306:   `MatGetColumnIJ()`.

8308:   Collective

8310:   Input Parameters:
8311: + mat        - the matrix
8312: . ncolors    - maximum color value
8313: . n          - number of entries in colorarray
8314: - colorarray - array indicating color for each column

8316:   Output Parameter:
8317: . iscoloring - coloring generated using colorarray information

8319:   Level: developer

8321: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8322: @*/
8323: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8324: {
8325:   PetscFunctionBegin;
8328:   PetscAssertPointer(colorarray, 4);
8329:   PetscAssertPointer(iscoloring, 5);
8330:   MatCheckPreallocated(mat, 1);

8332:   if (!mat->ops->coloringpatch) {
8333:     PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8334:   } else {
8335:     PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8336:   }
8337:   PetscFunctionReturn(PETSC_SUCCESS);
8338: }

8340: /*@
8341:   MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

8343:   Logically Collective

8345:   Input Parameter:
8346: . mat - the factored matrix to be reset

8348:   Level: developer

8350:   Notes:
8351:   This routine should be used only with factored matrices formed by in-place
8352:   factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8353:   format).  This option can save memory, for example, when solving nonlinear
8354:   systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8355:   ILU(0) preconditioner.

8357:   One can specify in-place ILU(0) factorization by calling
8358: .vb
8359:      PCType(pc,PCILU);
8360:      PCFactorSeUseInPlace(pc);
8361: .ve
8362:   or by using the options -pc_type ilu -pc_factor_in_place

8364:   In-place factorization ILU(0) can also be used as a local
8365:   solver for the blocks within the block Jacobi or additive Schwarz
8366:   methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
8367:   for details on setting local solver options.

8369:   Most users should employ the `KSP` interface for linear solvers
8370:   instead of working directly with matrix algebra routines such as this.
8371:   See, e.g., `KSPCreate()`.

8373: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8374: @*/
8375: PetscErrorCode MatSetUnfactored(Mat mat)
8376: {
8377:   PetscFunctionBegin;
8380:   MatCheckPreallocated(mat, 1);
8381:   mat->factortype = MAT_FACTOR_NONE;
8382:   if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8383:   PetscUseTypeMethod(mat, setunfactored);
8384:   PetscFunctionReturn(PETSC_SUCCESS);
8385: }

8387: /*@
8388:   MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8389:   as the original matrix.

8391:   Collective

8393:   Input Parameters:
8394: + mat   - the original matrix
8395: . isrow - parallel `IS` containing the rows this processor should obtain
8396: . 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.
8397: - cll   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

8399:   Output Parameter:
8400: . newmat - the new submatrix, of the same type as the original matrix

8402:   Level: advanced

8404:   Notes:
8405:   The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

8407:   Some matrix types place restrictions on the row and column indices, such
8408:   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;
8409:   for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

8411:   The index sets may not have duplicate entries.

8413:   The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8414:   the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8415:   to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8416:   will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
8417:   you are finished using it.

8419:   The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8420:   the input matrix.

8422:   If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

8424:   If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8425:   is used by `PCFIELDSPLIT` to allow easy nesting of its use.

8427:   Example usage:
8428:   Consider the following 8x8 matrix with 34 non-zero values, that is
8429:   assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8430:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8431:   as follows
8432: .vb
8433:             1  2  0  |  0  3  0  |  0  4
8434:     Proc0   0  5  6  |  7  0  0  |  8  0
8435:             9  0 10  | 11  0  0  | 12  0
8436:     -------------------------------------
8437:            13  0 14  | 15 16 17  |  0  0
8438:     Proc1   0 18  0  | 19 20 21  |  0  0
8439:             0  0  0  | 22 23  0  | 24  0
8440:     -------------------------------------
8441:     Proc2  25 26 27  |  0  0 28  | 29  0
8442:            30  0  0  | 31 32 33  |  0 34
8443: .ve

8445:   Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

8447: .vb
8448:             2  0  |  0  3  0  |  0
8449:     Proc0   5  6  |  7  0  0  |  8
8450:     -------------------------------
8451:     Proc1  18  0  | 19 20 21  |  0
8452:     -------------------------------
8453:     Proc2  26 27  |  0  0 28  | 29
8454:             0  0  | 31 32 33  |  0
8455: .ve

8457: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8458: @*/
8459: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8460: {
8461:   PetscMPIInt size;
8462:   Mat        *local;
8463:   IS          iscoltmp;
8464:   PetscBool   flg;

8466:   PetscFunctionBegin;
8470:   PetscAssertPointer(newmat, 5);
8473:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8474:   PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");

8476:   MatCheckPreallocated(mat, 1);
8477:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));

8479:   if (!iscol || isrow == iscol) {
8480:     PetscBool   stride;
8481:     PetscMPIInt grabentirematrix = 0, grab;
8482:     PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8483:     if (stride) {
8484:       PetscInt first, step, n, rstart, rend;
8485:       PetscCall(ISStrideGetInfo(isrow, &first, &step));
8486:       if (step == 1) {
8487:         PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8488:         if (rstart == first) {
8489:           PetscCall(ISGetLocalSize(isrow, &n));
8490:           if (n == rend - rstart) grabentirematrix = 1;
8491:         }
8492:       }
8493:     }
8494:     PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8495:     if (grab) {
8496:       PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8497:       if (cll == MAT_INITIAL_MATRIX) {
8498:         *newmat = mat;
8499:         PetscCall(PetscObjectReference((PetscObject)mat));
8500:       }
8501:       PetscFunctionReturn(PETSC_SUCCESS);
8502:     }
8503:   }

8505:   if (!iscol) {
8506:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8507:   } else {
8508:     iscoltmp = iscol;
8509:   }

8511:   /* if original matrix is on just one processor then use submatrix generated */
8512:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8513:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8514:     goto setproperties;
8515:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8516:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8517:     *newmat = *local;
8518:     PetscCall(PetscFree(local));
8519:     goto setproperties;
8520:   } else if (!mat->ops->createsubmatrix) {
8521:     /* Create a new matrix type that implements the operation using the full matrix */
8522:     PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8523:     switch (cll) {
8524:     case MAT_INITIAL_MATRIX:
8525:       PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8526:       break;
8527:     case MAT_REUSE_MATRIX:
8528:       PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8529:       break;
8530:     default:
8531:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8532:     }
8533:     PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8534:     goto setproperties;
8535:   }

8537:   PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8538:   PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8539:   PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));

8541: setproperties:
8542:   if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8543:     PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8544:     if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8545:   }
8546:   if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8547:   if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8548:   if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
8549:   PetscFunctionReturn(PETSC_SUCCESS);
8550: }

8552: /*@
8553:   MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

8555:   Not Collective

8557:   Input Parameters:
8558: + A - the matrix we wish to propagate options from
8559: - B - the matrix we wish to propagate options to

8561:   Level: beginner

8563:   Note:
8564:   Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`

8566: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8567: @*/
8568: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8569: {
8570:   PetscFunctionBegin;
8573:   B->symmetry_eternal            = A->symmetry_eternal;
8574:   B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8575:   B->symmetric                   = A->symmetric;
8576:   B->structurally_symmetric      = A->structurally_symmetric;
8577:   B->spd                         = A->spd;
8578:   B->hermitian                   = A->hermitian;
8579:   PetscFunctionReturn(PETSC_SUCCESS);
8580: }

8582: /*@
8583:   MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8584:   used during the assembly process to store values that belong to
8585:   other processors.

8587:   Not Collective

8589:   Input Parameters:
8590: + mat   - the matrix
8591: . size  - the initial size of the stash.
8592: - bsize - the initial size of the block-stash(if used).

8594:   Options Database Keys:
8595: + -matstash_initial_size <size> or <size0,size1,...sizep-1>            - set initial size
8596: - -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1> - set initial block size

8598:   Level: intermediate

8600:   Notes:
8601:   The block-stash is used for values set with `MatSetValuesBlocked()` while
8602:   the stash is used for values set with `MatSetValues()`

8604:   Run with the option -info and look for output of the form
8605:   MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8606:   to determine the appropriate value, MM, to use for size and
8607:   MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8608:   to determine the value, BMM to use for bsize

8610: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8611: @*/
8612: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8613: {
8614:   PetscFunctionBegin;
8617:   PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8618:   PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8619:   PetscFunctionReturn(PETSC_SUCCESS);
8620: }

8622: /*@
8623:   MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8624:   the matrix

8626:   Neighbor-wise Collective

8628:   Input Parameters:
8629: + A - the matrix
8630: . x - the vector to be multiplied by the interpolation operator
8631: - y - the vector to be added to the result

8633:   Output Parameter:
8634: . w - the resulting vector

8636:   Level: intermediate

8638:   Notes:
8639:   `w` may be the same vector as `y`.

8641:   This allows one to use either the restriction or interpolation (its transpose)
8642:   matrix to do the interpolation

8644: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8645: @*/
8646: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8647: {
8648:   PetscInt M, N, Ny;

8650:   PetscFunctionBegin;
8655:   PetscCall(MatGetSize(A, &M, &N));
8656:   PetscCall(VecGetSize(y, &Ny));
8657:   if (M == Ny) {
8658:     PetscCall(MatMultAdd(A, x, y, w));
8659:   } else {
8660:     PetscCall(MatMultTransposeAdd(A, x, y, w));
8661:   }
8662:   PetscFunctionReturn(PETSC_SUCCESS);
8663: }

8665: /*@
8666:   MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8667:   the matrix

8669:   Neighbor-wise Collective

8671:   Input Parameters:
8672: + A - the matrix
8673: - x - the vector to be interpolated

8675:   Output Parameter:
8676: . y - the resulting vector

8678:   Level: intermediate

8680:   Note:
8681:   This allows one to use either the restriction or interpolation (its transpose)
8682:   matrix to do the interpolation

8684: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8685: @*/
8686: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8687: {
8688:   PetscInt M, N, Ny;

8690:   PetscFunctionBegin;
8694:   PetscCall(MatGetSize(A, &M, &N));
8695:   PetscCall(VecGetSize(y, &Ny));
8696:   if (M == Ny) {
8697:     PetscCall(MatMult(A, x, y));
8698:   } else {
8699:     PetscCall(MatMultTranspose(A, x, y));
8700:   }
8701:   PetscFunctionReturn(PETSC_SUCCESS);
8702: }

8704: /*@
8705:   MatRestrict - $y = A*x$ or $A^T*x$

8707:   Neighbor-wise Collective

8709:   Input Parameters:
8710: + A - the matrix
8711: - x - the vector to be restricted

8713:   Output Parameter:
8714: . y - the resulting vector

8716:   Level: intermediate

8718:   Note:
8719:   This allows one to use either the restriction or interpolation (its transpose)
8720:   matrix to do the restriction

8722: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8723: @*/
8724: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8725: {
8726:   PetscInt M, N, Nx;

8728:   PetscFunctionBegin;
8732:   PetscCall(MatGetSize(A, &M, &N));
8733:   PetscCall(VecGetSize(x, &Nx));
8734:   if (M == Nx) {
8735:     PetscCall(MatMultTranspose(A, x, y));
8736:   } else {
8737:     PetscCall(MatMult(A, x, y));
8738:   }
8739:   PetscFunctionReturn(PETSC_SUCCESS);
8740: }

8742: /*@
8743:   MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`

8745:   Neighbor-wise Collective

8747:   Input Parameters:
8748: + A - the matrix
8749: . x - the input dense matrix to be multiplied
8750: - w - the input dense matrix to be added to the result

8752:   Output Parameter:
8753: . y - the output dense matrix

8755:   Level: intermediate

8757:   Note:
8758:   This allows one to use either the restriction or interpolation (its transpose)
8759:   matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8760:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8762: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8763: @*/
8764: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8765: {
8766:   PetscInt  M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8767:   PetscBool trans = PETSC_TRUE;
8768:   MatReuse  reuse = MAT_INITIAL_MATRIX;

8770:   PetscFunctionBegin;
8776:   PetscCall(MatGetSize(A, &M, &N));
8777:   PetscCall(MatGetSize(x, &Mx, &Nx));
8778:   if (N == Mx) trans = PETSC_FALSE;
8779:   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);
8780:   Mo = trans ? N : M;
8781:   if (*y) {
8782:     PetscCall(MatGetSize(*y, &My, &Ny));
8783:     if (Mo == My && Nx == Ny) {
8784:       reuse = MAT_REUSE_MATRIX;
8785:     } else {
8786:       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);
8787:       PetscCall(MatDestroy(y));
8788:     }
8789:   }

8791:   if (w && *y == w) { /* this is to minimize changes in PCMG */
8792:     PetscBool flg;

8794:     PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8795:     if (w) {
8796:       PetscInt My, Ny, Mw, Nw;

8798:       PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8799:       PetscCall(MatGetSize(*y, &My, &Ny));
8800:       PetscCall(MatGetSize(w, &Mw, &Nw));
8801:       if (!flg || My != Mw || Ny != Nw) w = NULL;
8802:     }
8803:     if (!w) {
8804:       PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8805:       PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8806:       PetscCall(PetscObjectDereference((PetscObject)w));
8807:     } else {
8808:       PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8809:     }
8810:   }
8811:   if (!trans) {
8812:     PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8813:   } else {
8814:     PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8815:   }
8816:   if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8817:   PetscFunctionReturn(PETSC_SUCCESS);
8818: }

8820: /*@
8821:   MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

8823:   Neighbor-wise Collective

8825:   Input Parameters:
8826: + A - the matrix
8827: - x - the input dense matrix

8829:   Output Parameter:
8830: . y - the output dense matrix

8832:   Level: intermediate

8834:   Note:
8835:   This allows one to use either the restriction or interpolation (its transpose)
8836:   matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8837:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8839: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8840: @*/
8841: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8842: {
8843:   PetscFunctionBegin;
8844:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8845:   PetscFunctionReturn(PETSC_SUCCESS);
8846: }

8848: /*@
8849:   MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`

8851:   Neighbor-wise Collective

8853:   Input Parameters:
8854: + A - the matrix
8855: - x - the input dense matrix

8857:   Output Parameter:
8858: . y - the output dense matrix

8860:   Level: intermediate

8862:   Note:
8863:   This allows one to use either the restriction or interpolation (its transpose)
8864:   matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8865:   otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.

8867: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8868: @*/
8869: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8870: {
8871:   PetscFunctionBegin;
8872:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8873:   PetscFunctionReturn(PETSC_SUCCESS);
8874: }

8876: /*@
8877:   MatGetNullSpace - retrieves the null space of a matrix.

8879:   Logically Collective

8881:   Input Parameters:
8882: + mat    - the matrix
8883: - nullsp - the null space object

8885:   Level: developer

8887: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8888: @*/
8889: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8890: {
8891:   PetscFunctionBegin;
8893:   PetscAssertPointer(nullsp, 2);
8894:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8895:   PetscFunctionReturn(PETSC_SUCCESS);
8896: }

8898: /*@C
8899:   MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices

8901:   Logically Collective

8903:   Input Parameters:
8904: + n   - the number of matrices
8905: - mat - the array of matrices

8907:   Output Parameters:
8908: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`

8910:   Level: developer

8912:   Note:
8913:   Call `MatRestoreNullspaces()` to provide these to another array of matrices

8915: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8916:           `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8917: @*/
8918: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8919: {
8920:   PetscFunctionBegin;
8921:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8922:   PetscAssertPointer(mat, 2);
8923:   PetscAssertPointer(nullsp, 3);

8925:   PetscCall(PetscCalloc1(3 * n, nullsp));
8926:   for (PetscInt i = 0; i < n; i++) {
8928:     (*nullsp)[i] = mat[i]->nullsp;
8929:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8930:     (*nullsp)[n + i] = mat[i]->nearnullsp;
8931:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8932:     (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8933:     PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8934:   }
8935:   PetscFunctionReturn(PETSC_SUCCESS);
8936: }

8938: /*@C
8939:   MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices

8941:   Logically Collective

8943:   Input Parameters:
8944: + n      - the number of matrices
8945: . mat    - the array of matrices
8946: - nullsp - an array of null spaces

8948:   Level: developer

8950:   Note:
8951:   Call `MatGetNullSpaces()` to create `nullsp`

8953: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8954:           `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8955: @*/
8956: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8957: {
8958:   PetscFunctionBegin;
8959:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8960:   PetscAssertPointer(mat, 2);
8961:   PetscAssertPointer(nullsp, 3);
8962:   PetscAssertPointer(*nullsp, 3);

8964:   for (PetscInt i = 0; i < n; i++) {
8966:     PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8967:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
8968:     PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
8969:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
8970:     PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
8971:     PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
8972:   }
8973:   PetscCall(PetscFree(*nullsp));
8974:   PetscFunctionReturn(PETSC_SUCCESS);
8975: }

8977: /*@
8978:   MatSetNullSpace - attaches a null space to a matrix.

8980:   Logically Collective

8982:   Input Parameters:
8983: + mat    - the matrix
8984: - nullsp - the null space object

8986:   Level: advanced

8988:   Notes:
8989:   This null space is used by the `KSP` linear solvers to solve singular systems.

8991:   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`

8993:   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
8994:   to zero but the linear system will still be solved in a least squares sense.

8996:   The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8997:   the domain of a matrix $A$ (from $R^n$ to $R^m$ ($m$ rows, $n$ columns) $R^n$ = the direct sum of the null space of $A$, $n(A)$, plus the range of $A^T$, $R(A^T)$.
8998:   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
8999:   $n(A^T))$ 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
9000:   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 $n(A^T)$.
9001:   This  $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.

9003:   If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9004:   `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9005:   routine also automatically calls `MatSetTransposeNullSpace()`.

9007:   The user should call `MatNullSpaceDestroy()`.

9009: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9010:           `KSPSetPCSide()`
9011: @*/
9012: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9013: {
9014:   PetscFunctionBegin;
9017:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9018:   PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9019:   mat->nullsp = nullsp;
9020:   if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9021:   PetscFunctionReturn(PETSC_SUCCESS);
9022: }

9024: /*@
9025:   MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

9027:   Logically Collective

9029:   Input Parameters:
9030: + mat    - the matrix
9031: - nullsp - the null space object

9033:   Level: developer

9035: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9036: @*/
9037: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9038: {
9039:   PetscFunctionBegin;
9042:   PetscAssertPointer(nullsp, 2);
9043:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9044:   PetscFunctionReturn(PETSC_SUCCESS);
9045: }

9047: /*@
9048:   MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix

9050:   Logically Collective

9052:   Input Parameters:
9053: + mat    - the matrix
9054: - nullsp - the null space object

9056:   Level: advanced

9058:   Notes:
9059:   This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.

9061:   See `MatSetNullSpace()`

9063: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9064: @*/
9065: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9066: {
9067:   PetscFunctionBegin;
9070:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9071:   PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9072:   mat->transnullsp = nullsp;
9073:   PetscFunctionReturn(PETSC_SUCCESS);
9074: }

9076: /*@
9077:   MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9078:   This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

9080:   Logically Collective

9082:   Input Parameters:
9083: + mat    - the matrix
9084: - nullsp - the null space object

9086:   Level: advanced

9088:   Notes:
9089:   Overwrites any previous near null space that may have been attached

9091:   You can remove the null space by calling this routine with an `nullsp` of `NULL`

9093: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9094: @*/
9095: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9096: {
9097:   PetscFunctionBegin;
9101:   MatCheckPreallocated(mat, 1);
9102:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9103:   PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9104:   mat->nearnullsp = nullsp;
9105:   PetscFunctionReturn(PETSC_SUCCESS);
9106: }

9108: /*@
9109:   MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`

9111:   Not Collective

9113:   Input Parameter:
9114: . mat - the matrix

9116:   Output Parameter:
9117: . nullsp - the null space object, `NULL` if not set

9119:   Level: advanced

9121: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9122: @*/
9123: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9124: {
9125:   PetscFunctionBegin;
9128:   PetscAssertPointer(nullsp, 2);
9129:   MatCheckPreallocated(mat, 1);
9130:   *nullsp = mat->nearnullsp;
9131:   PetscFunctionReturn(PETSC_SUCCESS);
9132: }

9134: /*@
9135:   MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

9137:   Collective

9139:   Input Parameters:
9140: + mat  - the matrix
9141: . row  - row/column permutation
9142: - info - information on desired factorization process

9144:   Level: developer

9146:   Notes:
9147:   Probably really in-place only when level of fill is zero, otherwise allocates
9148:   new space to store factored matrix and deletes previous memory.

9150:   Most users should employ the `KSP` interface for linear solvers
9151:   instead of working directly with matrix algebra routines such as this.
9152:   See, e.g., `KSPCreate()`.

9154:   Fortran Note:
9155:   A valid (non-null) `info` argument must be provided

9157: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9158: @*/
9159: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9160: {
9161:   PetscFunctionBegin;
9165:   PetscAssertPointer(info, 3);
9166:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9167:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9168:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9169:   MatCheckPreallocated(mat, 1);
9170:   PetscUseTypeMethod(mat, iccfactor, row, info);
9171:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9172:   PetscFunctionReturn(PETSC_SUCCESS);
9173: }

9175: /*@
9176:   MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9177:   ghosted ones.

9179:   Not Collective

9181:   Input Parameters:
9182: + mat  - the matrix
9183: - diag - the diagonal values, including ghost ones

9185:   Level: developer

9187:   Notes:
9188:   Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices

9190:   This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`

9192: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9193: @*/
9194: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9195: {
9196:   PetscMPIInt size;

9198:   PetscFunctionBegin;

9203:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9204:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9205:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9206:   if (size == 1) {
9207:     PetscInt n, m;
9208:     PetscCall(VecGetSize(diag, &n));
9209:     PetscCall(MatGetSize(mat, NULL, &m));
9210:     PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9211:     PetscCall(MatDiagonalScale(mat, NULL, diag));
9212:   } else {
9213:     PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9214:   }
9215:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9216:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9217:   PetscFunctionReturn(PETSC_SUCCESS);
9218: }

9220: /*@
9221:   MatGetInertia - Gets the inertia from a factored matrix

9223:   Collective

9225:   Input Parameter:
9226: . mat - the matrix

9228:   Output Parameters:
9229: + nneg  - number of negative eigenvalues
9230: . nzero - number of zero eigenvalues
9231: - npos  - number of positive eigenvalues

9233:   Level: advanced

9235:   Note:
9236:   Matrix must have been factored by `MatCholeskyFactor()`

9238: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9239: @*/
9240: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9241: {
9242:   PetscFunctionBegin;
9245:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9246:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9247:   PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9248:   PetscFunctionReturn(PETSC_SUCCESS);
9249: }

9251: /*@C
9252:   MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors

9254:   Neighbor-wise Collective

9256:   Input Parameters:
9257: + mat - the factored matrix obtained with `MatGetFactor()`
9258: - b   - the right-hand-side vectors

9260:   Output Parameter:
9261: . x - the result vectors

9263:   Level: developer

9265:   Note:
9266:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
9267:   call `MatSolves`(A,x,x).

9269: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9270: @*/
9271: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9272: {
9273:   PetscFunctionBegin;
9276:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9277:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9278:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);

9280:   MatCheckPreallocated(mat, 1);
9281:   PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9282:   PetscUseTypeMethod(mat, solves, b, x);
9283:   PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9284:   PetscFunctionReturn(PETSC_SUCCESS);
9285: }

9287: /*@
9288:   MatIsSymmetric - Test whether a matrix is symmetric

9290:   Collective

9292:   Input Parameters:
9293: + A   - the matrix to test
9294: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

9296:   Output Parameter:
9297: . flg - the result

9299:   Level: intermediate

9301:   Notes:
9302:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9304:   If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`

9306:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9307:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9309: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9310:           `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9311: @*/
9312: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9313: {
9314:   PetscFunctionBegin;
9316:   PetscAssertPointer(flg, 3);
9317:   if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9318:   else {
9319:     if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9320:     else PetscCall(MatIsTranspose(A, A, tol, flg));
9321:     if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9322:   }
9323:   PetscFunctionReturn(PETSC_SUCCESS);
9324: }

9326: /*@
9327:   MatIsHermitian - Test whether a matrix is Hermitian

9329:   Collective

9331:   Input Parameters:
9332: + A   - the matrix to test
9333: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

9335:   Output Parameter:
9336: . flg - the result

9338:   Level: intermediate

9340:   Notes:
9341:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9343:   If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`

9345:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9346:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)

9348: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9349:           `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9350: @*/
9351: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9352: {
9353:   PetscFunctionBegin;
9355:   PetscAssertPointer(flg, 3);
9356:   if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9357:   else {
9358:     if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9359:     else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9360:     if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9361:   }
9362:   PetscFunctionReturn(PETSC_SUCCESS);
9363: }

9365: /*@
9366:   MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state

9368:   Not Collective

9370:   Input Parameter:
9371: . A - the matrix to check

9373:   Output Parameters:
9374: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9375: - flg - the result (only valid if set is `PETSC_TRUE`)

9377:   Level: advanced

9379:   Notes:
9380:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9381:   if you want it explicitly checked

9383:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9384:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9386: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9387: @*/
9388: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9389: {
9390:   PetscFunctionBegin;
9392:   PetscAssertPointer(set, 2);
9393:   PetscAssertPointer(flg, 3);
9394:   if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9395:     *set = PETSC_TRUE;
9396:     *flg = PetscBool3ToBool(A->symmetric);
9397:   } else {
9398:     *set = PETSC_FALSE;
9399:   }
9400:   PetscFunctionReturn(PETSC_SUCCESS);
9401: }

9403: /*@
9404:   MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state

9406:   Not Collective

9408:   Input Parameter:
9409: . A - the matrix to check

9411:   Output Parameters:
9412: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9413: - flg - the result (only valid if set is `PETSC_TRUE`)

9415:   Level: advanced

9417:   Notes:
9418:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).

9420:   One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9421:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)

9423: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9424: @*/
9425: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9426: {
9427:   PetscFunctionBegin;
9429:   PetscAssertPointer(set, 2);
9430:   PetscAssertPointer(flg, 3);
9431:   if (A->spd != PETSC_BOOL3_UNKNOWN) {
9432:     *set = PETSC_TRUE;
9433:     *flg = PetscBool3ToBool(A->spd);
9434:   } else {
9435:     *set = PETSC_FALSE;
9436:   }
9437:   PetscFunctionReturn(PETSC_SUCCESS);
9438: }

9440: /*@
9441:   MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state

9443:   Not Collective

9445:   Input Parameter:
9446: . A - the matrix to check

9448:   Output Parameters:
9449: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9450: - flg - the result (only valid if set is `PETSC_TRUE`)

9452:   Level: advanced

9454:   Notes:
9455:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9456:   if you want it explicitly checked

9458:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9459:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9461: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9462: @*/
9463: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9464: {
9465:   PetscFunctionBegin;
9467:   PetscAssertPointer(set, 2);
9468:   PetscAssertPointer(flg, 3);
9469:   if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9470:     *set = PETSC_TRUE;
9471:     *flg = PetscBool3ToBool(A->hermitian);
9472:   } else {
9473:     *set = PETSC_FALSE;
9474:   }
9475:   PetscFunctionReturn(PETSC_SUCCESS);
9476: }

9478: /*@
9479:   MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

9481:   Collective

9483:   Input Parameter:
9484: . A - the matrix to test

9486:   Output Parameter:
9487: . flg - the result

9489:   Level: intermediate

9491:   Notes:
9492:   If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`

9494:   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
9495:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9497: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9498: @*/
9499: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9500: {
9501:   PetscFunctionBegin;
9503:   PetscAssertPointer(flg, 2);
9504:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9505:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9506:   } else {
9507:     PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9508:     PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9509:   }
9510:   PetscFunctionReturn(PETSC_SUCCESS);
9511: }

9513: /*@
9514:   MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state

9516:   Not Collective

9518:   Input Parameter:
9519: . A - the matrix to check

9521:   Output Parameters:
9522: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9523: - flg - the result (only valid if set is PETSC_TRUE)

9525:   Level: advanced

9527:   Notes:
9528:   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
9529:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9531:   Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)

9533: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9534: @*/
9535: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9536: {
9537:   PetscFunctionBegin;
9539:   PetscAssertPointer(set, 2);
9540:   PetscAssertPointer(flg, 3);
9541:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9542:     *set = PETSC_TRUE;
9543:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9544:   } else {
9545:     *set = PETSC_FALSE;
9546:   }
9547:   PetscFunctionReturn(PETSC_SUCCESS);
9548: }

9550: /*@
9551:   MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9552:   to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process

9554:   Not Collective

9556:   Input Parameter:
9557: . mat - the matrix

9559:   Output Parameters:
9560: + nstash    - the size of the stash
9561: . reallocs  - the number of additional mallocs incurred.
9562: . bnstash   - the size of the block stash
9563: - breallocs - the number of additional mallocs incurred.in the block stash

9565:   Level: advanced

9567: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9568: @*/
9569: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9570: {
9571:   PetscFunctionBegin;
9572:   PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9573:   PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9574:   PetscFunctionReturn(PETSC_SUCCESS);
9575: }

9577: /*@
9578:   MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9579:   parallel layout, `PetscLayout` for rows and columns

9581:   Collective

9583:   Input Parameter:
9584: . mat - the matrix

9586:   Output Parameters:
9587: + right - (optional) vector that the matrix can be multiplied against
9588: - left  - (optional) vector that the matrix vector product can be stored in

9590:   Level: advanced

9592:   Notes:
9593:   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()`.

9595:   These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed

9597: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9598: @*/
9599: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9600: {
9601:   PetscFunctionBegin;
9604:   if (mat->ops->getvecs) {
9605:     PetscUseTypeMethod(mat, getvecs, right, left);
9606:   } else {
9607:     if (right) {
9608:       PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9609:       PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9610:       PetscCall(VecSetType(*right, mat->defaultvectype));
9611: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9612:       if (mat->boundtocpu && mat->bindingpropagates) {
9613:         PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9614:         PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9615:       }
9616: #endif
9617:     }
9618:     if (left) {
9619:       PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9620:       PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9621:       PetscCall(VecSetType(*left, mat->defaultvectype));
9622: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9623:       if (mat->boundtocpu && mat->bindingpropagates) {
9624:         PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9625:         PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9626:       }
9627: #endif
9628:     }
9629:   }
9630:   PetscFunctionReturn(PETSC_SUCCESS);
9631: }

9633: /*@
9634:   MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9635:   with default values.

9637:   Not Collective

9639:   Input Parameter:
9640: . info - the `MatFactorInfo` data structure

9642:   Level: developer

9644:   Notes:
9645:   The solvers are generally used through the `KSP` and `PC` objects, for example
9646:   `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`

9648:   Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed

9650: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9651: @*/
9652: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9653: {
9654:   PetscFunctionBegin;
9655:   PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9656:   PetscFunctionReturn(PETSC_SUCCESS);
9657: }

9659: /*@
9660:   MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

9662:   Collective

9664:   Input Parameters:
9665: + mat - the factored matrix
9666: - is  - the index set defining the Schur indices (0-based)

9668:   Level: advanced

9670:   Notes:
9671:   Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.

9673:   You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.

9675:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9677: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9678:           `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9679: @*/
9680: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9681: {
9682:   PetscErrorCode (*f)(Mat, IS);

9684:   PetscFunctionBegin;
9689:   PetscCheckSameComm(mat, 1, is, 2);
9690:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9691:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9692:   PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9693:   PetscCall(MatDestroy(&mat->schur));
9694:   PetscCall((*f)(mat, is));
9695:   PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9696:   PetscFunctionReturn(PETSC_SUCCESS);
9697: }

9699: /*@
9700:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

9702:   Logically Collective

9704:   Input Parameters:
9705: + F      - the factored matrix obtained by calling `MatGetFactor()`
9706: . S      - location where to return the Schur complement, can be `NULL`
9707: - status - the status of the Schur complement matrix, can be `NULL`

9709:   Level: advanced

9711:   Notes:
9712:   You must call `MatFactorSetSchurIS()` before calling this routine.

9714:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9716:   The routine provides a copy of the Schur matrix stored within the solver data structures.
9717:   The caller must destroy the object when it is no longer needed.
9718:   If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.

9720:   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)

9722:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9724:   Developer Note:
9725:   The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9726:   matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

9728: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9729: @*/
9730: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9731: {
9732:   PetscFunctionBegin;
9734:   if (S) PetscAssertPointer(S, 2);
9735:   if (status) PetscAssertPointer(status, 3);
9736:   if (S) {
9737:     PetscErrorCode (*f)(Mat, Mat *);

9739:     PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9740:     if (f) {
9741:       PetscCall((*f)(F, S));
9742:     } else {
9743:       PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9744:     }
9745:   }
9746:   if (status) *status = F->schur_status;
9747:   PetscFunctionReturn(PETSC_SUCCESS);
9748: }

9750: /*@
9751:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

9753:   Logically Collective

9755:   Input Parameters:
9756: + F      - the factored matrix obtained by calling `MatGetFactor()`
9757: . S      - location where to return the Schur complement, can be `NULL`
9758: - status - the status of the Schur complement matrix, can be `NULL`

9760:   Level: advanced

9762:   Notes:
9763:   You must call `MatFactorSetSchurIS()` before calling this routine.

9765:   Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`

9767:   The routine returns a the Schur Complement stored within the data structures of the solver.

9769:   If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.

9771:   The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.

9773:   Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix

9775:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9777: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9778: @*/
9779: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9780: {
9781:   PetscFunctionBegin;
9783:   if (S) {
9784:     PetscAssertPointer(S, 2);
9785:     *S = F->schur;
9786:   }
9787:   if (status) {
9788:     PetscAssertPointer(status, 3);
9789:     *status = F->schur_status;
9790:   }
9791:   PetscFunctionReturn(PETSC_SUCCESS);
9792: }

9794: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9795: {
9796:   Mat S = F->schur;

9798:   PetscFunctionBegin;
9799:   switch (F->schur_status) {
9800:   case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9801:   case MAT_FACTOR_SCHUR_INVERTED:
9802:     if (S) {
9803:       S->ops->solve             = NULL;
9804:       S->ops->matsolve          = NULL;
9805:       S->ops->solvetranspose    = NULL;
9806:       S->ops->matsolvetranspose = NULL;
9807:       S->ops->solveadd          = NULL;
9808:       S->ops->solvetransposeadd = NULL;
9809:       S->factortype             = MAT_FACTOR_NONE;
9810:       PetscCall(PetscFree(S->solvertype));
9811:     }
9812:   case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9813:     break;
9814:   default:
9815:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9816:   }
9817:   PetscFunctionReturn(PETSC_SUCCESS);
9818: }

9820: /*@
9821:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`

9823:   Logically Collective

9825:   Input Parameters:
9826: + F      - the factored matrix obtained by calling `MatGetFactor()`
9827: . S      - location where the Schur complement is stored
9828: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)

9830:   Level: advanced

9832: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9833: @*/
9834: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9835: {
9836:   PetscFunctionBegin;
9838:   if (S) {
9840:     *S = NULL;
9841:   }
9842:   F->schur_status = status;
9843:   PetscCall(MatFactorUpdateSchurStatus_Private(F));
9844:   PetscFunctionReturn(PETSC_SUCCESS);
9845: }

9847: /*@
9848:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

9850:   Logically Collective

9852:   Input Parameters:
9853: + F   - the factored matrix obtained by calling `MatGetFactor()`
9854: . rhs - location where the right-hand side of the Schur complement system is stored
9855: - sol - location where the solution of the Schur complement system has to be returned

9857:   Level: advanced

9859:   Notes:
9860:   The sizes of the vectors should match the size of the Schur complement

9862:   Must be called after `MatFactorSetSchurIS()`

9864: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9865: @*/
9866: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9867: {
9868:   PetscFunctionBegin;
9875:   PetscCheckSameComm(F, 1, rhs, 2);
9876:   PetscCheckSameComm(F, 1, sol, 3);
9877:   PetscCall(MatFactorFactorizeSchurComplement(F));
9878:   switch (F->schur_status) {
9879:   case MAT_FACTOR_SCHUR_FACTORED:
9880:     PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9881:     break;
9882:   case MAT_FACTOR_SCHUR_INVERTED:
9883:     PetscCall(MatMultTranspose(F->schur, rhs, sol));
9884:     break;
9885:   default:
9886:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9887:   }
9888:   PetscFunctionReturn(PETSC_SUCCESS);
9889: }

9891: /*@
9892:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

9894:   Logically Collective

9896:   Input Parameters:
9897: + F   - the factored matrix obtained by calling `MatGetFactor()`
9898: . rhs - location where the right-hand side of the Schur complement system is stored
9899: - sol - location where the solution of the Schur complement system has to be returned

9901:   Level: advanced

9903:   Notes:
9904:   The sizes of the vectors should match the size of the Schur complement

9906:   Must be called after `MatFactorSetSchurIS()`

9908: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9909: @*/
9910: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9911: {
9912:   PetscFunctionBegin;
9919:   PetscCheckSameComm(F, 1, rhs, 2);
9920:   PetscCheckSameComm(F, 1, sol, 3);
9921:   PetscCall(MatFactorFactorizeSchurComplement(F));
9922:   switch (F->schur_status) {
9923:   case MAT_FACTOR_SCHUR_FACTORED:
9924:     PetscCall(MatSolve(F->schur, rhs, sol));
9925:     break;
9926:   case MAT_FACTOR_SCHUR_INVERTED:
9927:     PetscCall(MatMult(F->schur, rhs, sol));
9928:     break;
9929:   default:
9930:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9931:   }
9932:   PetscFunctionReturn(PETSC_SUCCESS);
9933: }

9935: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9936: #if PetscDefined(HAVE_CUDA)
9937: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9938: #endif

9940: /* Schur status updated in the interface */
9941: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9942: {
9943:   Mat S = F->schur;

9945:   PetscFunctionBegin;
9946:   if (S) {
9947:     PetscMPIInt size;
9948:     PetscBool   isdense, isdensecuda;

9950:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9951:     PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9952:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9953:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9954:     PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9955:     PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9956:     if (isdense) {
9957:       PetscCall(MatSeqDenseInvertFactors_Private(S));
9958:     } else if (isdensecuda) {
9959: #if defined(PETSC_HAVE_CUDA)
9960:       PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9961: #endif
9962:     }
9963:     // HIP??????????????
9964:     PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9965:   }
9966:   PetscFunctionReturn(PETSC_SUCCESS);
9967: }

9969: /*@
9970:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

9972:   Logically Collective

9974:   Input Parameter:
9975: . F - the factored matrix obtained by calling `MatGetFactor()`

9977:   Level: advanced

9979:   Notes:
9980:   Must be called after `MatFactorSetSchurIS()`.

9982:   Call `MatFactorGetSchurComplement()` or  `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.

9984: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9985: @*/
9986: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9987: {
9988:   PetscFunctionBegin;
9991:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9992:   PetscCall(MatFactorFactorizeSchurComplement(F));
9993:   PetscCall(MatFactorInvertSchurComplement_Private(F));
9994:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9995:   PetscFunctionReturn(PETSC_SUCCESS);
9996: }

9998: /*@
9999:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

10001:   Logically Collective

10003:   Input Parameter:
10004: . F - the factored matrix obtained by calling `MatGetFactor()`

10006:   Level: advanced

10008:   Note:
10009:   Must be called after `MatFactorSetSchurIS()`

10011: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10012: @*/
10013: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10014: {
10015:   MatFactorInfo info;

10017:   PetscFunctionBegin;
10020:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10021:   PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10022:   PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10023:   if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10024:     PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10025:   } else {
10026:     PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10027:   }
10028:   PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10029:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10030:   PetscFunctionReturn(PETSC_SUCCESS);
10031: }

10033: /*@
10034:   MatPtAP - Creates the matrix product $C = P^T * A * P$

10036:   Neighbor-wise Collective

10038:   Input Parameters:
10039: + A     - the matrix
10040: . P     - the projection matrix
10041: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10042: - 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
10043:           if the result is a dense matrix this is irrelevant

10045:   Output Parameter:
10046: . C - the product matrix

10048:   Level: intermediate

10050:   Notes:
10051:   C will be created and must be destroyed by the user with `MatDestroy()`.

10053:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

10055:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10057:   Developer Note:
10058:   For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.

10060: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10061: @*/
10062: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10063: {
10064:   PetscFunctionBegin;
10065:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10066:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10068:   if (scall == MAT_INITIAL_MATRIX) {
10069:     PetscCall(MatProductCreate(A, P, NULL, C));
10070:     PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10071:     PetscCall(MatProductSetAlgorithm(*C, "default"));
10072:     PetscCall(MatProductSetFill(*C, fill));

10074:     (*C)->product->api_user = PETSC_TRUE;
10075:     PetscCall(MatProductSetFromOptions(*C));
10076:     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);
10077:     PetscCall(MatProductSymbolic(*C));
10078:   } else { /* scall == MAT_REUSE_MATRIX */
10079:     PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10080:   }

10082:   PetscCall(MatProductNumeric(*C));
10083:   (*C)->symmetric = A->symmetric;
10084:   (*C)->spd       = A->spd;
10085:   PetscFunctionReturn(PETSC_SUCCESS);
10086: }

10088: /*@
10089:   MatRARt - Creates the matrix product $C = R * A * R^T$

10091:   Neighbor-wise Collective

10093:   Input Parameters:
10094: + A     - the matrix
10095: . R     - the projection matrix
10096: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10097: - fill  - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10098:           if the result is a dense matrix this is irrelevant

10100:   Output Parameter:
10101: . C - the product matrix

10103:   Level: intermediate

10105:   Notes:
10106:   `C` will be created and must be destroyed by the user with `MatDestroy()`.

10108:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

10110:   This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10111:   which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10112:   the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10113:   We recommend using `MatPtAP()` when possible.

10115:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10117: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10118: @*/
10119: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10120: {
10121:   PetscFunctionBegin;
10122:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10123:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10125:   if (scall == MAT_INITIAL_MATRIX) {
10126:     PetscCall(MatProductCreate(A, R, NULL, C));
10127:     PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10128:     PetscCall(MatProductSetAlgorithm(*C, "default"));
10129:     PetscCall(MatProductSetFill(*C, fill));

10131:     (*C)->product->api_user = PETSC_TRUE;
10132:     PetscCall(MatProductSetFromOptions(*C));
10133:     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);
10134:     PetscCall(MatProductSymbolic(*C));
10135:   } else { /* scall == MAT_REUSE_MATRIX */
10136:     PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10137:   }

10139:   PetscCall(MatProductNumeric(*C));
10140:   if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10141:   PetscFunctionReturn(PETSC_SUCCESS);
10142: }

10144: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10145: {
10146:   PetscBool flg = PETSC_TRUE;

10148:   PetscFunctionBegin;
10149:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10150:   if (scall == MAT_INITIAL_MATRIX) {
10151:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10152:     PetscCall(MatProductCreate(A, B, NULL, C));
10153:     PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10154:     PetscCall(MatProductSetFill(*C, fill));
10155:   } else { /* scall == MAT_REUSE_MATRIX */
10156:     Mat_Product *product = (*C)->product;

10158:     PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10159:     if (flg && product && product->type != ptype) {
10160:       PetscCall(MatProductClear(*C));
10161:       product = NULL;
10162:     }
10163:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10164:     if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10165:       PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10166:       PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10167:       product        = (*C)->product;
10168:       product->fill  = fill;
10169:       product->clear = PETSC_TRUE;
10170:     } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10171:       flg = PETSC_FALSE;
10172:       PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10173:     }
10174:   }
10175:   if (flg) {
10176:     (*C)->product->api_user = PETSC_TRUE;
10177:     PetscCall(MatProductSetType(*C, ptype));
10178:     PetscCall(MatProductSetFromOptions(*C));
10179:     PetscCall(MatProductSymbolic(*C));
10180:   }
10181:   PetscCall(MatProductNumeric(*C));
10182:   PetscFunctionReturn(PETSC_SUCCESS);
10183: }

10185: /*@
10186:   MatMatMult - Performs matrix-matrix multiplication C=A*B.

10188:   Neighbor-wise Collective

10190:   Input Parameters:
10191: + A     - the left matrix
10192: . B     - the right matrix
10193: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10194: - 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
10195:           if the result is a dense matrix this is irrelevant

10197:   Output Parameter:
10198: . C - the product matrix

10200:   Notes:
10201:   Unless scall is `MAT_REUSE_MATRIX` C will be created.

10203:   `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
10204:   call to this function with `MAT_INITIAL_MATRIX`.

10206:   To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.

10208:   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`,
10209:   rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.

10211:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10213:   Example of Usage:
10214: .vb
10215:      MatProductCreate(A,B,NULL,&C);
10216:      MatProductSetType(C,MATPRODUCT_AB);
10217:      MatProductSymbolic(C);
10218:      MatProductNumeric(C); // compute C=A * B
10219:      MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10220:      MatProductNumeric(C);
10221:      MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10222:      MatProductNumeric(C);
10223: .ve

10225:   Level: intermediate

10227: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10228: @*/
10229: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10230: {
10231:   PetscFunctionBegin;
10232:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10233:   PetscFunctionReturn(PETSC_SUCCESS);
10234: }

10236: /*@
10237:   MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.

10239:   Neighbor-wise Collective

10241:   Input Parameters:
10242: + A     - the left matrix
10243: . B     - the right matrix
10244: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10245: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

10247:   Output Parameter:
10248: . C - the product matrix

10250:   Options Database Key:
10251: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10252:               first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10253:               the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.

10255:   Level: intermediate

10257:   Notes:
10258:   C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10260:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call

10262:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10263:   actually needed.

10265:   This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10266:   and for pairs of `MATMPIDENSE` matrices.

10268:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`

10270:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10272: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10273: @*/
10274: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10275: {
10276:   PetscFunctionBegin;
10277:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10278:   if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10279:   PetscFunctionReturn(PETSC_SUCCESS);
10280: }

10282: /*@
10283:   MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.

10285:   Neighbor-wise Collective

10287:   Input Parameters:
10288: + A     - the left matrix
10289: . B     - the right matrix
10290: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10291: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known

10293:   Output Parameter:
10294: . C - the product matrix

10296:   Level: intermediate

10298:   Notes:
10299:   `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10301:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.

10303:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`

10305:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10306:   actually needed.

10308:   This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10309:   which inherit from `MATSEQAIJ`.  `C` will be of the same type as the input matrices.

10311:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10313: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10314: @*/
10315: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10316: {
10317:   PetscFunctionBegin;
10318:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10319:   PetscFunctionReturn(PETSC_SUCCESS);
10320: }

10322: /*@
10323:   MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.

10325:   Neighbor-wise Collective

10327:   Input Parameters:
10328: + A     - the left matrix
10329: . B     - the middle matrix
10330: . C     - the right matrix
10331: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10332: - 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
10333:           if the result is a dense matrix this is irrelevant

10335:   Output Parameter:
10336: . D - the product matrix

10338:   Level: intermediate

10340:   Notes:
10341:   Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.

10343:   `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call

10345:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`

10347:   To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10348:   actually needed.

10350:   If you have many matrices with the same non-zero structure to multiply, you
10351:   should use `MAT_REUSE_MATRIX` in all calls but the first

10353:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

10355: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10356: @*/
10357: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10358: {
10359:   PetscFunctionBegin;
10360:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10361:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10363:   if (scall == MAT_INITIAL_MATRIX) {
10364:     PetscCall(MatProductCreate(A, B, C, D));
10365:     PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10366:     PetscCall(MatProductSetAlgorithm(*D, "default"));
10367:     PetscCall(MatProductSetFill(*D, fill));

10369:     (*D)->product->api_user = PETSC_TRUE;
10370:     PetscCall(MatProductSetFromOptions(*D));
10371:     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,
10372:                ((PetscObject)C)->type_name);
10373:     PetscCall(MatProductSymbolic(*D));
10374:   } else { /* user may change input matrices when REUSE */
10375:     PetscCall(MatProductReplaceMats(A, B, C, *D));
10376:   }
10377:   PetscCall(MatProductNumeric(*D));
10378:   PetscFunctionReturn(PETSC_SUCCESS);
10379: }

10381: /*@
10382:   MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.

10384:   Collective

10386:   Input Parameters:
10387: + mat      - the matrix
10388: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10389: . subcomm  - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10390: - reuse    - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10392:   Output Parameter:
10393: . matredundant - redundant matrix

10395:   Level: advanced

10397:   Notes:
10398:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10399:   original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.

10401:   This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10402:   calling it.

10404:   `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.

10406: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10407: @*/
10408: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10409: {
10410:   MPI_Comm       comm;
10411:   PetscMPIInt    size;
10412:   PetscInt       mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10413:   Mat_Redundant *redund     = NULL;
10414:   PetscSubcomm   psubcomm   = NULL;
10415:   MPI_Comm       subcomm_in = subcomm;
10416:   Mat           *matseq;
10417:   IS             isrow, iscol;
10418:   PetscBool      newsubcomm = PETSC_FALSE;

10420:   PetscFunctionBegin;
10422:   if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10423:     PetscAssertPointer(*matredundant, 5);
10425:   }

10427:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10428:   if (size == 1 || nsubcomm == 1) {
10429:     if (reuse == MAT_INITIAL_MATRIX) {
10430:       PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10431:     } else {
10432:       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");
10433:       PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10434:     }
10435:     PetscFunctionReturn(PETSC_SUCCESS);
10436:   }

10438:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10439:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10440:   MatCheckPreallocated(mat, 1);

10442:   PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10443:   if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10444:     /* create psubcomm, then get subcomm */
10445:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10446:     PetscCallMPI(MPI_Comm_size(comm, &size));
10447:     PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);

10449:     PetscCall(PetscSubcommCreate(comm, &psubcomm));
10450:     PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10451:     PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10452:     PetscCall(PetscSubcommSetFromOptions(psubcomm));
10453:     PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10454:     newsubcomm = PETSC_TRUE;
10455:     PetscCall(PetscSubcommDestroy(&psubcomm));
10456:   }

10458:   /* get isrow, iscol and a local sequential matrix matseq[0] */
10459:   if (reuse == MAT_INITIAL_MATRIX) {
10460:     mloc_sub = PETSC_DECIDE;
10461:     nloc_sub = PETSC_DECIDE;
10462:     if (bs < 1) {
10463:       PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10464:       PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10465:     } else {
10466:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10467:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10468:     }
10469:     PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10470:     rstart = rend - mloc_sub;
10471:     PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10472:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10473:     PetscCall(ISSetIdentity(iscol));
10474:   } else { /* reuse == MAT_REUSE_MATRIX */
10475:     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");
10476:     /* retrieve subcomm */
10477:     PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10478:     redund = (*matredundant)->redundant;
10479:     isrow  = redund->isrow;
10480:     iscol  = redund->iscol;
10481:     matseq = redund->matseq;
10482:   }
10483:   PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));

10485:   /* get matredundant over subcomm */
10486:   if (reuse == MAT_INITIAL_MATRIX) {
10487:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));

10489:     /* create a supporting struct and attach it to C for reuse */
10490:     PetscCall(PetscNew(&redund));
10491:     (*matredundant)->redundant = redund;
10492:     redund->isrow              = isrow;
10493:     redund->iscol              = iscol;
10494:     redund->matseq             = matseq;
10495:     if (newsubcomm) {
10496:       redund->subcomm = subcomm;
10497:     } else {
10498:       redund->subcomm = MPI_COMM_NULL;
10499:     }
10500:   } else {
10501:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10502:   }
10503: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10504:   if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10505:     PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10506:     PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10507:   }
10508: #endif
10509:   PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10510:   PetscFunctionReturn(PETSC_SUCCESS);
10511: }

10513: /*@C
10514:   MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10515:   a given `Mat`. Each submatrix can span multiple procs.

10517:   Collective

10519:   Input Parameters:
10520: + mat     - the matrix
10521: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10522: - scall   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10524:   Output Parameter:
10525: . subMat - parallel sub-matrices each spanning a given `subcomm`

10527:   Level: advanced

10529:   Notes:
10530:   The submatrix partition across processors is dictated by `subComm` a
10531:   communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10532:   is not restricted to be grouped with consecutive original MPI processes.

10534:   Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10535:   map directly to the layout of the original matrix [wrt the local
10536:   row,col partitioning]. So the original 'DiagonalMat' naturally maps
10537:   into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10538:   the `subMat`. However the offDiagMat looses some columns - and this is
10539:   reconstructed with `MatSetValues()`

10541:   This is used by `PCBJACOBI` when a single block spans multiple MPI processes.

10543: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10544: @*/
10545: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10546: {
10547:   PetscMPIInt commsize, subCommSize;

10549:   PetscFunctionBegin;
10550:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10551:   PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10552:   PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);

10554:   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");
10555:   PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10556:   PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10557:   PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10558:   PetscFunctionReturn(PETSC_SUCCESS);
10559: }

10561: /*@
10562:   MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering

10564:   Not Collective

10566:   Input Parameters:
10567: + mat   - matrix to extract local submatrix from
10568: . isrow - local row indices for submatrix
10569: - iscol - local column indices for submatrix

10571:   Output Parameter:
10572: . submat - the submatrix

10574:   Level: intermediate

10576:   Notes:
10577:   `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.

10579:   Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`.  Its communicator may be
10580:   the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.

10582:   `submat` always implements `MatSetValuesLocal()`.  If `isrow` and `iscol` have the same block size, then
10583:   `MatSetValuesBlockedLocal()` will also be implemented.

10585:   `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10586:   Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.

10588: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10589: @*/
10590: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10591: {
10592:   PetscFunctionBegin;
10596:   PetscCheckSameComm(isrow, 2, iscol, 3);
10597:   PetscAssertPointer(submat, 4);
10598:   PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");

10600:   if (mat->ops->getlocalsubmatrix) {
10601:     PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10602:   } else {
10603:     PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10604:   }
10605:   PetscFunctionReturn(PETSC_SUCCESS);
10606: }

10608: /*@
10609:   MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`

10611:   Not Collective

10613:   Input Parameters:
10614: + mat    - matrix to extract local submatrix from
10615: . isrow  - local row indices for submatrix
10616: . iscol  - local column indices for submatrix
10617: - submat - the submatrix

10619:   Level: intermediate

10621: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10622: @*/
10623: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10624: {
10625:   PetscFunctionBegin;
10629:   PetscCheckSameComm(isrow, 2, iscol, 3);
10630:   PetscAssertPointer(submat, 4);

10633:   if (mat->ops->restorelocalsubmatrix) {
10634:     PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10635:   } else {
10636:     PetscCall(MatDestroy(submat));
10637:   }
10638:   *submat = NULL;
10639:   PetscFunctionReturn(PETSC_SUCCESS);
10640: }

10642: /*@
10643:   MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix

10645:   Collective

10647:   Input Parameter:
10648: . mat - the matrix

10650:   Output Parameter:
10651: . is - if any rows have zero diagonals this contains the list of them

10653:   Level: developer

10655: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10656: @*/
10657: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10658: {
10659:   PetscFunctionBegin;
10662:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10663:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10665:   if (!mat->ops->findzerodiagonals) {
10666:     Vec                diag;
10667:     const PetscScalar *a;
10668:     PetscInt          *rows;
10669:     PetscInt           rStart, rEnd, r, nrow = 0;

10671:     PetscCall(MatCreateVecs(mat, &diag, NULL));
10672:     PetscCall(MatGetDiagonal(mat, diag));
10673:     PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10674:     PetscCall(VecGetArrayRead(diag, &a));
10675:     for (r = 0; r < rEnd - rStart; ++r)
10676:       if (a[r] == 0.0) ++nrow;
10677:     PetscCall(PetscMalloc1(nrow, &rows));
10678:     nrow = 0;
10679:     for (r = 0; r < rEnd - rStart; ++r)
10680:       if (a[r] == 0.0) rows[nrow++] = r + rStart;
10681:     PetscCall(VecRestoreArrayRead(diag, &a));
10682:     PetscCall(VecDestroy(&diag));
10683:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10684:   } else {
10685:     PetscUseTypeMethod(mat, findzerodiagonals, is);
10686:   }
10687:   PetscFunctionReturn(PETSC_SUCCESS);
10688: }

10690: /*@
10691:   MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)

10693:   Collective

10695:   Input Parameter:
10696: . mat - the matrix

10698:   Output Parameter:
10699: . is - contains the list of rows with off block diagonal entries

10701:   Level: developer

10703: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10704: @*/
10705: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10706: {
10707:   PetscFunctionBegin;
10710:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10711:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10713:   PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10714:   PetscFunctionReturn(PETSC_SUCCESS);
10715: }

10717: /*@C
10718:   MatInvertBlockDiagonal - Inverts the block diagonal entries.

10720:   Collective; No Fortran Support

10722:   Input Parameter:
10723: . mat - the matrix

10725:   Output Parameter:
10726: . values - the block inverses in column major order (FORTRAN-like)

10728:   Level: advanced

10730:   Notes:
10731:   The size of the blocks is determined by the block size of the matrix.

10733:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10735:   The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size

10737: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10738: @*/
10739: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10740: {
10741:   PetscFunctionBegin;
10743:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10744:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10745:   PetscUseTypeMethod(mat, invertblockdiagonal, values);
10746:   PetscFunctionReturn(PETSC_SUCCESS);
10747: }

10749: /*@
10750:   MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.

10752:   Collective; No Fortran Support

10754:   Input Parameters:
10755: + mat     - the matrix
10756: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10757: - bsizes  - the size of each block on the process, set with `MatSetVariableBlockSizes()`

10759:   Output Parameter:
10760: . values - the block inverses in column major order (FORTRAN-like)

10762:   Level: advanced

10764:   Notes:
10765:   Use `MatInvertBlockDiagonal()` if all blocks have the same size

10767:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10769: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10770: @*/
10771: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10772: {
10773:   PetscFunctionBegin;
10775:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10776:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10777:   PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10778:   PetscFunctionReturn(PETSC_SUCCESS);
10779: }

10781: /*@
10782:   MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A

10784:   Collective

10786:   Input Parameters:
10787: + A - the matrix
10788: - C - matrix with inverted block diagonal of `A`.  This matrix should be created and may have its type set.

10790:   Level: advanced

10792:   Note:
10793:   The blocksize of the matrix is used to determine the blocks on the diagonal of `C`

10795: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10796: @*/
10797: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10798: {
10799:   const PetscScalar *vals;
10800:   PetscInt          *dnnz;
10801:   PetscInt           m, rstart, rend, bs, i, j;

10803:   PetscFunctionBegin;
10804:   PetscCall(MatInvertBlockDiagonal(A, &vals));
10805:   PetscCall(MatGetBlockSize(A, &bs));
10806:   PetscCall(MatGetLocalSize(A, &m, NULL));
10807:   PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10808:   PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10809:   PetscCall(PetscMalloc1(m / bs, &dnnz));
10810:   for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10811:   PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10812:   PetscCall(PetscFree(dnnz));
10813:   PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10814:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10815:   for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10816:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10817:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10818:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10819:   PetscFunctionReturn(PETSC_SUCCESS);
10820: }

10822: /*@
10823:   MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10824:   via `MatTransposeColoringCreate()`.

10826:   Collective

10828:   Input Parameter:
10829: . c - coloring context

10831:   Level: intermediate

10833: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10834: @*/
10835: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10836: {
10837:   MatTransposeColoring matcolor = *c;

10839:   PetscFunctionBegin;
10840:   if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10841:   if (--((PetscObject)matcolor)->refct > 0) {
10842:     matcolor = NULL;
10843:     PetscFunctionReturn(PETSC_SUCCESS);
10844:   }

10846:   PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10847:   PetscCall(PetscFree(matcolor->rows));
10848:   PetscCall(PetscFree(matcolor->den2sp));
10849:   PetscCall(PetscFree(matcolor->colorforcol));
10850:   PetscCall(PetscFree(matcolor->columns));
10851:   if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10852:   PetscCall(PetscHeaderDestroy(c));
10853:   PetscFunctionReturn(PETSC_SUCCESS);
10854: }

10856: /*@
10857:   MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10858:   a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10859:   `MatTransposeColoring` to sparse `B`.

10861:   Collective

10863:   Input Parameters:
10864: + coloring - coloring context created with `MatTransposeColoringCreate()`
10865: - B        - sparse matrix

10867:   Output Parameter:
10868: . Btdense - dense matrix $B^T$

10870:   Level: developer

10872:   Note:
10873:   These are used internally for some implementations of `MatRARt()`

10875: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10876: @*/
10877: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10878: {
10879:   PetscFunctionBegin;

10884:   PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10885:   PetscFunctionReturn(PETSC_SUCCESS);
10886: }

10888: /*@
10889:   MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10890:   a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10891:   in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10892:   $C_{sp}$ from $C_{den}$.

10894:   Collective

10896:   Input Parameters:
10897: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10898: - Cden        - matrix product of a sparse matrix and a dense matrix Btdense

10900:   Output Parameter:
10901: . Csp - sparse matrix

10903:   Level: developer

10905:   Note:
10906:   These are used internally for some implementations of `MatRARt()`

10908: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10909: @*/
10910: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10911: {
10912:   PetscFunctionBegin;

10917:   PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10918:   PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10919:   PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10920:   PetscFunctionReturn(PETSC_SUCCESS);
10921: }

10923: /*@
10924:   MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.

10926:   Collective

10928:   Input Parameters:
10929: + mat        - the matrix product C
10930: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`

10932:   Output Parameter:
10933: . color - the new coloring context

10935:   Level: intermediate

10937: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10938:           `MatTransColoringApplyDenToSp()`
10939: @*/
10940: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10941: {
10942:   MatTransposeColoring c;
10943:   MPI_Comm             comm;

10945:   PetscFunctionBegin;
10946:   PetscAssertPointer(color, 3);

10948:   PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10949:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10950:   PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10951:   c->ctype = iscoloring->ctype;
10952:   PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10953:   *color = c;
10954:   PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10955:   PetscFunctionReturn(PETSC_SUCCESS);
10956: }

10958: /*@
10959:   MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10960:   matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.

10962:   Not Collective

10964:   Input Parameter:
10965: . mat - the matrix

10967:   Output Parameter:
10968: . state - the current state

10970:   Level: intermediate

10972:   Notes:
10973:   You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10974:   different matrices

10976:   Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix

10978:   Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.

10980: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10981: @*/
10982: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10983: {
10984:   PetscFunctionBegin;
10986:   *state = mat->nonzerostate;
10987:   PetscFunctionReturn(PETSC_SUCCESS);
10988: }

10990: /*@
10991:   MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10992:   matrices from each processor

10994:   Collective

10996:   Input Parameters:
10997: + comm   - the communicators the parallel matrix will live on
10998: . seqmat - the input sequential matrices
10999: . n      - number of local columns (or `PETSC_DECIDE`)
11000: - reuse  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

11002:   Output Parameter:
11003: . mpimat - the parallel matrix generated

11005:   Level: developer

11007:   Note:
11008:   The number of columns of the matrix in EACH processor MUST be the same.

11010: .seealso: [](ch_matrices), `Mat`
11011: @*/
11012: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11013: {
11014:   PetscMPIInt size;

11016:   PetscFunctionBegin;
11017:   PetscCallMPI(MPI_Comm_size(comm, &size));
11018:   if (size == 1) {
11019:     if (reuse == MAT_INITIAL_MATRIX) {
11020:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11021:     } else {
11022:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11023:     }
11024:     PetscFunctionReturn(PETSC_SUCCESS);
11025:   }

11027:   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");

11029:   PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11030:   PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11031:   PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11032:   PetscFunctionReturn(PETSC_SUCCESS);
11033: }

11035: /*@
11036:   MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.

11038:   Collective

11040:   Input Parameters:
11041: + A - the matrix to create subdomains from
11042: - N - requested number of subdomains

11044:   Output Parameters:
11045: + n   - number of subdomains resulting on this MPI process
11046: - iss - `IS` list with indices of subdomains on this MPI process

11048:   Level: advanced

11050:   Note:
11051:   The number of subdomains must be smaller than the communicator size

11053: .seealso: [](ch_matrices), `Mat`, `IS`
11054: @*/
11055: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11056: {
11057:   MPI_Comm    comm, subcomm;
11058:   PetscMPIInt size, rank, color;
11059:   PetscInt    rstart, rend, k;

11061:   PetscFunctionBegin;
11062:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11063:   PetscCallMPI(MPI_Comm_size(comm, &size));
11064:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
11065:   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);
11066:   *n    = 1;
11067:   k     = size / N + (size % N > 0); /* There are up to k ranks to a color */
11068:   color = rank / k;
11069:   PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11070:   PetscCall(PetscMalloc1(1, iss));
11071:   PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11072:   PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11073:   PetscCallMPI(MPI_Comm_free(&subcomm));
11074:   PetscFunctionReturn(PETSC_SUCCESS);
11075: }

11077: /*@
11078:   MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.

11080:   If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11081:   If they are not the same, uses `MatMatMatMult()`.

11083:   Once the coarse grid problem is constructed, correct for interpolation operators
11084:   that are not of full rank, which can legitimately happen in the case of non-nested
11085:   geometric multigrid.

11087:   Input Parameters:
11088: + restrct     - restriction operator
11089: . dA          - fine grid matrix
11090: . interpolate - interpolation operator
11091: . reuse       - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11092: - fill        - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate

11094:   Output Parameter:
11095: . A - the Galerkin coarse matrix

11097:   Options Database Key:
11098: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used

11100:   Level: developer

11102:   Note:
11103:   The deprecated `PETSC_DEFAULT` in `fill` also means use the current value

11105: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11106: @*/
11107: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11108: {
11109:   IS  zerorows;
11110:   Vec diag;

11112:   PetscFunctionBegin;
11113:   PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11114:   /* Construct the coarse grid matrix */
11115:   if (interpolate == restrct) {
11116:     PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11117:   } else {
11118:     PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11119:   }

11121:   /* If the interpolation matrix is not of full rank, A will have zero rows.
11122:      This can legitimately happen in the case of non-nested geometric multigrid.
11123:      In that event, we set the rows of the matrix to the rows of the identity,
11124:      ignoring the equations (as the RHS will also be zero). */

11126:   PetscCall(MatFindZeroRows(*A, &zerorows));

11128:   if (zerorows != NULL) { /* if there are any zero rows */
11129:     PetscCall(MatCreateVecs(*A, &diag, NULL));
11130:     PetscCall(MatGetDiagonal(*A, diag));
11131:     PetscCall(VecISSet(diag, zerorows, 1.0));
11132:     PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11133:     PetscCall(VecDestroy(&diag));
11134:     PetscCall(ISDestroy(&zerorows));
11135:   }
11136:   PetscFunctionReturn(PETSC_SUCCESS);
11137: }

11139: /*@C
11140:   MatSetOperation - Allows user to set a matrix operation for any matrix type

11142:   Logically Collective

11144:   Input Parameters:
11145: + mat - the matrix
11146: . op  - the name of the operation
11147: - f   - the function that provides the operation

11149:   Level: developer

11151:   Example Usage:
11152: .vb
11153:   extern PetscErrorCode usermult(Mat, Vec, Vec);

11155:   PetscCall(MatCreateXXX(comm, ..., &A));
11156:   PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
11157: .ve

11159:   Notes:
11160:   See the file `include/petscmat.h` for a complete list of matrix
11161:   operations, which all have the form MATOP_<OPERATION>, where
11162:   <OPERATION> is the name (in all capital letters) of the
11163:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11165:   All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11166:   sequence as the usual matrix interface routines, since they
11167:   are intended to be accessed via the usual matrix interface
11168:   routines, e.g.,
11169: .vb
11170:   MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11171: .ve

11173:   In particular each function MUST return `PETSC_SUCCESS` on success and
11174:   nonzero on failure.

11176:   This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.

11178: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11179: @*/
11180: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11181: {
11182:   PetscFunctionBegin;
11184:   if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
11185:   (((void (**)(void))mat->ops)[op]) = f;
11186:   PetscFunctionReturn(PETSC_SUCCESS);
11187: }

11189: /*@C
11190:   MatGetOperation - Gets a matrix operation for any matrix type.

11192:   Not Collective

11194:   Input Parameters:
11195: + mat - the matrix
11196: - op  - the name of the operation

11198:   Output Parameter:
11199: . f - the function that provides the operation

11201:   Level: developer

11203:   Example Usage:
11204: .vb
11205:   PetscErrorCode (*usermult)(Mat, Vec, Vec);

11207:   MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11208: .ve

11210:   Notes:
11211:   See the file include/petscmat.h for a complete list of matrix
11212:   operations, which all have the form MATOP_<OPERATION>, where
11213:   <OPERATION> is the name (in all capital letters) of the
11214:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11216:   This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.

11218: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11219: @*/
11220: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11221: {
11222:   PetscFunctionBegin;
11224:   *f = (((void (**)(void))mat->ops)[op]);
11225:   PetscFunctionReturn(PETSC_SUCCESS);
11226: }

11228: /*@
11229:   MatHasOperation - Determines whether the given matrix supports the particular operation.

11231:   Not Collective

11233:   Input Parameters:
11234: + mat - the matrix
11235: - op  - the operation, for example, `MATOP_GET_DIAGONAL`

11237:   Output Parameter:
11238: . has - either `PETSC_TRUE` or `PETSC_FALSE`

11240:   Level: advanced

11242:   Note:
11243:   See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.

11245: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11246: @*/
11247: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11248: {
11249:   PetscFunctionBegin;
11251:   PetscAssertPointer(has, 3);
11252:   if (mat->ops->hasoperation) {
11253:     PetscUseTypeMethod(mat, hasoperation, op, has);
11254:   } else {
11255:     if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11256:     else {
11257:       *has = PETSC_FALSE;
11258:       if (op == MATOP_CREATE_SUBMATRIX) {
11259:         PetscMPIInt size;

11261:         PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11262:         if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11263:       }
11264:     }
11265:   }
11266:   PetscFunctionReturn(PETSC_SUCCESS);
11267: }

11269: /*@
11270:   MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent

11272:   Collective

11274:   Input Parameter:
11275: . mat - the matrix

11277:   Output Parameter:
11278: . cong - either `PETSC_TRUE` or `PETSC_FALSE`

11280:   Level: beginner

11282: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11283: @*/
11284: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11285: {
11286:   PetscFunctionBegin;
11289:   PetscAssertPointer(cong, 2);
11290:   if (!mat->rmap || !mat->cmap) {
11291:     *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11292:     PetscFunctionReturn(PETSC_SUCCESS);
11293:   }
11294:   if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11295:     PetscCall(PetscLayoutSetUp(mat->rmap));
11296:     PetscCall(PetscLayoutSetUp(mat->cmap));
11297:     PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11298:     if (*cong) mat->congruentlayouts = 1;
11299:     else mat->congruentlayouts = 0;
11300:   } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11301:   PetscFunctionReturn(PETSC_SUCCESS);
11302: }

11304: PetscErrorCode MatSetInf(Mat A)
11305: {
11306:   PetscFunctionBegin;
11307:   PetscUseTypeMethod(A, setinf);
11308:   PetscFunctionReturn(PETSC_SUCCESS);
11309: }

11311: /*@
11312:   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
11313:   and possibly removes small values from the graph structure.

11315:   Collective

11317:   Input Parameters:
11318: + A       - the matrix
11319: . sym     - `PETSC_TRUE` indicates that the graph should be symmetrized
11320: . scale   - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11321: . filter  - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11322: . num_idx - size of 'index' array
11323: - index   - array of block indices to use for graph strength of connection weight

11325:   Output Parameter:
11326: . graph - the resulting graph

11328:   Level: advanced

11330: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11331: @*/
11332: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11333: {
11334:   PetscFunctionBegin;
11338:   PetscAssertPointer(graph, 7);
11339:   PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11340:   PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11341:   PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11342:   PetscFunctionReturn(PETSC_SUCCESS);
11343: }

11345: /*@
11346:   MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11347:   meaning the same memory is used for the matrix, and no new memory is allocated.

11349:   Collective

11351:   Input Parameters:
11352: + A    - the matrix
11353: - 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

11355:   Level: intermediate

11357:   Developer Note:
11358:   The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11359:   of the arrays in the data structure are unneeded.

11361: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11362: @*/
11363: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11364: {
11365:   PetscFunctionBegin;
11367:   PetscUseTypeMethod(A, eliminatezeros, keep);
11368:   PetscFunctionReturn(PETSC_SUCCESS);
11369: }

11371: /*@C
11372:   MatGetCurrentMemType - Get the memory location of the matrix

11374:   Not Collective, but the result will be the same on all MPI processes

11376:   Input Parameter:
11377: . A - the matrix whose memory type we are checking

11379:   Output Parameter:
11380: . m - the memory type

11382:   Level: intermediate

11384: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11385: @*/
11386: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11387: {
11388:   PetscFunctionBegin;
11390:   PetscAssertPointer(m, 2);
11391:   if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11392:   else *m = PETSC_MEMTYPE_HOST;
11393:   PetscFunctionReturn(PETSC_SUCCESS);
11394: }