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:   MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in

110:   Logically Collective

112:   Input Parameter:
113: . mat - the factored matrix

115:   Output Parameters:
116: + pivot - the pivot value computed
117: - row   - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
118:          the share the matrix

120:   Level: advanced

122:   Notes:
123:   This routine does not work for factorizations done with external packages.

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

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

129: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
130: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
131: `MAT_FACTOR_NUMERIC_ZEROPIVOT`
132: @*/
133: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
134: {
135:   PetscFunctionBegin;
137:   PetscAssertPointer(pivot, 2);
138:   PetscAssertPointer(row, 3);
139:   *pivot = mat->factorerror_zeropivot_value;
140:   *row   = mat->factorerror_zeropivot_row;
141:   PetscFunctionReturn(PETSC_SUCCESS);
142: }

144: /*@
145:   MatFactorGetError - gets the error code from a factorization

147:   Logically Collective

149:   Input Parameter:
150: . mat - the factored matrix

152:   Output Parameter:
153: . err - the error code

155:   Level: advanced

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

160: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
161:           `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
162: @*/
163: PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
164: {
165:   PetscFunctionBegin;
167:   PetscAssertPointer(err, 2);
168:   *err = mat->factorerrortype;
169:   PetscFunctionReturn(PETSC_SUCCESS);
170: }

172: /*@
173:   MatFactorClearError - clears the error code in a factorization

175:   Logically Collective

177:   Input Parameter:
178: . mat - the factored matrix

180:   Level: developer

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

185: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
186:           `MatGetErrorCode()`, `MatFactorError`
187: @*/
188: PetscErrorCode MatFactorClearError(Mat mat)
189: {
190:   PetscFunctionBegin;
192:   mat->factorerrortype             = MAT_FACTOR_NOERROR;
193:   mat->factorerror_zeropivot_value = 0.0;
194:   mat->factorerror_zeropivot_row   = 0;
195:   PetscFunctionReturn(PETSC_SUCCESS);
196: }

198: PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
199: {
200:   Vec                r, l;
201:   const PetscScalar *al;
202:   PetscInt           i, nz, gnz, N, n, st;

204:   PetscFunctionBegin;
205:   PetscCall(MatCreateVecs(mat, &r, &l));
206:   if (!cols) { /* nonzero rows */
207:     PetscCall(MatGetOwnershipRange(mat, &st, NULL));
208:     PetscCall(MatGetSize(mat, &N, NULL));
209:     PetscCall(MatGetLocalSize(mat, &n, NULL));
210:     PetscCall(VecSet(l, 0.0));
211:     PetscCall(VecSetRandom(r, NULL));
212:     PetscCall(MatMult(mat, r, l));
213:     PetscCall(VecGetArrayRead(l, &al));
214:   } else { /* nonzero columns */
215:     PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
216:     PetscCall(MatGetSize(mat, NULL, &N));
217:     PetscCall(MatGetLocalSize(mat, NULL, &n));
218:     PetscCall(VecSet(r, 0.0));
219:     PetscCall(VecSetRandom(l, NULL));
220:     PetscCall(MatMultTranspose(mat, l, r));
221:     PetscCall(VecGetArrayRead(r, &al));
222:   }
223:   if (tol <= 0.0) {
224:     for (i = 0, nz = 0; i < n; i++)
225:       if (al[i] != 0.0) nz++;
226:   } else {
227:     for (i = 0, nz = 0; i < n; i++)
228:       if (PetscAbsScalar(al[i]) > tol) nz++;
229:   }
230:   PetscCallMPI(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
231:   if (gnz != N) {
232:     PetscInt *nzr;
233:     PetscCall(PetscMalloc1(nz, &nzr));
234:     if (nz) {
235:       if (tol < 0) {
236:         for (i = 0, nz = 0; i < n; i++)
237:           if (al[i] != 0.0) nzr[nz++] = i + st;
238:       } else {
239:         for (i = 0, nz = 0; i < n; i++)
240:           if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
241:       }
242:     }
243:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
244:   } else *nonzero = NULL;
245:   if (!cols) { /* nonzero rows */
246:     PetscCall(VecRestoreArrayRead(l, &al));
247:   } else {
248:     PetscCall(VecRestoreArrayRead(r, &al));
249:   }
250:   PetscCall(VecDestroy(&l));
251:   PetscCall(VecDestroy(&r));
252:   PetscFunctionReturn(PETSC_SUCCESS);
253: }

255: /*@
256:   MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix

258:   Input Parameter:
259: . mat - the matrix

261:   Output Parameter:
262: . keptrows - the rows that are not completely zero

264:   Level: intermediate

266:   Note:
267:   `keptrows` is set to `NULL` if all rows are nonzero.

269:   Developer Note:
270:   If `keptrows` is not `NULL`, it must be sorted.

272: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
273:  @*/
274: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
275: {
276:   PetscFunctionBegin;
279:   PetscAssertPointer(keptrows, 2);
280:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
281:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
282:   if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
283:   else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
284:   if (keptrows && *keptrows) PetscCall(ISSetInfo(*keptrows, IS_SORTED, IS_GLOBAL, PETSC_FALSE, PETSC_TRUE));
285:   PetscFunctionReturn(PETSC_SUCCESS);
286: }

288: /*@
289:   MatFindZeroRows - Locate all rows that are completely zero in the matrix

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

294:   Output Parameter:
295: . zerorows - the rows that are completely zero

297:   Level: intermediate

299:   Note:
300:   `zerorows` is set to `NULL` if no rows are zero.

302: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
303:  @*/
304: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
305: {
306:   IS       keptrows;
307:   PetscInt m, n;

309:   PetscFunctionBegin;
312:   PetscAssertPointer(zerorows, 2);
313:   PetscCall(MatFindNonzeroRows(mat, &keptrows));
314:   /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
315:      In keeping with this convention, we set zerorows to NULL if there are no zero
316:      rows. */
317:   if (keptrows == NULL) {
318:     *zerorows = NULL;
319:   } else {
320:     PetscCall(MatGetOwnershipRange(mat, &m, &n));
321:     PetscCall(ISComplement(keptrows, m, n, zerorows));
322:     PetscCall(ISDestroy(&keptrows));
323:   }
324:   PetscFunctionReturn(PETSC_SUCCESS);
325: }

327: /*@
328:   MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling

330:   Not Collective

332:   Input Parameter:
333: . A - the matrix

335:   Output Parameter:
336: . a - the diagonal part (which is a SEQUENTIAL matrix)

338:   Level: advanced

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

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

345: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
346: @*/
347: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
348: {
349:   PetscFunctionBegin;
352:   PetscAssertPointer(a, 2);
353:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
354:   if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
355:   else {
356:     PetscMPIInt size;

358:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
359:     PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
360:     *a = A;
361:   }
362:   PetscFunctionReturn(PETSC_SUCCESS);
363: }

365: /*@
366:   MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.

368:   Collective

370:   Input Parameter:
371: . mat - the matrix

373:   Output Parameter:
374: . trace - the sum of the diagonal entries

376:   Level: advanced

378: .seealso: [](ch_matrices), `Mat`
379: @*/
380: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
381: {
382:   Vec diag;

384:   PetscFunctionBegin;
386:   PetscAssertPointer(trace, 2);
387:   PetscCall(MatCreateVecs(mat, &diag, NULL));
388:   PetscCall(MatGetDiagonal(mat, diag));
389:   PetscCall(VecSum(diag, trace));
390:   PetscCall(VecDestroy(&diag));
391:   PetscFunctionReturn(PETSC_SUCCESS);
392: }

394: /*@
395:   MatRealPart - Zeros out the imaginary part of the matrix

397:   Logically Collective

399:   Input Parameter:
400: . mat - the matrix

402:   Level: advanced

404: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
405: @*/
406: PetscErrorCode MatRealPart(Mat mat)
407: {
408:   PetscFunctionBegin;
411:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
412:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
413:   MatCheckPreallocated(mat, 1);
414:   PetscUseTypeMethod(mat, realpart);
415:   PetscFunctionReturn(PETSC_SUCCESS);
416: }

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

421:   Collective

423:   Input Parameter:
424: . mat - the matrix

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

430:   Level: advanced

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

435: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
436: @*/
437: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
438: {
439:   PetscFunctionBegin;
442:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
443:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
444:   if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
445:   else {
446:     if (nghosts) *nghosts = 0;
447:     if (ghosts) *ghosts = NULL;
448:   }
449:   PetscFunctionReturn(PETSC_SUCCESS);
450: }

452: /*@
453:   MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

455:   Logically Collective

457:   Input Parameter:
458: . mat - the matrix

460:   Level: advanced

462: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
463: @*/
464: PetscErrorCode MatImaginaryPart(Mat mat)
465: {
466:   PetscFunctionBegin;
469:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
470:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
471:   MatCheckPreallocated(mat, 1);
472:   PetscUseTypeMethod(mat, imaginarypart);
473:   PetscFunctionReturn(PETSC_SUCCESS);
474: }

476: /*@
477:   MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices) in the nonzero structure

479:   Not Collective

481:   Input Parameter:
482: . mat - the matrix

484:   Output Parameters:
485: + missing - is any diagonal entry missing
486: - dd      - first diagonal entry that is missing (optional) on this process

488:   Level: advanced

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

493: .seealso: [](ch_matrices), `Mat`
494: @*/
495: PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
496: {
497:   PetscFunctionBegin;
500:   PetscAssertPointer(missing, 2);
501:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
502:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
503:   PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
504:   PetscFunctionReturn(PETSC_SUCCESS);
505: }

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

513:   Not Collective

515:   Input Parameters:
516: + mat - the matrix
517: - row - the row to get

519:   Output Parameters:
520: + ncols - if not `NULL`, the number of nonzeros in `row`
521: . cols  - if not `NULL`, the column numbers
522: - vals  - if not `NULL`, the numerical values

524:   Level: advanced

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

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

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

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

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

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

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

552:   Fortran Note:
553:   The calling sequence is
554: .vb
555:    MatGetRow(matrix,row,ncols,cols,values,ierr)
556:          Mat         matrix (input)
557:          PetscInt    row    (input)
558:          PetscInt    ncols  (output)
559:          PetscInt    cols(maxcols) (output)
560:          PetscScalar values(maxcols) output
561: .ve
562:   where maxcols >= maximum nonzeros in any row of the matrix.

564: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
565: @*/
566: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
567: {
568:   PetscInt incols;

570:   PetscFunctionBegin;
573:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
574:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
575:   MatCheckPreallocated(mat, 1);
576:   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);
577:   PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
578:   PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
579:   if (ncols) *ncols = incols;
580:   PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
581:   PetscFunctionReturn(PETSC_SUCCESS);
582: }

584: /*@
585:   MatConjugate - replaces the matrix values with their complex conjugates

587:   Logically Collective

589:   Input Parameter:
590: . mat - the matrix

592:   Level: advanced

594: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
595: @*/
596: PetscErrorCode MatConjugate(Mat mat)
597: {
598:   PetscFunctionBegin;
600:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
601:   if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
602:     PetscUseTypeMethod(mat, conjugate);
603:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
604:   }
605:   PetscFunctionReturn(PETSC_SUCCESS);
606: }

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

611:   Not Collective

613:   Input Parameters:
614: + mat   - the matrix
615: . row   - the row to get
616: . ncols - the number of nonzeros
617: . cols  - the columns of the nonzeros
618: - vals  - if nonzero the column values

620:   Level: advanced

622:   Notes:
623:   This routine should be called after you have finished examining the entries.

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

629:   Fortran Note:
630:   `MatRestoreRow()` MUST be called after `MatGetRow()`
631:   before another call to `MatGetRow()` can be made.

633: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
634: @*/
635: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
636: {
637:   PetscFunctionBegin;
639:   if (ncols) PetscAssertPointer(ncols, 3);
640:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
641:   if (!mat->ops->restorerow) PetscFunctionReturn(PETSC_SUCCESS);
642:   PetscUseTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
643:   if (ncols) *ncols = 0;
644:   if (cols) *cols = NULL;
645:   if (vals) *vals = NULL;
646:   PetscFunctionReturn(PETSC_SUCCESS);
647: }

649: /*@
650:   MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
651:   You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.

653:   Not Collective

655:   Input Parameter:
656: . mat - the matrix

658:   Level: advanced

660:   Note:
661:   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.

663: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
664: @*/
665: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
666: {
667:   PetscFunctionBegin;
670:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
671:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
672:   MatCheckPreallocated(mat, 1);
673:   if (!mat->ops->getrowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
674:   PetscUseTypeMethod(mat, getrowuppertriangular);
675:   PetscFunctionReturn(PETSC_SUCCESS);
676: }

678: /*@
679:   MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.

681:   Not Collective

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

686:   Level: advanced

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

691: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
692: @*/
693: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
694: {
695:   PetscFunctionBegin;
698:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
699:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
700:   MatCheckPreallocated(mat, 1);
701:   if (!mat->ops->restorerowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
702:   PetscUseTypeMethod(mat, restorerowuppertriangular);
703:   PetscFunctionReturn(PETSC_SUCCESS);
704: }

706: /*@
707:   MatSetOptionsPrefix - Sets the prefix used for searching for all
708:   `Mat` options in the database.

710:   Logically Collective

712:   Input Parameters:
713: + A      - the matrix
714: - prefix - the prefix to prepend to all option names

716:   Level: advanced

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

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

726: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
727: @*/
728: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
729: {
730:   PetscFunctionBegin;
732:   PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
733:   PetscFunctionReturn(PETSC_SUCCESS);
734: }

736: /*@
737:   MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
738:   for matrices created with `MatGetFactor()`

740:   Logically Collective

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

746:   Level: developer

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

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

755: .seealso: [](ch_matrices), `Mat`,   [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
756: @*/
757: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
758: {
759:   PetscFunctionBegin;
761:   if (prefix) {
762:     PetscAssertPointer(prefix, 2);
763:     PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
764:     if (prefix != A->factorprefix) {
765:       PetscCall(PetscFree(A->factorprefix));
766:       PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
767:     }
768:   } else PetscCall(PetscFree(A->factorprefix));
769:   PetscFunctionReturn(PETSC_SUCCESS);
770: }

772: /*@
773:   MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
774:   for matrices created with `MatGetFactor()`

776:   Logically Collective

778:   Input Parameters:
779: + A      - the matrix
780: - prefix - the prefix to prepend to all option names for the factored matrix

782:   Level: developer

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

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

791: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
792:           `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
793:           `MatSetOptionsPrefix()`
794: @*/
795: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
796: {
797:   size_t len1, len2, new_len;

799:   PetscFunctionBegin;
801:   if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
802:   if (!A->factorprefix) {
803:     PetscCall(MatSetOptionsPrefixFactor(A, prefix));
804:     PetscFunctionReturn(PETSC_SUCCESS);
805:   }
806:   PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");

808:   PetscCall(PetscStrlen(A->factorprefix, &len1));
809:   PetscCall(PetscStrlen(prefix, &len2));
810:   new_len = len1 + len2 + 1;
811:   PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
812:   PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
813:   PetscFunctionReturn(PETSC_SUCCESS);
814: }

816: /*@
817:   MatAppendOptionsPrefix - Appends to the prefix used for searching for all
818:   matrix options in the database.

820:   Logically Collective

822:   Input Parameters:
823: + A      - the matrix
824: - prefix - the prefix to prepend to all option names

826:   Level: advanced

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

832: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
833: @*/
834: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
835: {
836:   PetscFunctionBegin;
838:   PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
839:   PetscFunctionReturn(PETSC_SUCCESS);
840: }

842: /*@
843:   MatGetOptionsPrefix - Gets the prefix used for searching for all
844:   matrix options in the database.

846:   Not Collective

848:   Input Parameter:
849: . A - the matrix

851:   Output Parameter:
852: . prefix - pointer to the prefix string used

854:   Level: advanced

856:   Fortran Note:
857:   The user should pass in a string `prefix` of
858:   sufficient length to hold the prefix.

860: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
861: @*/
862: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
863: {
864:   PetscFunctionBegin;
866:   PetscAssertPointer(prefix, 2);
867:   PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
868:   PetscFunctionReturn(PETSC_SUCCESS);
869: }

871: /*@
872:   MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`

874:   Not Collective

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

879:   Output Parameter:
880: . state - the object state

882:   Level: advanced

884:   Note:
885:   Object state is an integer which gets increased every time
886:   the object is changed. By saving and later querying the object state
887:   one can determine whether information about the object is still current.

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

891: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
892: @*/
893: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
894: {
895:   PetscFunctionBegin;
897:   PetscAssertPointer(state, 2);
898:   PetscCall(PetscObjectStateGet((PetscObject)A, state));
899:   PetscFunctionReturn(PETSC_SUCCESS);
900: }

902: /*@
903:   MatResetPreallocation - Reset matrix to use the original nonzero pattern provided by the user.

905:   Collective

907:   Input Parameter:
908: . A - the matrix

910:   Level: beginner

912:   Notes:
913:   The allocated memory will be shrunk after calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.

915:   Users can reset the preallocation to access the original memory.

917:   Currently only supported for  `MATAIJ` matrices.

919: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
920: @*/
921: PetscErrorCode MatResetPreallocation(Mat A)
922: {
923:   PetscFunctionBegin;
926:   PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
927:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
928:   PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
929:   PetscFunctionReturn(PETSC_SUCCESS);
930: }

932: /*@
933:   MatSetUp - Sets up the internal matrix data structures for later use.

935:   Collective

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

940:   Level: intermediate

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

946:   This routine is called internally by other matrix functions when needed so rarely needs to be called by users

948: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
949: @*/
950: PetscErrorCode MatSetUp(Mat A)
951: {
952:   PetscFunctionBegin;
954:   if (!((PetscObject)A)->type_name) {
955:     PetscMPIInt size;

957:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
958:     PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
959:   }
960:   if (!A->preallocated) PetscTryTypeMethod(A, setup);
961:   PetscCall(PetscLayoutSetUp(A->rmap));
962:   PetscCall(PetscLayoutSetUp(A->cmap));
963:   A->preallocated = PETSC_TRUE;
964:   PetscFunctionReturn(PETSC_SUCCESS);
965: }

967: #if defined(PETSC_HAVE_SAWS)
968: #include <petscviewersaws.h>
969: #endif

971: /*
972:    If threadsafety is on extraneous matrices may be printed

974:    This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
975: */
976: #if !defined(PETSC_HAVE_THREADSAFETY)
977: static PetscInt insidematview = 0;
978: #endif

980: /*@
981:   MatViewFromOptions - View properties of the matrix based on options set in the options database

983:   Collective

985:   Input Parameters:
986: + A    - the matrix
987: . obj  - optional additional object that provides the options prefix to use
988: - name - command line option

990:   Options Database Key:
991: . -mat_view [viewertype]:... - the viewer and its options

993:   Level: intermediate

995:   Note:
996: .vb
997:     If no value is provided ascii:stdout is used
998:        ascii[:[filename][:[format][:append]]]    defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
999:                                                   for example ascii::ascii_info prints just the information about the object not all details
1000:                                                   unless :append is given filename opens in write mode, overwriting what was already there
1001:        binary[:[filename][:[format][:append]]]   defaults to the file binaryoutput
1002:        draw[:drawtype[:filename]]                for example, draw:tikz, draw:tikz:figure.tex  or draw:x
1003:        socket[:port]                             defaults to the standard output port
1004:        saws[:communicatorname]                    publishes object to the Scientific Application Webserver (SAWs)
1005: .ve

1007: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1008: @*/
1009: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1010: {
1011:   PetscFunctionBegin;
1013: #if !defined(PETSC_HAVE_THREADSAFETY)
1014:   if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1015: #endif
1016:   PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1017:   PetscFunctionReturn(PETSC_SUCCESS);
1018: }

1020: /*@
1021:   MatView - display information about a matrix in a variety ways

1023:   Collective on viewer

1025:   Input Parameters:
1026: + mat    - the matrix
1027: - viewer - visualization context

1029:   Options Database Keys:
1030: + -mat_view ::ascii_info           - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1031: . -mat_view ::ascii_info_detail    - Prints more detailed info
1032: . -mat_view                        - Prints matrix in ASCII format
1033: . -mat_view ::ascii_matlab         - Prints matrix in MATLAB format
1034: . -mat_view draw                   - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1035: . -display <name>                  - Sets display name (default is host)
1036: . -draw_pause <sec>                - Sets number of seconds to pause after display
1037: . -mat_view socket                 - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1038: . -viewer_socket_machine <machine> - -
1039: . -viewer_socket_port <port>       - -
1040: . -mat_view binary                 - save matrix to file in binary format
1041: - -viewer_binary_filename <name>   - -

1043:   Level: beginner

1045:   Notes:
1046:   The available visualization contexts include
1047: +    `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1048: .    `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1049: .    `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1050: -     `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure

1052:   The user can open alternative visualization contexts with
1053: +    `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1054: .    `PetscViewerBinaryOpen()` - Outputs matrix in binary to a
1055:   specified file; corresponding input uses `MatLoad()`
1056: .    `PetscViewerDrawOpen()` - Outputs nonzero matrix structure to
1057:   an X window display
1058: -    `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer.
1059:   Currently only the `MATSEQDENSE` and `MATAIJ`
1060:   matrix types support the Socket viewer.

1062:   The user can call `PetscViewerPushFormat()` to specify the output
1063:   format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1064:   `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`).  Available formats include
1065: +    `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1066: .    `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1067: .    `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1068: .    `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse
1069:   format common among all matrix types
1070: .    `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific
1071:   format (which is in many cases the same as the default)
1072: .    `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix
1073:   size and structure (not the matrix entries)
1074: -    `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about
1075:   the matrix structure (still not vector or matrix entries)

1077:   The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1078:   the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

1080:   In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).

1082:   See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1083:   viewer is used.

1085:   See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1086:   viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.

1088:   One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1089:   and then use the following mouse functions.
1090: .vb
1091:   left mouse: zoom in
1092:   middle mouse: zoom out
1093:   right mouse: continue with the simulation
1094: .ve

1096: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1097:           `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1098: @*/
1099: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1100: {
1101:   PetscInt          rows, cols, rbs, cbs;
1102:   PetscBool         isascii, isstring, issaws;
1103:   PetscViewerFormat format;
1104:   PetscMPIInt       size;

1106:   PetscFunctionBegin;
1109:   if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));

1112:   PetscCall(PetscViewerGetFormat(viewer, &format));
1113:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1114:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);

1116: #if !defined(PETSC_HAVE_THREADSAFETY)
1117:   insidematview++;
1118: #endif
1119:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1120:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1121:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1122:   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");

1124:   PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1125:   if (isascii) {
1126:     if (!mat->preallocated) {
1127:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1128: #if !defined(PETSC_HAVE_THREADSAFETY)
1129:       insidematview--;
1130: #endif
1131:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1132:       PetscFunctionReturn(PETSC_SUCCESS);
1133:     }
1134:     if (!mat->assembled) {
1135:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1136: #if !defined(PETSC_HAVE_THREADSAFETY)
1137:       insidematview--;
1138: #endif
1139:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1140:       PetscFunctionReturn(PETSC_SUCCESS);
1141:     }
1142:     PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1143:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1144:       MatNullSpace nullsp, transnullsp;

1146:       PetscCall(PetscViewerASCIIPushTab(viewer));
1147:       PetscCall(MatGetSize(mat, &rows, &cols));
1148:       PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1149:       if (rbs != 1 || cbs != 1) {
1150:         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" : ""));
1151:         else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1152:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1153:       if (mat->factortype) {
1154:         MatSolverType solver;
1155:         PetscCall(MatFactorGetSolverType(mat, &solver));
1156:         PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1157:       }
1158:       if (mat->ops->getinfo) {
1159:         MatInfo info;
1160:         PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1161:         PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1162:         if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1163:       }
1164:       PetscCall(MatGetNullSpace(mat, &nullsp));
1165:       PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1166:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached null space\n"));
1167:       if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached transposed null space\n"));
1168:       PetscCall(MatGetNearNullSpace(mat, &nullsp));
1169:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached near null space\n"));
1170:       PetscCall(PetscViewerASCIIPushTab(viewer));
1171:       PetscCall(MatProductView(mat, viewer));
1172:       PetscCall(PetscViewerASCIIPopTab(viewer));
1173:       if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1174:         IS tmp;

1176:         PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1177:         PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1178:         PetscCall(PetscViewerASCIIPushTab(viewer));
1179:         PetscCall(ISView(tmp, viewer));
1180:         PetscCall(PetscViewerASCIIPopTab(viewer));
1181:         PetscCall(ISDestroy(&tmp));
1182:       }
1183:     }
1184:   } else if (issaws) {
1185: #if defined(PETSC_HAVE_SAWS)
1186:     PetscMPIInt rank;

1188:     PetscCall(PetscObjectName((PetscObject)mat));
1189:     PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1190:     if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1191: #endif
1192:   } else if (isstring) {
1193:     const char *type;
1194:     PetscCall(MatGetType(mat, &type));
1195:     PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1196:     PetscTryTypeMethod(mat, view, viewer);
1197:   }
1198:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1199:     PetscCall(PetscViewerASCIIPushTab(viewer));
1200:     PetscUseTypeMethod(mat, viewnative, viewer);
1201:     PetscCall(PetscViewerASCIIPopTab(viewer));
1202:   } else if (mat->ops->view) {
1203:     PetscCall(PetscViewerASCIIPushTab(viewer));
1204:     PetscUseTypeMethod(mat, view, viewer);
1205:     PetscCall(PetscViewerASCIIPopTab(viewer));
1206:   }
1207:   if (isascii) {
1208:     PetscCall(PetscViewerGetFormat(viewer, &format));
1209:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1210:   }
1211:   PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1212: #if !defined(PETSC_HAVE_THREADSAFETY)
1213:   insidematview--;
1214: #endif
1215:   PetscFunctionReturn(PETSC_SUCCESS);
1216: }

1218: #if defined(PETSC_USE_DEBUG)
1219: #include <../src/sys/totalview/tv_data_display.h>
1220: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1221: {
1222:   TV_add_row("Local rows", "int", &mat->rmap->n);
1223:   TV_add_row("Local columns", "int", &mat->cmap->n);
1224:   TV_add_row("Global rows", "int", &mat->rmap->N);
1225:   TV_add_row("Global columns", "int", &mat->cmap->N);
1226:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1227:   return TV_format_OK;
1228: }
1229: #endif

1231: /*@
1232:   MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1233:   with `MatView()`.  The matrix format is determined from the options database.
1234:   Generates a parallel MPI matrix if the communicator has more than one
1235:   processor.  The default matrix type is `MATAIJ`.

1237:   Collective

1239:   Input Parameters:
1240: + mat    - the newly loaded matrix, this needs to have been created with `MatCreate()`
1241:             or some related function before a call to `MatLoad()`
1242: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer

1244:   Options Database Key:
1245: . -matload_block_size <bs> - set block size

1247:   Level: beginner

1249:   Notes:
1250:   If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1251:   `Mat` before calling this routine if you wish to set it from the options database.

1253:   `MatLoad()` automatically loads into the options database any options
1254:   given in the file filename.info where filename is the name of the file
1255:   that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1256:   file will be ignored if you use the -viewer_binary_skip_info option.

1258:   If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1259:   sets the default matrix type AIJ and sets the local and global sizes.
1260:   If type and/or size is already set, then the same are used.

1262:   In parallel, each processor can load a subset of rows (or the
1263:   entire matrix).  This routine is especially useful when a large
1264:   matrix is stored on disk and only part of it is desired on each
1265:   processor.  For example, a parallel solver may access only some of
1266:   the rows from each processor.  The algorithm used here reads
1267:   relatively small blocks of data rather than reading the entire
1268:   matrix and then subsetting it.

1270:   Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1271:   Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1272:   or the sequence like
1273: .vb
1274:     `PetscViewer` v;
1275:     `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1276:     `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1277:     `PetscViewerSetFromOptions`(v);
1278:     `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1279:     `PetscViewerFileSetName`(v,"datafile");
1280: .ve
1281:   The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1282: $ -viewer_type {binary, hdf5}

1284:   See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1285:   and src/mat/tutorials/ex10.c with the second approach.

1287:   In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1288:   is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1289:   Multiple objects, both matrices and vectors, can be stored within the same file.
1290:   Their `PetscObject` name is ignored; they are loaded in the order of their storage.

1292:   Most users should not need to know the details of the binary storage
1293:   format, since `MatLoad()` and `MatView()` completely hide these details.
1294:   But for anyone who is interested, the standard binary matrix storage
1295:   format is

1297: .vb
1298:     PetscInt    MAT_FILE_CLASSID
1299:     PetscInt    number of rows
1300:     PetscInt    number of columns
1301:     PetscInt    total number of nonzeros
1302:     PetscInt    *number nonzeros in each row
1303:     PetscInt    *column indices of all nonzeros (starting index is zero)
1304:     PetscScalar *values of all nonzeros
1305: .ve
1306:   If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1307:   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
1308:   case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.

1310:   PETSc automatically does the byte swapping for
1311:   machines that store the bytes reversed. Thus if you write your own binary
1312:   read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1313:   and `PetscBinaryWrite()` to see how this may be done.

1315:   In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1316:   Each processor's chunk is loaded independently by its owning MPI process.
1317:   Multiple objects, both matrices and vectors, can be stored within the same file.
1318:   They are looked up by their PetscObject name.

1320:   As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1321:   by default the same structure and naming of the AIJ arrays and column count
1322:   within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1323: $    save example.mat A b -v7.3
1324:   can be directly read by this routine (see Reference 1 for details).

1326:   Depending on your MATLAB version, this format might be a default,
1327:   otherwise you can set it as default in Preferences.

1329:   Unless -nocompression flag is used to save the file in MATLAB,
1330:   PETSc must be configured with ZLIB package.

1332:   See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

1334:   This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`

1336:   Corresponding `MatView()` is not yet implemented.

1338:   The loaded matrix is actually a transpose of the original one in MATLAB,
1339:   unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1340:   With this format, matrix is automatically transposed by PETSc,
1341:   unless the matrix is marked as SPD or symmetric
1342:   (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).

1344:   See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>

1346: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1347:  @*/
1348: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1349: {
1350:   PetscBool flg;

1352:   PetscFunctionBegin;

1356:   if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));

1358:   flg = PETSC_FALSE;
1359:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1360:   if (flg) {
1361:     PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1362:     PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1363:   }
1364:   flg = PETSC_FALSE;
1365:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1366:   if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));

1368:   PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1369:   PetscUseTypeMethod(mat, load, viewer);
1370:   PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1371:   PetscFunctionReturn(PETSC_SUCCESS);
1372: }

1374: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1375: {
1376:   Mat_Redundant *redund = *redundant;

1378:   PetscFunctionBegin;
1379:   if (redund) {
1380:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1381:       PetscCall(ISDestroy(&redund->isrow));
1382:       PetscCall(ISDestroy(&redund->iscol));
1383:       PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1384:     } else {
1385:       PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1386:       PetscCall(PetscFree(redund->sbuf_j));
1387:       PetscCall(PetscFree(redund->sbuf_a));
1388:       for (PetscInt i = 0; i < redund->nrecvs; i++) {
1389:         PetscCall(PetscFree(redund->rbuf_j[i]));
1390:         PetscCall(PetscFree(redund->rbuf_a[i]));
1391:       }
1392:       PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1393:     }

1395:     if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1396:     PetscCall(PetscFree(redund));
1397:   }
1398:   PetscFunctionReturn(PETSC_SUCCESS);
1399: }

1401: /*@
1402:   MatDestroy - Frees space taken by a matrix.

1404:   Collective

1406:   Input Parameter:
1407: . A - the matrix

1409:   Level: beginner

1411:   Developer Note:
1412:   Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1413:   `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1414:   `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1415:   if changes are needed here.

1417: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1418: @*/
1419: PetscErrorCode MatDestroy(Mat *A)
1420: {
1421:   PetscFunctionBegin;
1422:   if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1424:   if (--((PetscObject)*A)->refct > 0) {
1425:     *A = NULL;
1426:     PetscFunctionReturn(PETSC_SUCCESS);
1427:   }

1429:   /* if memory was published with SAWs then destroy it */
1430:   PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1431:   PetscTryTypeMethod(*A, destroy);

1433:   PetscCall(PetscFree((*A)->factorprefix));
1434:   PetscCall(PetscFree((*A)->defaultvectype));
1435:   PetscCall(PetscFree((*A)->defaultrandtype));
1436:   PetscCall(PetscFree((*A)->bsizes));
1437:   PetscCall(PetscFree((*A)->solvertype));
1438:   for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1439:   if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1440:   PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1441:   PetscCall(MatProductClear(*A));
1442:   PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1443:   PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1444:   PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1445:   PetscCall(MatDestroy(&(*A)->schur));
1446:   PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1447:   PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1448:   PetscCall(PetscHeaderDestroy(A));
1449:   PetscFunctionReturn(PETSC_SUCCESS);
1450: }

1452: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1453: /*@
1454:   MatSetValues - Inserts or adds a block of values into a matrix.
1455:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1456:   MUST be called after all calls to `MatSetValues()` have been completed.

1458:   Not Collective

1460:   Input Parameters:
1461: + mat  - the matrix
1462: . v    - a logically two-dimensional array of values
1463: . m    - the number of rows
1464: . idxm - the global indices of the rows
1465: . n    - the number of columns
1466: . idxn - the global indices of the columns
1467: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1469:   Level: beginner

1471:   Notes:
1472:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1474:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1475:   options cannot be mixed without intervening calls to the assembly
1476:   routines.

1478:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1479:   as well as in C.

1481:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1482:   simply ignored. This allows easily inserting element stiffness matrices
1483:   with homogeneous Dirichlet boundary conditions that you don't want represented
1484:   in the matrix.

1486:   Efficiency Alert:
1487:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1488:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1490:   Fortran Notes:
1491:   If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1492: .vb
1493:   MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES)
1494: .ve

1496:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

1498:   Developer Note:
1499:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1500:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1502: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1503:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1504: @*/
1505: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1506: {
1507:   PetscFunctionBeginHot;
1510:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1511:   PetscAssertPointer(idxm, 3);
1512:   PetscAssertPointer(idxn, 5);
1513:   MatCheckPreallocated(mat, 1);

1515:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1516:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

1518:   if (PetscDefined(USE_DEBUG)) {
1519:     PetscInt i, j;

1521:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1522:     if (v) {
1523:       for (i = 0; i < m; i++) {
1524:         for (j = 0; j < n; j++) {
1525:           if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1526: #if defined(PETSC_USE_COMPLEX)
1527:             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]);
1528: #else
1529:             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]);
1530: #endif
1531:         }
1532:       }
1533:     }
1534:     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);
1535:     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);
1536:   }

1538:   if (mat->assembled) {
1539:     mat->was_assembled = PETSC_TRUE;
1540:     mat->assembled     = PETSC_FALSE;
1541:   }
1542:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1543:   PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1544:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1545:   PetscFunctionReturn(PETSC_SUCCESS);
1546: }

1548: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1549: /*@
1550:   MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1551:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1552:   MUST be called after all calls to `MatSetValues()` have been completed.

1554:   Not Collective

1556:   Input Parameters:
1557: + mat  - the matrix
1558: . v    - a logically two-dimensional array of values
1559: . ism  - the rows to provide
1560: . isn  - the columns to provide
1561: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1563:   Level: beginner

1565:   Notes:
1566:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1568:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1569:   options cannot be mixed without intervening calls to the assembly
1570:   routines.

1572:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1573:   as well as in C.

1575:   Negative indices may be passed in `ism` and `isn`, these rows and columns are
1576:   simply ignored. This allows easily inserting element stiffness matrices
1577:   with homogeneous Dirichlet boundary conditions that you don't want represented
1578:   in the matrix.

1580:   Efficiency Alert:
1581:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1582:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1584:   This is currently not optimized for any particular `ISType`

1586:   Developer Note:
1587:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1588:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1590: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1591:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1592: @*/
1593: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1594: {
1595:   PetscInt        m, n;
1596:   const PetscInt *rows, *cols;

1598:   PetscFunctionBeginHot;
1600:   PetscCall(ISGetIndices(ism, &rows));
1601:   PetscCall(ISGetIndices(isn, &cols));
1602:   PetscCall(ISGetLocalSize(ism, &m));
1603:   PetscCall(ISGetLocalSize(isn, &n));
1604:   PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1605:   PetscCall(ISRestoreIndices(ism, &rows));
1606:   PetscCall(ISRestoreIndices(isn, &cols));
1607:   PetscFunctionReturn(PETSC_SUCCESS);
1608: }

1610: /*@
1611:   MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1612:   values into a matrix

1614:   Not Collective

1616:   Input Parameters:
1617: + mat - the matrix
1618: . row - the (block) row to set
1619: - v   - a logically two-dimensional array of values

1621:   Level: intermediate

1623:   Notes:
1624:   The values, `v`, are column-oriented (for the block version) and sorted

1626:   All the nonzero values in `row` must be provided

1628:   The matrix must have previously had its column indices set, likely by having been assembled.

1630:   `row` must belong to this MPI process

1632: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1633:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1634: @*/
1635: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1636: {
1637:   PetscInt globalrow;

1639:   PetscFunctionBegin;
1642:   PetscAssertPointer(v, 3);
1643:   PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1644:   PetscCall(MatSetValuesRow(mat, globalrow, v));
1645:   PetscFunctionReturn(PETSC_SUCCESS);
1646: }

1648: /*@
1649:   MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1650:   values into a matrix

1652:   Not Collective

1654:   Input Parameters:
1655: + mat - the matrix
1656: . row - the (block) row to set
1657: - 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

1659:   Level: advanced

1661:   Notes:
1662:   The values, `v`, are column-oriented for the block version.

1664:   All the nonzeros in `row` must be provided

1666:   THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

1668:   `row` must belong to this process

1670: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1671:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1672: @*/
1673: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1674: {
1675:   PetscFunctionBeginHot;
1678:   MatCheckPreallocated(mat, 1);
1679:   PetscAssertPointer(v, 3);
1680:   PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1681:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1682:   mat->insertmode = INSERT_VALUES;

1684:   if (mat->assembled) {
1685:     mat->was_assembled = PETSC_TRUE;
1686:     mat->assembled     = PETSC_FALSE;
1687:   }
1688:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1689:   PetscUseTypeMethod(mat, setvaluesrow, row, v);
1690:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1691:   PetscFunctionReturn(PETSC_SUCCESS);
1692: }

1694: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1695: /*@
1696:   MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1697:   Using structured grid indexing

1699:   Not Collective

1701:   Input Parameters:
1702: + mat  - the matrix
1703: . m    - number of rows being entered
1704: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1705: . n    - number of columns being entered
1706: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1707: . v    - a logically two-dimensional array of values
1708: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

1710:   Level: beginner

1712:   Notes:
1713:   By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

1715:   Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1716:   options cannot be mixed without intervening calls to the assembly
1717:   routines.

1719:   The grid coordinates are across the entire grid, not just the local portion

1721:   `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1722:   as well as in C.

1724:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1726:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1727:   or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1729:   The columns and rows in the stencil passed in MUST be contained within the
1730:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1731:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1732:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1733:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1735:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1736:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1737:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1738:   `DM_BOUNDARY_PERIODIC` boundary type.

1740:   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
1741:   a single value per point) you can skip filling those indices.

1743:   Inspired by the structured grid interface to the HYPRE package
1744:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1746:   Efficiency Alert:
1747:   The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1748:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1750:   Fortran Note:
1751:   `idxm` and `idxn` should be declared as
1752: $     MatStencil idxm(4,m),idxn(4,n)
1753:   and the values inserted using
1754: .vb
1755:     idxm(MatStencil_i,1) = i
1756:     idxm(MatStencil_j,1) = j
1757:     idxm(MatStencil_k,1) = k
1758:     idxm(MatStencil_c,1) = c
1759:     etc
1760: .ve

1762: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1763:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1764: @*/
1765: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1766: {
1767:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1768:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1769:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1771:   PetscFunctionBegin;
1772:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1775:   PetscAssertPointer(idxm, 3);
1776:   PetscAssertPointer(idxn, 5);

1778:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1779:     jdxm = buf;
1780:     jdxn = buf + m;
1781:   } else {
1782:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1783:     jdxm = bufm;
1784:     jdxn = bufn;
1785:   }
1786:   for (i = 0; i < m; i++) {
1787:     for (j = 0; j < 3 - sdim; j++) dxm++;
1788:     tmp = *dxm++ - starts[0];
1789:     for (j = 0; j < dim - 1; j++) {
1790:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1791:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1792:     }
1793:     if (mat->stencil.noc) dxm++;
1794:     jdxm[i] = tmp;
1795:   }
1796:   for (i = 0; i < n; i++) {
1797:     for (j = 0; j < 3 - sdim; j++) dxn++;
1798:     tmp = *dxn++ - starts[0];
1799:     for (j = 0; j < dim - 1; j++) {
1800:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1801:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1802:     }
1803:     if (mat->stencil.noc) dxn++;
1804:     jdxn[i] = tmp;
1805:   }
1806:   PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1807:   PetscCall(PetscFree2(bufm, bufn));
1808:   PetscFunctionReturn(PETSC_SUCCESS);
1809: }

1811: /*@
1812:   MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1813:   Using structured grid indexing

1815:   Not Collective

1817:   Input Parameters:
1818: + mat  - the matrix
1819: . m    - number of rows being entered
1820: . idxm - grid coordinates for matrix rows being entered
1821: . n    - number of columns being entered
1822: . idxn - grid coordinates for matrix columns being entered
1823: . v    - a logically two-dimensional array of values
1824: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

1826:   Level: beginner

1828:   Notes:
1829:   By default the values, `v`, are row-oriented and unsorted.
1830:   See `MatSetOption()` for other options.

1832:   Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1833:   options cannot be mixed without intervening calls to the assembly
1834:   routines.

1836:   The grid coordinates are across the entire grid, not just the local portion

1838:   `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1839:   as well as in C.

1841:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1843:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1844:   or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1846:   The columns and rows in the stencil passed in MUST be contained within the
1847:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1848:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1849:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1850:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1852:   Negative indices may be passed in idxm and idxn, these rows and columns are
1853:   simply ignored. This allows easily inserting element stiffness matrices
1854:   with homogeneous Dirichlet boundary conditions that you don't want represented
1855:   in the matrix.

1857:   Inspired by the structured grid interface to the HYPRE package
1858:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1860:   Fortran Note:
1861:   `idxm` and `idxn` should be declared as
1862: $     MatStencil idxm(4,m),idxn(4,n)
1863:   and the values inserted using
1864: .vb
1865:     idxm(MatStencil_i,1) = i
1866:     idxm(MatStencil_j,1) = j
1867:     idxm(MatStencil_k,1) = k
1868:    etc
1869: .ve

1871: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1872:           `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1873:           `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1874: @*/
1875: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1876: {
1877:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1878:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1879:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1881:   PetscFunctionBegin;
1882:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1885:   PetscAssertPointer(idxm, 3);
1886:   PetscAssertPointer(idxn, 5);
1887:   PetscAssertPointer(v, 6);

1889:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1890:     jdxm = buf;
1891:     jdxn = buf + m;
1892:   } else {
1893:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1894:     jdxm = bufm;
1895:     jdxn = bufn;
1896:   }
1897:   for (i = 0; i < m; i++) {
1898:     for (j = 0; j < 3 - sdim; j++) dxm++;
1899:     tmp = *dxm++ - starts[0];
1900:     for (j = 0; j < sdim - 1; j++) {
1901:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1902:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1903:     }
1904:     dxm++;
1905:     jdxm[i] = tmp;
1906:   }
1907:   for (i = 0; i < n; i++) {
1908:     for (j = 0; j < 3 - sdim; j++) dxn++;
1909:     tmp = *dxn++ - starts[0];
1910:     for (j = 0; j < sdim - 1; j++) {
1911:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1912:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1913:     }
1914:     dxn++;
1915:     jdxn[i] = tmp;
1916:   }
1917:   PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1918:   PetscCall(PetscFree2(bufm, bufn));
1919:   PetscFunctionReturn(PETSC_SUCCESS);
1920: }

1922: /*@
1923:   MatSetStencil - Sets the grid information for setting values into a matrix via
1924:   `MatSetValuesStencil()`

1926:   Not Collective

1928:   Input Parameters:
1929: + mat    - the matrix
1930: . dim    - dimension of the grid 1, 2, or 3
1931: . dims   - number of grid points in x, y, and z direction, including ghost points on your processor
1932: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1933: - dof    - number of degrees of freedom per node

1935:   Level: beginner

1937:   Notes:
1938:   Inspired by the structured grid interface to the HYPRE package
1939:   (www.llnl.gov/CASC/hyper)

1941:   For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1942:   user.

1944: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1945:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1946: @*/
1947: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1948: {
1949:   PetscFunctionBegin;
1951:   PetscAssertPointer(dims, 3);
1952:   PetscAssertPointer(starts, 4);

1954:   mat->stencil.dim = dim + (dof > 1);
1955:   for (PetscInt i = 0; i < dim; i++) {
1956:     mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
1957:     mat->stencil.starts[i] = starts[dim - i - 1];
1958:   }
1959:   mat->stencil.dims[dim]   = dof;
1960:   mat->stencil.starts[dim] = 0;
1961:   mat->stencil.noc         = (PetscBool)(dof == 1);
1962:   PetscFunctionReturn(PETSC_SUCCESS);
1963: }

1965: /*@
1966:   MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

1968:   Not Collective

1970:   Input Parameters:
1971: + mat  - the matrix
1972: . v    - a logically two-dimensional array of values
1973: . m    - the number of block rows
1974: . idxm - the global block indices
1975: . n    - the number of block columns
1976: . idxn - the global block indices
1977: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

1979:   Level: intermediate

1981:   Notes:
1982:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
1983:   MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

1985:   The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
1986:   NOT the total number of rows/columns; for example, if the block size is 2 and
1987:   you are passing in values for rows 2,3,4,5  then `m` would be 2 (not 4).
1988:   The values in `idxm` would be 1 2; that is the first index for each block divided by
1989:   the block size.

1991:   You must call `MatSetBlockSize()` when constructing this matrix (before
1992:   preallocating it).

1994:   By default the values, `v`, are row-oriented, so the layout of
1995:   `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.

1997:   Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
1998:   options cannot be mixed without intervening calls to the assembly
1999:   routines.

2001:   `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2002:   as well as in C.

2004:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2005:   simply ignored. This allows easily inserting element stiffness matrices
2006:   with homogeneous Dirichlet boundary conditions that you don't want represented
2007:   in the matrix.

2009:   Each time an entry is set within a sparse matrix via `MatSetValues()`,
2010:   internal searching must be done to determine where to place the
2011:   data in the matrix storage space.  By instead inserting blocks of
2012:   entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2013:   reduced.

2015:   Example:
2016: .vb
2017:    Suppose m=n=2 and block size(bs) = 2 The array is

2019:    1  2  | 3  4
2020:    5  6  | 7  8
2021:    - - - | - - -
2022:    9  10 | 11 12
2023:    13 14 | 15 16

2025:    v[] should be passed in like
2026:    v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

2028:   If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2029:    v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2030: .ve

2032:   Fortran Notes:
2033:   If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2034: .vb
2035:   MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES)
2036: .ve

2038:   If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2040: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2041: @*/
2042: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2043: {
2044:   PetscFunctionBeginHot;
2047:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2048:   PetscAssertPointer(idxm, 3);
2049:   PetscAssertPointer(idxn, 5);
2050:   MatCheckPreallocated(mat, 1);
2051:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2052:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2053:   if (PetscDefined(USE_DEBUG)) {
2054:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2055:     PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2056:   }
2057:   if (PetscDefined(USE_DEBUG)) {
2058:     PetscInt rbs, cbs, M, N, i;
2059:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2060:     PetscCall(MatGetSize(mat, &M, &N));
2061:     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);
2062:     for (i = 0; i < n; i++)
2063:       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);
2064:   }
2065:   if (mat->assembled) {
2066:     mat->was_assembled = PETSC_TRUE;
2067:     mat->assembled     = PETSC_FALSE;
2068:   }
2069:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2070:   if (mat->ops->setvaluesblocked) {
2071:     PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2072:   } else {
2073:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2074:     PetscInt i, j, bs, cbs;

2076:     PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2077:     if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2078:       iidxm = buf;
2079:       iidxn = buf + m * bs;
2080:     } else {
2081:       PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2082:       iidxm = bufr;
2083:       iidxn = bufc;
2084:     }
2085:     for (i = 0; i < m; i++) {
2086:       for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2087:     }
2088:     if (m != n || bs != cbs || idxm != idxn) {
2089:       for (i = 0; i < n; i++) {
2090:         for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2091:       }
2092:     } else iidxn = iidxm;
2093:     PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2094:     PetscCall(PetscFree2(bufr, bufc));
2095:   }
2096:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2097:   PetscFunctionReturn(PETSC_SUCCESS);
2098: }

2100: /*@
2101:   MatGetValues - Gets a block of local values from a matrix.

2103:   Not Collective; can only return values that are owned by the give process

2105:   Input Parameters:
2106: + mat  - the matrix
2107: . v    - a logically two-dimensional array for storing the values
2108: . m    - the number of rows
2109: . idxm - the  global indices of the rows
2110: . n    - the number of columns
2111: - idxn - the global indices of the columns

2113:   Level: advanced

2115:   Notes:
2116:   The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2117:   The values, `v`, are then returned in a row-oriented format,
2118:   analogous to that used by default in `MatSetValues()`.

2120:   `MatGetValues()` uses 0-based row and column numbers in
2121:   Fortran as well as in C.

2123:   `MatGetValues()` requires that the matrix has been assembled
2124:   with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
2125:   `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2126:   without intermediate matrix assembly.

2128:   Negative row or column indices will be ignored and those locations in `v` will be
2129:   left unchanged.

2131:   For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2132:   That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2133:   from `MatGetOwnershipRange`(mat,&rstart,&rend).

2135: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2136: @*/
2137: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2138: {
2139:   PetscFunctionBegin;
2142:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2143:   PetscAssertPointer(idxm, 3);
2144:   PetscAssertPointer(idxn, 5);
2145:   PetscAssertPointer(v, 6);
2146:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2147:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2148:   MatCheckPreallocated(mat, 1);

2150:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2151:   PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2152:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2153:   PetscFunctionReturn(PETSC_SUCCESS);
2154: }

2156: /*@
2157:   MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2158:   defined previously by `MatSetLocalToGlobalMapping()`

2160:   Not Collective

2162:   Input Parameters:
2163: + mat  - the matrix
2164: . nrow - number of rows
2165: . irow - the row local indices
2166: . ncol - number of columns
2167: - icol - the column local indices

2169:   Output Parameter:
2170: . y - a logically two-dimensional array of values

2172:   Level: advanced

2174:   Notes:
2175:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

2177:   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,
2178:   are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2179:   determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2180:   with `MatSetLocalToGlobalMapping()`.

2182:   Developer Note:
2183:   This is labelled with C so does not automatically generate Fortran stubs and interfaces
2184:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2186: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2187:           `MatSetValuesLocal()`, `MatGetValues()`
2188: @*/
2189: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2190: {
2191:   PetscFunctionBeginHot;
2194:   MatCheckPreallocated(mat, 1);
2195:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2196:   PetscAssertPointer(irow, 3);
2197:   PetscAssertPointer(icol, 5);
2198:   if (PetscDefined(USE_DEBUG)) {
2199:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2200:     PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2201:   }
2202:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2203:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2204:   if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2205:   else {
2206:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2207:     if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2208:       irowm = buf;
2209:       icolm = buf + nrow;
2210:     } else {
2211:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2212:       irowm = bufr;
2213:       icolm = bufc;
2214:     }
2215:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2216:     PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2217:     PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2218:     PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2219:     PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2220:     PetscCall(PetscFree2(bufr, bufc));
2221:   }
2222:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2223:   PetscFunctionReturn(PETSC_SUCCESS);
2224: }

2226: /*@
2227:   MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2228:   the same size. Currently, this can only be called once and creates the given matrix.

2230:   Not Collective

2232:   Input Parameters:
2233: + mat  - the matrix
2234: . nb   - the number of blocks
2235: . bs   - the number of rows (and columns) in each block
2236: . rows - a concatenation of the rows for each block
2237: - v    - a concatenation of logically two-dimensional arrays of values

2239:   Level: advanced

2241:   Notes:
2242:   `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values

2244:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

2246: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2247:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2248: @*/
2249: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2250: {
2251:   PetscFunctionBegin;
2254:   PetscAssertPointer(rows, 4);
2255:   PetscAssertPointer(v, 5);
2256:   PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

2258:   PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2259:   if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2260:   else {
2261:     for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2262:   }
2263:   PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2264:   PetscFunctionReturn(PETSC_SUCCESS);
2265: }

2267: /*@
2268:   MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2269:   the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2270:   using a local (per-processor) numbering.

2272:   Not Collective

2274:   Input Parameters:
2275: + x        - the matrix
2276: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2277: - cmapping - column mapping

2279:   Level: intermediate

2281:   Note:
2282:   If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix

2284: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2285: @*/
2286: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2287: {
2288:   PetscFunctionBegin;
2293:   if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2294:   else {
2295:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2296:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2297:   }
2298:   PetscFunctionReturn(PETSC_SUCCESS);
2299: }

2301: /*@
2302:   MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`

2304:   Not Collective

2306:   Input Parameter:
2307: . A - the matrix

2309:   Output Parameters:
2310: + rmapping - row mapping
2311: - cmapping - column mapping

2313:   Level: advanced

2315: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2316: @*/
2317: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2318: {
2319:   PetscFunctionBegin;
2322:   if (rmapping) {
2323:     PetscAssertPointer(rmapping, 2);
2324:     *rmapping = A->rmap->mapping;
2325:   }
2326:   if (cmapping) {
2327:     PetscAssertPointer(cmapping, 3);
2328:     *cmapping = A->cmap->mapping;
2329:   }
2330:   PetscFunctionReturn(PETSC_SUCCESS);
2331: }

2333: /*@
2334:   MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix

2336:   Logically Collective

2338:   Input Parameters:
2339: + A    - the matrix
2340: . rmap - row layout
2341: - cmap - column layout

2343:   Level: advanced

2345:   Note:
2346:   The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.

2348: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2349: @*/
2350: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2351: {
2352:   PetscFunctionBegin;
2354:   PetscCall(PetscLayoutReference(rmap, &A->rmap));
2355:   PetscCall(PetscLayoutReference(cmap, &A->cmap));
2356:   PetscFunctionReturn(PETSC_SUCCESS);
2357: }

2359: /*@
2360:   MatGetLayouts - Gets the `PetscLayout` objects for rows and columns

2362:   Not Collective

2364:   Input Parameter:
2365: . A - the matrix

2367:   Output Parameters:
2368: + rmap - row layout
2369: - cmap - column layout

2371:   Level: advanced

2373: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2374: @*/
2375: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2376: {
2377:   PetscFunctionBegin;
2380:   if (rmap) {
2381:     PetscAssertPointer(rmap, 2);
2382:     *rmap = A->rmap;
2383:   }
2384:   if (cmap) {
2385:     PetscAssertPointer(cmap, 3);
2386:     *cmap = A->cmap;
2387:   }
2388:   PetscFunctionReturn(PETSC_SUCCESS);
2389: }

2391: /*@
2392:   MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2393:   using a local numbering of the rows and columns.

2395:   Not Collective

2397:   Input Parameters:
2398: + mat  - the matrix
2399: . nrow - number of rows
2400: . irow - the row local indices
2401: . ncol - number of columns
2402: . icol - the column local indices
2403: . y    - a logically two-dimensional array of values
2404: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2406:   Level: intermediate

2408:   Notes:
2409:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

2411:   Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2412:   options cannot be mixed without intervening calls to the assembly
2413:   routines.

2415:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2416:   MUST be called after all calls to `MatSetValuesLocal()` have been completed.

2418:   Fortran Notes:
2419:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2420: .vb
2421:   MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES)
2422: .ve

2424:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2426:   Developer Note:
2427:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2428:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2430: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2431:           `MatGetValuesLocal()`
2432: @*/
2433: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2434: {
2435:   PetscFunctionBeginHot;
2438:   MatCheckPreallocated(mat, 1);
2439:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2440:   PetscAssertPointer(irow, 3);
2441:   PetscAssertPointer(icol, 5);
2442:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2443:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2444:   if (PetscDefined(USE_DEBUG)) {
2445:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2446:     PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2447:   }

2449:   if (mat->assembled) {
2450:     mat->was_assembled = PETSC_TRUE;
2451:     mat->assembled     = PETSC_FALSE;
2452:   }
2453:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2454:   if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2455:   else {
2456:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2457:     const PetscInt *irowm, *icolm;

2459:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2460:       bufr  = buf;
2461:       bufc  = buf + nrow;
2462:       irowm = bufr;
2463:       icolm = bufc;
2464:     } else {
2465:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2466:       irowm = bufr;
2467:       icolm = bufc;
2468:     }
2469:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2470:     else irowm = irow;
2471:     if (mat->cmap->mapping) {
2472:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2473:         PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2474:       } else icolm = irowm;
2475:     } else icolm = icol;
2476:     PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2477:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2478:   }
2479:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2480:   PetscFunctionReturn(PETSC_SUCCESS);
2481: }

2483: /*@
2484:   MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2485:   using a local ordering of the nodes a block at a time.

2487:   Not Collective

2489:   Input Parameters:
2490: + mat  - the matrix
2491: . nrow - number of rows
2492: . irow - the row local indices
2493: . ncol - number of columns
2494: . icol - the column local indices
2495: . y    - a logically two-dimensional array of values
2496: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2498:   Level: intermediate

2500:   Notes:
2501:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2502:   before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

2504:   Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2505:   options cannot be mixed without intervening calls to the assembly
2506:   routines.

2508:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2509:   MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

2511:   Fortran Notes:
2512:   If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2513: .vb
2514:   MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES)
2515: .ve

2517:   If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array

2519:   Developer Note:
2520:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2521:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2523: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2524:           `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2525: @*/
2526: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2527: {
2528:   PetscFunctionBeginHot;
2531:   MatCheckPreallocated(mat, 1);
2532:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2533:   PetscAssertPointer(irow, 3);
2534:   PetscAssertPointer(icol, 5);
2535:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2536:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2537:   if (PetscDefined(USE_DEBUG)) {
2538:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2539:     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);
2540:   }

2542:   if (mat->assembled) {
2543:     mat->was_assembled = PETSC_TRUE;
2544:     mat->assembled     = PETSC_FALSE;
2545:   }
2546:   if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2547:     PetscInt irbs, rbs;
2548:     PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2549:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2550:     PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2551:   }
2552:   if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2553:     PetscInt icbs, cbs;
2554:     PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2555:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2556:     PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2557:   }
2558:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2559:   if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2560:   else {
2561:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2562:     const PetscInt *irowm, *icolm;

2564:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2565:       bufr  = buf;
2566:       bufc  = buf + nrow;
2567:       irowm = bufr;
2568:       icolm = bufc;
2569:     } else {
2570:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2571:       irowm = bufr;
2572:       icolm = bufc;
2573:     }
2574:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2575:     else irowm = irow;
2576:     if (mat->cmap->mapping) {
2577:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2578:         PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2579:       } else icolm = irowm;
2580:     } else icolm = icol;
2581:     PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2582:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2583:   }
2584:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2585:   PetscFunctionReturn(PETSC_SUCCESS);
2586: }

2588: /*@
2589:   MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal

2591:   Collective

2593:   Input Parameters:
2594: + mat - the matrix
2595: - x   - the vector to be multiplied

2597:   Output Parameter:
2598: . y - the result

2600:   Level: developer

2602:   Note:
2603:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2604:   call `MatMultDiagonalBlock`(A,y,y).

2606: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2607: @*/
2608: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2609: {
2610:   PetscFunctionBegin;

2616:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2617:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2618:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2619:   MatCheckPreallocated(mat, 1);

2621:   PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2622:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2623:   PetscFunctionReturn(PETSC_SUCCESS);
2624: }

2626: /*@
2627:   MatMult - Computes the matrix-vector product, $y = Ax$.

2629:   Neighbor-wise Collective

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

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

2638:   Level: beginner

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

2644: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2645: @*/
2646: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2647: {
2648:   PetscFunctionBegin;
2652:   VecCheckAssembled(x);
2654:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2655:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2656:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2657:   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);
2658:   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);
2659:   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);
2660:   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);
2661:   PetscCall(VecSetErrorIfLocked(y, 3));
2662:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2663:   MatCheckPreallocated(mat, 1);

2665:   PetscCall(VecLockReadPush(x));
2666:   PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2667:   PetscUseTypeMethod(mat, mult, x, y);
2668:   PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2669:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2670:   PetscCall(VecLockReadPop(x));
2671:   PetscFunctionReturn(PETSC_SUCCESS);
2672: }

2674: /*@
2675:   MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.

2677:   Neighbor-wise Collective

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

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

2686:   Level: beginner

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

2692:   For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2693:   use `MatMultHermitianTranspose()`

2695: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2696: @*/
2697: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2698: {
2699:   PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;

2701:   PetscFunctionBegin;
2705:   VecCheckAssembled(x);

2708:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2709:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2710:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2711:   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);
2712:   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);
2713:   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);
2714:   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);
2715:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2716:   MatCheckPreallocated(mat, 1);

2718:   if (!mat->ops->multtranspose) {
2719:     if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2720:     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);
2721:   } else op = mat->ops->multtranspose;
2722:   PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2723:   PetscCall(VecLockReadPush(x));
2724:   PetscCall((*op)(mat, x, y));
2725:   PetscCall(VecLockReadPop(x));
2726:   PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2727:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2728:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2729:   PetscFunctionReturn(PETSC_SUCCESS);
2730: }

2732: /*@
2733:   MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.

2735:   Neighbor-wise Collective

2737:   Input Parameters:
2738: + mat - the matrix
2739: - x   - the vector to be multiplied

2741:   Output Parameter:
2742: . y - the result

2744:   Level: beginner

2746:   Notes:
2747:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2748:   call `MatMultHermitianTranspose`(A,y,y).

2750:   Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2752:   For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

2754: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2755: @*/
2756: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2757: {
2758:   PetscFunctionBegin;

2764:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2765:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2766:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2767:   PetscCheck(mat->cmap->N == y->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, y->map->N);
2768:   PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
2769:   PetscCheck(mat->cmap->n == y->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec y: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->n, y->map->n);
2770:   PetscCheck(mat->rmap->n == x->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, x->map->n);
2771:   MatCheckPreallocated(mat, 1);

2773:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2774: #if defined(PETSC_USE_COMPLEX)
2775:   if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2776:     PetscCall(VecLockReadPush(x));
2777:     if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2778:     else PetscUseTypeMethod(mat, mult, x, y);
2779:     PetscCall(VecLockReadPop(x));
2780:   } else {
2781:     Vec w;
2782:     PetscCall(VecDuplicate(x, &w));
2783:     PetscCall(VecCopy(x, w));
2784:     PetscCall(VecConjugate(w));
2785:     PetscCall(MatMultTranspose(mat, w, y));
2786:     PetscCall(VecDestroy(&w));
2787:     PetscCall(VecConjugate(y));
2788:   }
2789:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2790: #else
2791:   PetscCall(MatMultTranspose(mat, x, y));
2792: #endif
2793:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2794:   PetscFunctionReturn(PETSC_SUCCESS);
2795: }

2797: /*@
2798:   MatMultAdd -  Computes $v3 = v2 + A * v1$.

2800:   Neighbor-wise Collective

2802:   Input Parameters:
2803: + mat - the matrix
2804: . v1  - the vector to be multiplied by `mat`
2805: - v2  - the vector to be added to the result

2807:   Output Parameter:
2808: . v3 - the result

2810:   Level: beginner

2812:   Note:
2813:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2814:   call `MatMultAdd`(A,v1,v2,v1).

2816: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2817: @*/
2818: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2819: {
2820:   PetscFunctionBegin;

2827:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2828:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2829:   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);
2830:   /* 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);
2831:      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); */
2832:   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);
2833:   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);
2834:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2835:   MatCheckPreallocated(mat, 1);

2837:   PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2838:   PetscCall(VecLockReadPush(v1));
2839:   PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2840:   PetscCall(VecLockReadPop(v1));
2841:   PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2842:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2843:   PetscFunctionReturn(PETSC_SUCCESS);
2844: }

2846: /*@
2847:   MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.

2849:   Neighbor-wise Collective

2851:   Input Parameters:
2852: + mat - the matrix
2853: . v1  - the vector to be multiplied by the transpose of the matrix
2854: - v2  - the vector to be added to the result

2856:   Output Parameter:
2857: . v3 - the result

2859:   Level: beginner

2861:   Note:
2862:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2863:   call `MatMultTransposeAdd`(A,v1,v2,v1).

2865: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2866: @*/
2867: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2868: {
2869:   PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

2871:   PetscFunctionBegin;

2878:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2879:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2880:   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);
2881:   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);
2882:   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);
2883:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2884:   PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2885:   MatCheckPreallocated(mat, 1);

2887:   PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2888:   PetscCall(VecLockReadPush(v1));
2889:   PetscCall((*op)(mat, v1, v2, v3));
2890:   PetscCall(VecLockReadPop(v1));
2891:   PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2892:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2893:   PetscFunctionReturn(PETSC_SUCCESS);
2894: }

2896: /*@
2897:   MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.

2899:   Neighbor-wise Collective

2901:   Input Parameters:
2902: + mat - the matrix
2903: . v1  - the vector to be multiplied by the Hermitian transpose
2904: - v2  - the vector to be added to the result

2906:   Output Parameter:
2907: . v3 - the result

2909:   Level: beginner

2911:   Note:
2912:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2913:   call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

2915: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2916: @*/
2917: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2918: {
2919:   PetscFunctionBegin;

2926:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2927:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2928:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2929:   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);
2930:   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);
2931:   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);
2932:   MatCheckPreallocated(mat, 1);

2934:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2935:   PetscCall(VecLockReadPush(v1));
2936:   if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2937:   else {
2938:     Vec w, z;
2939:     PetscCall(VecDuplicate(v1, &w));
2940:     PetscCall(VecCopy(v1, w));
2941:     PetscCall(VecConjugate(w));
2942:     PetscCall(VecDuplicate(v3, &z));
2943:     PetscCall(MatMultTranspose(mat, w, z));
2944:     PetscCall(VecDestroy(&w));
2945:     PetscCall(VecConjugate(z));
2946:     if (v2 != v3) {
2947:       PetscCall(VecWAXPY(v3, 1.0, v2, z));
2948:     } else {
2949:       PetscCall(VecAXPY(v3, 1.0, z));
2950:     }
2951:     PetscCall(VecDestroy(&z));
2952:   }
2953:   PetscCall(VecLockReadPop(v1));
2954:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2955:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2956:   PetscFunctionReturn(PETSC_SUCCESS);
2957: }

2959: /*@
2960:   MatGetFactorType - gets the type of factorization a matrix is

2962:   Not Collective

2964:   Input Parameter:
2965: . mat - the matrix

2967:   Output Parameter:
2968: . 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`

2970:   Level: intermediate

2972: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2973:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2974: @*/
2975: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
2976: {
2977:   PetscFunctionBegin;
2980:   PetscAssertPointer(t, 2);
2981:   *t = mat->factortype;
2982:   PetscFunctionReturn(PETSC_SUCCESS);
2983: }

2985: /*@
2986:   MatSetFactorType - sets the type of factorization a matrix is

2988:   Logically Collective

2990:   Input Parameters:
2991: + mat - the matrix
2992: - 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`

2994:   Level: intermediate

2996: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2997:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2998: @*/
2999: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3000: {
3001:   PetscFunctionBegin;
3004:   mat->factortype = t;
3005:   PetscFunctionReturn(PETSC_SUCCESS);
3006: }

3008: /*@
3009:   MatGetInfo - Returns information about matrix storage (number of
3010:   nonzeros, memory, etc.).

3012:   Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

3014:   Input Parameters:
3015: + mat  - the matrix
3016: - 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)

3018:   Output Parameter:
3019: . info - matrix information context

3021:   Options Database Key:
3022: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

3024:   Level: intermediate

3026:   Notes:
3027:   The `MatInfo` context contains a variety of matrix data, including
3028:   number of nonzeros allocated and used, number of mallocs during
3029:   matrix assembly, etc.  Additional information for factored matrices
3030:   is provided (such as the fill ratio, number of mallocs during
3031:   factorization, etc.).

3033:   Example:
3034:   See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3035:   data within the `MatInfo` context.  For example,
3036: .vb
3037:       MatInfo info;
3038:       Mat     A;
3039:       double  mal, nz_a, nz_u;

3041:       MatGetInfo(A, MAT_LOCAL, &info);
3042:       mal  = info.mallocs;
3043:       nz_a = info.nz_allocated;
3044: .ve

3046:   Fortran Note:
3047:   Declare info as a `MatInfo` array of dimension `MAT_INFO_SIZE`, and then extract the parameters
3048:   of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
3049:   a complete list of parameter names.
3050: .vb
3051:       MatInfo info(MAT_INFO_SIZE)
3052:       double  precision mal, nz_a
3053:       Mat     A
3054:       integer ierr

3056:       call MatGetInfo(A, MAT_LOCAL, info, ierr)
3057:       mal = info(MAT_INFO_MALLOCS)
3058:       nz_a = info(MAT_INFO_NZ_ALLOCATED)
3059: .ve

3061: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3062: @*/
3063: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3064: {
3065:   PetscFunctionBegin;
3068:   PetscAssertPointer(info, 3);
3069:   MatCheckPreallocated(mat, 1);
3070:   PetscUseTypeMethod(mat, getinfo, flag, info);
3071:   PetscFunctionReturn(PETSC_SUCCESS);
3072: }

3074: /*
3075:    This is used by external packages where it is not easy to get the info from the actual
3076:    matrix factorization.
3077: */
3078: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3079: {
3080:   PetscFunctionBegin;
3081:   PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3082:   PetscFunctionReturn(PETSC_SUCCESS);
3083: }

3085: /*@
3086:   MatLUFactor - Performs in-place LU factorization of matrix.

3088:   Collective

3090:   Input Parameters:
3091: + mat  - the matrix
3092: . row  - row permutation
3093: . col  - column permutation
3094: - info - options for factorization, includes
3095: .vb
3096:           fill - expected fill as ratio of original fill.
3097:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3098:                    Run with the option -info to determine an optimal value to use
3099: .ve

3101:   Level: developer

3103:   Notes:
3104:   Most users should employ the `KSP` interface for linear solvers
3105:   instead of working directly with matrix algebra routines such as this.
3106:   See, e.g., `KSPCreate()`.

3108:   This changes the state of the matrix to a factored matrix; it cannot be used
3109:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3111:   This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3112:   when not using `KSP`.

3114:   Developer Note:
3115:   The Fortran interface is not autogenerated as the
3116:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3118: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3119:           `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3120: @*/
3121: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3122: {
3123:   MatFactorInfo tinfo;

3125:   PetscFunctionBegin;
3129:   if (info) PetscAssertPointer(info, 4);
3131:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3132:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3133:   MatCheckPreallocated(mat, 1);
3134:   if (!info) {
3135:     PetscCall(MatFactorInfoInitialize(&tinfo));
3136:     info = &tinfo;
3137:   }

3139:   PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3140:   PetscUseTypeMethod(mat, lufactor, row, col, info);
3141:   PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3142:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3143:   PetscFunctionReturn(PETSC_SUCCESS);
3144: }

3146: /*@
3147:   MatILUFactor - Performs in-place ILU factorization of matrix.

3149:   Collective

3151:   Input Parameters:
3152: + mat  - the matrix
3153: . row  - row permutation
3154: . col  - column permutation
3155: - info - structure containing
3156: .vb
3157:       levels - number of levels of fill.
3158:       expected fill - as ratio of original fill.
3159:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3160:                 missing diagonal entries)
3161: .ve

3163:   Level: developer

3165:   Notes:
3166:   Most users should employ the `KSP` interface for linear solvers
3167:   instead of working directly with matrix algebra routines such as this.
3168:   See, e.g., `KSPCreate()`.

3170:   Probably really in-place only when level of fill is zero, otherwise allocates
3171:   new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3172:   when not using `KSP`.

3174:   Developer Note:
3175:   The Fortran interface is not autogenerated as the
3176:   interface definition cannot be generated correctly [due to MatFactorInfo]

3178: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3179: @*/
3180: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3181: {
3182:   PetscFunctionBegin;
3186:   PetscAssertPointer(info, 4);
3188:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3189:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3190:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3191:   MatCheckPreallocated(mat, 1);

3193:   PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3194:   PetscUseTypeMethod(mat, ilufactor, row, col, info);
3195:   PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3196:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3197:   PetscFunctionReturn(PETSC_SUCCESS);
3198: }

3200: /*@
3201:   MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3202:   Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

3204:   Collective

3206:   Input Parameters:
3207: + fact - the factor matrix obtained with `MatGetFactor()`
3208: . mat  - the matrix
3209: . row  - the row permutation
3210: . col  - the column permutation
3211: - info - options for factorization, includes
3212: .vb
3213:           fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3214:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3215: .ve

3217:   Level: developer

3219:   Notes:
3220:   See [Matrix Factorization](sec_matfactor) for additional information about factorizations

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

3226:   Developer Note:
3227:   The Fortran interface is not autogenerated as the
3228:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3230: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3231: @*/
3232: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3233: {
3234:   MatFactorInfo tinfo;

3236:   PetscFunctionBegin;
3241:   if (info) PetscAssertPointer(info, 5);
3244:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3245:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3246:   MatCheckPreallocated(mat, 2);
3247:   if (!info) {
3248:     PetscCall(MatFactorInfoInitialize(&tinfo));
3249:     info = &tinfo;
3250:   }

3252:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3253:   PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3254:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3255:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3256:   PetscFunctionReturn(PETSC_SUCCESS);
3257: }

3259: /*@
3260:   MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3261:   Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

3263:   Collective

3265:   Input Parameters:
3266: + fact - the factor matrix obtained with `MatGetFactor()`
3267: . mat  - the matrix
3268: - info - options for factorization

3270:   Level: developer

3272:   Notes:
3273:   See `MatLUFactor()` for in-place factorization.  See
3274:   `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

3276:   Most users should employ the `KSP` interface for linear solvers
3277:   instead of working directly with matrix algebra routines such as this.
3278:   See, e.g., `KSPCreate()`.

3280:   Developer Note:
3281:   The Fortran interface is not autogenerated as the
3282:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3284: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3285: @*/
3286: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3287: {
3288:   MatFactorInfo tinfo;

3290:   PetscFunctionBegin;
3295:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3296:   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,
3297:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3299:   MatCheckPreallocated(mat, 2);
3300:   if (!info) {
3301:     PetscCall(MatFactorInfoInitialize(&tinfo));
3302:     info = &tinfo;
3303:   }

3305:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3306:   else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3307:   PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3308:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3309:   else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3310:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3311:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3312:   PetscFunctionReturn(PETSC_SUCCESS);
3313: }

3315: /*@
3316:   MatCholeskyFactor - Performs in-place Cholesky factorization of a
3317:   symmetric matrix.

3319:   Collective

3321:   Input Parameters:
3322: + mat  - the matrix
3323: . perm - row and column permutations
3324: - info - expected fill as ratio of original fill

3326:   Level: developer

3328:   Notes:
3329:   See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
3330:   `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

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

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

3340: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3341:           `MatGetOrdering()`
3342: @*/
3343: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3344: {
3345:   MatFactorInfo tinfo;

3347:   PetscFunctionBegin;
3350:   if (info) PetscAssertPointer(info, 3);
3352:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3353:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3354:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3355:   MatCheckPreallocated(mat, 1);
3356:   if (!info) {
3357:     PetscCall(MatFactorInfoInitialize(&tinfo));
3358:     info = &tinfo;
3359:   }

3361:   PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3362:   PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3363:   PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3364:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3365:   PetscFunctionReturn(PETSC_SUCCESS);
3366: }

3368: /*@
3369:   MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3370:   of a symmetric matrix.

3372:   Collective

3374:   Input Parameters:
3375: + fact - the factor matrix obtained with `MatGetFactor()`
3376: . mat  - the matrix
3377: . perm - row and column permutations
3378: - info - options for factorization, includes
3379: .vb
3380:           fill - expected fill as ratio of original fill.
3381:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3382:                    Run with the option -info to determine an optimal value to use
3383: .ve

3385:   Level: developer

3387:   Notes:
3388:   See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
3389:   `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

3391:   Most users should employ the `KSP` interface for linear solvers
3392:   instead of working directly with matrix algebra routines such as this.
3393:   See, e.g., `KSPCreate()`.

3395:   Developer Note:
3396:   The Fortran interface is not autogenerated as the
3397:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3399: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3400:           `MatGetOrdering()`
3401: @*/
3402: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3403: {
3404:   MatFactorInfo tinfo;

3406:   PetscFunctionBegin;
3410:   if (info) PetscAssertPointer(info, 4);
3413:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3414:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3415:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3416:   MatCheckPreallocated(mat, 2);
3417:   if (!info) {
3418:     PetscCall(MatFactorInfoInitialize(&tinfo));
3419:     info = &tinfo;
3420:   }

3422:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3423:   PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3424:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3425:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3426:   PetscFunctionReturn(PETSC_SUCCESS);
3427: }

3429: /*@
3430:   MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3431:   of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3432:   `MatCholeskyFactorSymbolic()`.

3434:   Collective

3436:   Input Parameters:
3437: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3438: . mat  - the initial matrix that is to be factored
3439: - info - options for factorization

3441:   Level: developer

3443:   Note:
3444:   Most users should employ the `KSP` interface for linear solvers
3445:   instead of working directly with matrix algebra routines such as this.
3446:   See, e.g., `KSPCreate()`.

3448:   Developer Note:
3449:   The Fortran interface is not autogenerated as the
3450:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3452: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3453: @*/
3454: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3455: {
3456:   MatFactorInfo tinfo;

3458:   PetscFunctionBegin;
3463:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3464:   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,
3465:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3466:   MatCheckPreallocated(mat, 2);
3467:   if (!info) {
3468:     PetscCall(MatFactorInfoInitialize(&tinfo));
3469:     info = &tinfo;
3470:   }

3472:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3473:   else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3474:   PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3475:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3476:   else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3477:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3478:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3479:   PetscFunctionReturn(PETSC_SUCCESS);
3480: }

3482: /*@
3483:   MatQRFactor - Performs in-place QR factorization of matrix.

3485:   Collective

3487:   Input Parameters:
3488: + mat  - the matrix
3489: . col  - column permutation
3490: - info - options for factorization, includes
3491: .vb
3492:           fill - expected fill as ratio of original fill.
3493:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3494:                    Run with the option -info to determine an optimal value to use
3495: .ve

3497:   Level: developer

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

3504:   This changes the state of the matrix to a factored matrix; it cannot be used
3505:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3507:   Developer Note:
3508:   The Fortran interface is not autogenerated as the
3509:   interface definition cannot be generated correctly [due to MatFactorInfo]

3511: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3512:           `MatSetUnfactored()`
3513: @*/
3514: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3515: {
3516:   PetscFunctionBegin;
3519:   if (info) PetscAssertPointer(info, 3);
3521:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3522:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3523:   MatCheckPreallocated(mat, 1);
3524:   PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3525:   PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3526:   PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3527:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3528:   PetscFunctionReturn(PETSC_SUCCESS);
3529: }

3531: /*@
3532:   MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3533:   Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

3535:   Collective

3537:   Input Parameters:
3538: + fact - the factor matrix obtained with `MatGetFactor()`
3539: . mat  - the matrix
3540: . col  - column permutation
3541: - info - options for factorization, includes
3542: .vb
3543:           fill - expected fill as ratio of original fill.
3544:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3545:                    Run with the option -info to determine an optimal value to use
3546: .ve

3548:   Level: developer

3550:   Note:
3551:   Most users should employ the `KSP` interface for linear solvers
3552:   instead of working directly with matrix algebra routines such as this.
3553:   See, e.g., `KSPCreate()`.

3555:   Developer Note:
3556:   The Fortran interface is not autogenerated as the
3557:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3559: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3560: @*/
3561: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3562: {
3563:   MatFactorInfo tinfo;

3565:   PetscFunctionBegin;
3569:   if (info) PetscAssertPointer(info, 4);
3572:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3573:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3574:   MatCheckPreallocated(mat, 2);
3575:   if (!info) {
3576:     PetscCall(MatFactorInfoInitialize(&tinfo));
3577:     info = &tinfo;
3578:   }

3580:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3581:   PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3582:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3583:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3584:   PetscFunctionReturn(PETSC_SUCCESS);
3585: }

3587: /*@
3588:   MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3589:   Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

3591:   Collective

3593:   Input Parameters:
3594: + fact - the factor matrix obtained with `MatGetFactor()`
3595: . mat  - the matrix
3596: - info - options for factorization

3598:   Level: developer

3600:   Notes:
3601:   See `MatQRFactor()` for in-place factorization.

3603:   Most users should employ the `KSP` interface for linear solvers
3604:   instead of working directly with matrix algebra routines such as this.
3605:   See, e.g., `KSPCreate()`.

3607:   Developer Note:
3608:   The Fortran interface is not autogenerated as the
3609:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3611: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3612: @*/
3613: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3614: {
3615:   MatFactorInfo tinfo;

3617:   PetscFunctionBegin;
3622:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3623:   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,
3624:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3626:   MatCheckPreallocated(mat, 2);
3627:   if (!info) {
3628:     PetscCall(MatFactorInfoInitialize(&tinfo));
3629:     info = &tinfo;
3630:   }

3632:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3633:   else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3634:   PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3635:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3636:   else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3637:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3638:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3639:   PetscFunctionReturn(PETSC_SUCCESS);
3640: }

3642: /*@
3643:   MatSolve - Solves $A x = b$, given a factored matrix.

3645:   Neighbor-wise Collective

3647:   Input Parameters:
3648: + mat - the factored matrix
3649: - b   - the right-hand-side vector

3651:   Output Parameter:
3652: . x - the result vector

3654:   Level: developer

3656:   Notes:
3657:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3658:   call `MatSolve`(A,x,x).

3660:   Most users should employ the `KSP` interface for linear solvers
3661:   instead of working directly with matrix algebra routines such as this.
3662:   See, e.g., `KSPCreate()`.

3664: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3665: @*/
3666: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3667: {
3668:   PetscFunctionBegin;
3673:   PetscCheckSameComm(mat, 1, b, 2);
3674:   PetscCheckSameComm(mat, 1, x, 3);
3675:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3676:   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);
3677:   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);
3678:   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);
3679:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3680:   MatCheckPreallocated(mat, 1);

3682:   PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3683:   PetscCall(VecFlag(x, mat->factorerrortype));
3684:   if (mat->factorerrortype) {
3685:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3686:   } else PetscUseTypeMethod(mat, solve, b, x);
3687:   PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3688:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3689:   PetscFunctionReturn(PETSC_SUCCESS);
3690: }

3692: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3693: {
3694:   Vec      b, x;
3695:   PetscInt N, i;
3696:   PetscErrorCode (*f)(Mat, Vec, Vec);
3697:   PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

3699:   PetscFunctionBegin;
3700:   if (A->factorerrortype) {
3701:     PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3702:     PetscCall(MatSetInf(X));
3703:     PetscFunctionReturn(PETSC_SUCCESS);
3704:   }
3705:   f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3706:   PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3707:   PetscCall(MatBoundToCPU(A, &Abound));
3708:   if (!Abound) {
3709:     PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3710:     PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3711:   }
3712: #if PetscDefined(HAVE_CUDA)
3713:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3714:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3715: #elif PetscDefined(HAVE_HIP)
3716:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3717:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3718: #endif
3719:   PetscCall(MatGetSize(B, NULL, &N));
3720:   for (i = 0; i < N; i++) {
3721:     PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3722:     PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3723:     PetscCall((*f)(A, b, x));
3724:     PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3725:     PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3726:   }
3727:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3728:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3729:   PetscFunctionReturn(PETSC_SUCCESS);
3730: }

3732: /*@
3733:   MatMatSolve - Solves $A X = B$, given a factored matrix.

3735:   Neighbor-wise Collective

3737:   Input Parameters:
3738: + A - the factored matrix
3739: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

3741:   Output Parameter:
3742: . X - the result matrix (dense matrix)

3744:   Level: developer

3746:   Note:
3747:   If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3748:   otherwise, `B` and `X` cannot be the same.

3750: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3751: @*/
3752: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3753: {
3754:   PetscFunctionBegin;
3759:   PetscCheckSameComm(A, 1, B, 2);
3760:   PetscCheckSameComm(A, 1, X, 3);
3761:   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);
3762:   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);
3763:   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");
3764:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3765:   MatCheckPreallocated(A, 1);

3767:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3768:   if (!A->ops->matsolve) {
3769:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3770:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3771:   } else PetscUseTypeMethod(A, matsolve, B, X);
3772:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3773:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3774:   PetscFunctionReturn(PETSC_SUCCESS);
3775: }

3777: /*@
3778:   MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.

3780:   Neighbor-wise Collective

3782:   Input Parameters:
3783: + A - the factored matrix
3784: - B - the right-hand-side matrix  (`MATDENSE` matrix)

3786:   Output Parameter:
3787: . X - the result matrix (dense matrix)

3789:   Level: developer

3791:   Note:
3792:   The matrices `B` and `X` cannot be the same.  I.e., one cannot
3793:   call `MatMatSolveTranspose`(A,X,X).

3795: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3796: @*/
3797: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3798: {
3799:   PetscFunctionBegin;
3804:   PetscCheckSameComm(A, 1, B, 2);
3805:   PetscCheckSameComm(A, 1, X, 3);
3806:   PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3807:   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);
3808:   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);
3809:   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);
3810:   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");
3811:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3812:   MatCheckPreallocated(A, 1);

3814:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3815:   if (!A->ops->matsolvetranspose) {
3816:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3817:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3818:   } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3819:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3820:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3821:   PetscFunctionReturn(PETSC_SUCCESS);
3822: }

3824: /*@
3825:   MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.

3827:   Neighbor-wise Collective

3829:   Input Parameters:
3830: + A  - the factored matrix
3831: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`

3833:   Output Parameter:
3834: . X - the result matrix (dense matrix)

3836:   Level: developer

3838:   Note:
3839:   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
3840:   format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

3842: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3843: @*/
3844: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3845: {
3846:   PetscFunctionBegin;
3851:   PetscCheckSameComm(A, 1, Bt, 2);
3852:   PetscCheckSameComm(A, 1, X, 3);

3854:   PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3855:   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);
3856:   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);
3857:   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");
3858:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3859:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3860:   MatCheckPreallocated(A, 1);

3862:   PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3863:   PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3864:   PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3865:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3866:   PetscFunctionReturn(PETSC_SUCCESS);
3867: }

3869: /*@
3870:   MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3871:   $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3873:   Neighbor-wise Collective

3875:   Input Parameters:
3876: + mat - the factored matrix
3877: - b   - the right-hand-side vector

3879:   Output Parameter:
3880: . x - the result vector

3882:   Level: developer

3884:   Notes:
3885:   `MatSolve()` should be used for most applications, as it performs
3886:   a forward solve followed by a backward solve.

3888:   The vectors `b` and `x` cannot be the same,  i.e., one cannot
3889:   call `MatForwardSolve`(A,x,x).

3891:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3892:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3893:   `MatForwardSolve()` solves $U^T*D y = b$, and
3894:   `MatBackwardSolve()` solves $U x = y$.
3895:   Thus they do not provide a symmetric preconditioner.

3897: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3898: @*/
3899: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3900: {
3901:   PetscFunctionBegin;
3906:   PetscCheckSameComm(mat, 1, b, 2);
3907:   PetscCheckSameComm(mat, 1, x, 3);
3908:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3909:   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);
3910:   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);
3911:   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);
3912:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3913:   MatCheckPreallocated(mat, 1);

3915:   PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3916:   PetscUseTypeMethod(mat, forwardsolve, b, x);
3917:   PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3918:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3919:   PetscFunctionReturn(PETSC_SUCCESS);
3920: }

3922: /*@
3923:   MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3924:   $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3926:   Neighbor-wise Collective

3928:   Input Parameters:
3929: + mat - the factored matrix
3930: - b   - the right-hand-side vector

3932:   Output Parameter:
3933: . x - the result vector

3935:   Level: developer

3937:   Notes:
3938:   `MatSolve()` should be used for most applications, as it performs
3939:   a forward solve followed by a backward solve.

3941:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3942:   call `MatBackwardSolve`(A,x,x).

3944:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3945:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3946:   `MatForwardSolve()` solves $U^T*D y = b$, and
3947:   `MatBackwardSolve()` solves $U x = y$.
3948:   Thus they do not provide a symmetric preconditioner.

3950: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3951: @*/
3952: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3953: {
3954:   PetscFunctionBegin;
3959:   PetscCheckSameComm(mat, 1, b, 2);
3960:   PetscCheckSameComm(mat, 1, x, 3);
3961:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3962:   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);
3963:   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);
3964:   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);
3965:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3966:   MatCheckPreallocated(mat, 1);

3968:   PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3969:   PetscUseTypeMethod(mat, backwardsolve, b, x);
3970:   PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3971:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3972:   PetscFunctionReturn(PETSC_SUCCESS);
3973: }

3975: /*@
3976:   MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.

3978:   Neighbor-wise Collective

3980:   Input Parameters:
3981: + mat - the factored matrix
3982: . b   - the right-hand-side vector
3983: - y   - the vector to be added to

3985:   Output Parameter:
3986: . x - the result vector

3988:   Level: developer

3990:   Note:
3991:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3992:   call `MatSolveAdd`(A,x,y,x).

3994: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3995: @*/
3996: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
3997: {
3998:   PetscScalar one = 1.0;
3999:   Vec         tmp;

4001:   PetscFunctionBegin;
4007:   PetscCheckSameComm(mat, 1, b, 2);
4008:   PetscCheckSameComm(mat, 1, y, 3);
4009:   PetscCheckSameComm(mat, 1, x, 4);
4010:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4011:   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);
4012:   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);
4013:   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);
4014:   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);
4015:   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);
4016:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4017:   MatCheckPreallocated(mat, 1);

4019:   PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4020:   PetscCall(VecFlag(x, mat->factorerrortype));
4021:   if (mat->factorerrortype) {
4022:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4023:   } else if (mat->ops->solveadd) {
4024:     PetscUseTypeMethod(mat, solveadd, b, y, x);
4025:   } else {
4026:     /* do the solve then the add manually */
4027:     if (x != y) {
4028:       PetscCall(MatSolve(mat, b, x));
4029:       PetscCall(VecAXPY(x, one, y));
4030:     } else {
4031:       PetscCall(VecDuplicate(x, &tmp));
4032:       PetscCall(VecCopy(x, tmp));
4033:       PetscCall(MatSolve(mat, b, x));
4034:       PetscCall(VecAXPY(x, one, tmp));
4035:       PetscCall(VecDestroy(&tmp));
4036:     }
4037:   }
4038:   PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4039:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4040:   PetscFunctionReturn(PETSC_SUCCESS);
4041: }

4043: /*@
4044:   MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.

4046:   Neighbor-wise Collective

4048:   Input Parameters:
4049: + mat - the factored matrix
4050: - b   - the right-hand-side vector

4052:   Output Parameter:
4053: . x - the result vector

4055:   Level: developer

4057:   Notes:
4058:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4059:   call `MatSolveTranspose`(A,x,x).

4061:   Most users should employ the `KSP` interface for linear solvers
4062:   instead of working directly with matrix algebra routines such as this.
4063:   See, e.g., `KSPCreate()`.

4065: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4066: @*/
4067: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4068: {
4069:   PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

4071:   PetscFunctionBegin;
4076:   PetscCheckSameComm(mat, 1, b, 2);
4077:   PetscCheckSameComm(mat, 1, x, 3);
4078:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4079:   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);
4080:   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);
4081:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4082:   MatCheckPreallocated(mat, 1);
4083:   PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4084:   PetscCall(VecFlag(x, mat->factorerrortype));
4085:   if (mat->factorerrortype) {
4086:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4087:   } else {
4088:     PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4089:     PetscCall((*f)(mat, b, x));
4090:   }
4091:   PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4092:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4093:   PetscFunctionReturn(PETSC_SUCCESS);
4094: }

4096: /*@
4097:   MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4098:   factored matrix.

4100:   Neighbor-wise Collective

4102:   Input Parameters:
4103: + mat - the factored matrix
4104: . b   - the right-hand-side vector
4105: - y   - the vector to be added to

4107:   Output Parameter:
4108: . x - the result vector

4110:   Level: developer

4112:   Note:
4113:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4114:   call `MatSolveTransposeAdd`(A,x,y,x).

4116: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4117: @*/
4118: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4119: {
4120:   PetscScalar one = 1.0;
4121:   Vec         tmp;
4122:   PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

4124:   PetscFunctionBegin;
4130:   PetscCheckSameComm(mat, 1, b, 2);
4131:   PetscCheckSameComm(mat, 1, y, 3);
4132:   PetscCheckSameComm(mat, 1, x, 4);
4133:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4134:   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);
4135:   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);
4136:   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);
4137:   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);
4138:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4139:   MatCheckPreallocated(mat, 1);

4141:   PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4142:   PetscCall(VecFlag(x, mat->factorerrortype));
4143:   if (mat->factorerrortype) {
4144:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4145:   } else if (f) {
4146:     PetscCall((*f)(mat, b, y, x));
4147:   } else {
4148:     /* do the solve then the add manually */
4149:     if (x != y) {
4150:       PetscCall(MatSolveTranspose(mat, b, x));
4151:       PetscCall(VecAXPY(x, one, y));
4152:     } else {
4153:       PetscCall(VecDuplicate(x, &tmp));
4154:       PetscCall(VecCopy(x, tmp));
4155:       PetscCall(MatSolveTranspose(mat, b, x));
4156:       PetscCall(VecAXPY(x, one, tmp));
4157:       PetscCall(VecDestroy(&tmp));
4158:     }
4159:   }
4160:   PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4161:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4162:   PetscFunctionReturn(PETSC_SUCCESS);
4163: }

4165: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4166: /*@
4167:   MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

4169:   Neighbor-wise Collective

4171:   Input Parameters:
4172: + mat   - the matrix
4173: . b     - the right-hand side
4174: . omega - the relaxation factor
4175: . flag  - flag indicating the type of SOR (see below)
4176: . shift - diagonal shift
4177: . its   - the number of iterations
4178: - lits  - the number of local iterations

4180:   Output Parameter:
4181: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

4183:   SOR Flags:
4184: +     `SOR_FORWARD_SWEEP` - forward SOR
4185: .     `SOR_BACKWARD_SWEEP` - backward SOR
4186: .     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4187: .     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4188: .     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4189: .     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4190: .     `SOR_EISENSTAT` - SOR with Eisenstat trick
4191: .     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4192:   upper/lower triangular part of matrix to
4193:   vector (with omega)
4194: -     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

4196:   Level: developer

4198:   Notes:
4199:   `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4200:   `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4201:   on each processor.

4203:   Application programmers will not generally use `MatSOR()` directly,
4204:   but instead will employ the `KSP`/`PC` interface.

4206:   For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

4208:   Most users should employ the `KSP` interface for linear solvers
4209:   instead of working directly with matrix algebra routines such as this.
4210:   See, e.g., `KSPCreate()`.

4212:   Vectors `x` and `b` CANNOT be the same

4214:   The flags are implemented as bitwise inclusive or operations.
4215:   For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4216:   to specify a zero initial guess for SSOR.

4218:   Developer Note:
4219:   We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

4221: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4222: @*/
4223: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4224: {
4225:   PetscFunctionBegin;
4230:   PetscCheckSameComm(mat, 1, b, 2);
4231:   PetscCheckSameComm(mat, 1, x, 8);
4232:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4233:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4234:   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);
4235:   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);
4236:   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);
4237:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4238:   PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4239:   PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

4241:   MatCheckPreallocated(mat, 1);
4242:   PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4243:   PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4244:   PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4245:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4246:   PetscFunctionReturn(PETSC_SUCCESS);
4247: }

4249: /*
4250:       Default matrix copy routine.
4251: */
4252: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4253: {
4254:   PetscInt           i, rstart = 0, rend = 0, nz;
4255:   const PetscInt    *cwork;
4256:   const PetscScalar *vwork;

4258:   PetscFunctionBegin;
4259:   if (B->assembled) PetscCall(MatZeroEntries(B));
4260:   if (str == SAME_NONZERO_PATTERN) {
4261:     PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4262:     for (i = rstart; i < rend; i++) {
4263:       PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4264:       PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4265:       PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4266:     }
4267:   } else {
4268:     PetscCall(MatAYPX(B, 0.0, A, str));
4269:   }
4270:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4271:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4272:   PetscFunctionReturn(PETSC_SUCCESS);
4273: }

4275: /*@
4276:   MatCopy - Copies a matrix to another matrix.

4278:   Collective

4280:   Input Parameters:
4281: + A   - the matrix
4282: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

4284:   Output Parameter:
4285: . B - where the copy is put

4287:   Level: intermediate

4289:   Notes:
4290:   If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.

4292:   `MatCopy()` copies the matrix entries of a matrix to another existing
4293:   matrix (after first zeroing the second matrix).  A related routine is
4294:   `MatConvert()`, which first creates a new matrix and then copies the data.

4296: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4297: @*/
4298: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4299: {
4300:   PetscInt i;

4302:   PetscFunctionBegin;
4307:   PetscCheckSameComm(A, 1, B, 2);
4308:   MatCheckPreallocated(B, 2);
4309:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4310:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4311:   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,
4312:              A->cmap->N, B->cmap->N);
4313:   MatCheckPreallocated(A, 1);
4314:   if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

4316:   PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4317:   if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4318:   else PetscCall(MatCopy_Basic(A, B, str));

4320:   B->stencil.dim = A->stencil.dim;
4321:   B->stencil.noc = A->stencil.noc;
4322:   for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4323:     B->stencil.dims[i]   = A->stencil.dims[i];
4324:     B->stencil.starts[i] = A->stencil.starts[i];
4325:   }

4327:   PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4328:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4329:   PetscFunctionReturn(PETSC_SUCCESS);
4330: }

4332: /*@
4333:   MatConvert - Converts a matrix to another matrix, either of the same
4334:   or different type.

4336:   Collective

4338:   Input Parameters:
4339: + mat     - the matrix
4340: . newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
4341:             same type as the original matrix.
4342: - reuse   - denotes if the destination matrix is to be created or reused.
4343:             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
4344:             `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).

4346:   Output Parameter:
4347: . M - pointer to place new matrix

4349:   Level: intermediate

4351:   Notes:
4352:   `MatConvert()` first creates a new matrix and then copies the data from
4353:   the first matrix.  A related routine is `MatCopy()`, which copies the matrix
4354:   entries of one matrix to another already existing matrix context.

4356:   Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4357:   the MPI communicator of the generated matrix is always the same as the communicator
4358:   of the input matrix.

4360: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4361: @*/
4362: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4363: {
4364:   PetscBool  sametype, issame, flg;
4365:   PetscBool3 issymmetric, ishermitian;
4366:   char       convname[256], mtype[256];
4367:   Mat        B;

4369:   PetscFunctionBegin;
4372:   PetscAssertPointer(M, 4);
4373:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4374:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4375:   MatCheckPreallocated(mat, 1);

4377:   PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4378:   if (flg) newtype = mtype;

4380:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4381:   PetscCall(PetscStrcmp(newtype, "same", &issame));
4382:   PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4383:   if (reuse == MAT_REUSE_MATRIX) {
4385:     PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4386:   }

4388:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4389:     PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4390:     PetscFunctionReturn(PETSC_SUCCESS);
4391:   }

4393:   /* Cache Mat options because some converters use MatHeaderReplace  */
4394:   issymmetric = mat->symmetric;
4395:   ishermitian = mat->hermitian;

4397:   if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4398:     PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4399:     PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4400:   } else {
4401:     PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4402:     const char *prefix[3]                                 = {"seq", "mpi", ""};
4403:     PetscInt    i;
4404:     /*
4405:        Order of precedence:
4406:        0) See if newtype is a superclass of the current matrix.
4407:        1) See if a specialized converter is known to the current matrix.
4408:        2) See if a specialized converter is known to the desired matrix class.
4409:        3) See if a good general converter is registered for the desired class
4410:           (as of 6/27/03 only MATMPIADJ falls into this category).
4411:        4) See if a good general converter is known for the current matrix.
4412:        5) Use a really basic converter.
4413:     */

4415:     /* 0) See if newtype is a superclass of the current matrix.
4416:           i.e mat is mpiaij and newtype is aij */
4417:     for (i = 0; i < 2; i++) {
4418:       PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4419:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4420:       PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4421:       PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4422:       if (flg) {
4423:         if (reuse == MAT_INPLACE_MATRIX) {
4424:           PetscCall(PetscInfo(mat, "Early return\n"));
4425:           PetscFunctionReturn(PETSC_SUCCESS);
4426:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4427:           PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4428:           PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4429:           PetscFunctionReturn(PETSC_SUCCESS);
4430:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4431:           PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4432:           PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4433:           PetscFunctionReturn(PETSC_SUCCESS);
4434:         }
4435:       }
4436:     }
4437:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4438:     for (i = 0; i < 3; i++) {
4439:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4440:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4441:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4442:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4443:       PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4444:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4445:       PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4446:       PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4447:       if (conv) goto foundconv;
4448:     }

4450:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4451:     PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4452:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4453:     PetscCall(MatSetType(B, newtype));
4454:     for (i = 0; i < 3; i++) {
4455:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4456:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4457:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4458:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4459:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4460:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4461:       PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4462:       PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4463:       if (conv) {
4464:         PetscCall(MatDestroy(&B));
4465:         goto foundconv;
4466:       }
4467:     }

4469:     /* 3) See if a good general converter is registered for the desired class */
4470:     conv = B->ops->convertfrom;
4471:     PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4472:     PetscCall(MatDestroy(&B));
4473:     if (conv) goto foundconv;

4475:     /* 4) See if a good general converter is known for the current matrix */
4476:     if (mat->ops->convert) conv = mat->ops->convert;
4477:     PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4478:     if (conv) goto foundconv;

4480:     /* 5) Use a really basic converter. */
4481:     PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4482:     conv = MatConvert_Basic;

4484:   foundconv:
4485:     PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4486:     PetscCall((*conv)(mat, newtype, reuse, M));
4487:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4488:       /* the block sizes must be same if the mappings are copied over */
4489:       (*M)->rmap->bs = mat->rmap->bs;
4490:       (*M)->cmap->bs = mat->cmap->bs;
4491:       PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4492:       PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4493:       (*M)->rmap->mapping = mat->rmap->mapping;
4494:       (*M)->cmap->mapping = mat->cmap->mapping;
4495:     }
4496:     (*M)->stencil.dim = mat->stencil.dim;
4497:     (*M)->stencil.noc = mat->stencil.noc;
4498:     for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4499:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4500:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4501:     }
4502:     PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4503:   }
4504:   PetscCall(PetscObjectStateIncrease((PetscObject)*M));

4506:   /* Copy Mat options */
4507:   if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4508:   else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4509:   if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4510:   else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4511:   PetscFunctionReturn(PETSC_SUCCESS);
4512: }

4514: /*@
4515:   MatFactorGetSolverType - Returns name of the package providing the factorization routines

4517:   Not Collective

4519:   Input Parameter:
4520: . mat - the matrix, must be a factored matrix

4522:   Output Parameter:
4523: . type - the string name of the package (do not free this string)

4525:   Level: intermediate

4527:   Fortran Note:
4528:   Pass in an empty string that is long enough and the package name will be copied into it.

4530: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4531: @*/
4532: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4533: {
4534:   PetscErrorCode (*conv)(Mat, MatSolverType *);

4536:   PetscFunctionBegin;
4539:   PetscAssertPointer(type, 2);
4540:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4541:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4542:   if (conv) PetscCall((*conv)(mat, type));
4543:   else *type = MATSOLVERPETSC;
4544:   PetscFunctionReturn(PETSC_SUCCESS);
4545: }

4547: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4548: struct _MatSolverTypeForSpecifcType {
4549:   MatType mtype;
4550:   /* no entry for MAT_FACTOR_NONE */
4551:   PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4552:   MatSolverTypeForSpecifcType next;
4553: };

4555: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4556: struct _MatSolverTypeHolder {
4557:   char                       *name;
4558:   MatSolverTypeForSpecifcType handlers;
4559:   MatSolverTypeHolder         next;
4560: };

4562: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4564: /*@C
4565:   MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

4567:   Logically Collective, No Fortran Support

4569:   Input Parameters:
4570: + package      - name of the package, for example petsc or superlu
4571: . mtype        - the matrix type that works with this package
4572: . ftype        - the type of factorization supported by the package
4573: - createfactor - routine that will create the factored matrix ready to be used

4575:   Level: developer

4577: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4578:   `MatGetFactor()`
4579: @*/
4580: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4581: {
4582:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
4583:   PetscBool                   flg;
4584:   MatSolverTypeForSpecifcType inext, iprev = NULL;

4586:   PetscFunctionBegin;
4587:   PetscCall(MatInitializePackage());
4588:   if (!next) {
4589:     PetscCall(PetscNew(&MatSolverTypeHolders));
4590:     PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4591:     PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4592:     PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4593:     MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4594:     PetscFunctionReturn(PETSC_SUCCESS);
4595:   }
4596:   while (next) {
4597:     PetscCall(PetscStrcasecmp(package, next->name, &flg));
4598:     if (flg) {
4599:       PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4600:       inext = next->handlers;
4601:       while (inext) {
4602:         PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4603:         if (flg) {
4604:           inext->createfactor[(int)ftype - 1] = createfactor;
4605:           PetscFunctionReturn(PETSC_SUCCESS);
4606:         }
4607:         iprev = inext;
4608:         inext = inext->next;
4609:       }
4610:       PetscCall(PetscNew(&iprev->next));
4611:       PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4612:       iprev->next->createfactor[(int)ftype - 1] = createfactor;
4613:       PetscFunctionReturn(PETSC_SUCCESS);
4614:     }
4615:     prev = next;
4616:     next = next->next;
4617:   }
4618:   PetscCall(PetscNew(&prev->next));
4619:   PetscCall(PetscStrallocpy(package, &prev->next->name));
4620:   PetscCall(PetscNew(&prev->next->handlers));
4621:   PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4622:   prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4623:   PetscFunctionReturn(PETSC_SUCCESS);
4624: }

4626: /*@C
4627:   MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

4629:   Input Parameters:
4630: + 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
4631: . ftype - the type of factorization supported by the type
4632: - mtype - the matrix type that works with this type

4634:   Output Parameters:
4635: + foundtype    - `PETSC_TRUE` if the type was registered
4636: . foundmtype   - `PETSC_TRUE` if the type supports the requested mtype
4637: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

4639:   Calling sequence of `createfactor`:
4640: + A     - the matrix providing the factor matrix
4641: . ftype - the `MatFactorType` of the factor requested
4642: - B     - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`

4644:   Level: developer

4646:   Note:
4647:   When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4648:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4649:   For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.

4651: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4652:           `MatInitializePackage()`
4653: @*/
4654: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4655: {
4656:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4657:   PetscBool                   flg;
4658:   MatSolverTypeForSpecifcType inext;

4660:   PetscFunctionBegin;
4661:   if (foundtype) *foundtype = PETSC_FALSE;
4662:   if (foundmtype) *foundmtype = PETSC_FALSE;
4663:   if (createfactor) *createfactor = NULL;

4665:   if (type) {
4666:     while (next) {
4667:       PetscCall(PetscStrcasecmp(type, next->name, &flg));
4668:       if (flg) {
4669:         if (foundtype) *foundtype = PETSC_TRUE;
4670:         inext = next->handlers;
4671:         while (inext) {
4672:           PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4673:           if (flg) {
4674:             if (foundmtype) *foundmtype = PETSC_TRUE;
4675:             if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4676:             PetscFunctionReturn(PETSC_SUCCESS);
4677:           }
4678:           inext = inext->next;
4679:         }
4680:       }
4681:       next = next->next;
4682:     }
4683:   } else {
4684:     while (next) {
4685:       inext = next->handlers;
4686:       while (inext) {
4687:         PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4688:         if (flg && inext->createfactor[(int)ftype - 1]) {
4689:           if (foundtype) *foundtype = PETSC_TRUE;
4690:           if (foundmtype) *foundmtype = PETSC_TRUE;
4691:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4692:           PetscFunctionReturn(PETSC_SUCCESS);
4693:         }
4694:         inext = inext->next;
4695:       }
4696:       next = next->next;
4697:     }
4698:     /* try with base classes inext->mtype */
4699:     next = MatSolverTypeHolders;
4700:     while (next) {
4701:       inext = next->handlers;
4702:       while (inext) {
4703:         PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4704:         if (flg && inext->createfactor[(int)ftype - 1]) {
4705:           if (foundtype) *foundtype = PETSC_TRUE;
4706:           if (foundmtype) *foundmtype = PETSC_TRUE;
4707:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4708:           PetscFunctionReturn(PETSC_SUCCESS);
4709:         }
4710:         inext = inext->next;
4711:       }
4712:       next = next->next;
4713:     }
4714:   }
4715:   PetscFunctionReturn(PETSC_SUCCESS);
4716: }

4718: PetscErrorCode MatSolverTypeDestroy(void)
4719: {
4720:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
4721:   MatSolverTypeForSpecifcType inext, iprev;

4723:   PetscFunctionBegin;
4724:   while (next) {
4725:     PetscCall(PetscFree(next->name));
4726:     inext = next->handlers;
4727:     while (inext) {
4728:       PetscCall(PetscFree(inext->mtype));
4729:       iprev = inext;
4730:       inext = inext->next;
4731:       PetscCall(PetscFree(iprev));
4732:     }
4733:     prev = next;
4734:     next = next->next;
4735:     PetscCall(PetscFree(prev));
4736:   }
4737:   MatSolverTypeHolders = NULL;
4738:   PetscFunctionReturn(PETSC_SUCCESS);
4739: }

4741: /*@
4742:   MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

4744:   Logically Collective

4746:   Input Parameter:
4747: . mat - the matrix

4749:   Output Parameter:
4750: . flg - `PETSC_TRUE` if uses the ordering

4752:   Level: developer

4754:   Note:
4755:   Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4756:   packages do not, thus we want to skip generating the ordering when it is not needed or used.

4758: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4759: @*/
4760: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4761: {
4762:   PetscFunctionBegin;
4763:   *flg = mat->canuseordering;
4764:   PetscFunctionReturn(PETSC_SUCCESS);
4765: }

4767: /*@
4768:   MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

4770:   Logically Collective

4772:   Input Parameters:
4773: + mat   - the matrix obtained with `MatGetFactor()`
4774: - ftype - the factorization type to be used

4776:   Output Parameter:
4777: . otype - the preferred ordering type

4779:   Level: developer

4781: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4782: @*/
4783: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4784: {
4785:   PetscFunctionBegin;
4786:   *otype = mat->preferredordering[ftype];
4787:   PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4788:   PetscFunctionReturn(PETSC_SUCCESS);
4789: }

4791: /*@
4792:   MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()

4794:   Collective

4796:   Input Parameters:
4797: + mat   - the matrix
4798: . 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
4799:           the other criteria is returned
4800: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4802:   Output Parameter:
4803: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.

4805:   Options Database Keys:
4806: + -pc_factor_mat_solver_type <type>             - choose the type at run time. When using `KSP` solvers
4807: - -mat_factor_bind_factorization <host, device> - Where to do matrix factorization? Default is device (might consume more device memory.
4808:                                                   One can choose host to save device memory). Currently only supported with `MATSEQAIJCUSPARSE` matrices.

4810:   Level: intermediate

4812:   Notes:
4813:   The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4814:   types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.

4816:   Users usually access the factorization solvers via `KSP`

4818:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4819:   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

4821:   When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4822:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4823:   For example if one configuration had --download-mumps while a different one had --download-superlu_dist.

4825:   Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4826:   where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4827:   call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

4829:   Developer Note:
4830:   This should actually be called `MatCreateFactor()` since it creates a new factor object

4832: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4833:           `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4834:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4835: @*/
4836: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4837: {
4838:   PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4839:   PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

4841:   PetscFunctionBegin;

4845:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4846:   MatCheckPreallocated(mat, 1);

4848:   PetscCall(MatIsShell(mat, &shell));
4849:   if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4850:   if (hasop) {
4851:     PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4852:     PetscFunctionReturn(PETSC_SUCCESS);
4853:   }

4855:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4856:   if (!foundtype) {
4857:     if (type) {
4858:       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],
4859:               ((PetscObject)mat)->type_name, type);
4860:     } else {
4861:       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);
4862:     }
4863:   }
4864:   PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4865:   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);

4867:   PetscCall((*conv)(mat, ftype, f));
4868:   if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4869:   PetscFunctionReturn(PETSC_SUCCESS);
4870: }

4872: /*@
4873:   MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type

4875:   Not Collective

4877:   Input Parameters:
4878: + mat   - the matrix
4879: . type  - name of solver type, for example, superlu, petsc (to use PETSc's default)
4880: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4882:   Output Parameter:
4883: . flg - PETSC_TRUE if the factorization is available

4885:   Level: intermediate

4887:   Notes:
4888:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4889:   such as pastix, superlu, mumps etc.

4891:   PETSc must have been ./configure to use the external solver, using the option --download-package

4893:   Developer Note:
4894:   This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object

4896: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4897:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4898: @*/
4899: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4900: {
4901:   PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

4903:   PetscFunctionBegin;
4905:   PetscAssertPointer(flg, 4);

4907:   *flg = PETSC_FALSE;
4908:   if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);

4910:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4911:   MatCheckPreallocated(mat, 1);

4913:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4914:   *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4915:   PetscFunctionReturn(PETSC_SUCCESS);
4916: }

4918: /*@
4919:   MatDuplicate - Duplicates a matrix including the non-zero structure.

4921:   Collective

4923:   Input Parameters:
4924: + mat - the matrix
4925: - op  - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4926:         See the manual page for `MatDuplicateOption()` for an explanation of these options.

4928:   Output Parameter:
4929: . M - pointer to place new matrix

4931:   Level: intermediate

4933:   Notes:
4934:   You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.

4936:   If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.

4938:   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.

4940:   When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4941:   is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4942:   User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.

4944: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4945: @*/
4946: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4947: {
4948:   Mat         B;
4949:   VecType     vtype;
4950:   PetscInt    i;
4951:   PetscObject dm, container_h, container_d;
4952:   void (*viewf)(void);

4954:   PetscFunctionBegin;
4957:   PetscAssertPointer(M, 3);
4958:   PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4959:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4960:   MatCheckPreallocated(mat, 1);

4962:   *M = NULL;
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, PetscContainerUserDestroyDefault));
5322:   PetscFunctionReturn(PETSC_SUCCESS);
5323: }

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

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

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`. 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.

5349:   If mat is unchanged from the last call this function returns immediately without recomputing the result

5351:   If you only need the symbolic transpose, and not the numerical values, use `MatTransposeSymbolic()`

5353: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5354:           `MatTransposeSymbolic()`, `MatCreateTranspose()`
5355: @*/
5356: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5357: {
5358:   PetscContainer  rB = NULL;
5359:   MatParentState *rb = NULL;

5361:   PetscFunctionBegin;
5364:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5365:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5366:   PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5367:   PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5368:   MatCheckPreallocated(mat, 1);
5369:   if (reuse == MAT_REUSE_MATRIX) {
5370:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5371:     PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5372:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5373:     PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5374:     if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5375:   }

5377:   PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5378:   if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5379:     PetscUseTypeMethod(mat, transpose, reuse, B);
5380:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5381:   }
5382:   PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

5384:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5385:   if (reuse != MAT_INPLACE_MATRIX) {
5386:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5387:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5388:     rb->state        = ((PetscObject)mat)->state;
5389:     rb->nonzerostate = mat->nonzerostate;
5390:   }
5391:   PetscFunctionReturn(PETSC_SUCCESS);
5392: }

5394: /*@
5395:   MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

5397:   Collective

5399:   Input Parameter:
5400: . A - the matrix to transpose

5402:   Output Parameter:
5403: . 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
5404:       numerical portion.

5406:   Level: intermediate

5408:   Note:
5409:   This is not supported for many matrix types, use `MatTranspose()` in those cases

5411: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5412: @*/
5413: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5414: {
5415:   PetscFunctionBegin;
5418:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5419:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5420:   PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5421:   PetscUseTypeMethod(A, transposesymbolic, B);
5422:   PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

5424:   PetscCall(MatTransposeSetPrecursor(A, *B));
5425:   PetscFunctionReturn(PETSC_SUCCESS);
5426: }

5428: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5429: {
5430:   PetscContainer  rB;
5431:   MatParentState *rb;

5433:   PetscFunctionBegin;
5436:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5437:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5438:   PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5439:   PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5440:   PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5441:   PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5442:   PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5443:   PetscFunctionReturn(PETSC_SUCCESS);
5444: }

5446: /*@
5447:   MatIsTranspose - Test whether a matrix is another one's transpose,
5448:   or its own, in which case it tests symmetry.

5450:   Collective

5452:   Input Parameters:
5453: + A   - the matrix to test
5454: . B   - the matrix to test against, this can equal the first parameter
5455: - tol - tolerance, differences between entries smaller than this are counted as zero

5457:   Output Parameter:
5458: . flg - the result

5460:   Level: intermediate

5462:   Notes:
5463:   The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5464:   test involves parallel copies of the block off-diagonal parts of the matrix.

5466: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5467: @*/
5468: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5469: {
5470:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5472:   PetscFunctionBegin;
5475:   PetscAssertPointer(flg, 4);
5476:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5477:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5478:   *flg = PETSC_FALSE;
5479:   if (f && g) {
5480:     PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5481:     PetscCall((*f)(A, B, tol, flg));
5482:   } else {
5483:     MatType mattype;

5485:     PetscCall(MatGetType(f ? B : A, &mattype));
5486:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5487:   }
5488:   PetscFunctionReturn(PETSC_SUCCESS);
5489: }

5491: /*@
5492:   MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

5494:   Collective

5496:   Input Parameters:
5497: + mat   - the matrix to transpose and complex conjugate
5498: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5500:   Output Parameter:
5501: . B - the Hermitian transpose

5503:   Level: intermediate

5505: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5506: @*/
5507: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5508: {
5509:   PetscFunctionBegin;
5510:   PetscCall(MatTranspose(mat, reuse, B));
5511: #if defined(PETSC_USE_COMPLEX)
5512:   PetscCall(MatConjugate(*B));
5513: #endif
5514:   PetscFunctionReturn(PETSC_SUCCESS);
5515: }

5517: /*@
5518:   MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

5520:   Collective

5522:   Input Parameters:
5523: + A   - the matrix to test
5524: . B   - the matrix to test against, this can equal the first parameter
5525: - tol - tolerance, differences between entries smaller than this are counted as zero

5527:   Output Parameter:
5528: . flg - the result

5530:   Level: intermediate

5532:   Notes:
5533:   Only available for `MATAIJ` matrices.

5535:   The sequential algorithm
5536:   has a running time of the order of the number of nonzeros; the parallel
5537:   test involves parallel copies of the block off-diagonal parts of the matrix.

5539: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5540: @*/
5541: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5542: {
5543:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5545:   PetscFunctionBegin;
5548:   PetscAssertPointer(flg, 4);
5549:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5550:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5551:   if (f && g) {
5552:     PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5553:     PetscCall((*f)(A, B, tol, flg));
5554:   }
5555:   PetscFunctionReturn(PETSC_SUCCESS);
5556: }

5558: /*@
5559:   MatPermute - Creates a new matrix with rows and columns permuted from the
5560:   original.

5562:   Collective

5564:   Input Parameters:
5565: + mat - the matrix to permute
5566: . row - row permutation, each processor supplies only the permutation for its rows
5567: - col - column permutation, each processor supplies only the permutation for its columns

5569:   Output Parameter:
5570: . B - the permuted matrix

5572:   Level: advanced

5574:   Note:
5575:   The index sets map from row/col of permuted matrix to row/col of original matrix.
5576:   The index sets should be on the same communicator as mat and have the same local sizes.

5578:   Developer Note:
5579:   If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5580:   exploit the fact that row and col are permutations, consider implementing the
5581:   more general `MatCreateSubMatrix()` instead.

5583: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5584: @*/
5585: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5586: {
5587:   PetscFunctionBegin;
5592:   PetscAssertPointer(B, 4);
5593:   PetscCheckSameComm(mat, 1, row, 2);
5594:   if (row != col) PetscCheckSameComm(row, 2, col, 3);
5595:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5596:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5597:   PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5598:   MatCheckPreallocated(mat, 1);

5600:   if (mat->ops->permute) {
5601:     PetscUseTypeMethod(mat, permute, row, col, B);
5602:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5603:   } else {
5604:     PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5605:   }
5606:   PetscFunctionReturn(PETSC_SUCCESS);
5607: }

5609: /*@
5610:   MatEqual - Compares two matrices.

5612:   Collective

5614:   Input Parameters:
5615: + A - the first matrix
5616: - B - the second matrix

5618:   Output Parameter:
5619: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

5621:   Level: intermediate

5623: .seealso: [](ch_matrices), `Mat`
5624: @*/
5625: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5626: {
5627:   PetscFunctionBegin;
5632:   PetscAssertPointer(flg, 3);
5633:   PetscCheckSameComm(A, 1, B, 2);
5634:   MatCheckPreallocated(A, 1);
5635:   MatCheckPreallocated(B, 2);
5636:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5637:   PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5638:   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,
5639:              B->cmap->N);
5640:   if (A->ops->equal && A->ops->equal == B->ops->equal) {
5641:     PetscUseTypeMethod(A, equal, B, flg);
5642:   } else {
5643:     PetscCall(MatMultEqual(A, B, 10, flg));
5644:   }
5645:   PetscFunctionReturn(PETSC_SUCCESS);
5646: }

5648: /*@
5649:   MatDiagonalScale - Scales a matrix on the left and right by diagonal
5650:   matrices that are stored as vectors.  Either of the two scaling
5651:   matrices can be `NULL`.

5653:   Collective

5655:   Input Parameters:
5656: + mat - the matrix to be scaled
5657: . l   - the left scaling vector (or `NULL`)
5658: - r   - the right scaling vector (or `NULL`)

5660:   Level: intermediate

5662:   Note:
5663:   `MatDiagonalScale()` computes $A = LAR$, where
5664:   L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5665:   The L scales the rows of the matrix, the R scales the columns of the matrix.

5667: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5668: @*/
5669: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5670: {
5671:   PetscFunctionBegin;
5674:   if (l) {
5676:     PetscCheckSameComm(mat, 1, l, 2);
5677:   }
5678:   if (r) {
5680:     PetscCheckSameComm(mat, 1, r, 3);
5681:   }
5682:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5683:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5684:   MatCheckPreallocated(mat, 1);
5685:   if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

5687:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5688:   PetscUseTypeMethod(mat, diagonalscale, l, r);
5689:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5690:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5691:   if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5692:   PetscFunctionReturn(PETSC_SUCCESS);
5693: }

5695: /*@
5696:   MatScale - Scales all elements of a matrix by a given number.

5698:   Logically Collective

5700:   Input Parameters:
5701: + mat - the matrix to be scaled
5702: - a   - the scaling value

5704:   Level: intermediate

5706: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5707: @*/
5708: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5709: {
5710:   PetscFunctionBegin;
5713:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5714:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5716:   MatCheckPreallocated(mat, 1);

5718:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5719:   if (a != (PetscScalar)1.0) {
5720:     PetscUseTypeMethod(mat, scale, a);
5721:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5722:   }
5723:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5724:   PetscFunctionReturn(PETSC_SUCCESS);
5725: }

5727: /*@
5728:   MatNorm - Calculates various norms of a matrix.

5730:   Collective

5732:   Input Parameters:
5733: + mat  - the matrix
5734: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

5736:   Output Parameter:
5737: . nrm - the resulting norm

5739:   Level: intermediate

5741: .seealso: [](ch_matrices), `Mat`
5742: @*/
5743: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5744: {
5745:   PetscFunctionBegin;
5748:   PetscAssertPointer(nrm, 3);

5750:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5751:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5752:   MatCheckPreallocated(mat, 1);

5754:   PetscUseTypeMethod(mat, norm, type, nrm);
5755:   PetscFunctionReturn(PETSC_SUCCESS);
5756: }

5758: /*
5759:      This variable is used to prevent counting of MatAssemblyBegin() that
5760:    are called from within a MatAssemblyEnd().
5761: */
5762: static PetscInt MatAssemblyEnd_InUse = 0;
5763: /*@
5764:   MatAssemblyBegin - Begins assembling the matrix.  This routine should
5765:   be called after completing all calls to `MatSetValues()`.

5767:   Collective

5769:   Input Parameters:
5770: + mat  - the matrix
5771: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5773:   Level: beginner

5775:   Notes:
5776:   `MatSetValues()` generally caches the values that belong to other MPI processes.  The matrix is ready to
5777:   use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

5779:   Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5780:   in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5781:   using the matrix.

5783:   ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5784:   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
5785:   a global collective operation requiring all processes that share the matrix.

5787:   Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5788:   out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5789:   before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

5791: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5792: @*/
5793: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5794: {
5795:   PetscFunctionBegin;
5798:   MatCheckPreallocated(mat, 1);
5799:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5800:   if (mat->assembled) {
5801:     mat->was_assembled = PETSC_TRUE;
5802:     mat->assembled     = PETSC_FALSE;
5803:   }

5805:   if (!MatAssemblyEnd_InUse) {
5806:     PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5807:     PetscTryTypeMethod(mat, assemblybegin, type);
5808:     PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5809:   } else PetscTryTypeMethod(mat, assemblybegin, type);
5810:   PetscFunctionReturn(PETSC_SUCCESS);
5811: }

5813: /*@
5814:   MatAssembled - Indicates if a matrix has been assembled and is ready for
5815:   use; for example, in matrix-vector product.

5817:   Not Collective

5819:   Input Parameter:
5820: . mat - the matrix

5822:   Output Parameter:
5823: . assembled - `PETSC_TRUE` or `PETSC_FALSE`

5825:   Level: advanced

5827: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5828: @*/
5829: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5830: {
5831:   PetscFunctionBegin;
5833:   PetscAssertPointer(assembled, 2);
5834:   *assembled = mat->assembled;
5835:   PetscFunctionReturn(PETSC_SUCCESS);
5836: }

5838: /*@
5839:   MatAssemblyEnd - Completes assembling the matrix.  This routine should
5840:   be called after `MatAssemblyBegin()`.

5842:   Collective

5844:   Input Parameters:
5845: + mat  - the matrix
5846: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5848:   Options Database Keys:
5849: + -mat_view ::ascii_info             - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5850: . -mat_view ::ascii_info_detail      - Prints more detailed info
5851: . -mat_view                          - Prints matrix in ASCII format
5852: . -mat_view ::ascii_matlab           - Prints matrix in MATLAB format
5853: . -mat_view draw                     - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5854: . -display <name>                    - Sets display name (default is host)
5855: . -draw_pause <sec>                  - Sets number of seconds to pause after display
5856: . -mat_view socket                   - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5857: . -viewer_socket_machine <machine>   - Machine to use for socket
5858: . -viewer_socket_port <port>         - Port number to use for socket
5859: - -mat_view binary:filename[:append] - Save matrix to file in binary format

5861:   Level: beginner

5863: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5864: @*/
5865: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5866: {
5867:   static PetscInt inassm = 0;
5868:   PetscBool       flg    = PETSC_FALSE;

5870:   PetscFunctionBegin;

5874:   inassm++;
5875:   MatAssemblyEnd_InUse++;
5876:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5877:     PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5878:     PetscTryTypeMethod(mat, assemblyend, type);
5879:     PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5880:   } else PetscTryTypeMethod(mat, assemblyend, type);

5882:   /* Flush assembly is not a true assembly */
5883:   if (type != MAT_FLUSH_ASSEMBLY) {
5884:     if (mat->num_ass) {
5885:       if (!mat->symmetry_eternal) {
5886:         mat->symmetric = PETSC_BOOL3_UNKNOWN;
5887:         mat->hermitian = PETSC_BOOL3_UNKNOWN;
5888:       }
5889:       if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5890:       if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5891:     }
5892:     mat->num_ass++;
5893:     mat->assembled        = PETSC_TRUE;
5894:     mat->ass_nonzerostate = mat->nonzerostate;
5895:   }

5897:   mat->insertmode = NOT_SET_VALUES;
5898:   MatAssemblyEnd_InUse--;
5899:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5900:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5901:     PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

5903:     if (mat->checksymmetryonassembly) {
5904:       PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5905:       if (flg) {
5906:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5907:       } else {
5908:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5909:       }
5910:     }
5911:     if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5912:   }
5913:   inassm--;
5914:   PetscFunctionReturn(PETSC_SUCCESS);
5915: }

5917: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5918: /*@
5919:   MatSetOption - Sets a parameter option for a matrix. Some options
5920:   may be specific to certain storage formats.  Some options
5921:   determine how values will be inserted (or added). Sorted,
5922:   row-oriented input will generally assemble the fastest. The default
5923:   is row-oriented.

5925:   Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

5927:   Input Parameters:
5928: + mat - the matrix
5929: . op  - the option, one of those listed below (and possibly others),
5930: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5932:   Options Describing Matrix Structure:
5933: + `MAT_SPD`                         - symmetric positive definite
5934: . `MAT_SYMMETRIC`                   - symmetric in terms of both structure and value
5935: . `MAT_HERMITIAN`                   - transpose is the complex conjugation
5936: . `MAT_STRUCTURALLY_SYMMETRIC`      - symmetric nonzero structure
5937: . `MAT_SYMMETRY_ETERNAL`            - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5938: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5939: . `MAT_SPD_ETERNAL`                 - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

5941:    These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5942:    do not need to be computed (usually at a high cost)

5944:    Options For Use with `MatSetValues()`:
5945:    Insert a logically dense subblock, which can be
5946: . `MAT_ROW_ORIENTED`                - row-oriented (default)

5948:    These options reflect the data you pass in with `MatSetValues()`; it has
5949:    nothing to do with how the data is stored internally in the matrix
5950:    data structure.

5952:    When (re)assembling a matrix, we can restrict the input for
5953:    efficiency/debugging purposes.  These options include
5954: . `MAT_NEW_NONZERO_LOCATIONS`       - additional insertions will be allowed if they generate a new nonzero (slow)
5955: . `MAT_FORCE_DIAGONAL_ENTRIES`      - forces diagonal entries to be allocated
5956: . `MAT_IGNORE_OFF_PROC_ENTRIES`     - drops off-processor entries
5957: . `MAT_NEW_NONZERO_LOCATION_ERR`    - generates an error for new matrix entry
5958: . `MAT_USE_HASH_TABLE`              - uses a hash table to speed up matrix assembly
5959: . `MAT_NO_OFF_PROC_ENTRIES`         - you know each process will only set values for its own rows, will generate an error if
5960:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5961:         performance for very large process counts.
5962: - `MAT_SUBSET_OFF_PROC_ENTRIES`     - you know that the first assembly after setting this flag will set a superset
5963:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5964:         functions, instead sending only neighbor messages.

5966:   Level: intermediate

5968:   Notes:
5969:   Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

5971:   Some options are relevant only for particular matrix types and
5972:   are thus ignored by others.  Other options are not supported by
5973:   certain matrix types and will generate an error message if set.

5975:   If using Fortran to compute a matrix, one may need to
5976:   use the column-oriented option (or convert to the row-oriented
5977:   format).

5979:   `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5980:   that would generate a new entry in the nonzero structure is instead
5981:   ignored.  Thus, if memory has not already been allocated for this particular
5982:   data, then the insertion is ignored. For dense matrices, in which
5983:   the entire array is allocated, no entries are ever ignored.
5984:   Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

5986:   `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
5987:   that would generate a new entry in the nonzero structure instead produces
5988:   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

5990:   `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
5991:   that would generate a new entry that has not been preallocated will
5992:   instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
5993:   only.) This is a useful flag when debugging matrix memory preallocation.
5994:   If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

5996:   `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
5997:   other processors should be dropped, rather than stashed.
5998:   This is useful if you know that the "owning" processor is also
5999:   always generating the correct matrix entries, so that PETSc need
6000:   not transfer duplicate entries generated on another processor.

6002:   `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6003:   searches during matrix assembly. When this flag is set, the hash table
6004:   is created during the first matrix assembly. This hash table is
6005:   used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6006:   to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6007:   should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6008:   supported by `MATMPIBAIJ` format only.

6010:   `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6011:   are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`

6013:   `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6014:   a zero location in the matrix

6016:   `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

6018:   `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6019:   zero row routines and thus improves performance for very large process counts.

6021:   `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6022:   part of the matrix (since they should match the upper triangular part).

6024:   `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6025:   single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6026:   with finite difference schemes with non-periodic boundary conditions.

6028:   Developer Note:
6029:   `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6030:   places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6031:   to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6032:   not changed.

6034: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6035: @*/
6036: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6037: {
6038:   PetscFunctionBegin;
6040:   if (op > 0) {
6043:   }

6045:   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);

6047:   switch (op) {
6048:   case MAT_FORCE_DIAGONAL_ENTRIES:
6049:     mat->force_diagonals = flg;
6050:     PetscFunctionReturn(PETSC_SUCCESS);
6051:   case MAT_NO_OFF_PROC_ENTRIES:
6052:     mat->nooffprocentries = flg;
6053:     PetscFunctionReturn(PETSC_SUCCESS);
6054:   case MAT_SUBSET_OFF_PROC_ENTRIES:
6055:     mat->assembly_subset = flg;
6056:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6057: #if !defined(PETSC_HAVE_MPIUNI)
6058:       PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6059: #endif
6060:       mat->stash.first_assembly_done = PETSC_FALSE;
6061:     }
6062:     PetscFunctionReturn(PETSC_SUCCESS);
6063:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6064:     mat->nooffproczerorows = flg;
6065:     PetscFunctionReturn(PETSC_SUCCESS);
6066:   case MAT_SPD:
6067:     if (flg) {
6068:       mat->spd                    = PETSC_BOOL3_TRUE;
6069:       mat->symmetric              = PETSC_BOOL3_TRUE;
6070:       mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6071:     } else {
6072:       mat->spd = PETSC_BOOL3_FALSE;
6073:     }
6074:     break;
6075:   case MAT_SYMMETRIC:
6076:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6077:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6078: #if !defined(PETSC_USE_COMPLEX)
6079:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6080: #endif
6081:     break;
6082:   case MAT_HERMITIAN:
6083:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6084:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6085: #if !defined(PETSC_USE_COMPLEX)
6086:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6087: #endif
6088:     break;
6089:   case MAT_STRUCTURALLY_SYMMETRIC:
6090:     mat->structurally_symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6091:     break;
6092:   case MAT_SYMMETRY_ETERNAL:
6093:     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");
6094:     mat->symmetry_eternal = flg;
6095:     if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6096:     break;
6097:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6098:     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");
6099:     mat->structural_symmetry_eternal = flg;
6100:     break;
6101:   case MAT_SPD_ETERNAL:
6102:     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");
6103:     mat->spd_eternal = flg;
6104:     if (flg) {
6105:       mat->structural_symmetry_eternal = PETSC_TRUE;
6106:       mat->symmetry_eternal            = PETSC_TRUE;
6107:     }
6108:     break;
6109:   case MAT_STRUCTURE_ONLY:
6110:     mat->structure_only = flg;
6111:     break;
6112:   case MAT_SORTED_FULL:
6113:     mat->sortedfull = flg;
6114:     break;
6115:   default:
6116:     break;
6117:   }
6118:   PetscTryTypeMethod(mat, setoption, op, flg);
6119:   PetscFunctionReturn(PETSC_SUCCESS);
6120: }

6122: /*@
6123:   MatGetOption - Gets a parameter option that has been set for a matrix.

6125:   Logically Collective

6127:   Input Parameters:
6128: + mat - the matrix
6129: - op  - the option, this only responds to certain options, check the code for which ones

6131:   Output Parameter:
6132: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

6134:   Level: intermediate

6136:   Notes:
6137:   Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

6139:   Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6140:   `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

6142: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6143:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6144: @*/
6145: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6146: {
6147:   PetscFunctionBegin;

6151:   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);
6152:   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()");

6154:   switch (op) {
6155:   case MAT_NO_OFF_PROC_ENTRIES:
6156:     *flg = mat->nooffprocentries;
6157:     break;
6158:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6159:     *flg = mat->nooffproczerorows;
6160:     break;
6161:   case MAT_SYMMETRIC:
6162:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6163:     break;
6164:   case MAT_HERMITIAN:
6165:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6166:     break;
6167:   case MAT_STRUCTURALLY_SYMMETRIC:
6168:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6169:     break;
6170:   case MAT_SPD:
6171:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6172:     break;
6173:   case MAT_SYMMETRY_ETERNAL:
6174:     *flg = mat->symmetry_eternal;
6175:     break;
6176:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6177:     *flg = mat->symmetry_eternal;
6178:     break;
6179:   default:
6180:     break;
6181:   }
6182:   PetscFunctionReturn(PETSC_SUCCESS);
6183: }

6185: /*@
6186:   MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
6187:   this routine retains the old nonzero structure.

6189:   Logically Collective

6191:   Input Parameter:
6192: . mat - the matrix

6194:   Level: intermediate

6196:   Note:
6197:   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.
6198:   See the Performance chapter of the users manual for information on preallocating matrices.

6200: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6201: @*/
6202: PetscErrorCode MatZeroEntries(Mat mat)
6203: {
6204:   PetscFunctionBegin;
6207:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6208:   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");
6209:   MatCheckPreallocated(mat, 1);

6211:   PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6212:   PetscUseTypeMethod(mat, zeroentries);
6213:   PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6214:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6215:   PetscFunctionReturn(PETSC_SUCCESS);
6216: }

6218: /*@
6219:   MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6220:   of a set of rows and columns of a matrix.

6222:   Collective

6224:   Input Parameters:
6225: + mat     - the matrix
6226: . numRows - the number of rows/columns to zero
6227: . rows    - the global row indices
6228: . diag    - value put in the diagonal of the eliminated rows
6229: . x       - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6230: - b       - optional vector of the right-hand side, that will be adjusted by provided solution entries

6232:   Level: intermediate

6234:   Notes:
6235:   This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6237:   For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6238:   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

6240:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6241:   Krylov method to take advantage of the known solution on the zeroed rows.

6243:   For the parallel case, all processes that share the matrix (i.e.,
6244:   those in the communicator used for matrix creation) MUST call this
6245:   routine, regardless of whether any rows being zeroed are owned by
6246:   them.

6248:   Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6249:   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
6250:   missing.

6252:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6253:   list only rows local to itself).

6255:   The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

6257: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6258:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6259: @*/
6260: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6261: {
6262:   PetscFunctionBegin;
6265:   if (numRows) PetscAssertPointer(rows, 3);
6266:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6267:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6268:   MatCheckPreallocated(mat, 1);

6270:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6271:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6272:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6273:   PetscFunctionReturn(PETSC_SUCCESS);
6274: }

6276: /*@
6277:   MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6278:   of a set of rows and columns of a matrix.

6280:   Collective

6282:   Input Parameters:
6283: + mat  - the matrix
6284: . is   - the rows to zero
6285: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6286: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6287: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6289:   Level: intermediate

6291:   Note:
6292:   See `MatZeroRowsColumns()` for details on how this routine operates.

6294: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6295:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6296: @*/
6297: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6298: {
6299:   PetscInt        numRows;
6300:   const PetscInt *rows;

6302:   PetscFunctionBegin;
6307:   PetscCall(ISGetLocalSize(is, &numRows));
6308:   PetscCall(ISGetIndices(is, &rows));
6309:   PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6310:   PetscCall(ISRestoreIndices(is, &rows));
6311:   PetscFunctionReturn(PETSC_SUCCESS);
6312: }

6314: /*@
6315:   MatZeroRows - Zeros all entries (except possibly the main diagonal)
6316:   of a set of rows of a matrix.

6318:   Collective

6320:   Input Parameters:
6321: + mat     - the matrix
6322: . numRows - the number of rows to zero
6323: . rows    - the global row indices
6324: . diag    - value put in the diagonal of the zeroed rows
6325: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6326: - b       - optional vector of right-hand side, that will be adjusted by provided solution entries

6328:   Level: intermediate

6330:   Notes:
6331:   This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6333:   For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

6335:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6336:   Krylov method to take advantage of the known solution on the zeroed rows.

6338:   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)
6339:   from the matrix.

6341:   Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6342:   but does not release memory.  Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense and block diagonal
6343:   formats this does not alter the nonzero structure.

6345:   If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6346:   of the matrix is not changed the values are
6347:   merely zeroed.

6349:   The user can set a value in the diagonal entry (or for the `MATAIJ` format
6350:   formats can optionally remove the main diagonal entry from the
6351:   nonzero structure as well, by passing 0.0 as the final argument).

6353:   For the parallel case, all processes that share the matrix (i.e.,
6354:   those in the communicator used for matrix creation) MUST call this
6355:   routine, regardless of whether any rows being zeroed are owned by
6356:   them.

6358:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6359:   list only rows local to itself).

6361:   You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6362:   owns that are to be zeroed. This saves a global synchronization in the implementation.

6364: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6365:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6366: @*/
6367: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6368: {
6369:   PetscFunctionBegin;
6372:   if (numRows) PetscAssertPointer(rows, 3);
6373:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6374:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6375:   MatCheckPreallocated(mat, 1);

6377:   PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6378:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6379:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6380:   PetscFunctionReturn(PETSC_SUCCESS);
6381: }

6383: /*@
6384:   MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6385:   of a set of rows of a matrix.

6387:   Collective

6389:   Input Parameters:
6390: + mat  - the matrix
6391: . is   - index set of rows to remove (if `NULL` then no row is removed)
6392: . diag - value put in all diagonals of eliminated rows
6393: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6394: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6396:   Level: intermediate

6398:   Note:
6399:   See `MatZeroRows()` for details on how this routine operates.

6401: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6402:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6403: @*/
6404: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6405: {
6406:   PetscInt        numRows = 0;
6407:   const PetscInt *rows    = NULL;

6409:   PetscFunctionBegin;
6412:   if (is) {
6414:     PetscCall(ISGetLocalSize(is, &numRows));
6415:     PetscCall(ISGetIndices(is, &rows));
6416:   }
6417:   PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6418:   if (is) PetscCall(ISRestoreIndices(is, &rows));
6419:   PetscFunctionReturn(PETSC_SUCCESS);
6420: }

6422: /*@
6423:   MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6424:   of a set of rows of a matrix. These rows must be local to the process.

6426:   Collective

6428:   Input Parameters:
6429: + mat     - the matrix
6430: . numRows - the number of rows to remove
6431: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6432: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6433: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6434: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6436:   Level: intermediate

6438:   Notes:
6439:   See `MatZeroRows()` for details on how this routine operates.

6441:   The grid coordinates are across the entire grid, not just the local portion

6443:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6444:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6445:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6446:   `DM_BOUNDARY_PERIODIC` boundary type.

6448:   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
6449:   a single value per point) you can skip filling those indices.

6451:   Fortran Note:
6452:   `idxm` and `idxn` should be declared as
6453: $     MatStencil idxm(4, m)
6454:   and the values inserted using
6455: .vb
6456:     idxm(MatStencil_i, 1) = i
6457:     idxm(MatStencil_j, 1) = j
6458:     idxm(MatStencil_k, 1) = k
6459:     idxm(MatStencil_c, 1) = c
6460:    etc
6461: .ve

6463: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6464:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6465: @*/
6466: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6467: {
6468:   PetscInt  dim    = mat->stencil.dim;
6469:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6470:   PetscInt *dims   = mat->stencil.dims + 1;
6471:   PetscInt *starts = mat->stencil.starts;
6472:   PetscInt *dxm    = (PetscInt *)rows;
6473:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6475:   PetscFunctionBegin;
6478:   if (numRows) PetscAssertPointer(rows, 3);

6480:   PetscCall(PetscMalloc1(numRows, &jdxm));
6481:   for (i = 0; i < numRows; ++i) {
6482:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6483:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6484:     /* Local index in X dir */
6485:     tmp = *dxm++ - starts[0];
6486:     /* Loop over remaining dimensions */
6487:     for (j = 0; j < dim - 1; ++j) {
6488:       /* If nonlocal, set index to be negative */
6489:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6490:       /* Update local index */
6491:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6492:     }
6493:     /* Skip component slot if necessary */
6494:     if (mat->stencil.noc) dxm++;
6495:     /* Local row number */
6496:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6497:   }
6498:   PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6499:   PetscCall(PetscFree(jdxm));
6500:   PetscFunctionReturn(PETSC_SUCCESS);
6501: }

6503: /*@
6504:   MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6505:   of a set of rows and columns of a matrix.

6507:   Collective

6509:   Input Parameters:
6510: + mat     - the matrix
6511: . numRows - the number of rows/columns to remove
6512: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6513: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6514: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6515: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6517:   Level: intermediate

6519:   Notes:
6520:   See `MatZeroRowsColumns()` for details on how this routine operates.

6522:   The grid coordinates are across the entire grid, not just the local portion

6524:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6525:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6526:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6527:   `DM_BOUNDARY_PERIODIC` boundary type.

6529:   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
6530:   a single value per point) you can skip filling those indices.

6532:   Fortran Note:
6533:   `idxm` and `idxn` should be declared as
6534: $     MatStencil idxm(4, m)
6535:   and the values inserted using
6536: .vb
6537:     idxm(MatStencil_i, 1) = i
6538:     idxm(MatStencil_j, 1) = j
6539:     idxm(MatStencil_k, 1) = k
6540:     idxm(MatStencil_c, 1) = c
6541:     etc
6542: .ve

6544: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6545:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6546: @*/
6547: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6548: {
6549:   PetscInt  dim    = mat->stencil.dim;
6550:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6551:   PetscInt *dims   = mat->stencil.dims + 1;
6552:   PetscInt *starts = mat->stencil.starts;
6553:   PetscInt *dxm    = (PetscInt *)rows;
6554:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6556:   PetscFunctionBegin;
6559:   if (numRows) PetscAssertPointer(rows, 3);

6561:   PetscCall(PetscMalloc1(numRows, &jdxm));
6562:   for (i = 0; i < numRows; ++i) {
6563:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6564:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6565:     /* Local index in X dir */
6566:     tmp = *dxm++ - starts[0];
6567:     /* Loop over remaining dimensions */
6568:     for (j = 0; j < dim - 1; ++j) {
6569:       /* If nonlocal, set index to be negative */
6570:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6571:       /* Update local index */
6572:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6573:     }
6574:     /* Skip component slot if necessary */
6575:     if (mat->stencil.noc) dxm++;
6576:     /* Local row number */
6577:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6578:   }
6579:   PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6580:   PetscCall(PetscFree(jdxm));
6581:   PetscFunctionReturn(PETSC_SUCCESS);
6582: }

6584: /*@
6585:   MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6586:   of a set of rows of a matrix; using local numbering of rows.

6588:   Collective

6590:   Input Parameters:
6591: + mat     - the matrix
6592: . numRows - the number of rows to remove
6593: . rows    - the local row indices
6594: . diag    - value put in all diagonals of eliminated rows
6595: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6596: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6598:   Level: intermediate

6600:   Notes:
6601:   Before calling `MatZeroRowsLocal()`, the user must first set the
6602:   local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

6604:   See `MatZeroRows()` for details on how this routine operates.

6606: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6607:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6608: @*/
6609: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6610: {
6611:   PetscFunctionBegin;
6614:   if (numRows) PetscAssertPointer(rows, 3);
6615:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6616:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6617:   MatCheckPreallocated(mat, 1);

6619:   if (mat->ops->zerorowslocal) {
6620:     PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6621:   } else {
6622:     IS              is, newis;
6623:     const PetscInt *newRows;

6625:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6626:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6627:     PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6628:     PetscCall(ISGetIndices(newis, &newRows));
6629:     PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6630:     PetscCall(ISRestoreIndices(newis, &newRows));
6631:     PetscCall(ISDestroy(&newis));
6632:     PetscCall(ISDestroy(&is));
6633:   }
6634:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6635:   PetscFunctionReturn(PETSC_SUCCESS);
6636: }

6638: /*@
6639:   MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6640:   of a set of rows of a matrix; using local numbering of rows.

6642:   Collective

6644:   Input Parameters:
6645: + mat  - the matrix
6646: . is   - index set of rows to remove
6647: . diag - value put in all diagonals of eliminated rows
6648: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6649: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6651:   Level: intermediate

6653:   Notes:
6654:   Before calling `MatZeroRowsLocalIS()`, the user must first set the
6655:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6657:   See `MatZeroRows()` for details on how this routine operates.

6659: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6660:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6661: @*/
6662: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6663: {
6664:   PetscInt        numRows;
6665:   const PetscInt *rows;

6667:   PetscFunctionBegin;
6671:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6672:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6673:   MatCheckPreallocated(mat, 1);

6675:   PetscCall(ISGetLocalSize(is, &numRows));
6676:   PetscCall(ISGetIndices(is, &rows));
6677:   PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6678:   PetscCall(ISRestoreIndices(is, &rows));
6679:   PetscFunctionReturn(PETSC_SUCCESS);
6680: }

6682: /*@
6683:   MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6684:   of a set of rows and columns of a matrix; using local numbering of rows.

6686:   Collective

6688:   Input Parameters:
6689: + mat     - the matrix
6690: . numRows - the number of rows to remove
6691: . rows    - the global row indices
6692: . diag    - value put in all diagonals of eliminated rows
6693: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6694: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6696:   Level: intermediate

6698:   Notes:
6699:   Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6700:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6702:   See `MatZeroRowsColumns()` for details on how this routine operates.

6704: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6705:           `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6706: @*/
6707: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6708: {
6709:   IS              is, newis;
6710:   const PetscInt *newRows;

6712:   PetscFunctionBegin;
6715:   if (numRows) PetscAssertPointer(rows, 3);
6716:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6717:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6718:   MatCheckPreallocated(mat, 1);

6720:   PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6721:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6722:   PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6723:   PetscCall(ISGetIndices(newis, &newRows));
6724:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6725:   PetscCall(ISRestoreIndices(newis, &newRows));
6726:   PetscCall(ISDestroy(&newis));
6727:   PetscCall(ISDestroy(&is));
6728:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6729:   PetscFunctionReturn(PETSC_SUCCESS);
6730: }

6732: /*@
6733:   MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6734:   of a set of rows and columns of a matrix; using local numbering of rows.

6736:   Collective

6738:   Input Parameters:
6739: + mat  - the matrix
6740: . is   - index set of rows to remove
6741: . diag - value put in all diagonals of eliminated rows
6742: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6743: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6745:   Level: intermediate

6747:   Notes:
6748:   Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6749:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6751:   See `MatZeroRowsColumns()` for details on how this routine operates.

6753: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6754:           `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6755: @*/
6756: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6757: {
6758:   PetscInt        numRows;
6759:   const PetscInt *rows;

6761:   PetscFunctionBegin;
6765:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6766:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6767:   MatCheckPreallocated(mat, 1);

6769:   PetscCall(ISGetLocalSize(is, &numRows));
6770:   PetscCall(ISGetIndices(is, &rows));
6771:   PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6772:   PetscCall(ISRestoreIndices(is, &rows));
6773:   PetscFunctionReturn(PETSC_SUCCESS);
6774: }

6776: /*@
6777:   MatGetSize - Returns the numbers of rows and columns in a matrix.

6779:   Not Collective

6781:   Input Parameter:
6782: . mat - the matrix

6784:   Output Parameters:
6785: + m - the number of global rows
6786: - n - the number of global columns

6788:   Level: beginner

6790:   Note:
6791:   Both output parameters can be `NULL` on input.

6793: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6794: @*/
6795: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6796: {
6797:   PetscFunctionBegin;
6799:   if (m) *m = mat->rmap->N;
6800:   if (n) *n = mat->cmap->N;
6801:   PetscFunctionReturn(PETSC_SUCCESS);
6802: }

6804: /*@
6805:   MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6806:   of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

6808:   Not Collective

6810:   Input Parameter:
6811: . mat - the matrix

6813:   Output Parameters:
6814: + m - the number of local rows, use `NULL` to not obtain this value
6815: - n - the number of local columns, use `NULL` to not obtain this value

6817:   Level: beginner

6819: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6820: @*/
6821: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6822: {
6823:   PetscFunctionBegin;
6825:   if (m) PetscAssertPointer(m, 2);
6826:   if (n) PetscAssertPointer(n, 3);
6827:   if (m) *m = mat->rmap->n;
6828:   if (n) *n = mat->cmap->n;
6829:   PetscFunctionReturn(PETSC_SUCCESS);
6830: }

6832: /*@
6833:   MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6834:   vector one multiplies this matrix by that are owned by this processor.

6836:   Not Collective, unless matrix has not been allocated, then collective

6838:   Input Parameter:
6839: . mat - the matrix

6841:   Output Parameters:
6842: + m - the global index of the first local column, use `NULL` to not obtain this value
6843: - n - one more than the global index of the last local column, use `NULL` to not obtain this value

6845:   Level: developer

6847:   Notes:
6848:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6850:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6851:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6853:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6854:   the local values in the matrix.

6856:   Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6857:   Layouts](sec_matlayout) for details on matrix layouts.

6859: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6860:           `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6861: @*/
6862: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6863: {
6864:   PetscFunctionBegin;
6867:   if (m) PetscAssertPointer(m, 2);
6868:   if (n) PetscAssertPointer(n, 3);
6869:   MatCheckPreallocated(mat, 1);
6870:   if (m) *m = mat->cmap->rstart;
6871:   if (n) *n = mat->cmap->rend;
6872:   PetscFunctionReturn(PETSC_SUCCESS);
6873: }

6875: /*@
6876:   MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6877:   this MPI process.

6879:   Not Collective

6881:   Input Parameter:
6882: . mat - the matrix

6884:   Output Parameters:
6885: + m - the global index of the first local row, use `NULL` to not obtain this value
6886: - n - one more than the global index of the last local row, use `NULL` to not obtain this value

6888:   Level: beginner

6890:   Notes:
6891:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6893:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6894:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6896:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6897:   the local values in the matrix.

6899:   The high argument is one more than the last element stored locally.

6901:   For all matrices  it returns the range of matrix rows associated with rows of a vector that
6902:   would contain the result of a matrix vector product with this matrix. See [Matrix
6903:   Layouts](sec_matlayout) for details on matrix layouts.

6905: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6906:           `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6907: @*/
6908: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6909: {
6910:   PetscFunctionBegin;
6913:   if (m) PetscAssertPointer(m, 2);
6914:   if (n) PetscAssertPointer(n, 3);
6915:   MatCheckPreallocated(mat, 1);
6916:   if (m) *m = mat->rmap->rstart;
6917:   if (n) *n = mat->rmap->rend;
6918:   PetscFunctionReturn(PETSC_SUCCESS);
6919: }

6921: /*@C
6922:   MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6923:   `MATSCALAPACK`, returns the range of matrix rows owned by each process.

6925:   Not Collective, unless matrix has not been allocated

6927:   Input Parameter:
6928: . mat - the matrix

6930:   Output Parameter:
6931: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6932:            where `size` is the number of MPI processes used by `mat`

6934:   Level: beginner

6936:   Notes:
6937:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6939:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6940:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6942:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6943:   the local values in the matrix.

6945:   For all matrices  it returns the ranges of matrix rows associated with rows of a vector that
6946:   would contain the result of a matrix vector product with this matrix. See [Matrix
6947:   Layouts](sec_matlayout) for details on matrix layouts.

6949: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6950:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6951:           `DMDAGetGhostCorners()`, `DM`
6952: @*/
6953: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6954: {
6955:   PetscFunctionBegin;
6958:   MatCheckPreallocated(mat, 1);
6959:   PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6960:   PetscFunctionReturn(PETSC_SUCCESS);
6961: }

6963: /*@C
6964:   MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6965:   vector one multiplies this vector by that are owned by each processor.

6967:   Not Collective, unless matrix has not been allocated

6969:   Input Parameter:
6970: . mat - the matrix

6972:   Output Parameter:
6973: . ranges - start of each processors portion plus one more than the total length at the end

6975:   Level: beginner

6977:   Notes:
6978:   If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.

6980:   If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6981:   If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.

6983:   For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6984:   the local values in the matrix.

6986:   Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
6987:   Layouts](sec_matlayout) for details on matrix layouts.

6989: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
6990:           `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
6991:           `DMDAGetGhostCorners()`, `DM`
6992: @*/
6993: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
6994: {
6995:   PetscFunctionBegin;
6998:   MatCheckPreallocated(mat, 1);
6999:   PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7000:   PetscFunctionReturn(PETSC_SUCCESS);
7001: }

7003: /*@
7004:   MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.

7006:   Not Collective

7008:   Input Parameter:
7009: . A - matrix

7011:   Output Parameters:
7012: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7013: - cols - columns in which this process owns elements, use `NULL` to not obtain this value

7015:   Level: intermediate

7017:   Note:
7018:   You should call `ISDestroy()` on the returned `IS`

7020:   For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7021:   returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7022:   `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7023:   details on matrix layouts.

7025: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7026: @*/
7027: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7028: {
7029:   PetscErrorCode (*f)(Mat, IS *, IS *);

7031:   PetscFunctionBegin;
7034:   MatCheckPreallocated(A, 1);
7035:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7036:   if (f) {
7037:     PetscCall((*f)(A, rows, cols));
7038:   } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7039:     if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7040:     if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7041:   }
7042:   PetscFunctionReturn(PETSC_SUCCESS);
7043: }

7045: /*@
7046:   MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7047:   Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7048:   to complete the factorization.

7050:   Collective

7052:   Input Parameters:
7053: + fact - the factorized matrix obtained with `MatGetFactor()`
7054: . mat  - the matrix
7055: . row  - row permutation
7056: . col  - column permutation
7057: - info - structure containing
7058: .vb
7059:       levels - number of levels of fill.
7060:       expected fill - as ratio of original fill.
7061:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7062:                 missing diagonal entries)
7063: .ve

7065:   Level: developer

7067:   Notes:
7068:   See [Matrix Factorization](sec_matfactor) for additional information.

7070:   Most users should employ the `KSP` interface for linear solvers
7071:   instead of working directly with matrix algebra routines such as this.
7072:   See, e.g., `KSPCreate()`.

7074:   Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`

7076:   Developer Note:
7077:   The Fortran interface is not autogenerated as the
7078:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

7080: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7081:           `MatGetOrdering()`, `MatFactorInfo`
7082: @*/
7083: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7084: {
7085:   PetscFunctionBegin;
7090:   PetscAssertPointer(info, 5);
7091:   PetscAssertPointer(fact, 1);
7092:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7093:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7094:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7095:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7096:   MatCheckPreallocated(mat, 2);

7098:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7099:   PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7100:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7101:   PetscFunctionReturn(PETSC_SUCCESS);
7102: }

7104: /*@
7105:   MatICCFactorSymbolic - Performs symbolic incomplete
7106:   Cholesky factorization for a symmetric matrix.  Use
7107:   `MatCholeskyFactorNumeric()` to complete the factorization.

7109:   Collective

7111:   Input Parameters:
7112: + fact - the factorized matrix obtained with `MatGetFactor()`
7113: . mat  - the matrix to be factored
7114: . perm - row and column permutation
7115: - info - structure containing
7116: .vb
7117:       levels - number of levels of fill.
7118:       expected fill - as ratio of original fill.
7119: .ve

7121:   Level: developer

7123:   Notes:
7124:   Most users should employ the `KSP` interface for linear solvers
7125:   instead of working directly with matrix algebra routines such as this.
7126:   See, e.g., `KSPCreate()`.

7128:   This uses the definition of level of fill as in Y. Saad {cite}`saad2003`

7130:   Developer Note:
7131:   The Fortran interface is not autogenerated as the
7132:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

7134: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7135: @*/
7136: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7137: {
7138:   PetscFunctionBegin;
7142:   PetscAssertPointer(info, 4);
7143:   PetscAssertPointer(fact, 1);
7144:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7145:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7146:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7147:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7148:   MatCheckPreallocated(mat, 2);

7150:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7151:   PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7152:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7153:   PetscFunctionReturn(PETSC_SUCCESS);
7154: }

7156: /*@C
7157:   MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7158:   points to an array of valid matrices, they may be reused to store the new
7159:   submatrices.

7161:   Collective

7163:   Input Parameters:
7164: + mat   - the matrix
7165: . n     - the number of submatrixes to be extracted (on this processor, may be zero)
7166: . irow  - index set of rows to extract
7167: . icol  - index set of columns to extract
7168: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7170:   Output Parameter:
7171: . submat - the array of submatrices

7173:   Level: advanced

7175:   Notes:
7176:   `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7177:   (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7178:   to extract a parallel submatrix.

7180:   Some matrix types place restrictions on the row and column
7181:   indices, such as that they be sorted or that they be equal to each other.

7183:   The index sets may not have duplicate entries.

7185:   When extracting submatrices from a parallel matrix, each processor can
7186:   form a different submatrix by setting the rows and columns of its
7187:   individual index sets according to the local submatrix desired.

7189:   When finished using the submatrices, the user should destroy
7190:   them with `MatDestroySubMatrices()`.

7192:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7193:   original matrix has not changed from that last call to `MatCreateSubMatrices()`.

7195:   This routine creates the matrices in submat; you should NOT create them before
7196:   calling it. It also allocates the array of matrix pointers submat.

7198:   For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7199:   request one row/column in a block, they must request all rows/columns that are in
7200:   that block. For example, if the block size is 2 you cannot request just row 0 and
7201:   column 0.

7203:   Fortran Note:
7204:   One must pass in as `submat` a `Mat` array of size at least `n`+1.

7206: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7207: @*/
7208: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7209: {
7210:   PetscInt  i;
7211:   PetscBool eq;

7213:   PetscFunctionBegin;
7216:   if (n) {
7217:     PetscAssertPointer(irow, 3);
7219:     PetscAssertPointer(icol, 4);
7221:   }
7222:   PetscAssertPointer(submat, 6);
7223:   if (n && scall == MAT_REUSE_MATRIX) {
7224:     PetscAssertPointer(*submat, 6);
7226:   }
7227:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7228:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7229:   MatCheckPreallocated(mat, 1);
7230:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7231:   PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7232:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7233:   for (i = 0; i < n; i++) {
7234:     (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7235:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7236:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7237: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7238:     if (mat->boundtocpu && mat->bindingpropagates) {
7239:       PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7240:       PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7241:     }
7242: #endif
7243:   }
7244:   PetscFunctionReturn(PETSC_SUCCESS);
7245: }

7247: /*@C
7248:   MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of `IS` that may live on subcomms).

7250:   Collective

7252:   Input Parameters:
7253: + mat   - the matrix
7254: . n     - the number of submatrixes to be extracted
7255: . irow  - index set of rows to extract
7256: . icol  - index set of columns to extract
7257: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7259:   Output Parameter:
7260: . submat - the array of submatrices

7262:   Level: advanced

7264:   Note:
7265:   This is used by `PCGASM`

7267: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7268: @*/
7269: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7270: {
7271:   PetscInt  i;
7272:   PetscBool eq;

7274:   PetscFunctionBegin;
7277:   if (n) {
7278:     PetscAssertPointer(irow, 3);
7280:     PetscAssertPointer(icol, 4);
7282:   }
7283:   PetscAssertPointer(submat, 6);
7284:   if (n && scall == MAT_REUSE_MATRIX) {
7285:     PetscAssertPointer(*submat, 6);
7287:   }
7288:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7289:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7290:   MatCheckPreallocated(mat, 1);

7292:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7293:   PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7294:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7295:   for (i = 0; i < n; i++) {
7296:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7297:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7298:   }
7299:   PetscFunctionReturn(PETSC_SUCCESS);
7300: }

7302: /*@C
7303:   MatDestroyMatrices - Destroys an array of matrices.

7305:   Collective

7307:   Input Parameters:
7308: + n   - the number of local matrices
7309: - mat - the matrices (this is a pointer to the array of matrices)

7311:   Level: advanced

7313:   Notes:
7314:   Frees not only the matrices, but also the array that contains the matrices

7316:   For matrices obtained with  `MatCreateSubMatrices()` use `MatDestroySubMatrices()`

7318:   Fortran Note:
7319:   Does not free the `mat` array.

7321: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7322: @*/
7323: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7324: {
7325:   PetscInt i;

7327:   PetscFunctionBegin;
7328:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7329:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7330:   PetscAssertPointer(mat, 2);

7332:   for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

7334:   /* memory is allocated even if n = 0 */
7335:   PetscCall(PetscFree(*mat));
7336:   PetscFunctionReturn(PETSC_SUCCESS);
7337: }

7339: /*@C
7340:   MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

7342:   Collective

7344:   Input Parameters:
7345: + n   - the number of local matrices
7346: - mat - the matrices (this is a pointer to the array of matrices, just to match the calling
7347:                        sequence of `MatCreateSubMatrices()`)

7349:   Level: advanced

7351:   Note:
7352:   Frees not only the matrices, but also the array that contains the matrices

7354:   Fortran Note:
7355:   Does not free the `mat` array.

7357: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7358: @*/
7359: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7360: {
7361:   Mat mat0;

7363:   PetscFunctionBegin;
7364:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7365:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7366:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7367:   PetscAssertPointer(mat, 2);

7369:   mat0 = (*mat)[0];
7370:   if (mat0 && mat0->ops->destroysubmatrices) {
7371:     PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7372:   } else {
7373:     PetscCall(MatDestroyMatrices(n, mat));
7374:   }
7375:   PetscFunctionReturn(PETSC_SUCCESS);
7376: }

7378: /*@
7379:   MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

7381:   Collective

7383:   Input Parameter:
7384: . mat - the matrix

7386:   Output Parameter:
7387: . matstruct - the sequential matrix with the nonzero structure of `mat`

7389:   Level: developer

7391: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7392: @*/
7393: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7394: {
7395:   PetscFunctionBegin;
7397:   PetscAssertPointer(matstruct, 2);

7400:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7401:   MatCheckPreallocated(mat, 1);

7403:   PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7404:   PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7405:   PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7406:   PetscFunctionReturn(PETSC_SUCCESS);
7407: }

7409: /*@C
7410:   MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

7412:   Collective

7414:   Input Parameter:
7415: . mat - the matrix

7417:   Level: advanced

7419:   Note:
7420:   This is not needed, one can just call `MatDestroy()`

7422: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7423: @*/
7424: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7425: {
7426:   PetscFunctionBegin;
7427:   PetscAssertPointer(mat, 1);
7428:   PetscCall(MatDestroy(mat));
7429:   PetscFunctionReturn(PETSC_SUCCESS);
7430: }

7432: /*@
7433:   MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7434:   replaces the index sets by larger ones that represent submatrices with
7435:   additional overlap.

7437:   Collective

7439:   Input Parameters:
7440: + mat - the matrix
7441: . n   - the number of index sets
7442: . is  - the array of index sets (these index sets will changed during the call)
7443: - ov  - the additional overlap requested

7445:   Options Database Key:
7446: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7448:   Level: developer

7450:   Note:
7451:   The computed overlap preserves the matrix block sizes when the blocks are square.
7452:   That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7453:   that block are included in the overlap regardless of whether each specific column would increase the overlap.

7455: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7456: @*/
7457: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7458: {
7459:   PetscInt i, bs, cbs;

7461:   PetscFunctionBegin;
7465:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7466:   if (n) {
7467:     PetscAssertPointer(is, 3);
7469:   }
7470:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7471:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7472:   MatCheckPreallocated(mat, 1);

7474:   if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7475:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7476:   PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7477:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7478:   PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7479:   if (bs == cbs) {
7480:     for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7481:   }
7482:   PetscFunctionReturn(PETSC_SUCCESS);
7483: }

7485: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

7487: /*@
7488:   MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7489:   a sub communicator, replaces the index sets by larger ones that represent submatrices with
7490:   additional overlap.

7492:   Collective

7494:   Input Parameters:
7495: + mat - the matrix
7496: . n   - the number of index sets
7497: . is  - the array of index sets (these index sets will changed during the call)
7498: - ov  - the additional overlap requested

7500:   `   Options Database Key:
7501: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7503:   Level: developer

7505: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7506: @*/
7507: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7508: {
7509:   PetscInt i;

7511:   PetscFunctionBegin;
7514:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7515:   if (n) {
7516:     PetscAssertPointer(is, 3);
7518:   }
7519:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7520:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7521:   MatCheckPreallocated(mat, 1);
7522:   if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7523:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7524:   for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7525:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7526:   PetscFunctionReturn(PETSC_SUCCESS);
7527: }

7529: /*@
7530:   MatGetBlockSize - Returns the matrix block size.

7532:   Not Collective

7534:   Input Parameter:
7535: . mat - the matrix

7537:   Output Parameter:
7538: . bs - block size

7540:   Level: intermediate

7542:   Notes:
7543:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

7545:   If the block size has not been set yet this routine returns 1.

7547: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7548: @*/
7549: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7550: {
7551:   PetscFunctionBegin;
7553:   PetscAssertPointer(bs, 2);
7554:   *bs = PetscAbs(mat->rmap->bs);
7555:   PetscFunctionReturn(PETSC_SUCCESS);
7556: }

7558: /*@
7559:   MatGetBlockSizes - Returns the matrix block row and column sizes.

7561:   Not Collective

7563:   Input Parameter:
7564: . mat - the matrix

7566:   Output Parameters:
7567: + rbs - row block size
7568: - cbs - column block size

7570:   Level: intermediate

7572:   Notes:
7573:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7574:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7576:   If a block size has not been set yet this routine returns 1.

7578: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7579: @*/
7580: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7581: {
7582:   PetscFunctionBegin;
7584:   if (rbs) PetscAssertPointer(rbs, 2);
7585:   if (cbs) PetscAssertPointer(cbs, 3);
7586:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7587:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7588:   PetscFunctionReturn(PETSC_SUCCESS);
7589: }

7591: /*@
7592:   MatSetBlockSize - Sets the matrix block size.

7594:   Logically Collective

7596:   Input Parameters:
7597: + mat - the matrix
7598: - bs  - block size

7600:   Level: intermediate

7602:   Notes:
7603:   Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7604:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7606:   For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7607:   is compatible with the matrix local sizes.

7609: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7610: @*/
7611: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7612: {
7613:   PetscFunctionBegin;
7616:   PetscCall(MatSetBlockSizes(mat, bs, bs));
7617:   PetscFunctionReturn(PETSC_SUCCESS);
7618: }

7620: typedef struct {
7621:   PetscInt         n;
7622:   IS              *is;
7623:   Mat             *mat;
7624:   PetscObjectState nonzerostate;
7625:   Mat              C;
7626: } EnvelopeData;

7628: static PetscErrorCode EnvelopeDataDestroy(void *ptr)
7629: {
7630:   EnvelopeData *edata = (EnvelopeData *)ptr;

7632:   PetscFunctionBegin;
7633:   for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7634:   PetscCall(PetscFree(edata->is));
7635:   PetscCall(PetscFree(edata));
7636:   PetscFunctionReturn(PETSC_SUCCESS);
7637: }

7639: /*@
7640:   MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7641:   the sizes of these blocks in the matrix. An individual block may lie over several processes.

7643:   Collective

7645:   Input Parameter:
7646: . mat - the matrix

7648:   Level: intermediate

7650:   Notes:
7651:   There can be zeros within the blocks

7653:   The blocks can overlap between processes, including laying on more than two processes

7655: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7656: @*/
7657: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7658: {
7659:   PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7660:   PetscInt          *diag, *odiag, sc;
7661:   VecScatter         scatter;
7662:   PetscScalar       *seqv;
7663:   const PetscScalar *parv;
7664:   const PetscInt    *ia, *ja;
7665:   PetscBool          set, flag, done;
7666:   Mat                AA = mat, A;
7667:   MPI_Comm           comm;
7668:   PetscMPIInt        rank, size, tag;
7669:   MPI_Status         status;
7670:   PetscContainer     container;
7671:   EnvelopeData      *edata;
7672:   Vec                seq, par;
7673:   IS                 isglobal;

7675:   PetscFunctionBegin;
7677:   PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7678:   if (!set || !flag) {
7679:     /* TODO: only needs nonzero structure of transpose */
7680:     PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7681:     PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7682:   }
7683:   PetscCall(MatAIJGetLocalMat(AA, &A));
7684:   PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7685:   PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

7687:   PetscCall(MatGetLocalSize(mat, &n, NULL));
7688:   PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7689:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7690:   PetscCallMPI(MPI_Comm_size(comm, &size));
7691:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

7693:   PetscCall(PetscMalloc2(n, &sizes, n, &starts));

7695:   if (rank > 0) {
7696:     PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7697:     PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7698:   }
7699:   PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7700:   for (i = 0; i < n; i++) {
7701:     env = PetscMax(env, ja[ia[i + 1] - 1]);
7702:     II  = rstart + i;
7703:     if (env == II) {
7704:       starts[lblocks]  = tbs;
7705:       sizes[lblocks++] = 1 + II - tbs;
7706:       tbs              = 1 + II;
7707:     }
7708:   }
7709:   if (rank < size - 1) {
7710:     PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7711:     PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7712:   }

7714:   PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7715:   if (!set || !flag) PetscCall(MatDestroy(&AA));
7716:   PetscCall(MatDestroy(&A));

7718:   PetscCall(PetscNew(&edata));
7719:   PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7720:   edata->n = lblocks;
7721:   /* create IS needed for extracting blocks from the original matrix */
7722:   PetscCall(PetscMalloc1(lblocks, &edata->is));
7723:   for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

7725:   /* Create the resulting inverse matrix structure with preallocation information */
7726:   PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7727:   PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7728:   PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7729:   PetscCall(MatSetType(edata->C, MATAIJ));

7731:   /* Communicate the start and end of each row, from each block to the correct rank */
7732:   /* TODO: Use PetscSF instead of VecScatter */
7733:   for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7734:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7735:   PetscCall(VecGetArrayWrite(seq, &seqv));
7736:   for (PetscInt i = 0; i < lblocks; i++) {
7737:     for (PetscInt j = 0; j < sizes[i]; j++) {
7738:       seqv[cnt]     = starts[i];
7739:       seqv[cnt + 1] = starts[i] + sizes[i];
7740:       cnt += 2;
7741:     }
7742:   }
7743:   PetscCall(VecRestoreArrayWrite(seq, &seqv));
7744:   PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7745:   sc -= cnt;
7746:   PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7747:   PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7748:   PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7749:   PetscCall(ISDestroy(&isglobal));
7750:   PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7751:   PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7752:   PetscCall(VecScatterDestroy(&scatter));
7753:   PetscCall(VecDestroy(&seq));
7754:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7755:   PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7756:   PetscCall(VecGetArrayRead(par, &parv));
7757:   cnt = 0;
7758:   PetscCall(MatGetSize(mat, NULL, &n));
7759:   for (PetscInt i = 0; i < mat->rmap->n; i++) {
7760:     PetscInt start, end, d = 0, od = 0;

7762:     start = (PetscInt)PetscRealPart(parv[cnt]);
7763:     end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
7764:     cnt += 2;

7766:     if (start < cstart) {
7767:       od += cstart - start + n - cend;
7768:       d += cend - cstart;
7769:     } else if (start < cend) {
7770:       od += n - cend;
7771:       d += cend - start;
7772:     } else od += n - start;
7773:     if (end <= cstart) {
7774:       od -= cstart - end + n - cend;
7775:       d -= cend - cstart;
7776:     } else if (end < cend) {
7777:       od -= n - cend;
7778:       d -= cend - end;
7779:     } else od -= n - end;

7781:     odiag[i] = od;
7782:     diag[i]  = d;
7783:   }
7784:   PetscCall(VecRestoreArrayRead(par, &parv));
7785:   PetscCall(VecDestroy(&par));
7786:   PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7787:   PetscCall(PetscFree2(diag, odiag));
7788:   PetscCall(PetscFree2(sizes, starts));

7790:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7791:   PetscCall(PetscContainerSetPointer(container, edata));
7792:   PetscCall(PetscContainerSetUserDestroy(container, (PetscErrorCode (*)(void *))EnvelopeDataDestroy));
7793:   PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7794:   PetscCall(PetscObjectDereference((PetscObject)container));
7795:   PetscFunctionReturn(PETSC_SUCCESS);
7796: }

7798: /*@
7799:   MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

7801:   Collective

7803:   Input Parameters:
7804: + A     - the matrix
7805: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine

7807:   Output Parameter:
7808: . C - matrix with inverted block diagonal of `A`

7810:   Level: advanced

7812:   Note:
7813:   For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

7815: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7816: @*/
7817: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7818: {
7819:   PetscContainer   container;
7820:   EnvelopeData    *edata;
7821:   PetscObjectState nonzerostate;

7823:   PetscFunctionBegin;
7824:   PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7825:   if (!container) {
7826:     PetscCall(MatComputeVariableBlockEnvelope(A));
7827:     PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7828:   }
7829:   PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7830:   PetscCall(MatGetNonzeroState(A, &nonzerostate));
7831:   PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7832:   PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

7834:   PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7835:   *C = edata->C;

7837:   for (PetscInt i = 0; i < edata->n; i++) {
7838:     Mat          D;
7839:     PetscScalar *dvalues;

7841:     PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7842:     PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7843:     PetscCall(MatSeqDenseInvert(D));
7844:     PetscCall(MatDenseGetArray(D, &dvalues));
7845:     PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7846:     PetscCall(MatDestroy(&D));
7847:   }
7848:   PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7849:   PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7850:   PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7851:   PetscFunctionReturn(PETSC_SUCCESS);
7852: }

7854: /*@
7855:   MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

7857:   Not Collective

7859:   Input Parameters:
7860: + mat     - the matrix
7861: . nblocks - the number of blocks on this process, each block can only exist on a single process
7862: - bsizes  - the block sizes

7864:   Level: intermediate

7866:   Notes:
7867:   Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

7869:   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.

7871: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7872:           `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7873: @*/
7874: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7875: {
7876:   PetscInt ncnt = 0, nlocal;

7878:   PetscFunctionBegin;
7880:   PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7881:   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);
7882:   for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7883:   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);
7884:   PetscCall(PetscFree(mat->bsizes));
7885:   mat->nblocks = nblocks;
7886:   PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7887:   PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7888:   PetscFunctionReturn(PETSC_SUCCESS);
7889: }

7891: /*@C
7892:   MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7894:   Not Collective; No Fortran Support

7896:   Input Parameter:
7897: . mat - the matrix

7899:   Output Parameters:
7900: + nblocks - the number of blocks on this process
7901: - bsizes  - the block sizes

7903:   Level: intermediate

7905: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7906: @*/
7907: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7908: {
7909:   PetscFunctionBegin;
7911:   if (nblocks) *nblocks = mat->nblocks;
7912:   if (bsizes) *bsizes = mat->bsizes;
7913:   PetscFunctionReturn(PETSC_SUCCESS);
7914: }

7916: /*@
7917:   MatSetBlockSizes - Sets the matrix block row and column sizes.

7919:   Logically Collective

7921:   Input Parameters:
7922: + mat - the matrix
7923: . rbs - row block size
7924: - cbs - column block size

7926:   Level: intermediate

7928:   Notes:
7929:   Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7930:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7931:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7933:   For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7934:   are compatible with the matrix local sizes.

7936:   The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

7938: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7939: @*/
7940: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7941: {
7942:   PetscFunctionBegin;
7946:   PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7947:   if (mat->rmap->refcnt) {
7948:     ISLocalToGlobalMapping l2g  = NULL;
7949:     PetscLayout            nmap = NULL;

7951:     PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7952:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7953:     PetscCall(PetscLayoutDestroy(&mat->rmap));
7954:     mat->rmap          = nmap;
7955:     mat->rmap->mapping = l2g;
7956:   }
7957:   if (mat->cmap->refcnt) {
7958:     ISLocalToGlobalMapping l2g  = NULL;
7959:     PetscLayout            nmap = NULL;

7961:     PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7962:     if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7963:     PetscCall(PetscLayoutDestroy(&mat->cmap));
7964:     mat->cmap          = nmap;
7965:     mat->cmap->mapping = l2g;
7966:   }
7967:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7968:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7969:   PetscFunctionReturn(PETSC_SUCCESS);
7970: }

7972: /*@
7973:   MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

7975:   Logically Collective

7977:   Input Parameters:
7978: + mat     - the matrix
7979: . fromRow - matrix from which to copy row block size
7980: - fromCol - matrix from which to copy column block size (can be same as fromRow)

7982:   Level: developer

7984: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
7985: @*/
7986: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
7987: {
7988:   PetscFunctionBegin;
7992:   if (fromRow->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
7993:   if (fromCol->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
7994:   PetscFunctionReturn(PETSC_SUCCESS);
7995: }

7997: /*@
7998:   MatResidual - Default routine to calculate the residual r = b - Ax

8000:   Collective

8002:   Input Parameters:
8003: + mat - the matrix
8004: . b   - the right-hand-side
8005: - x   - the approximate solution

8007:   Output Parameter:
8008: . r - location to store the residual

8010:   Level: developer

8012: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8013: @*/
8014: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8015: {
8016:   PetscFunctionBegin;
8022:   MatCheckPreallocated(mat, 1);
8023:   PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8024:   if (!mat->ops->residual) {
8025:     PetscCall(MatMult(mat, x, r));
8026:     PetscCall(VecAYPX(r, -1.0, b));
8027:   } else {
8028:     PetscUseTypeMethod(mat, residual, b, x, r);
8029:   }
8030:   PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8031:   PetscFunctionReturn(PETSC_SUCCESS);
8032: }

8034: /*MC
8035:     MatGetRowIJF90 - Obtains the compressed row storage i and j indices for the local rows of a sparse matrix

8037:     Synopsis:
8038:     MatGetRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

8040:     Not Collective

8042:     Input Parameters:
8043: +   A - the matrix
8044: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
8045: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8046: -   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8047:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8048:                  always used.

8050:     Output Parameters:
8051: +   n - number of local rows in the (possibly compressed) matrix
8052: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
8053: .   ja - the column indices
8054: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8055:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8057:     Level: developer

8059:     Note:
8060:     Use  `MatRestoreRowIJF90()` when you no longer need access to the data

8062: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatRestoreRowIJF90()`
8063: M*/

8065: /*MC
8066:     MatRestoreRowIJF90 - restores the compressed row storage i and j indices for the local rows of a sparse matrix obtained with `MatGetRowIJF90()`

8068:     Synopsis:
8069:     MatRestoreRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

8071:     Not Collective

8073:     Input Parameters:
8074: +   A - the  matrix
8075: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
8076: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8077:     inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8078:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8079:                  always used.
8080: .   n - number of local rows in the (possibly compressed) matrix
8081: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
8082: .   ja - the column indices
8083: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8084:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8086:     Level: developer

8088: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatGetRowIJF90()`
8089: M*/

8091: /*@C
8092:   MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

8094:   Collective

8096:   Input Parameters:
8097: + mat             - the matrix
8098: . shift           - 0 or 1 indicating we want the indices starting at 0 or 1
8099: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8100: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
8101:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8102:                  always used.

8104:   Output Parameters:
8105: + n    - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8106: . 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
8107: . ja   - the column indices, use `NULL` if not needed
8108: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8109:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8111:   Level: developer

8113:   Notes:
8114:   You CANNOT change any of the ia[] or ja[] values.

8116:   Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

8118:   Fortran Notes:
8119:   Use
8120: .vb
8121:     PetscInt, pointer :: ia(:),ja(:)
8122:     call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8123:     ! Access the ith and jth entries via ia(i) and ja(j)
8124: .ve

8126:   `MatGetRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatGetRowIJF90()`

8128: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetRowIJF90()`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8129: @*/
8130: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8131: {
8132:   PetscFunctionBegin;
8135:   if (n) PetscAssertPointer(n, 5);
8136:   if (ia) PetscAssertPointer(ia, 6);
8137:   if (ja) PetscAssertPointer(ja, 7);
8138:   if (done) PetscAssertPointer(done, 8);
8139:   MatCheckPreallocated(mat, 1);
8140:   if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8141:   else {
8142:     if (done) *done = PETSC_TRUE;
8143:     PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8144:     PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8145:     PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8146:   }
8147:   PetscFunctionReturn(PETSC_SUCCESS);
8148: }

8150: /*@C
8151:   MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

8153:   Collective

8155:   Input Parameters:
8156: + mat             - the matrix
8157: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8158: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8159:                 symmetrized
8160: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8161:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8162:                  always used.
8163: . n               - number of columns in the (possibly compressed) matrix
8164: . ia              - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8165: - ja              - the row indices

8167:   Output Parameter:
8168: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

8170:   Level: developer

8172: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8173: @*/
8174: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8175: {
8176:   PetscFunctionBegin;
8179:   PetscAssertPointer(n, 5);
8180:   if (ia) PetscAssertPointer(ia, 6);
8181:   if (ja) PetscAssertPointer(ja, 7);
8182:   PetscAssertPointer(done, 8);
8183:   MatCheckPreallocated(mat, 1);
8184:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8185:   else {
8186:     *done = PETSC_TRUE;
8187:     PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8188:   }
8189:   PetscFunctionReturn(PETSC_SUCCESS);
8190: }

8192: /*@C
8193:   MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

8195:   Collective

8197:   Input Parameters:
8198: + mat             - the matrix
8199: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8200: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8201: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8202:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8203:                     always used.
8204: . n               - size of (possibly compressed) matrix
8205: . ia              - the row pointers
8206: - ja              - the column indices

8208:   Output Parameter:
8209: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8211:   Level: developer

8213:   Note:
8214:   This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8215:   us of the array after it has been restored. If you pass `NULL`, it will
8216:   not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

8218:   Fortran Note:
8219:   `MatRestoreRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatRestoreRowIJF90()`

8221: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreRowIJF90()`, `MatRestoreColumnIJ()`
8222: @*/
8223: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8224: {
8225:   PetscFunctionBegin;
8228:   if (ia) PetscAssertPointer(ia, 6);
8229:   if (ja) PetscAssertPointer(ja, 7);
8230:   if (done) PetscAssertPointer(done, 8);
8231:   MatCheckPreallocated(mat, 1);

8233:   if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8234:   else {
8235:     if (done) *done = PETSC_TRUE;
8236:     PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8237:     if (n) *n = 0;
8238:     if (ia) *ia = NULL;
8239:     if (ja) *ja = NULL;
8240:   }
8241:   PetscFunctionReturn(PETSC_SUCCESS);
8242: }

8244: /*@C
8245:   MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

8247:   Collective

8249:   Input Parameters:
8250: + mat             - the matrix
8251: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8252: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8253: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8254:                     inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8255:                     always used.

8257:   Output Parameters:
8258: + n    - size of (possibly compressed) matrix
8259: . ia   - the column pointers
8260: . ja   - the row indices
8261: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8263:   Level: developer

8265: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8266: @*/
8267: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8268: {
8269:   PetscFunctionBegin;
8272:   if (ia) PetscAssertPointer(ia, 6);
8273:   if (ja) PetscAssertPointer(ja, 7);
8274:   PetscAssertPointer(done, 8);
8275:   MatCheckPreallocated(mat, 1);

8277:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8278:   else {
8279:     *done = PETSC_TRUE;
8280:     PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8281:     if (n) *n = 0;
8282:     if (ia) *ia = NULL;
8283:     if (ja) *ja = NULL;
8284:   }
8285:   PetscFunctionReturn(PETSC_SUCCESS);
8286: }

8288: /*@
8289:   MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8290:   `MatGetColumnIJ()`.

8292:   Collective

8294:   Input Parameters:
8295: + mat        - the matrix
8296: . ncolors    - maximum color value
8297: . n          - number of entries in colorarray
8298: - colorarray - array indicating color for each column

8300:   Output Parameter:
8301: . iscoloring - coloring generated using colorarray information

8303:   Level: developer

8305: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8306: @*/
8307: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8308: {
8309:   PetscFunctionBegin;
8312:   PetscAssertPointer(colorarray, 4);
8313:   PetscAssertPointer(iscoloring, 5);
8314:   MatCheckPreallocated(mat, 1);

8316:   if (!mat->ops->coloringpatch) {
8317:     PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8318:   } else {
8319:     PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8320:   }
8321:   PetscFunctionReturn(PETSC_SUCCESS);
8322: }

8324: /*@
8325:   MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

8327:   Logically Collective

8329:   Input Parameter:
8330: . mat - the factored matrix to be reset

8332:   Level: developer

8334:   Notes:
8335:   This routine should be used only with factored matrices formed by in-place
8336:   factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8337:   format).  This option can save memory, for example, when solving nonlinear
8338:   systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8339:   ILU(0) preconditioner.

8341:   One can specify in-place ILU(0) factorization by calling
8342: .vb
8343:      PCType(pc,PCILU);
8344:      PCFactorSeUseInPlace(pc);
8345: .ve
8346:   or by using the options -pc_type ilu -pc_factor_in_place

8348:   In-place factorization ILU(0) can also be used as a local
8349:   solver for the blocks within the block Jacobi or additive Schwarz
8350:   methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
8351:   for details on setting local solver options.

8353:   Most users should employ the `KSP` interface for linear solvers
8354:   instead of working directly with matrix algebra routines such as this.
8355:   See, e.g., `KSPCreate()`.

8357: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8358: @*/
8359: PetscErrorCode MatSetUnfactored(Mat mat)
8360: {
8361:   PetscFunctionBegin;
8364:   MatCheckPreallocated(mat, 1);
8365:   mat->factortype = MAT_FACTOR_NONE;
8366:   if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8367:   PetscUseTypeMethod(mat, setunfactored);
8368:   PetscFunctionReturn(PETSC_SUCCESS);
8369: }

8371: /*MC
8372:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran

8374:     Synopsis:
8375:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8377:     Not Collective

8379:     Input Parameter:
8380: .   x - matrix

8382:     Output Parameters:
8383: +   xx_v - the Fortran pointer to the array
8384: -   ierr - error code

8386:     Example of Usage:
8387: .vb
8388:       PetscScalar, pointer xx_v(:,:)
8389:       ....
8390:       call MatDenseGetArrayF90(x,xx_v,ierr)
8391:       a = xx_v(3)
8392:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
8393: .ve

8395:     Level: advanced

8397: .seealso: [](ch_matrices), `Mat`, `MatDenseRestoreArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJGetArrayF90()`
8398: M*/

8400: /*MC
8401:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
8402:     accessed with `MatDenseGetArrayF90()`.

8404:     Synopsis:
8405:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8407:     Not Collective

8409:     Input Parameters:
8410: +   x - matrix
8411: -   xx_v - the Fortran90 pointer to the array

8413:     Output Parameter:
8414: .   ierr - error code

8416:     Example of Usage:
8417: .vb
8418:        PetscScalar, pointer xx_v(:,:)
8419:        ....
8420:        call MatDenseGetArrayF90(x,xx_v,ierr)
8421:        a = xx_v(3)
8422:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
8423: .ve

8425:     Level: advanced

8427: .seealso: [](ch_matrices), `Mat`, `MatDenseGetArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJRestoreArrayF90()`
8428: M*/

8430: /*MC
8431:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran.

8433:     Synopsis:
8434:     MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

8436:     Not Collective

8438:     Input Parameter:
8439: .   x - matrix

8441:     Output Parameters:
8442: +   xx_v - the Fortran pointer to the array
8443: -   ierr - error code

8445:     Example of Usage:
8446: .vb
8447:       PetscScalar, pointer xx_v(:)
8448:       ....
8449:       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8450:       a = xx_v(3)
8451:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8452: .ve

8454:     Level: advanced

8456: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseGetArrayF90()`
8457: M*/

8459: /*MC
8460:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
8461:     accessed with `MatSeqAIJGetArrayF90()`.

8463:     Synopsis:
8464:     MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

8466:     Not Collective

8468:     Input Parameters:
8469: +   x - matrix
8470: -   xx_v - the Fortran90 pointer to the array

8472:     Output Parameter:
8473: .   ierr - error code

8475:     Example of Usage:
8476: .vb
8477:        PetscScalar, pointer xx_v(:)
8478:        ....
8479:        call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8480:        a = xx_v(3)
8481:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8482: .ve

8484:     Level: advanced

8486: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseRestoreArrayF90()`
8487: M*/

8489: /*@
8490:   MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8491:   as the original matrix.

8493:   Collective

8495:   Input Parameters:
8496: + mat   - the original matrix
8497: . isrow - parallel `IS` containing the rows this processor should obtain
8498: . 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.
8499: - cll   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

8501:   Output Parameter:
8502: . newmat - the new submatrix, of the same type as the original matrix

8504:   Level: advanced

8506:   Notes:
8507:   The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

8509:   Some matrix types place restrictions on the row and column indices, such
8510:   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;
8511:   for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

8513:   The index sets may not have duplicate entries.

8515:   The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8516:   the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8517:   to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8518:   will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
8519:   you are finished using it.

8521:   The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8522:   the input matrix.

8524:   If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

8526:   If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8527:   is used by `PCFIELDSPLIT` to allow easy nesting of its use.

8529:   Example usage:
8530:   Consider the following 8x8 matrix with 34 non-zero values, that is
8531:   assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8532:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8533:   as follows
8534: .vb
8535:             1  2  0  |  0  3  0  |  0  4
8536:     Proc0   0  5  6  |  7  0  0  |  8  0
8537:             9  0 10  | 11  0  0  | 12  0
8538:     -------------------------------------
8539:            13  0 14  | 15 16 17  |  0  0
8540:     Proc1   0 18  0  | 19 20 21  |  0  0
8541:             0  0  0  | 22 23  0  | 24  0
8542:     -------------------------------------
8543:     Proc2  25 26 27  |  0  0 28  | 29  0
8544:            30  0  0  | 31 32 33  |  0 34
8545: .ve

8547:   Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

8549: .vb
8550:             2  0  |  0  3  0  |  0
8551:     Proc0   5  6  |  7  0  0  |  8
8552:     -------------------------------
8553:     Proc1  18  0  | 19 20 21  |  0
8554:     -------------------------------
8555:     Proc2  26 27  |  0  0 28  | 29
8556:             0  0  | 31 32 33  |  0
8557: .ve

8559: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8560: @*/
8561: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8562: {
8563:   PetscMPIInt size;
8564:   Mat        *local;
8565:   IS          iscoltmp;
8566:   PetscBool   flg;

8568:   PetscFunctionBegin;
8572:   PetscAssertPointer(newmat, 5);
8575:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8576:   PetscCheck(cll != MAT_IGNORE_MATRIX,