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_SetValuesBatch;
 40: PetscLogEvent MAT_ViennaCLCopyToGPU;
 41: PetscLogEvent MAT_CUDACopyToGPU, MAT_HIPCopyToGPU;
 42: PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
 43: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
 44: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
 45: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
 46: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;

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

 50: /*@
 51:   MatSetRandom - Sets all components of a matrix to random numbers.

 53:   Logically Collective

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

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

 67:   Level: intermediate

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

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

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

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

 82:   PetscFunctionBegin;
 86:   MatCheckPreallocated(x, 1);

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

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

106: /*@
107:   MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in

109:   Logically Collective

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

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

119:   Level: advanced

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

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

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

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

143: /*@
144:   MatFactorGetError - gets the error code from a factorization

146:   Logically Collective

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

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

154:   Level: advanced

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

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

171: /*@
172:   MatFactorClearError - clears the error code in a factorization

174:   Logically Collective

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

179:   Level: developer

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

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

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

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

254: /*@
255:   MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix

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

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

263:   Level: intermediate

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

268: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
269:  @*/
270: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
271: {
272:   PetscFunctionBegin;
275:   PetscAssertPointer(keptrows, 2);
276:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
277:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
278:   if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
279:   else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
280:   PetscFunctionReturn(PETSC_SUCCESS);
281: }

283: /*@
284:   MatFindZeroRows - Locate all rows that are completely zero in the matrix

286:   Input Parameter:
287: . mat - the matrix

289:   Output Parameter:
290: . zerorows - the rows that are completely zero

292:   Level: intermediate

294:   Note:
295:   `zerorows` is set to `NULL` if no rows are zero.

297: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
298:  @*/
299: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
300: {
301:   IS       keptrows;
302:   PetscInt m, n;

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

322: /*@
323:   MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling

325:   Not Collective

327:   Input Parameter:
328: . A - the matrix

330:   Output Parameter:
331: . a - the diagonal part (which is a SEQUENTIAL matrix)

333:   Level: advanced

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

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

340: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
341: @*/
342: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
343: {
344:   PetscFunctionBegin;
347:   PetscAssertPointer(a, 2);
348:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
349:   if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
350:   else {
351:     PetscMPIInt size;

353:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
354:     PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
355:     *a = A;
356:   }
357:   PetscFunctionReturn(PETSC_SUCCESS);
358: }

360: /*@
361:   MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.

363:   Collective

365:   Input Parameter:
366: . mat - the matrix

368:   Output Parameter:
369: . trace - the sum of the diagonal entries

371:   Level: advanced

373: .seealso: [](ch_matrices), `Mat`
374: @*/
375: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
376: {
377:   Vec diag;

379:   PetscFunctionBegin;
381:   PetscAssertPointer(trace, 2);
382:   PetscCall(MatCreateVecs(mat, &diag, NULL));
383:   PetscCall(MatGetDiagonal(mat, diag));
384:   PetscCall(VecSum(diag, trace));
385:   PetscCall(VecDestroy(&diag));
386:   PetscFunctionReturn(PETSC_SUCCESS);
387: }

389: /*@
390:   MatRealPart - Zeros out the imaginary part of the matrix

392:   Logically Collective

394:   Input Parameter:
395: . mat - the matrix

397:   Level: advanced

399: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
400: @*/
401: PetscErrorCode MatRealPart(Mat mat)
402: {
403:   PetscFunctionBegin;
406:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
407:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
408:   MatCheckPreallocated(mat, 1);
409:   PetscUseTypeMethod(mat, realpart);
410:   PetscFunctionReturn(PETSC_SUCCESS);
411: }

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

416:   Collective

418:   Input Parameter:
419: . mat - the matrix

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

425:   Level: advanced

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

430: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
431: @*/
432: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
433: {
434:   PetscFunctionBegin;
437:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
438:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
439:   if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
440:   else {
441:     if (nghosts) *nghosts = 0;
442:     if (ghosts) *ghosts = NULL;
443:   }
444:   PetscFunctionReturn(PETSC_SUCCESS);
445: }

447: /*@
448:   MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

450:   Logically Collective

452:   Input Parameter:
453: . mat - the matrix

455:   Level: advanced

457: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
458: @*/
459: PetscErrorCode MatImaginaryPart(Mat mat)
460: {
461:   PetscFunctionBegin;
464:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
465:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
466:   MatCheckPreallocated(mat, 1);
467:   PetscUseTypeMethod(mat, imaginarypart);
468:   PetscFunctionReturn(PETSC_SUCCESS);
469: }

471: /*@
472:   MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices) in the nonzero structure

474:   Not Collective

476:   Input Parameter:
477: . mat - the matrix

479:   Output Parameters:
480: + missing - is any diagonal entry missing
481: - dd      - first diagonal entry that is missing (optional) on this process

483:   Level: advanced

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

488: .seealso: [](ch_matrices), `Mat`
489: @*/
490: PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
491: {
492:   PetscFunctionBegin;
495:   PetscAssertPointer(missing, 2);
496:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
497:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
498:   PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
499:   PetscFunctionReturn(PETSC_SUCCESS);
500: }

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

508:   Not Collective

510:   Input Parameters:
511: + mat - the matrix
512: - row - the row to get

514:   Output Parameters:
515: + ncols - if not `NULL`, the number of nonzeros in `row`
516: . cols  - if not `NULL`, the column numbers
517: - vals  - if not `NULL`, the numerical values

519:   Level: advanced

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

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

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

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

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

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

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

547:   Fortran Note:
548:   The calling sequence is
549: .vb
550:    MatGetRow(matrix,row,ncols,cols,values,ierr)
551:          Mat     matrix (input)
552:          integer row    (input)
553:          integer ncols  (output)
554:          integer cols(maxcols) (output)
555:          double precision (or double complex) values(maxcols) output
556: .ve
557:   where maxcols >= maximum nonzeros in any row of the matrix.

559: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
560: @*/
561: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
562: {
563:   PetscInt incols;

565:   PetscFunctionBegin;
568:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
569:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
570:   MatCheckPreallocated(mat, 1);
571:   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);
572:   PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
573:   PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
574:   if (ncols) *ncols = incols;
575:   PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
576:   PetscFunctionReturn(PETSC_SUCCESS);
577: }

579: /*@
580:   MatConjugate - replaces the matrix values with their complex conjugates

582:   Logically Collective

584:   Input Parameter:
585: . mat - the matrix

587:   Level: advanced

589: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
590: @*/
591: PetscErrorCode MatConjugate(Mat mat)
592: {
593:   PetscFunctionBegin;
595:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
596:   if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
597:     PetscUseTypeMethod(mat, conjugate);
598:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
599:   }
600:   PetscFunctionReturn(PETSC_SUCCESS);
601: }

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

606:   Not Collective

608:   Input Parameters:
609: + mat   - the matrix
610: . row   - the row to get
611: . ncols - the number of nonzeros
612: . cols  - the columns of the nonzeros
613: - vals  - if nonzero the column values

615:   Level: advanced

617:   Notes:
618:   This routine should be called after you have finished examining the entries.

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

624:   Fortran Notes:
625:   The calling sequence is
626: .vb
627:    MatRestoreRow(matrix,row,ncols,cols,values,ierr)
628:       Mat     matrix (input)
629:       integer row    (input)
630:       integer ncols  (output)
631:       integer cols(maxcols) (output)
632:       double precision (or double complex) values(maxcols) output
633: .ve
634:   Where maxcols >= maximum nonzeros in any row of the matrix.

636:   In Fortran `MatRestoreRow()` MUST be called after `MatGetRow()`
637:   before another call to `MatGetRow()` can be made.

639: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
640: @*/
641: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
642: {
643:   PetscFunctionBegin;
645:   if (ncols) PetscAssertPointer(ncols, 3);
646:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
647:   if (!mat->ops->restorerow) PetscFunctionReturn(PETSC_SUCCESS);
648:   PetscUseTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
649:   if (ncols) *ncols = 0;
650:   if (cols) *cols = NULL;
651:   if (vals) *vals = NULL;
652:   PetscFunctionReturn(PETSC_SUCCESS);
653: }

655: /*@
656:   MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
657:   You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.

659:   Not Collective

661:   Input Parameter:
662: . mat - the matrix

664:   Level: advanced

666:   Note:
667:   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.

669: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
670: @*/
671: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
672: {
673:   PetscFunctionBegin;
676:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
677:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
678:   MatCheckPreallocated(mat, 1);
679:   if (!mat->ops->getrowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
680:   PetscUseTypeMethod(mat, getrowuppertriangular);
681:   PetscFunctionReturn(PETSC_SUCCESS);
682: }

684: /*@
685:   MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.

687:   Not Collective

689:   Input Parameter:
690: . mat - the matrix

692:   Level: advanced

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

697: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
698: @*/
699: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
700: {
701:   PetscFunctionBegin;
704:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
705:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
706:   MatCheckPreallocated(mat, 1);
707:   if (!mat->ops->restorerowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
708:   PetscUseTypeMethod(mat, restorerowuppertriangular);
709:   PetscFunctionReturn(PETSC_SUCCESS);
710: }

712: /*@C
713:   MatSetOptionsPrefix - Sets the prefix used for searching for all
714:   `Mat` options in the database.

716:   Logically Collective

718:   Input Parameters:
719: + A      - the matrix
720: - prefix - the prefix to prepend to all option names

722:   Level: advanced

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

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

732: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
733: @*/
734: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
735: {
736:   PetscFunctionBegin;
738:   PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
739:   PetscFunctionReturn(PETSC_SUCCESS);
740: }

742: /*@C
743:   MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
744:   for matrices created with `MatGetFactor()`

746:   Logically Collective

748:   Input Parameters:
749: + A      - the matrix
750: - prefix - the prefix to prepend to all option names for the factored matrix

752:   Level: developer

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

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

761: .seealso: [](ch_matrices), `Mat`,   [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
762: @*/
763: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
764: {
765:   PetscFunctionBegin;
767:   if (prefix) {
768:     PetscAssertPointer(prefix, 2);
769:     PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
770:     if (prefix != A->factorprefix) {
771:       PetscCall(PetscFree(A->factorprefix));
772:       PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
773:     }
774:   } else PetscCall(PetscFree(A->factorprefix));
775:   PetscFunctionReturn(PETSC_SUCCESS);
776: }

778: /*@C
779:   MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
780:   for matrices created with `MatGetFactor()`

782:   Logically Collective

784:   Input Parameters:
785: + A      - the matrix
786: - prefix - the prefix to prepend to all option names for the factored matrix

788:   Level: developer

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

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

797: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
798:           `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
799:           `MatSetOptionsPrefix()`
800: @*/
801: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
802: {
803:   size_t len1, len2, new_len;

805:   PetscFunctionBegin;
807:   if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
808:   if (!A->factorprefix) {
809:     PetscCall(MatSetOptionsPrefixFactor(A, prefix));
810:     PetscFunctionReturn(PETSC_SUCCESS);
811:   }
812:   PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");

814:   PetscCall(PetscStrlen(A->factorprefix, &len1));
815:   PetscCall(PetscStrlen(prefix, &len2));
816:   new_len = len1 + len2 + 1;
817:   PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
818:   PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
819:   PetscFunctionReturn(PETSC_SUCCESS);
820: }

822: /*@C
823:   MatAppendOptionsPrefix - Appends to the prefix used for searching for all
824:   matrix options in the database.

826:   Logically Collective

828:   Input Parameters:
829: + A      - the matrix
830: - prefix - the prefix to prepend to all option names

832:   Level: advanced

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

838: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
839: @*/
840: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
841: {
842:   PetscFunctionBegin;
844:   PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
845:   PetscFunctionReturn(PETSC_SUCCESS);
846: }

848: /*@C
849:   MatGetOptionsPrefix - Gets the prefix used for searching for all
850:   matrix options in the database.

852:   Not Collective

854:   Input Parameter:
855: . A - the matrix

857:   Output Parameter:
858: . prefix - pointer to the prefix string used

860:   Level: advanced

862:   Fortran Note:
863:   The user should pass in a string `prefix` of
864:   sufficient length to hold the prefix.

866: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
867: @*/
868: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
869: {
870:   PetscFunctionBegin;
872:   PetscAssertPointer(prefix, 2);
873:   PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
874:   PetscFunctionReturn(PETSC_SUCCESS);
875: }

877: /*@
878:   MatResetPreallocation - Reset matrix to use the original nonzero pattern provided by the user.

880:   Collective

882:   Input Parameter:
883: . A - the matrix

885:   Level: beginner

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

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

892:   Currently only supported for  `MATAIJ` matrices.

894: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
895: @*/
896: PetscErrorCode MatResetPreallocation(Mat A)
897: {
898:   PetscFunctionBegin;
901:   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()");
902:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
903:   PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
904:   PetscFunctionReturn(PETSC_SUCCESS);
905: }

907: /*@
908:   MatSetUp - Sets up the internal matrix data structures for later use.

910:   Collective

912:   Input Parameter:
913: . A - the matrix

915:   Level: intermediate

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

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

923: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
924: @*/
925: PetscErrorCode MatSetUp(Mat A)
926: {
927:   PetscFunctionBegin;
929:   if (!((PetscObject)A)->type_name) {
930:     PetscMPIInt size;

932:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
933:     PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
934:   }
935:   if (!A->preallocated) PetscTryTypeMethod(A, setup);
936:   PetscCall(PetscLayoutSetUp(A->rmap));
937:   PetscCall(PetscLayoutSetUp(A->cmap));
938:   A->preallocated = PETSC_TRUE;
939:   PetscFunctionReturn(PETSC_SUCCESS);
940: }

942: #if defined(PETSC_HAVE_SAWS)
943: #include <petscviewersaws.h>
944: #endif

946: /*
947:    If threadsafety is on extraneous matrices may be printed

949:    This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
950: */
951: #if !defined(PETSC_HAVE_THREADSAFETY)
952: static PetscInt insidematview = 0;
953: #endif

955: /*@C
956:   MatViewFromOptions - View properties of the matrix based on options set in the options database

958:   Collective

960:   Input Parameters:
961: + A    - the matrix
962: . obj  - optional additional object that provides the options prefix to use
963: - name - command line option

965:   Options Database Key:
966: . -mat_view [viewertype]:... - the viewer and its options

968:   Level: intermediate

970:   Note:
971: .vb
972:     If no value is provided ascii:stdout is used
973:        ascii[:[filename][:[format][:append]]]    defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
974:                                                   for example ascii::ascii_info prints just the information about the object not all details
975:                                                   unless :append is given filename opens in write mode, overwriting what was already there
976:        binary[:[filename][:[format][:append]]]   defaults to the file binaryoutput
977:        draw[:drawtype[:filename]]                for example, draw:tikz, draw:tikz:figure.tex  or draw:x
978:        socket[:port]                             defaults to the standard output port
979:        saws[:communicatorname]                    publishes object to the Scientific Application Webserver (SAWs)
980: .ve

982: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
983: @*/
984: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
985: {
986:   PetscFunctionBegin;
988: #if !defined(PETSC_HAVE_THREADSAFETY)
989:   if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
990: #endif
991:   PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
992:   PetscFunctionReturn(PETSC_SUCCESS);
993: }

995: /*@C
996:   MatView - display information about a matrix in a variety ways

998:   Collective on viewer

1000:   Input Parameters:
1001: + mat    - the matrix
1002: - viewer - visualization context

1004:   Options Database Keys:
1005: + -mat_view ::ascii_info           - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1006: . -mat_view ::ascii_info_detail    - Prints more detailed info
1007: . -mat_view                        - Prints matrix in ASCII format
1008: . -mat_view ::ascii_matlab         - Prints matrix in MATLAB format
1009: . -mat_view draw                   - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1010: . -display <name>                  - Sets display name (default is host)
1011: . -draw_pause <sec>                - Sets number of seconds to pause after display
1012: . -mat_view socket                 - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1013: . -viewer_socket_machine <machine> - -
1014: . -viewer_socket_port <port>       - -
1015: . -mat_view binary                 - save matrix to file in binary format
1016: - -viewer_binary_filename <name>   - -

1018:   Level: beginner

1020:   Notes:
1021:   The available visualization contexts include
1022: +    `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1023: .    `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1024: .    `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1025: -     `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure

1027:   The user can open alternative visualization contexts with
1028: +    `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1029: .    `PetscViewerBinaryOpen()` - Outputs matrix in binary to a
1030:   specified file; corresponding input uses `MatLoad()`
1031: .    `PetscViewerDrawOpen()` - Outputs nonzero matrix structure to
1032:   an X window display
1033: -    `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer.
1034:   Currently only the `MATSEQDENSE` and `MATAIJ`
1035:   matrix types support the Socket viewer.

1037:   The user can call `PetscViewerPushFormat()` to specify the output
1038:   format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1039:   `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`).  Available formats include
1040: +    `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1041: .    `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1042: .    `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1043: .    `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse
1044:   format common among all matrix types
1045: .    `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific
1046:   format (which is in many cases the same as the default)
1047: .    `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix
1048:   size and structure (not the matrix entries)
1049: -    `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about
1050:   the matrix structure

1052:   The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1053:   the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

1055:   In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).

1057:   See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1058:   viewer is used.

1060:   See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1061:   viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.

1063:   One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1064:   and then use the following mouse functions.
1065: .vb
1066:   left mouse: zoom in
1067:   middle mouse: zoom out
1068:   right mouse: continue with the simulation
1069: .ve

1071: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1072:           `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1073: @*/
1074: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1075: {
1076:   PetscInt          rows, cols, rbs, cbs;
1077:   PetscBool         isascii, isstring, issaws;
1078:   PetscViewerFormat format;
1079:   PetscMPIInt       size;

1081:   PetscFunctionBegin;
1084:   if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));

1087:   PetscCall(PetscViewerGetFormat(viewer, &format));
1088:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1089:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);

1091: #if !defined(PETSC_HAVE_THREADSAFETY)
1092:   insidematview++;
1093: #endif
1094:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1095:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1096:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1097:   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");

1099:   PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1100:   if (isascii) {
1101:     if (!mat->preallocated) {
1102:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1103: #if !defined(PETSC_HAVE_THREADSAFETY)
1104:       insidematview--;
1105: #endif
1106:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1107:       PetscFunctionReturn(PETSC_SUCCESS);
1108:     }
1109:     if (!mat->assembled) {
1110:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1111: #if !defined(PETSC_HAVE_THREADSAFETY)
1112:       insidematview--;
1113: #endif
1114:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1115:       PetscFunctionReturn(PETSC_SUCCESS);
1116:     }
1117:     PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1118:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1119:       MatNullSpace nullsp, transnullsp;

1121:       PetscCall(PetscViewerASCIIPushTab(viewer));
1122:       PetscCall(MatGetSize(mat, &rows, &cols));
1123:       PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1124:       if (rbs != 1 || cbs != 1) {
1125:         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" : ""));
1126:         else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1127:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1128:       if (mat->factortype) {
1129:         MatSolverType solver;
1130:         PetscCall(MatFactorGetSolverType(mat, &solver));
1131:         PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1132:       }
1133:       if (mat->ops->getinfo) {
1134:         MatInfo info;
1135:         PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1136:         PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1137:         if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1138:       }
1139:       PetscCall(MatGetNullSpace(mat, &nullsp));
1140:       PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1141:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached null space\n"));
1142:       if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached transposed null space\n"));
1143:       PetscCall(MatGetNearNullSpace(mat, &nullsp));
1144:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached near null space\n"));
1145:       PetscCall(PetscViewerASCIIPushTab(viewer));
1146:       PetscCall(MatProductView(mat, viewer));
1147:       PetscCall(PetscViewerASCIIPopTab(viewer));
1148:       if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1149:         IS tmp;

1151:         PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1152:         PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1153:         PetscCall(PetscViewerASCIIPushTab(viewer));
1154:         PetscCall(ISView(tmp, viewer));
1155:         PetscCall(PetscViewerASCIIPopTab(viewer));
1156:         PetscCall(ISDestroy(&tmp));
1157:       }
1158:     }
1159:   } else if (issaws) {
1160: #if defined(PETSC_HAVE_SAWS)
1161:     PetscMPIInt rank;

1163:     PetscCall(PetscObjectName((PetscObject)mat));
1164:     PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1165:     if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1166: #endif
1167:   } else if (isstring) {
1168:     const char *type;
1169:     PetscCall(MatGetType(mat, &type));
1170:     PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1171:     PetscTryTypeMethod(mat, view, viewer);
1172:   }
1173:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1174:     PetscCall(PetscViewerASCIIPushTab(viewer));
1175:     PetscUseTypeMethod(mat, viewnative, viewer);
1176:     PetscCall(PetscViewerASCIIPopTab(viewer));
1177:   } else if (mat->ops->view) {
1178:     PetscCall(PetscViewerASCIIPushTab(viewer));
1179:     PetscUseTypeMethod(mat, view, viewer);
1180:     PetscCall(PetscViewerASCIIPopTab(viewer));
1181:   }
1182:   if (isascii) {
1183:     PetscCall(PetscViewerGetFormat(viewer, &format));
1184:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1185:   }
1186:   PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1187: #if !defined(PETSC_HAVE_THREADSAFETY)
1188:   insidematview--;
1189: #endif
1190:   PetscFunctionReturn(PETSC_SUCCESS);
1191: }

1193: #if defined(PETSC_USE_DEBUG)
1194: #include <../src/sys/totalview/tv_data_display.h>
1195: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1196: {
1197:   TV_add_row("Local rows", "int", &mat->rmap->n);
1198:   TV_add_row("Local columns", "int", &mat->cmap->n);
1199:   TV_add_row("Global rows", "int", &mat->rmap->N);
1200:   TV_add_row("Global columns", "int", &mat->cmap->N);
1201:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1202:   return TV_format_OK;
1203: }
1204: #endif

1206: /*@C
1207:   MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1208:   with `MatView()`.  The matrix format is determined from the options database.
1209:   Generates a parallel MPI matrix if the communicator has more than one
1210:   processor.  The default matrix type is `MATAIJ`.

1212:   Collective

1214:   Input Parameters:
1215: + mat    - the newly loaded matrix, this needs to have been created with `MatCreate()`
1216:             or some related function before a call to `MatLoad()`
1217: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer

1219:   Options Database Key:
1220: . -matload_block_size <bs> - set block size

1222:   Level: beginner

1224:   Notes:
1225:   If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1226:   `Mat` before calling this routine if you wish to set it from the options database.

1228:   `MatLoad()` automatically loads into the options database any options
1229:   given in the file filename.info where filename is the name of the file
1230:   that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1231:   file will be ignored if you use the -viewer_binary_skip_info option.

1233:   If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1234:   sets the default matrix type AIJ and sets the local and global sizes.
1235:   If type and/or size is already set, then the same are used.

1237:   In parallel, each processor can load a subset of rows (or the
1238:   entire matrix).  This routine is especially useful when a large
1239:   matrix is stored on disk and only part of it is desired on each
1240:   processor.  For example, a parallel solver may access only some of
1241:   the rows from each processor.  The algorithm used here reads
1242:   relatively small blocks of data rather than reading the entire
1243:   matrix and then subsetting it.

1245:   Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1246:   Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1247:   or the sequence like
1248: .vb
1249:     `PetscViewer` v;
1250:     `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1251:     `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1252:     `PetscViewerSetFromOptions`(v);
1253:     `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1254:     `PetscViewerFileSetName`(v,"datafile");
1255: .ve
1256:   The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1257: $ -viewer_type {binary, hdf5}

1259:   See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1260:   and src/mat/tutorials/ex10.c with the second approach.

1262:   In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1263:   is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1264:   Multiple objects, both matrices and vectors, can be stored within the same file.
1265:   Their `PetscObject` name is ignored; they are loaded in the order of their storage.

1267:   Most users should not need to know the details of the binary storage
1268:   format, since `MatLoad()` and `MatView()` completely hide these details.
1269:   But for anyone who is interested, the standard binary matrix storage
1270:   format is

1272: .vb
1273:     PetscInt    MAT_FILE_CLASSID
1274:     PetscInt    number of rows
1275:     PetscInt    number of columns
1276:     PetscInt    total number of nonzeros
1277:     PetscInt    *number nonzeros in each row
1278:     PetscInt    *column indices of all nonzeros (starting index is zero)
1279:     PetscScalar *values of all nonzeros
1280: .ve
1281:   If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1282:   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
1283:   case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.

1285:   PETSc automatically does the byte swapping for
1286:   machines that store the bytes reversed. Thus if you write your own binary
1287:   read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1288:   and `PetscBinaryWrite()` to see how this may be done.

1290:   In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1291:   Each processor's chunk is loaded independently by its owning MPI process.
1292:   Multiple objects, both matrices and vectors, can be stored within the same file.
1293:   They are looked up by their PetscObject name.

1295:   As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1296:   by default the same structure and naming of the AIJ arrays and column count
1297:   within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1298: $    save example.mat A b -v7.3
1299:   can be directly read by this routine (see Reference 1 for details).

1301:   Depending on your MATLAB version, this format might be a default,
1302:   otherwise you can set it as default in Preferences.

1304:   Unless -nocompression flag is used to save the file in MATLAB,
1305:   PETSc must be configured with ZLIB package.

1307:   See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

1309:   This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`

1311:   Corresponding `MatView()` is not yet implemented.

1313:   The loaded matrix is actually a transpose of the original one in MATLAB,
1314:   unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1315:   With this format, matrix is automatically transposed by PETSc,
1316:   unless the matrix is marked as SPD or symmetric
1317:   (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).

1319:   See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>

1321: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1322:  @*/
1323: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1324: {
1325:   PetscBool flg;

1327:   PetscFunctionBegin;

1331:   if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));

1333:   flg = PETSC_FALSE;
1334:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1335:   if (flg) {
1336:     PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1337:     PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1338:   }
1339:   flg = PETSC_FALSE;
1340:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1341:   if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));

1343:   PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1344:   PetscUseTypeMethod(mat, load, viewer);
1345:   PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1346:   PetscFunctionReturn(PETSC_SUCCESS);
1347: }

1349: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1350: {
1351:   Mat_Redundant *redund = *redundant;

1353:   PetscFunctionBegin;
1354:   if (redund) {
1355:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1356:       PetscCall(ISDestroy(&redund->isrow));
1357:       PetscCall(ISDestroy(&redund->iscol));
1358:       PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1359:     } else {
1360:       PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1361:       PetscCall(PetscFree(redund->sbuf_j));
1362:       PetscCall(PetscFree(redund->sbuf_a));
1363:       for (PetscInt i = 0; i < redund->nrecvs; i++) {
1364:         PetscCall(PetscFree(redund->rbuf_j[i]));
1365:         PetscCall(PetscFree(redund->rbuf_a[i]));
1366:       }
1367:       PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1368:     }

1370:     if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1371:     PetscCall(PetscFree(redund));
1372:   }
1373:   PetscFunctionReturn(PETSC_SUCCESS);
1374: }

1376: /*@C
1377:   MatDestroy - Frees space taken by a matrix.

1379:   Collective

1381:   Input Parameter:
1382: . A - the matrix

1384:   Level: beginner

1386:   Developer Note:
1387:   Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1388:   `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1389:   `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1390:   if changes are needed here.

1392: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1393: @*/
1394: PetscErrorCode MatDestroy(Mat *A)
1395: {
1396:   PetscFunctionBegin;
1397:   if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1399:   if (--((PetscObject)*A)->refct > 0) {
1400:     *A = NULL;
1401:     PetscFunctionReturn(PETSC_SUCCESS);
1402:   }

1404:   /* if memory was published with SAWs then destroy it */
1405:   PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1406:   PetscTryTypeMethod(*A, destroy);

1408:   PetscCall(PetscFree((*A)->factorprefix));
1409:   PetscCall(PetscFree((*A)->defaultvectype));
1410:   PetscCall(PetscFree((*A)->defaultrandtype));
1411:   PetscCall(PetscFree((*A)->bsizes));
1412:   PetscCall(PetscFree((*A)->solvertype));
1413:   for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1414:   if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1415:   PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1416:   PetscCall(MatProductClear(*A));
1417:   PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1418:   PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1419:   PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1420:   PetscCall(MatDestroy(&(*A)->schur));
1421:   PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1422:   PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1423:   PetscCall(PetscHeaderDestroy(A));
1424:   PetscFunctionReturn(PETSC_SUCCESS);
1425: }

1427: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1428: /*@C
1429:   MatSetValues - Inserts or adds a block of values into a matrix.
1430:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1431:   MUST be called after all calls to `MatSetValues()` have been completed.

1433:   Not Collective

1435:   Input Parameters:
1436: + mat  - the matrix
1437: . v    - a logically two-dimensional array of values
1438: . m    - the number of rows
1439: . idxm - the global indices of the rows
1440: . n    - the number of columns
1441: . idxn - the global indices of the columns
1442: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1444:   Level: beginner

1446:   Notes:
1447:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1449:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1450:   options cannot be mixed without intervening calls to the assembly
1451:   routines.

1453:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1454:   as well as in C.

1456:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1457:   simply ignored. This allows easily inserting element stiffness matrices
1458:   with homogeneous Dirichlet boundary conditions that you don't want represented
1459:   in the matrix.

1461:   Efficiency Alert:
1462:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1463:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1465:   Developer Note:
1466:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1467:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1469: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1470:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1471: @*/
1472: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1473: {
1474:   PetscFunctionBeginHot;
1477:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1478:   PetscAssertPointer(idxm, 3);
1479:   PetscAssertPointer(idxn, 5);
1480:   MatCheckPreallocated(mat, 1);

1482:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1483:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

1485:   if (PetscDefined(USE_DEBUG)) {
1486:     PetscInt i, j;

1488:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1489:     if (v) {
1490:       for (i = 0; i < m; i++) {
1491:         for (j = 0; j < n; j++) {
1492:           if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1493: #if defined(PETSC_USE_COMPLEX)
1494:             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]);
1495: #else
1496:             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]);
1497: #endif
1498:         }
1499:       }
1500:     }
1501:     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);
1502:     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);
1503:   }

1505:   if (mat->assembled) {
1506:     mat->was_assembled = PETSC_TRUE;
1507:     mat->assembled     = PETSC_FALSE;
1508:   }
1509:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1510:   PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1511:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1512:   PetscFunctionReturn(PETSC_SUCCESS);
1513: }

1515: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1516: /*@C
1517:   MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1518:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1519:   MUST be called after all calls to `MatSetValues()` have been completed.

1521:   Not Collective

1523:   Input Parameters:
1524: + mat  - the matrix
1525: . v    - a logically two-dimensional array of values
1526: . ism  - the rows to provide
1527: . isn  - the columns to provide
1528: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1530:   Level: beginner

1532:   Notes:
1533:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1535:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1536:   options cannot be mixed without intervening calls to the assembly
1537:   routines.

1539:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1540:   as well as in C.

1542:   Negative indices may be passed in `ism` and `isn`, these rows and columns are
1543:   simply ignored. This allows easily inserting element stiffness matrices
1544:   with homogeneous Dirichlet boundary conditions that you don't want represented
1545:   in the matrix.

1547:   Efficiency Alert:
1548:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1549:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1551:   This is currently not optimized for any particular `ISType`

1553:   Developer Note:
1554:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1555:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1557: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1558:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1559: @*/
1560: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1561: {
1562:   PetscInt        m, n;
1563:   const PetscInt *rows, *cols;

1565:   PetscFunctionBeginHot;
1567:   PetscCall(ISGetIndices(ism, &rows));
1568:   PetscCall(ISGetIndices(isn, &cols));
1569:   PetscCall(ISGetLocalSize(ism, &m));
1570:   PetscCall(ISGetLocalSize(isn, &n));
1571:   PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1572:   PetscCall(ISRestoreIndices(ism, &rows));
1573:   PetscCall(ISRestoreIndices(isn, &cols));
1574:   PetscFunctionReturn(PETSC_SUCCESS);
1575: }

1577: /*@
1578:   MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1579:   values into a matrix

1581:   Not Collective

1583:   Input Parameters:
1584: + mat - the matrix
1585: . row - the (block) row to set
1586: - v   - a logically two-dimensional array of values

1588:   Level: intermediate

1590:   Notes:
1591:   The values, `v`, are column-oriented (for the block version) and sorted

1593:   All the nonzero values in `row` must be provided

1595:   The matrix must have previously had its column indices set, likely by having been assembled.

1597:   `row` must belong to this MPI process

1599: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1600:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1601: @*/
1602: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1603: {
1604:   PetscInt globalrow;

1606:   PetscFunctionBegin;
1609:   PetscAssertPointer(v, 3);
1610:   PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1611:   PetscCall(MatSetValuesRow(mat, globalrow, v));
1612:   PetscFunctionReturn(PETSC_SUCCESS);
1613: }

1615: /*@
1616:   MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1617:   values into a matrix

1619:   Not Collective

1621:   Input Parameters:
1622: + mat - the matrix
1623: . row - the (block) row to set
1624: - 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

1626:   Level: advanced

1628:   Notes:
1629:   The values, `v`, are column-oriented for the block version.

1631:   All the nonzeros in `row` must be provided

1633:   THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

1635:   `row` must belong to this process

1637: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1638:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1639: @*/
1640: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1641: {
1642:   PetscFunctionBeginHot;
1645:   MatCheckPreallocated(mat, 1);
1646:   PetscAssertPointer(v, 3);
1647:   PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1648:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1649:   mat->insertmode = INSERT_VALUES;

1651:   if (mat->assembled) {
1652:     mat->was_assembled = PETSC_TRUE;
1653:     mat->assembled     = PETSC_FALSE;
1654:   }
1655:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1656:   PetscUseTypeMethod(mat, setvaluesrow, row, v);
1657:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1658:   PetscFunctionReturn(PETSC_SUCCESS);
1659: }

1661: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1662: /*@
1663:   MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1664:   Using structured grid indexing

1666:   Not Collective

1668:   Input Parameters:
1669: + mat  - the matrix
1670: . m    - number of rows being entered
1671: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1672: . n    - number of columns being entered
1673: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1674: . v    - a logically two-dimensional array of values
1675: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

1677:   Level: beginner

1679:   Notes:
1680:   By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

1682:   Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1683:   options cannot be mixed without intervening calls to the assembly
1684:   routines.

1686:   The grid coordinates are across the entire grid, not just the local portion

1688:   `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1689:   as well as in C.

1691:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1693:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1694:   or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1696:   The columns and rows in the stencil passed in MUST be contained within the
1697:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1698:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1699:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1700:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1702:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1703:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1704:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1705:   `DM_BOUNDARY_PERIODIC` boundary type.

1707:   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
1708:   a single value per point) you can skip filling those indices.

1710:   Inspired by the structured grid interface to the HYPRE package
1711:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1713:   Efficiency Alert:
1714:   The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1715:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1717:   Fortran Note:
1718:   `idxm` and `idxn` should be declared as
1719: $     MatStencil idxm(4,m),idxn(4,n)
1720:   and the values inserted using
1721: .vb
1722:     idxm(MatStencil_i,1) = i
1723:     idxm(MatStencil_j,1) = j
1724:     idxm(MatStencil_k,1) = k
1725:     idxm(MatStencil_c,1) = c
1726:     etc
1727: .ve

1729: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1730:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1731: @*/
1732: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1733: {
1734:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1735:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1736:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1738:   PetscFunctionBegin;
1739:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1742:   PetscAssertPointer(idxm, 3);
1743:   PetscAssertPointer(idxn, 5);

1745:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1746:     jdxm = buf;
1747:     jdxn = buf + m;
1748:   } else {
1749:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1750:     jdxm = bufm;
1751:     jdxn = bufn;
1752:   }
1753:   for (i = 0; i < m; i++) {
1754:     for (j = 0; j < 3 - sdim; j++) dxm++;
1755:     tmp = *dxm++ - starts[0];
1756:     for (j = 0; j < dim - 1; j++) {
1757:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1758:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1759:     }
1760:     if (mat->stencil.noc) dxm++;
1761:     jdxm[i] = tmp;
1762:   }
1763:   for (i = 0; i < n; i++) {
1764:     for (j = 0; j < 3 - sdim; j++) dxn++;
1765:     tmp = *dxn++ - starts[0];
1766:     for (j = 0; j < dim - 1; j++) {
1767:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1768:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1769:     }
1770:     if (mat->stencil.noc) dxn++;
1771:     jdxn[i] = tmp;
1772:   }
1773:   PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1774:   PetscCall(PetscFree2(bufm, bufn));
1775:   PetscFunctionReturn(PETSC_SUCCESS);
1776: }

1778: /*@
1779:   MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1780:   Using structured grid indexing

1782:   Not Collective

1784:   Input Parameters:
1785: + mat  - the matrix
1786: . m    - number of rows being entered
1787: . idxm - grid coordinates for matrix rows being entered
1788: . n    - number of columns being entered
1789: . idxn - grid coordinates for matrix columns being entered
1790: . v    - a logically two-dimensional array of values
1791: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

1793:   Level: beginner

1795:   Notes:
1796:   By default the values, `v`, are row-oriented and unsorted.
1797:   See `MatSetOption()` for other options.

1799:   Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1800:   options cannot be mixed without intervening calls to the assembly
1801:   routines.

1803:   The grid coordinates are across the entire grid, not just the local portion

1805:   `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1806:   as well as in C.

1808:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1810:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1811:   or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1813:   The columns and rows in the stencil passed in MUST be contained within the
1814:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1815:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1816:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1817:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1819:   Negative indices may be passed in idxm and idxn, these rows and columns are
1820:   simply ignored. This allows easily inserting element stiffness matrices
1821:   with homogeneous Dirichlet boundary conditions that you don't want represented
1822:   in the matrix.

1824:   Inspired by the structured grid interface to the HYPRE package
1825:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1827:   Fortran Note:
1828:   `idxm` and `idxn` should be declared as
1829: $     MatStencil idxm(4,m),idxn(4,n)
1830:   and the values inserted using
1831: .vb
1832:     idxm(MatStencil_i,1) = i
1833:     idxm(MatStencil_j,1) = j
1834:     idxm(MatStencil_k,1) = k
1835:    etc
1836: .ve

1838: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1839:           `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1840:           `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1841: @*/
1842: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1843: {
1844:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1845:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1846:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1848:   PetscFunctionBegin;
1849:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1852:   PetscAssertPointer(idxm, 3);
1853:   PetscAssertPointer(idxn, 5);
1854:   PetscAssertPointer(v, 6);

1856:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1857:     jdxm = buf;
1858:     jdxn = buf + m;
1859:   } else {
1860:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1861:     jdxm = bufm;
1862:     jdxn = bufn;
1863:   }
1864:   for (i = 0; i < m; i++) {
1865:     for (j = 0; j < 3 - sdim; j++) dxm++;
1866:     tmp = *dxm++ - starts[0];
1867:     for (j = 0; j < sdim - 1; j++) {
1868:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1869:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1870:     }
1871:     dxm++;
1872:     jdxm[i] = tmp;
1873:   }
1874:   for (i = 0; i < n; i++) {
1875:     for (j = 0; j < 3 - sdim; j++) dxn++;
1876:     tmp = *dxn++ - starts[0];
1877:     for (j = 0; j < sdim - 1; j++) {
1878:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1879:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1880:     }
1881:     dxn++;
1882:     jdxn[i] = tmp;
1883:   }
1884:   PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1885:   PetscCall(PetscFree2(bufm, bufn));
1886:   PetscFunctionReturn(PETSC_SUCCESS);
1887: }

1889: /*@
1890:   MatSetStencil - Sets the grid information for setting values into a matrix via
1891:   `MatSetValuesStencil()`

1893:   Not Collective

1895:   Input Parameters:
1896: + mat    - the matrix
1897: . dim    - dimension of the grid 1, 2, or 3
1898: . dims   - number of grid points in x, y, and z direction, including ghost points on your processor
1899: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1900: - dof    - number of degrees of freedom per node

1902:   Level: beginner

1904:   Notes:
1905:   Inspired by the structured grid interface to the HYPRE package
1906:   (www.llnl.gov/CASC/hyper)

1908:   For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1909:   user.

1911: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1912:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1913: @*/
1914: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1915: {
1916:   PetscFunctionBegin;
1918:   PetscAssertPointer(dims, 3);
1919:   PetscAssertPointer(starts, 4);

1921:   mat->stencil.dim = dim + (dof > 1);
1922:   for (PetscInt i = 0; i < dim; i++) {
1923:     mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
1924:     mat->stencil.starts[i] = starts[dim - i - 1];
1925:   }
1926:   mat->stencil.dims[dim]   = dof;
1927:   mat->stencil.starts[dim] = 0;
1928:   mat->stencil.noc         = (PetscBool)(dof == 1);
1929:   PetscFunctionReturn(PETSC_SUCCESS);
1930: }

1932: /*@C
1933:   MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

1935:   Not Collective

1937:   Input Parameters:
1938: + mat  - the matrix
1939: . v    - a logically two-dimensional array of values
1940: . m    - the number of block rows
1941: . idxm - the global block indices
1942: . n    - the number of block columns
1943: . idxn - the global block indices
1944: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

1946:   Level: intermediate

1948:   Notes:
1949:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
1950:   MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

1952:   The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
1953:   NOT the total number of rows/columns; for example, if the block size is 2 and
1954:   you are passing in values for rows 2,3,4,5  then `m` would be 2 (not 4).
1955:   The values in `idxm` would be 1 2; that is the first index for each block divided by
1956:   the block size.

1958:   You must call `MatSetBlockSize()` when constructing this matrix (before
1959:   preallocating it).

1961:   By default the values, `v`, are row-oriented, so the layout of
1962:   `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.

1964:   Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
1965:   options cannot be mixed without intervening calls to the assembly
1966:   routines.

1968:   `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
1969:   as well as in C.

1971:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1972:   simply ignored. This allows easily inserting element stiffness matrices
1973:   with homogeneous Dirichlet boundary conditions that you don't want represented
1974:   in the matrix.

1976:   Each time an entry is set within a sparse matrix via `MatSetValues()`,
1977:   internal searching must be done to determine where to place the
1978:   data in the matrix storage space.  By instead inserting blocks of
1979:   entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
1980:   reduced.

1982:   Example:
1983: .vb
1984:    Suppose m=n=2 and block size(bs) = 2 The array is

1986:    1  2  | 3  4
1987:    5  6  | 7  8
1988:    - - - | - - -
1989:    9  10 | 11 12
1990:    13 14 | 15 16

1992:    v[] should be passed in like
1993:    v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

1995:   If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
1996:    v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
1997: .ve

1999: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2000: @*/
2001: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2002: {
2003:   PetscFunctionBeginHot;
2006:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2007:   PetscAssertPointer(idxm, 3);
2008:   PetscAssertPointer(idxn, 5);
2009:   MatCheckPreallocated(mat, 1);
2010:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2011:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2012:   if (PetscDefined(USE_DEBUG)) {
2013:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2014:     PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2015:   }
2016:   if (PetscDefined(USE_DEBUG)) {
2017:     PetscInt rbs, cbs, M, N, i;
2018:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2019:     PetscCall(MatGetSize(mat, &M, &N));
2020:     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);
2021:     for (i = 0; i < n; i++)
2022:       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);
2023:   }
2024:   if (mat->assembled) {
2025:     mat->was_assembled = PETSC_TRUE;
2026:     mat->assembled     = PETSC_FALSE;
2027:   }
2028:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2029:   if (mat->ops->setvaluesblocked) {
2030:     PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2031:   } else {
2032:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2033:     PetscInt i, j, bs, cbs;

2035:     PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2036:     if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2037:       iidxm = buf;
2038:       iidxn = buf + m * bs;
2039:     } else {
2040:       PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2041:       iidxm = bufr;
2042:       iidxn = bufc;
2043:     }
2044:     for (i = 0; i < m; i++) {
2045:       for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2046:     }
2047:     if (m != n || bs != cbs || idxm != idxn) {
2048:       for (i = 0; i < n; i++) {
2049:         for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2050:       }
2051:     } else iidxn = iidxm;
2052:     PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2053:     PetscCall(PetscFree2(bufr, bufc));
2054:   }
2055:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2056:   PetscFunctionReturn(PETSC_SUCCESS);
2057: }

2059: /*@C
2060:   MatGetValues - Gets a block of local values from a matrix.

2062:   Not Collective; can only return values that are owned by the give process

2064:   Input Parameters:
2065: + mat  - the matrix
2066: . v    - a logically two-dimensional array for storing the values
2067: . m    - the number of rows
2068: . idxm - the  global indices of the rows
2069: . n    - the number of columns
2070: - idxn - the global indices of the columns

2072:   Level: advanced

2074:   Notes:
2075:   The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2076:   The values, `v`, are then returned in a row-oriented format,
2077:   analogous to that used by default in `MatSetValues()`.

2079:   `MatGetValues()` uses 0-based row and column numbers in
2080:   Fortran as well as in C.

2082:   `MatGetValues()` requires that the matrix has been assembled
2083:   with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
2084:   `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2085:   without intermediate matrix assembly.

2087:   Negative row or column indices will be ignored and those locations in `v` will be
2088:   left unchanged.

2090:   For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2091:   That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2092:   from `MatGetOwnershipRange`(mat,&rstart,&rend).

2094: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2095: @*/
2096: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2097: {
2098:   PetscFunctionBegin;
2101:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2102:   PetscAssertPointer(idxm, 3);
2103:   PetscAssertPointer(idxn, 5);
2104:   PetscAssertPointer(v, 6);
2105:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2106:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2107:   MatCheckPreallocated(mat, 1);

2109:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2110:   PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2111:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2112:   PetscFunctionReturn(PETSC_SUCCESS);
2113: }

2115: /*@C
2116:   MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2117:   defined previously by `MatSetLocalToGlobalMapping()`

2119:   Not Collective

2121:   Input Parameters:
2122: + mat  - the matrix
2123: . nrow - number of rows
2124: . irow - the row local indices
2125: . ncol - number of columns
2126: - icol - the column local indices

2128:   Output Parameter:
2129: . y - a logically two-dimensional array of values

2131:   Level: advanced

2133:   Notes:
2134:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

2136:   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,
2137:   are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2138:   determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2139:   with `MatSetLocalToGlobalMapping()`.

2141:   Developer Note:
2142:   This is labelled with C so does not automatically generate Fortran stubs and interfaces
2143:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2145: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2146:           `MatSetValuesLocal()`, `MatGetValues()`
2147: @*/
2148: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2149: {
2150:   PetscFunctionBeginHot;
2153:   MatCheckPreallocated(mat, 1);
2154:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2155:   PetscAssertPointer(irow, 3);
2156:   PetscAssertPointer(icol, 5);
2157:   if (PetscDefined(USE_DEBUG)) {
2158:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2159:     PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2160:   }
2161:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2162:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2163:   if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2164:   else {
2165:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2166:     if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2167:       irowm = buf;
2168:       icolm = buf + nrow;
2169:     } else {
2170:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2171:       irowm = bufr;
2172:       icolm = bufc;
2173:     }
2174:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2175:     PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2176:     PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2177:     PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2178:     PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2179:     PetscCall(PetscFree2(bufr, bufc));
2180:   }
2181:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2182:   PetscFunctionReturn(PETSC_SUCCESS);
2183: }

2185: /*@
2186:   MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2187:   the same size. Currently, this can only be called once and creates the given matrix.

2189:   Not Collective

2191:   Input Parameters:
2192: + mat  - the matrix
2193: . nb   - the number of blocks
2194: . bs   - the number of rows (and columns) in each block
2195: . rows - a concatenation of the rows for each block
2196: - v    - a concatenation of logically two-dimensional arrays of values

2198:   Level: advanced

2200:   Notes:
2201:   `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values

2203:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

2205: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2206:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2207: @*/
2208: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2209: {
2210:   PetscFunctionBegin;
2213:   PetscAssertPointer(rows, 4);
2214:   PetscAssertPointer(v, 5);
2215:   PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

2217:   PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2218:   if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2219:   else {
2220:     for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2221:   }
2222:   PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2223:   PetscFunctionReturn(PETSC_SUCCESS);
2224: }

2226: /*@
2227:   MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2228:   the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2229:   using a local (per-processor) numbering.

2231:   Not Collective

2233:   Input Parameters:
2234: + x        - the matrix
2235: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2236: - cmapping - column mapping

2238:   Level: intermediate

2240:   Note:
2241:   If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix

2243: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2244: @*/
2245: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2246: {
2247:   PetscFunctionBegin;
2252:   if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2253:   else {
2254:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2255:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2256:   }
2257:   PetscFunctionReturn(PETSC_SUCCESS);
2258: }

2260: /*@
2261:   MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`

2263:   Not Collective

2265:   Input Parameter:
2266: . A - the matrix

2268:   Output Parameters:
2269: + rmapping - row mapping
2270: - cmapping - column mapping

2272:   Level: advanced

2274: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2275: @*/
2276: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2277: {
2278:   PetscFunctionBegin;
2281:   if (rmapping) {
2282:     PetscAssertPointer(rmapping, 2);
2283:     *rmapping = A->rmap->mapping;
2284:   }
2285:   if (cmapping) {
2286:     PetscAssertPointer(cmapping, 3);
2287:     *cmapping = A->cmap->mapping;
2288:   }
2289:   PetscFunctionReturn(PETSC_SUCCESS);
2290: }

2292: /*@
2293:   MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix

2295:   Logically Collective

2297:   Input Parameters:
2298: + A    - the matrix
2299: . rmap - row layout
2300: - cmap - column layout

2302:   Level: advanced

2304:   Note:
2305:   The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.

2307: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2308: @*/
2309: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2310: {
2311:   PetscFunctionBegin;
2313:   PetscCall(PetscLayoutReference(rmap, &A->rmap));
2314:   PetscCall(PetscLayoutReference(cmap, &A->cmap));
2315:   PetscFunctionReturn(PETSC_SUCCESS);
2316: }

2318: /*@
2319:   MatGetLayouts - Gets the `PetscLayout` objects for rows and columns

2321:   Not Collective

2323:   Input Parameter:
2324: . A - the matrix

2326:   Output Parameters:
2327: + rmap - row layout
2328: - cmap - column layout

2330:   Level: advanced

2332: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2333: @*/
2334: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2335: {
2336:   PetscFunctionBegin;
2339:   if (rmap) {
2340:     PetscAssertPointer(rmap, 2);
2341:     *rmap = A->rmap;
2342:   }
2343:   if (cmap) {
2344:     PetscAssertPointer(cmap, 3);
2345:     *cmap = A->cmap;
2346:   }
2347:   PetscFunctionReturn(PETSC_SUCCESS);
2348: }

2350: /*@C
2351:   MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2352:   using a local numbering of the rows and columns.

2354:   Not Collective

2356:   Input Parameters:
2357: + mat  - the matrix
2358: . nrow - number of rows
2359: . irow - the row local indices
2360: . ncol - number of columns
2361: . icol - the column local indices
2362: . y    - a logically two-dimensional array of values
2363: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2365:   Level: intermediate

2367:   Notes:
2368:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

2370:   Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2371:   options cannot be mixed without intervening calls to the assembly
2372:   routines.

2374:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2375:   MUST be called after all calls to `MatSetValuesLocal()` have been completed.

2377:   Developer Note:
2378:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2379:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2381: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2382:           `MatGetValuesLocal()`
2383: @*/
2384: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2385: {
2386:   PetscFunctionBeginHot;
2389:   MatCheckPreallocated(mat, 1);
2390:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2391:   PetscAssertPointer(irow, 3);
2392:   PetscAssertPointer(icol, 5);
2393:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2394:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2395:   if (PetscDefined(USE_DEBUG)) {
2396:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2397:     PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2398:   }

2400:   if (mat->assembled) {
2401:     mat->was_assembled = PETSC_TRUE;
2402:     mat->assembled     = PETSC_FALSE;
2403:   }
2404:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2405:   if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2406:   else {
2407:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2408:     const PetscInt *irowm, *icolm;

2410:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2411:       bufr  = buf;
2412:       bufc  = buf + nrow;
2413:       irowm = bufr;
2414:       icolm = bufc;
2415:     } else {
2416:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2417:       irowm = bufr;
2418:       icolm = bufc;
2419:     }
2420:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2421:     else irowm = irow;
2422:     if (mat->cmap->mapping) {
2423:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2424:         PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2425:       } else icolm = irowm;
2426:     } else icolm = icol;
2427:     PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2428:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2429:   }
2430:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2431:   PetscFunctionReturn(PETSC_SUCCESS);
2432: }

2434: /*@C
2435:   MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2436:   using a local ordering of the nodes a block at a time.

2438:   Not Collective

2440:   Input Parameters:
2441: + mat  - the matrix
2442: . nrow - number of rows
2443: . irow - the row local indices
2444: . ncol - number of columns
2445: . icol - the column local indices
2446: . y    - a logically two-dimensional array of values
2447: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2449:   Level: intermediate

2451:   Notes:
2452:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2453:   before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

2455:   Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2456:   options cannot be mixed without intervening calls to the assembly
2457:   routines.

2459:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2460:   MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

2462:   Developer Note:
2463:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2464:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

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

2485:   if (mat->assembled) {
2486:     mat->was_assembled = PETSC_TRUE;
2487:     mat->assembled     = PETSC_FALSE;
2488:   }
2489:   if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2490:     PetscInt irbs, rbs;
2491:     PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2492:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2493:     PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2494:   }
2495:   if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2496:     PetscInt icbs, cbs;
2497:     PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2498:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2499:     PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2500:   }
2501:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2502:   if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2503:   else {
2504:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2505:     const PetscInt *irowm, *icolm;

2507:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2508:       bufr  = buf;
2509:       bufc  = buf + nrow;
2510:       irowm = bufr;
2511:       icolm = bufc;
2512:     } else {
2513:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2514:       irowm = bufr;
2515:       icolm = bufc;
2516:     }
2517:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2518:     else irowm = irow;
2519:     if (mat->cmap->mapping) {
2520:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2521:         PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2522:       } else icolm = irowm;
2523:     } else icolm = icol;
2524:     PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2525:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2526:   }
2527:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2528:   PetscFunctionReturn(PETSC_SUCCESS);
2529: }

2531: /*@
2532:   MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal

2534:   Collective

2536:   Input Parameters:
2537: + mat - the matrix
2538: - x   - the vector to be multiplied

2540:   Output Parameter:
2541: . y - the result

2543:   Level: developer

2545:   Note:
2546:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2547:   call `MatMultDiagonalBlock`(A,y,y).

2549: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2550: @*/
2551: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2552: {
2553:   PetscFunctionBegin;

2559:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2560:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2561:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2562:   MatCheckPreallocated(mat, 1);

2564:   PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2565:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2566:   PetscFunctionReturn(PETSC_SUCCESS);
2567: }

2569: /*@
2570:   MatMult - Computes the matrix-vector product, $y = Ax$.

2572:   Neighbor-wise Collective

2574:   Input Parameters:
2575: + mat - the matrix
2576: - x   - the vector to be multiplied

2578:   Output Parameter:
2579: . y - the result

2581:   Level: beginner

2583:   Note:
2584:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2585:   call `MatMult`(A,y,y).

2587: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2588: @*/
2589: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2590: {
2591:   PetscFunctionBegin;
2595:   VecCheckAssembled(x);
2597:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2598:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2599:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2600:   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);
2601:   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);
2602:   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);
2603:   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);
2604:   PetscCall(VecSetErrorIfLocked(y, 3));
2605:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2606:   MatCheckPreallocated(mat, 1);

2608:   PetscCall(VecLockReadPush(x));
2609:   PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2610:   PetscUseTypeMethod(mat, mult, x, y);
2611:   PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2612:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2613:   PetscCall(VecLockReadPop(x));
2614:   PetscFunctionReturn(PETSC_SUCCESS);
2615: }

2617: /*@
2618:   MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.

2620:   Neighbor-wise Collective

2622:   Input Parameters:
2623: + mat - the matrix
2624: - x   - the vector to be multiplied

2626:   Output Parameter:
2627: . y - the result

2629:   Level: beginner

2631:   Notes:
2632:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2633:   call `MatMultTranspose`(A,y,y).

2635:   For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2636:   use `MatMultHermitianTranspose()`

2638: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2639: @*/
2640: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2641: {
2642:   PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;

2644:   PetscFunctionBegin;
2648:   VecCheckAssembled(x);

2651:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2652:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2653:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2654:   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);
2655:   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);
2656:   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);
2657:   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);
2658:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2659:   MatCheckPreallocated(mat, 1);

2661:   if (!mat->ops->multtranspose) {
2662:     if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2663:     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);
2664:   } else op = mat->ops->multtranspose;
2665:   PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2666:   PetscCall(VecLockReadPush(x));
2667:   PetscCall((*op)(mat, x, y));
2668:   PetscCall(VecLockReadPop(x));
2669:   PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2670:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2671:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2672:   PetscFunctionReturn(PETSC_SUCCESS);
2673: }

2675: /*@
2676:   MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.

2678:   Neighbor-wise Collective

2680:   Input Parameters:
2681: + mat - the matrix
2682: - x   - the vector to be multiplied

2684:   Output Parameter:
2685: . y - the result

2687:   Level: beginner

2689:   Notes:
2690:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2691:   call `MatMultHermitianTranspose`(A,y,y).

2693:   Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2695:   For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

2697: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2698: @*/
2699: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2700: {
2701:   PetscFunctionBegin;

2707:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2708:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2709:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2710:   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);
2711:   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);
2712:   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);
2713:   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);
2714:   MatCheckPreallocated(mat, 1);

2716:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2717: #if defined(PETSC_USE_COMPLEX)
2718:   if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2719:     PetscCall(VecLockReadPush(x));
2720:     if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2721:     else PetscUseTypeMethod(mat, mult, x, y);
2722:     PetscCall(VecLockReadPop(x));
2723:   } else {
2724:     Vec w;
2725:     PetscCall(VecDuplicate(x, &w));
2726:     PetscCall(VecCopy(x, w));
2727:     PetscCall(VecConjugate(w));
2728:     PetscCall(MatMultTranspose(mat, w, y));
2729:     PetscCall(VecDestroy(&w));
2730:     PetscCall(VecConjugate(y));
2731:   }
2732:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2733: #else
2734:   PetscCall(MatMultTranspose(mat, x, y));
2735: #endif
2736:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2737:   PetscFunctionReturn(PETSC_SUCCESS);
2738: }

2740: /*@
2741:   MatMultAdd -  Computes $v3 = v2 + A * v1$.

2743:   Neighbor-wise Collective

2745:   Input Parameters:
2746: + mat - the matrix
2747: . v1  - the vector to be multiplied by `mat`
2748: - v2  - the vector to be added to the result

2750:   Output Parameter:
2751: . v3 - the result

2753:   Level: beginner

2755:   Note:
2756:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2757:   call `MatMultAdd`(A,v1,v2,v1).

2759: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2760: @*/
2761: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2762: {
2763:   PetscFunctionBegin;

2770:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2771:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2772:   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);
2773:   /* 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);
2774:      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); */
2775:   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);
2776:   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);
2777:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2778:   MatCheckPreallocated(mat, 1);

2780:   PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2781:   PetscCall(VecLockReadPush(v1));
2782:   PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2783:   PetscCall(VecLockReadPop(v1));
2784:   PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2785:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2786:   PetscFunctionReturn(PETSC_SUCCESS);
2787: }

2789: /*@
2790:   MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.

2792:   Neighbor-wise Collective

2794:   Input Parameters:
2795: + mat - the matrix
2796: . v1  - the vector to be multiplied by the transpose of the matrix
2797: - v2  - the vector to be added to the result

2799:   Output Parameter:
2800: . v3 - the result

2802:   Level: beginner

2804:   Note:
2805:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2806:   call `MatMultTransposeAdd`(A,v1,v2,v1).

2808: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2809: @*/
2810: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2811: {
2812:   PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

2814:   PetscFunctionBegin;

2821:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2822:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2823:   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);
2824:   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);
2825:   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);
2826:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2827:   PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2828:   MatCheckPreallocated(mat, 1);

2830:   PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2831:   PetscCall(VecLockReadPush(v1));
2832:   PetscCall((*op)(mat, v1, v2, v3));
2833:   PetscCall(VecLockReadPop(v1));
2834:   PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2835:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2836:   PetscFunctionReturn(PETSC_SUCCESS);
2837: }

2839: /*@
2840:   MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.

2842:   Neighbor-wise Collective

2844:   Input Parameters:
2845: + mat - the matrix
2846: . v1  - the vector to be multiplied by the Hermitian transpose
2847: - v2  - the vector to be added to the result

2849:   Output Parameter:
2850: . v3 - the result

2852:   Level: beginner

2854:   Note:
2855:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2856:   call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

2858: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2859: @*/
2860: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2861: {
2862:   PetscFunctionBegin;

2869:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2870:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2871:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2872:   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);
2873:   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);
2874:   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);
2875:   MatCheckPreallocated(mat, 1);

2877:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2878:   PetscCall(VecLockReadPush(v1));
2879:   if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2880:   else {
2881:     Vec w, z;
2882:     PetscCall(VecDuplicate(v1, &w));
2883:     PetscCall(VecCopy(v1, w));
2884:     PetscCall(VecConjugate(w));
2885:     PetscCall(VecDuplicate(v3, &z));
2886:     PetscCall(MatMultTranspose(mat, w, z));
2887:     PetscCall(VecDestroy(&w));
2888:     PetscCall(VecConjugate(z));
2889:     if (v2 != v3) {
2890:       PetscCall(VecWAXPY(v3, 1.0, v2, z));
2891:     } else {
2892:       PetscCall(VecAXPY(v3, 1.0, z));
2893:     }
2894:     PetscCall(VecDestroy(&z));
2895:   }
2896:   PetscCall(VecLockReadPop(v1));
2897:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2898:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2899:   PetscFunctionReturn(PETSC_SUCCESS);
2900: }

2902: /*@C
2903:   MatGetFactorType - gets the type of factorization a matrix is

2905:   Not Collective

2907:   Input Parameter:
2908: . mat - the matrix

2910:   Output Parameter:
2911: . 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`

2913:   Level: intermediate

2915: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2916:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2917: @*/
2918: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
2919: {
2920:   PetscFunctionBegin;
2923:   PetscAssertPointer(t, 2);
2924:   *t = mat->factortype;
2925:   PetscFunctionReturn(PETSC_SUCCESS);
2926: }

2928: /*@C
2929:   MatSetFactorType - sets the type of factorization a matrix is

2931:   Logically Collective

2933:   Input Parameters:
2934: + mat - the matrix
2935: - 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`

2937:   Level: intermediate

2939: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2940:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2941: @*/
2942: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
2943: {
2944:   PetscFunctionBegin;
2947:   mat->factortype = t;
2948:   PetscFunctionReturn(PETSC_SUCCESS);
2949: }

2951: /*@C
2952:   MatGetInfo - Returns information about matrix storage (number of
2953:   nonzeros, memory, etc.).

2955:   Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

2957:   Input Parameters:
2958: + mat  - the matrix
2959: - 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)

2961:   Output Parameter:
2962: . info - matrix information context

2964:   Options Database Key:
2965: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

2967:   Notes:
2968:   The `MatInfo` context contains a variety of matrix data, including
2969:   number of nonzeros allocated and used, number of mallocs during
2970:   matrix assembly, etc.  Additional information for factored matrices
2971:   is provided (such as the fill ratio, number of mallocs during
2972:   factorization, etc.).

2974:   Example:
2975:   See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
2976:   data within the MatInfo context.  For example,
2977: .vb
2978:       MatInfo info;
2979:       Mat     A;
2980:       double  mal, nz_a, nz_u;

2982:       MatGetInfo(A, MAT_LOCAL, &info);
2983:       mal  = info.mallocs;
2984:       nz_a = info.nz_allocated;
2985: .ve

2987:   Fortran users should declare info as a double precision
2988:   array of dimension `MAT_INFO_SIZE`, and then extract the parameters
2989:   of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
2990:   a complete list of parameter names.
2991: .vb
2992:       double  precision info(MAT_INFO_SIZE)
2993:       double  precision mal, nz_a
2994:       Mat     A
2995:       integer ierr

2997:       call MatGetInfo(A, MAT_LOCAL, info, ierr)
2998:       mal = info(MAT_INFO_MALLOCS)
2999:       nz_a = info(MAT_INFO_NZ_ALLOCATED)
3000: .ve

3002:   Level: intermediate

3004:   Developer Note:
3005:   The Fortran interface is not autogenerated as the
3006:   interface definition cannot be generated correctly [due to `MatInfo` argument]

3008: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3009: @*/
3010: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3011: {
3012:   PetscFunctionBegin;
3015:   PetscAssertPointer(info, 3);
3016:   MatCheckPreallocated(mat, 1);
3017:   PetscUseTypeMethod(mat, getinfo, flag, info);
3018:   PetscFunctionReturn(PETSC_SUCCESS);
3019: }

3021: /*
3022:    This is used by external packages where it is not easy to get the info from the actual
3023:    matrix factorization.
3024: */
3025: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3026: {
3027:   PetscFunctionBegin;
3028:   PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3029:   PetscFunctionReturn(PETSC_SUCCESS);
3030: }

3032: /*@C
3033:   MatLUFactor - Performs in-place LU factorization of matrix.

3035:   Collective

3037:   Input Parameters:
3038: + mat  - the matrix
3039: . row  - row permutation
3040: . col  - column permutation
3041: - info - options for factorization, includes
3042: .vb
3043:           fill - expected fill as ratio of original fill.
3044:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3045:                    Run with the option -info to determine an optimal value to use
3046: .ve

3048:   Level: developer

3050:   Notes:
3051:   Most users should employ the `KSP` interface for linear solvers
3052:   instead of working directly with matrix algebra routines such as this.
3053:   See, e.g., `KSPCreate()`.

3055:   This changes the state of the matrix to a factored matrix; it cannot be used
3056:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3058:   This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3059:   when not using `KSP`.

3061:   Developer Note:
3062:   The Fortran interface is not autogenerated as the
3063:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3065: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3066:           `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3067: @*/
3068: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3069: {
3070:   MatFactorInfo tinfo;

3072:   PetscFunctionBegin;
3076:   if (info) PetscAssertPointer(info, 4);
3078:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3079:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3080:   MatCheckPreallocated(mat, 1);
3081:   if (!info) {
3082:     PetscCall(MatFactorInfoInitialize(&tinfo));
3083:     info = &tinfo;
3084:   }

3086:   PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3087:   PetscUseTypeMethod(mat, lufactor, row, col, info);
3088:   PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3089:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3090:   PetscFunctionReturn(PETSC_SUCCESS);
3091: }

3093: /*@C
3094:   MatILUFactor - Performs in-place ILU factorization of matrix.

3096:   Collective

3098:   Input Parameters:
3099: + mat  - the matrix
3100: . row  - row permutation
3101: . col  - column permutation
3102: - info - structure containing
3103: .vb
3104:       levels - number of levels of fill.
3105:       expected fill - as ratio of original fill.
3106:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3107:                 missing diagonal entries)
3108: .ve

3110:   Level: developer

3112:   Notes:
3113:   Most users should employ the `KSP` interface for linear solvers
3114:   instead of working directly with matrix algebra routines such as this.
3115:   See, e.g., `KSPCreate()`.

3117:   Probably really in-place only when level of fill is zero, otherwise allocates
3118:   new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3119:   when not using `KSP`.

3121:   Developer Note:
3122:   The Fortran interface is not autogenerated as the
3123:   interface definition cannot be generated correctly [due to MatFactorInfo]

3125: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3126: @*/
3127: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3128: {
3129:   PetscFunctionBegin;
3133:   PetscAssertPointer(info, 4);
3135:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3136:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3137:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3138:   MatCheckPreallocated(mat, 1);

3140:   PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3141:   PetscUseTypeMethod(mat, ilufactor, row, col, info);
3142:   PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3143:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3144:   PetscFunctionReturn(PETSC_SUCCESS);
3145: }

3147: /*@C
3148:   MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3149:   Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

3151:   Collective

3153:   Input Parameters:
3154: + fact - the factor matrix obtained with `MatGetFactor()`
3155: . mat  - the matrix
3156: . row  - the row permutation
3157: . col  - the column permutation
3158: - info - options for factorization, includes
3159: .vb
3160:           fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3161:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3162: .ve

3164:   Level: developer

3166:   Notes:
3167:   See [Matrix Factorization](sec_matfactor) for additional information about factorizations

3169:   Most users should employ the simplified `KSP` interface for linear solvers
3170:   instead of working directly with matrix algebra routines such as this.
3171:   See, e.g., `KSPCreate()`.

3173:   Developer Note:
3174:   The Fortran interface is not autogenerated as the
3175:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3177: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3178: @*/
3179: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3180: {
3181:   MatFactorInfo tinfo;

3183:   PetscFunctionBegin;
3188:   if (info) PetscAssertPointer(info, 5);
3191:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3192:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3193:   MatCheckPreallocated(mat, 2);
3194:   if (!info) {
3195:     PetscCall(MatFactorInfoInitialize(&tinfo));
3196:     info = &tinfo;
3197:   }

3199:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3200:   PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3201:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3202:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3203:   PetscFunctionReturn(PETSC_SUCCESS);
3204: }

3206: /*@C
3207:   MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3208:   Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

3210:   Collective

3212:   Input Parameters:
3213: + fact - the factor matrix obtained with `MatGetFactor()`
3214: . mat  - the matrix
3215: - info - options for factorization

3217:   Level: developer

3219:   Notes:
3220:   See `MatLUFactor()` for in-place factorization.  See
3221:   `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

3223:   Most users should employ the `KSP` interface for linear solvers
3224:   instead of working directly with matrix algebra routines such as this.
3225:   See, e.g., `KSPCreate()`.

3227:   Developer Note:
3228:   The Fortran interface is not autogenerated as the
3229:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3231: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3232: @*/
3233: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3234: {
3235:   MatFactorInfo tinfo;

3237:   PetscFunctionBegin;
3242:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3243:   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,
3244:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3246:   MatCheckPreallocated(mat, 2);
3247:   if (!info) {
3248:     PetscCall(MatFactorInfoInitialize(&tinfo));
3249:     info = &tinfo;
3250:   }

3252:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3253:   else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3254:   PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3255:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3256:   else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3257:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3258:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3259:   PetscFunctionReturn(PETSC_SUCCESS);
3260: }

3262: /*@C
3263:   MatCholeskyFactor - Performs in-place Cholesky factorization of a
3264:   symmetric matrix.

3266:   Collective

3268:   Input Parameters:
3269: + mat  - the matrix
3270: . perm - row and column permutations
3271: - info - expected fill as ratio of original fill

3273:   Level: developer

3275:   Notes:
3276:   See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
3277:   `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

3279:   Most users should employ the `KSP` interface for linear solvers
3280:   instead of working directly with matrix algebra routines such as this.
3281:   See, e.g., `KSPCreate()`.

3283:   Developer Note:
3284:   The Fortran interface is not autogenerated as the
3285:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3287: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3288:           `MatGetOrdering()`
3289: @*/
3290: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3291: {
3292:   MatFactorInfo tinfo;

3294:   PetscFunctionBegin;
3297:   if (info) PetscAssertPointer(info, 3);
3299:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3300:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3301:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3302:   MatCheckPreallocated(mat, 1);
3303:   if (!info) {
3304:     PetscCall(MatFactorInfoInitialize(&tinfo));
3305:     info = &tinfo;
3306:   }

3308:   PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3309:   PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3310:   PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3311:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3312:   PetscFunctionReturn(PETSC_SUCCESS);
3313: }

3315: /*@C
3316:   MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3317:   of a symmetric matrix.

3319:   Collective

3321:   Input Parameters:
3322: + fact - the factor matrix obtained with `MatGetFactor()`
3323: . mat  - the matrix
3324: . perm - row and column permutations
3325: - info - options for factorization, includes
3326: .vb
3327:           fill - expected fill as ratio of original fill.
3328:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3329:                    Run with the option -info to determine an optimal value to use
3330: .ve

3332:   Level: developer

3334:   Notes:
3335:   See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
3336:   `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

3338:   Most users should employ the `KSP` interface for linear solvers
3339:   instead of working directly with matrix algebra routines such as this.
3340:   See, e.g., `KSPCreate()`.

3342:   Developer Note:
3343:   The Fortran interface is not autogenerated as the
3344:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3346: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3347:           `MatGetOrdering()`
3348: @*/
3349: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3350: {
3351:   MatFactorInfo tinfo;

3353:   PetscFunctionBegin;
3357:   if (info) PetscAssertPointer(info, 4);
3360:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3361:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3362:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3363:   MatCheckPreallocated(mat, 2);
3364:   if (!info) {
3365:     PetscCall(MatFactorInfoInitialize(&tinfo));
3366:     info = &tinfo;
3367:   }

3369:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3370:   PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3371:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3372:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3373:   PetscFunctionReturn(PETSC_SUCCESS);
3374: }

3376: /*@C
3377:   MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3378:   of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3379:   `MatCholeskyFactorSymbolic()`.

3381:   Collective

3383:   Input Parameters:
3384: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3385: . mat  - the initial matrix that is to be factored
3386: - info - options for factorization

3388:   Level: developer

3390:   Note:
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()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3400: @*/
3401: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3402: {
3403:   MatFactorInfo tinfo;

3405:   PetscFunctionBegin;
3410:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3411:   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,
3412:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3413:   MatCheckPreallocated(mat, 2);
3414:   if (!info) {
3415:     PetscCall(MatFactorInfoInitialize(&tinfo));
3416:     info = &tinfo;
3417:   }

3419:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3420:   else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3421:   PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3422:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3423:   else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3424:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3425:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3426:   PetscFunctionReturn(PETSC_SUCCESS);
3427: }

3429: /*@
3430:   MatQRFactor - Performs in-place QR factorization of matrix.

3432:   Collective

3434:   Input Parameters:
3435: + mat  - the matrix
3436: . col  - column permutation
3437: - info - options for factorization, includes
3438: .vb
3439:           fill - expected fill as ratio of original fill.
3440:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3441:                    Run with the option -info to determine an optimal value to use
3442: .ve

3444:   Level: developer

3446:   Notes:
3447:   Most users should employ the `KSP` interface for linear solvers
3448:   instead of working directly with matrix algebra routines such as this.
3449:   See, e.g., `KSPCreate()`.

3451:   This changes the state of the matrix to a factored matrix; it cannot be used
3452:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3454:   Developer Note:
3455:   The Fortran interface is not autogenerated as the
3456:   interface definition cannot be generated correctly [due to MatFactorInfo]

3458: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3459:           `MatSetUnfactored()`
3460: @*/
3461: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3462: {
3463:   PetscFunctionBegin;
3466:   if (info) PetscAssertPointer(info, 3);
3468:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3469:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3470:   MatCheckPreallocated(mat, 1);
3471:   PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3472:   PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3473:   PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3474:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3475:   PetscFunctionReturn(PETSC_SUCCESS);
3476: }

3478: /*@
3479:   MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3480:   Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

3482:   Collective

3484:   Input Parameters:
3485: + fact - the factor matrix obtained with `MatGetFactor()`
3486: . mat  - the matrix
3487: . col  - column permutation
3488: - info - options for factorization, includes
3489: .vb
3490:           fill - expected fill as ratio of original fill.
3491:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3492:                    Run with the option -info to determine an optimal value to use
3493: .ve

3495:   Level: developer

3497:   Note:
3498:   Most users should employ the `KSP` interface for linear solvers
3499:   instead of working directly with matrix algebra routines such as this.
3500:   See, e.g., `KSPCreate()`.

3502:   Developer Note:
3503:   The Fortran interface is not autogenerated as the
3504:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3506: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3507: @*/
3508: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3509: {
3510:   MatFactorInfo tinfo;

3512:   PetscFunctionBegin;
3516:   if (info) PetscAssertPointer(info, 4);
3519:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3520:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3521:   MatCheckPreallocated(mat, 2);
3522:   if (!info) {
3523:     PetscCall(MatFactorInfoInitialize(&tinfo));
3524:     info = &tinfo;
3525:   }

3527:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3528:   PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3529:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3530:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3531:   PetscFunctionReturn(PETSC_SUCCESS);
3532: }

3534: /*@
3535:   MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3536:   Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

3538:   Collective

3540:   Input Parameters:
3541: + fact - the factor matrix obtained with `MatGetFactor()`
3542: . mat  - the matrix
3543: - info - options for factorization

3545:   Level: developer

3547:   Notes:
3548:   See `MatQRFactor()` for in-place factorization.

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

3554:   Developer Note:
3555:   The Fortran interface is not autogenerated as the
3556:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3558: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3559: @*/
3560: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3561: {
3562:   MatFactorInfo tinfo;

3564:   PetscFunctionBegin;
3569:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3570:   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,
3571:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3573:   MatCheckPreallocated(mat, 2);
3574:   if (!info) {
3575:     PetscCall(MatFactorInfoInitialize(&tinfo));
3576:     info = &tinfo;
3577:   }

3579:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3580:   else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3581:   PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3582:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3583:   else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3584:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3585:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3586:   PetscFunctionReturn(PETSC_SUCCESS);
3587: }

3589: /*@
3590:   MatSolve - Solves $A x = b$, given a factored matrix.

3592:   Neighbor-wise Collective

3594:   Input Parameters:
3595: + mat - the factored matrix
3596: - b   - the right-hand-side vector

3598:   Output Parameter:
3599: . x - the result vector

3601:   Level: developer

3603:   Notes:
3604:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3605:   call `MatSolve`(A,x,x).

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

3611: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3612: @*/
3613: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3614: {
3615:   PetscFunctionBegin;
3620:   PetscCheckSameComm(mat, 1, b, 2);
3621:   PetscCheckSameComm(mat, 1, x, 3);
3622:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3623:   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);
3624:   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);
3625:   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);
3626:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3627:   MatCheckPreallocated(mat, 1);

3629:   PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3630:   if (mat->factorerrortype) {
3631:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3632:     PetscCall(VecSetInf(x));
3633:   } else PetscUseTypeMethod(mat, solve, b, x);
3634:   PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3635:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3636:   PetscFunctionReturn(PETSC_SUCCESS);
3637: }

3639: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3640: {
3641:   Vec      b, x;
3642:   PetscInt N, i;
3643:   PetscErrorCode (*f)(Mat, Vec, Vec);
3644:   PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

3646:   PetscFunctionBegin;
3647:   if (A->factorerrortype) {
3648:     PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3649:     PetscCall(MatSetInf(X));
3650:     PetscFunctionReturn(PETSC_SUCCESS);
3651:   }
3652:   f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3653:   PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3654:   PetscCall(MatBoundToCPU(A, &Abound));
3655:   if (!Abound) {
3656:     PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3657:     PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3658:   }
3659: #if PetscDefined(HAVE_CUDA)
3660:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3661:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3662: #elif PetscDefined(HAVE_HIP)
3663:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3664:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3665: #endif
3666:   PetscCall(MatGetSize(B, NULL, &N));
3667:   for (i = 0; i < N; i++) {
3668:     PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3669:     PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3670:     PetscCall((*f)(A, b, x));
3671:     PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3672:     PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3673:   }
3674:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3675:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3676:   PetscFunctionReturn(PETSC_SUCCESS);
3677: }

3679: /*@
3680:   MatMatSolve - Solves $A X = B$, given a factored matrix.

3682:   Neighbor-wise Collective

3684:   Input Parameters:
3685: + A - the factored matrix
3686: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

3688:   Output Parameter:
3689: . X - the result matrix (dense matrix)

3691:   Level: developer

3693:   Note:
3694:   If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3695:   otherwise, `B` and `X` cannot be the same.

3697: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3698: @*/
3699: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3700: {
3701:   PetscFunctionBegin;
3706:   PetscCheckSameComm(A, 1, B, 2);
3707:   PetscCheckSameComm(A, 1, X, 3);
3708:   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);
3709:   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);
3710:   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");
3711:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3712:   MatCheckPreallocated(A, 1);

3714:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3715:   if (!A->ops->matsolve) {
3716:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3717:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3718:   } else PetscUseTypeMethod(A, matsolve, B, X);
3719:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3720:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3721:   PetscFunctionReturn(PETSC_SUCCESS);
3722: }

3724: /*@
3725:   MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.

3727:   Neighbor-wise Collective

3729:   Input Parameters:
3730: + A - the factored matrix
3731: - B - the right-hand-side matrix  (`MATDENSE` matrix)

3733:   Output Parameter:
3734: . X - the result matrix (dense matrix)

3736:   Level: developer

3738:   Note:
3739:   The matrices `B` and `X` cannot be the same.  I.e., one cannot
3740:   call `MatMatSolveTranspose`(A,X,X).

3742: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3743: @*/
3744: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3745: {
3746:   PetscFunctionBegin;
3751:   PetscCheckSameComm(A, 1, B, 2);
3752:   PetscCheckSameComm(A, 1, X, 3);
3753:   PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3754:   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);
3755:   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);
3756:   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);
3757:   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");
3758:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3759:   MatCheckPreallocated(A, 1);

3761:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3762:   if (!A->ops->matsolvetranspose) {
3763:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3764:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3765:   } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3766:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3767:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3768:   PetscFunctionReturn(PETSC_SUCCESS);
3769: }

3771: /*@
3772:   MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.

3774:   Neighbor-wise Collective

3776:   Input Parameters:
3777: + A  - the factored matrix
3778: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`

3780:   Output Parameter:
3781: . X - the result matrix (dense matrix)

3783:   Level: developer

3785:   Note:
3786:   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
3787:   format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

3789: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3790: @*/
3791: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3792: {
3793:   PetscFunctionBegin;
3798:   PetscCheckSameComm(A, 1, Bt, 2);
3799:   PetscCheckSameComm(A, 1, X, 3);

3801:   PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3802:   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);
3803:   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);
3804:   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");
3805:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3806:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3807:   MatCheckPreallocated(A, 1);

3809:   PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3810:   PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3811:   PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3812:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3813:   PetscFunctionReturn(PETSC_SUCCESS);
3814: }

3816: /*@
3817:   MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3818:   $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,

3820:   Neighbor-wise Collective

3822:   Input Parameters:
3823: + mat - the factored matrix
3824: - b   - the right-hand-side vector

3826:   Output Parameter:
3827: . x - the result vector

3829:   Level: developer

3831:   Notes:
3832:   `MatSolve()` should be used for most applications, as it performs
3833:   a forward solve followed by a backward solve.

3835:   The vectors `b` and `x` cannot be the same,  i.e., one cannot
3836:   call `MatForwardSolve`(A,x,x).

3838:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3839:   the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3840:   `MatForwardSolve()` solves $U^T*D y = b$, and
3841:   `MatBackwardSolve()` solves $U x = y$.
3842:   Thus they do not provide a symmetric preconditioner.

3844: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3845: @*/
3846: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3847: {
3848:   PetscFunctionBegin;
3853:   PetscCheckSameComm(mat, 1, b, 2);
3854:   PetscCheckSameComm(mat, 1, x, 3);
3855:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3856:   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);
3857:   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);
3858:   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);
3859:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3860:   MatCheckPreallocated(mat, 1);

3862:   PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3863:   PetscUseTypeMethod(mat, forwardsolve, b, x);
3864:   PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3865:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3866:   PetscFunctionReturn(PETSC_SUCCESS);
3867: }

3869: /*@
3870:   MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3871:   $D^(1/2) U x = b$, 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 `MatBackwardSolve`(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`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3898: @*/
3899: PetscErrorCode MatBackwardSolve(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_BackwardSolve, mat, b, x, 0));
3916:   PetscUseTypeMethod(mat, backwardsolve, b, x);
3917:   PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3918:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3919:   PetscFunctionReturn(PETSC_SUCCESS);
3920: }

3922: /*@
3923:   MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.

3925:   Neighbor-wise Collective

3927:   Input Parameters:
3928: + mat - the factored matrix
3929: . b   - the right-hand-side vector
3930: - y   - the vector to be added to

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

3935:   Level: developer

3937:   Note:
3938:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3939:   call `MatSolveAdd`(A,x,y,x).

3941: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3942: @*/
3943: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
3944: {
3945:   PetscScalar one = 1.0;
3946:   Vec         tmp;

3948:   PetscFunctionBegin;
3954:   PetscCheckSameComm(mat, 1, b, 2);
3955:   PetscCheckSameComm(mat, 1, y, 3);
3956:   PetscCheckSameComm(mat, 1, x, 4);
3957:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3958:   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);
3959:   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);
3960:   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);
3961:   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);
3962:   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);
3963:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3964:   MatCheckPreallocated(mat, 1);

3966:   PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
3967:   if (mat->factorerrortype) {
3968:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3969:     PetscCall(VecSetInf(x));
3970:   } else if (mat->ops->solveadd) {
3971:     PetscUseTypeMethod(mat, solveadd, b, y, x);
3972:   } else {
3973:     /* do the solve then the add manually */
3974:     if (x != y) {
3975:       PetscCall(MatSolve(mat, b, x));
3976:       PetscCall(VecAXPY(x, one, y));
3977:     } else {
3978:       PetscCall(VecDuplicate(x, &tmp));
3979:       PetscCall(VecCopy(x, tmp));
3980:       PetscCall(MatSolve(mat, b, x));
3981:       PetscCall(VecAXPY(x, one, tmp));
3982:       PetscCall(VecDestroy(&tmp));
3983:     }
3984:   }
3985:   PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
3986:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3987:   PetscFunctionReturn(PETSC_SUCCESS);
3988: }

3990: /*@
3991:   MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.

3993:   Neighbor-wise Collective

3995:   Input Parameters:
3996: + mat - the factored matrix
3997: - b   - the right-hand-side vector

3999:   Output Parameter:
4000: . x - the result vector

4002:   Level: developer

4004:   Notes:
4005:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4006:   call `MatSolveTranspose`(A,x,x).

4008:   Most users should employ the `KSP` interface for linear solvers
4009:   instead of working directly with matrix algebra routines such as this.
4010:   See, e.g., `KSPCreate()`.

4012: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4013: @*/
4014: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4015: {
4016:   PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

4018:   PetscFunctionBegin;
4023:   PetscCheckSameComm(mat, 1, b, 2);
4024:   PetscCheckSameComm(mat, 1, x, 3);
4025:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4026:   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);
4027:   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);
4028:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4029:   MatCheckPreallocated(mat, 1);
4030:   PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4031:   if (mat->factorerrortype) {
4032:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4033:     PetscCall(VecSetInf(x));
4034:   } else {
4035:     PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4036:     PetscCall((*f)(mat, b, x));
4037:   }
4038:   PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4039:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4040:   PetscFunctionReturn(PETSC_SUCCESS);
4041: }

4043: /*@
4044:   MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4045:   factored matrix.

4047:   Neighbor-wise Collective

4049:   Input Parameters:
4050: + mat - the factored matrix
4051: . b   - the right-hand-side vector
4052: - y   - the vector to be added to

4054:   Output Parameter:
4055: . x - the result vector

4057:   Level: developer

4059:   Note:
4060:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4061:   call `MatSolveTransposeAdd`(A,x,y,x).

4063: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4064: @*/
4065: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4066: {
4067:   PetscScalar one = 1.0;
4068:   Vec         tmp;
4069:   PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

4071:   PetscFunctionBegin;
4077:   PetscCheckSameComm(mat, 1, b, 2);
4078:   PetscCheckSameComm(mat, 1, y, 3);
4079:   PetscCheckSameComm(mat, 1, x, 4);
4080:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4081:   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);
4082:   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);
4083:   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);
4084:   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);
4085:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4086:   MatCheckPreallocated(mat, 1);

4088:   PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4089:   if (mat->factorerrortype) {
4090:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4091:     PetscCall(VecSetInf(x));
4092:   } else if (f) {
4093:     PetscCall((*f)(mat, b, y, x));
4094:   } else {
4095:     /* do the solve then the add manually */
4096:     if (x != y) {
4097:       PetscCall(MatSolveTranspose(mat, b, x));
4098:       PetscCall(VecAXPY(x, one, y));
4099:     } else {
4100:       PetscCall(VecDuplicate(x, &tmp));
4101:       PetscCall(VecCopy(x, tmp));
4102:       PetscCall(MatSolveTranspose(mat, b, x));
4103:       PetscCall(VecAXPY(x, one, tmp));
4104:       PetscCall(VecDestroy(&tmp));
4105:     }
4106:   }
4107:   PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4108:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4109:   PetscFunctionReturn(PETSC_SUCCESS);
4110: }

4112: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4113: /*@
4114:   MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

4116:   Neighbor-wise Collective

4118:   Input Parameters:
4119: + mat   - the matrix
4120: . b     - the right-hand side
4121: . omega - the relaxation factor
4122: . flag  - flag indicating the type of SOR (see below)
4123: . shift - diagonal shift
4124: . its   - the number of iterations
4125: - lits  - the number of local iterations

4127:   Output Parameter:
4128: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

4130:   SOR Flags:
4131: +     `SOR_FORWARD_SWEEP` - forward SOR
4132: .     `SOR_BACKWARD_SWEEP` - backward SOR
4133: .     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4134: .     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4135: .     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4136: .     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4137: .     `SOR_EISENSTAT` - SOR with Eisenstat trick
4138: .     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4139:   upper/lower triangular part of matrix to
4140:   vector (with omega)
4141: -     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

4143:   Level: developer

4145:   Notes:
4146:   `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4147:   `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4148:   on each processor.

4150:   Application programmers will not generally use `MatSOR()` directly,
4151:   but instead will employ the `KSP`/`PC` interface.

4153:   For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

4155:   Most users should employ the `KSP` interface for linear solvers
4156:   instead of working directly with matrix algebra routines such as this.
4157:   See, e.g., `KSPCreate()`.

4159:   Vectors `x` and `b` CANNOT be the same

4161:   The flags are implemented as bitwise inclusive or operations.
4162:   For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4163:   to specify a zero initial guess for SSOR.

4165:   Developer Note:
4166:   We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

4168: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4169: @*/
4170: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4171: {
4172:   PetscFunctionBegin;
4177:   PetscCheckSameComm(mat, 1, b, 2);
4178:   PetscCheckSameComm(mat, 1, x, 8);
4179:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4180:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4181:   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);
4182:   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);
4183:   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);
4184:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4185:   PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4186:   PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

4188:   MatCheckPreallocated(mat, 1);
4189:   PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4190:   PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4191:   PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4192:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4193:   PetscFunctionReturn(PETSC_SUCCESS);
4194: }

4196: /*
4197:       Default matrix copy routine.
4198: */
4199: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4200: {
4201:   PetscInt           i, rstart = 0, rend = 0, nz;
4202:   const PetscInt    *cwork;
4203:   const PetscScalar *vwork;

4205:   PetscFunctionBegin;
4206:   if (B->assembled) PetscCall(MatZeroEntries(B));
4207:   if (str == SAME_NONZERO_PATTERN) {
4208:     PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4209:     for (i = rstart; i < rend; i++) {
4210:       PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4211:       PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4212:       PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4213:     }
4214:   } else {
4215:     PetscCall(MatAYPX(B, 0.0, A, str));
4216:   }
4217:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4218:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4219:   PetscFunctionReturn(PETSC_SUCCESS);
4220: }

4222: /*@
4223:   MatCopy - Copies a matrix to another matrix.

4225:   Collective

4227:   Input Parameters:
4228: + A   - the matrix
4229: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

4231:   Output Parameter:
4232: . B - where the copy is put

4234:   Level: intermediate

4236:   Notes:
4237:   If you use `SAME_NONZERO_PATTERN` then the two matrices must have the same nonzero pattern or the routine will crash.

4239:   `MatCopy()` copies the matrix entries of a matrix to another existing
4240:   matrix (after first zeroing the second matrix).  A related routine is
4241:   `MatConvert()`, which first creates a new matrix and then copies the data.

4243: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4244: @*/
4245: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4246: {
4247:   PetscInt i;

4249:   PetscFunctionBegin;
4254:   PetscCheckSameComm(A, 1, B, 2);
4255:   MatCheckPreallocated(B, 2);
4256:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4257:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4258:   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,
4259:              A->cmap->N, B->cmap->N);
4260:   MatCheckPreallocated(A, 1);
4261:   if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

4263:   PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4264:   if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4265:   else PetscCall(MatCopy_Basic(A, B, str));

4267:   B->stencil.dim = A->stencil.dim;
4268:   B->stencil.noc = A->stencil.noc;
4269:   for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4270:     B->stencil.dims[i]   = A->stencil.dims[i];
4271:     B->stencil.starts[i] = A->stencil.starts[i];
4272:   }

4274:   PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4275:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4276:   PetscFunctionReturn(PETSC_SUCCESS);
4277: }

4279: /*@C
4280:   MatConvert - Converts a matrix to another matrix, either of the same
4281:   or different type.

4283:   Collective

4285:   Input Parameters:
4286: + mat     - the matrix
4287: . newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
4288:    same type as the original matrix.
4289: - reuse   - denotes if the destination matrix is to be created or reused.
4290:    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
4291:    `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).

4293:   Output Parameter:
4294: . M - pointer to place new matrix

4296:   Level: intermediate

4298:   Notes:
4299:   `MatConvert()` first creates a new matrix and then copies the data from
4300:   the first matrix.  A related routine is `MatCopy()`, which copies the matrix
4301:   entries of one matrix to another already existing matrix context.

4303:   Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4304:   the MPI communicator of the generated matrix is always the same as the communicator
4305:   of the input matrix.

4307: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4308: @*/
4309: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4310: {
4311:   PetscBool  sametype, issame, flg;
4312:   PetscBool3 issymmetric, ishermitian;
4313:   char       convname[256], mtype[256];
4314:   Mat        B;

4316:   PetscFunctionBegin;
4319:   PetscAssertPointer(M, 4);
4320:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4321:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4322:   MatCheckPreallocated(mat, 1);

4324:   PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4325:   if (flg) newtype = mtype;

4327:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4328:   PetscCall(PetscStrcmp(newtype, "same", &issame));
4329:   PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4330:   if (reuse == MAT_REUSE_MATRIX) {
4332:     PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4333:   }

4335:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4336:     PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4337:     PetscFunctionReturn(PETSC_SUCCESS);
4338:   }

4340:   /* Cache Mat options because some converters use MatHeaderReplace  */
4341:   issymmetric = mat->symmetric;
4342:   ishermitian = mat->hermitian;

4344:   if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4345:     PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4346:     PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4347:   } else {
4348:     PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4349:     const char *prefix[3]                                 = {"seq", "mpi", ""};
4350:     PetscInt    i;
4351:     /*
4352:        Order of precedence:
4353:        0) See if newtype is a superclass of the current matrix.
4354:        1) See if a specialized converter is known to the current matrix.
4355:        2) See if a specialized converter is known to the desired matrix class.
4356:        3) See if a good general converter is registered for the desired class
4357:           (as of 6/27/03 only MATMPIADJ falls into this category).
4358:        4) See if a good general converter is known for the current matrix.
4359:        5) Use a really basic converter.
4360:     */

4362:     /* 0) See if newtype is a superclass of the current matrix.
4363:           i.e mat is mpiaij and newtype is aij */
4364:     for (i = 0; i < 2; i++) {
4365:       PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4366:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4367:       PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4368:       PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4369:       if (flg) {
4370:         if (reuse == MAT_INPLACE_MATRIX) {
4371:           PetscCall(PetscInfo(mat, "Early return\n"));
4372:           PetscFunctionReturn(PETSC_SUCCESS);
4373:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4374:           PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4375:           PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4376:           PetscFunctionReturn(PETSC_SUCCESS);
4377:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4378:           PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4379:           PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4380:           PetscFunctionReturn(PETSC_SUCCESS);
4381:         }
4382:       }
4383:     }
4384:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4385:     for (i = 0; i < 3; i++) {
4386:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4387:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4388:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4389:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4390:       PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4391:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4392:       PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4393:       PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4394:       if (conv) goto foundconv;
4395:     }

4397:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4398:     PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4399:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4400:     PetscCall(MatSetType(B, newtype));
4401:     for (i = 0; i < 3; i++) {
4402:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4403:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4404:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4405:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4406:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4407:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4408:       PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4409:       PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4410:       if (conv) {
4411:         PetscCall(MatDestroy(&B));
4412:         goto foundconv;
4413:       }
4414:     }

4416:     /* 3) See if a good general converter is registered for the desired class */
4417:     conv = B->ops->convertfrom;
4418:     PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4419:     PetscCall(MatDestroy(&B));
4420:     if (conv) goto foundconv;

4422:     /* 4) See if a good general converter is known for the current matrix */
4423:     if (mat->ops->convert) conv = mat->ops->convert;
4424:     PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4425:     if (conv) goto foundconv;

4427:     /* 5) Use a really basic converter. */
4428:     PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4429:     conv = MatConvert_Basic;

4431:   foundconv:
4432:     PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4433:     PetscCall((*conv)(mat, newtype, reuse, M));
4434:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4435:       /* the block sizes must be same if the mappings are copied over */
4436:       (*M)->rmap->bs = mat->rmap->bs;
4437:       (*M)->cmap->bs = mat->cmap->bs;
4438:       PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4439:       PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4440:       (*M)->rmap->mapping = mat->rmap->mapping;
4441:       (*M)->cmap->mapping = mat->cmap->mapping;
4442:     }
4443:     (*M)->stencil.dim = mat->stencil.dim;
4444:     (*M)->stencil.noc = mat->stencil.noc;
4445:     for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4446:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4447:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4448:     }
4449:     PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4450:   }
4451:   PetscCall(PetscObjectStateIncrease((PetscObject)*M));

4453:   /* Copy Mat options */
4454:   if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4455:   else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4456:   if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4457:   else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4458:   PetscFunctionReturn(PETSC_SUCCESS);
4459: }

4461: /*@C
4462:   MatFactorGetSolverType - Returns name of the package providing the factorization routines

4464:   Not Collective

4466:   Input Parameter:
4467: . mat - the matrix, must be a factored matrix

4469:   Output Parameter:
4470: . type - the string name of the package (do not free this string)

4472:   Level: intermediate

4474:   Fortran Note:
4475:   Pass in an empty string and the package name will be copied into it. Make sure the string is long enough.

4477: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4478: @*/
4479: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4480: {
4481:   PetscErrorCode (*conv)(Mat, MatSolverType *);

4483:   PetscFunctionBegin;
4486:   PetscAssertPointer(type, 2);
4487:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4488:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4489:   if (conv) PetscCall((*conv)(mat, type));
4490:   else *type = MATSOLVERPETSC;
4491:   PetscFunctionReturn(PETSC_SUCCESS);
4492: }

4494: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4495: struct _MatSolverTypeForSpecifcType {
4496:   MatType mtype;
4497:   /* no entry for MAT_FACTOR_NONE */
4498:   PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4499:   MatSolverTypeForSpecifcType next;
4500: };

4502: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4503: struct _MatSolverTypeHolder {
4504:   char                       *name;
4505:   MatSolverTypeForSpecifcType handlers;
4506:   MatSolverTypeHolder         next;
4507: };

4509: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4511: /*@C
4512:   MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

4514:   Input Parameters:
4515: + package      - name of the package, for example petsc or superlu
4516: . mtype        - the matrix type that works with this package
4517: . ftype        - the type of factorization supported by the package
4518: - createfactor - routine that will create the factored matrix ready to be used

4520:   Level: developer

4522: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4523:   `MatGetFactor()`
4524: @*/
4525: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4526: {
4527:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
4528:   PetscBool                   flg;
4529:   MatSolverTypeForSpecifcType inext, iprev = NULL;

4531:   PetscFunctionBegin;
4532:   PetscCall(MatInitializePackage());
4533:   if (!next) {
4534:     PetscCall(PetscNew(&MatSolverTypeHolders));
4535:     PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4536:     PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4537:     PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4538:     MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4539:     PetscFunctionReturn(PETSC_SUCCESS);
4540:   }
4541:   while (next) {
4542:     PetscCall(PetscStrcasecmp(package, next->name, &flg));
4543:     if (flg) {
4544:       PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4545:       inext = next->handlers;
4546:       while (inext) {
4547:         PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4548:         if (flg) {
4549:           inext->createfactor[(int)ftype - 1] = createfactor;
4550:           PetscFunctionReturn(PETSC_SUCCESS);
4551:         }
4552:         iprev = inext;
4553:         inext = inext->next;
4554:       }
4555:       PetscCall(PetscNew(&iprev->next));
4556:       PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4557:       iprev->next->createfactor[(int)ftype - 1] = createfactor;
4558:       PetscFunctionReturn(PETSC_SUCCESS);
4559:     }
4560:     prev = next;
4561:     next = next->next;
4562:   }
4563:   PetscCall(PetscNew(&prev->next));
4564:   PetscCall(PetscStrallocpy(package, &prev->next->name));
4565:   PetscCall(PetscNew(&prev->next->handlers));
4566:   PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4567:   prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4568:   PetscFunctionReturn(PETSC_SUCCESS);
4569: }

4571: /*@C
4572:   MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

4574:   Input Parameters:
4575: + 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
4576: . ftype - the type of factorization supported by the type
4577: - mtype - the matrix type that works with this type

4579:   Output Parameters:
4580: + foundtype    - `PETSC_TRUE` if the type was registered
4581: . foundmtype   - `PETSC_TRUE` if the type supports the requested mtype
4582: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

4584:   Calling sequence of `createfactor`:
4585: + A     - the matrix providing the factor matrix
4586: . mtype - the `MatType` of the factor requested
4587: - B     - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`

4589:   Level: developer

4591:   Note:
4592:   When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4593:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4594:   For example if one configuration had --download-mumps while a different one had --download-superlu_dist.

4596: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4597:           `MatInitializePackage()`
4598: @*/
4599: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType mtype, Mat *B))
4600: {
4601:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4602:   PetscBool                   flg;
4603:   MatSolverTypeForSpecifcType inext;

4605:   PetscFunctionBegin;
4606:   if (foundtype) *foundtype = PETSC_FALSE;
4607:   if (foundmtype) *foundmtype = PETSC_FALSE;
4608:   if (createfactor) *createfactor = NULL;

4610:   if (type) {
4611:     while (next) {
4612:       PetscCall(PetscStrcasecmp(type, next->name, &flg));
4613:       if (flg) {
4614:         if (foundtype) *foundtype = PETSC_TRUE;
4615:         inext = next->handlers;
4616:         while (inext) {
4617:           PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4618:           if (flg) {
4619:             if (foundmtype) *foundmtype = PETSC_TRUE;
4620:             if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4621:             PetscFunctionReturn(PETSC_SUCCESS);
4622:           }
4623:           inext = inext->next;
4624:         }
4625:       }
4626:       next = next->next;
4627:     }
4628:   } else {
4629:     while (next) {
4630:       inext = next->handlers;
4631:       while (inext) {
4632:         PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4633:         if (flg && inext->createfactor[(int)ftype - 1]) {
4634:           if (foundtype) *foundtype = PETSC_TRUE;
4635:           if (foundmtype) *foundmtype = PETSC_TRUE;
4636:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4637:           PetscFunctionReturn(PETSC_SUCCESS);
4638:         }
4639:         inext = inext->next;
4640:       }
4641:       next = next->next;
4642:     }
4643:     /* try with base classes inext->mtype */
4644:     next = MatSolverTypeHolders;
4645:     while (next) {
4646:       inext = next->handlers;
4647:       while (inext) {
4648:         PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4649:         if (flg && inext->createfactor[(int)ftype - 1]) {
4650:           if (foundtype) *foundtype = PETSC_TRUE;
4651:           if (foundmtype) *foundmtype = PETSC_TRUE;
4652:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4653:           PetscFunctionReturn(PETSC_SUCCESS);
4654:         }
4655:         inext = inext->next;
4656:       }
4657:       next = next->next;
4658:     }
4659:   }
4660:   PetscFunctionReturn(PETSC_SUCCESS);
4661: }

4663: PetscErrorCode MatSolverTypeDestroy(void)
4664: {
4665:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
4666:   MatSolverTypeForSpecifcType inext, iprev;

4668:   PetscFunctionBegin;
4669:   while (next) {
4670:     PetscCall(PetscFree(next->name));
4671:     inext = next->handlers;
4672:     while (inext) {
4673:       PetscCall(PetscFree(inext->mtype));
4674:       iprev = inext;
4675:       inext = inext->next;
4676:       PetscCall(PetscFree(iprev));
4677:     }
4678:     prev = next;
4679:     next = next->next;
4680:     PetscCall(PetscFree(prev));
4681:   }
4682:   MatSolverTypeHolders = NULL;
4683:   PetscFunctionReturn(PETSC_SUCCESS);
4684: }

4686: /*@C
4687:   MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

4689:   Logically Collective

4691:   Input Parameter:
4692: . mat - the matrix

4694:   Output Parameter:
4695: . flg - `PETSC_TRUE` if uses the ordering

4697:   Level: developer

4699:   Note:
4700:   Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4701:   packages do not, thus we want to skip generating the ordering when it is not needed or used.

4703: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4704: @*/
4705: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4706: {
4707:   PetscFunctionBegin;
4708:   *flg = mat->canuseordering;
4709:   PetscFunctionReturn(PETSC_SUCCESS);
4710: }

4712: /*@C
4713:   MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

4715:   Logically Collective

4717:   Input Parameters:
4718: + mat   - the matrix obtained with `MatGetFactor()`
4719: - ftype - the factorization type to be used

4721:   Output Parameter:
4722: . otype - the preferred ordering type

4724:   Level: developer

4726: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4727: @*/
4728: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4729: {
4730:   PetscFunctionBegin;
4731:   *otype = mat->preferredordering[ftype];
4732:   PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4733:   PetscFunctionReturn(PETSC_SUCCESS);
4734: }

4736: /*@C
4737:   MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()

4739:   Collective

4741:   Input Parameters:
4742: + mat   - the matrix
4743: . 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
4744:           the other criteria is returned
4745: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4747:   Output Parameter:
4748: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.

4750:   Options Database Keys:
4751: + -pc_factor_mat_solver_type <type>             - choose the type at run time. When using `KSP` solvers
4752: - -mat_factor_bind_factorization <host, device> - Where to do matrix factorization? Default is device (might consume more device memory.
4753:                                                   One can choose host to save device memory). Currently only supported with `MATSEQAIJCUSPARSE` matrices.

4755:   Level: intermediate

4757:   Notes:
4758:   The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4759:   types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.

4761:   Users usually access the factorization solvers via `KSP`

4763:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4764:   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

4766:   When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4767:   Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4768:   For example if one configuration had --download-mumps while a different one had --download-superlu_dist.

4770:   Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4771:   where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4772:   call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

4774:   Developer Note:
4775:   This should actually be called `MatCreateFactor()` since it creates a new factor object

4777: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4778:           `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4779:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4780: @*/
4781: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4782: {
4783:   PetscBool foundtype, foundmtype;
4784:   PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

4786:   PetscFunctionBegin;

4790:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4791:   MatCheckPreallocated(mat, 1);

4793:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4794:   if (!foundtype) {
4795:     if (type) {
4796:       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],
4797:               ((PetscObject)mat)->type_name, type);
4798:     } else {
4799:       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);
4800:     }
4801:   }
4802:   PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4803:   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);

4805:   PetscCall((*conv)(mat, ftype, f));
4806:   if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4807:   PetscFunctionReturn(PETSC_SUCCESS);
4808: }

4810: /*@C
4811:   MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type

4813:   Not Collective

4815:   Input Parameters:
4816: + mat   - the matrix
4817: . type  - name of solver type, for example, superlu, petsc (to use PETSc's default)
4818: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4820:   Output Parameter:
4821: . flg - PETSC_TRUE if the factorization is available

4823:   Level: intermediate

4825:   Notes:
4826:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4827:   such as pastix, superlu, mumps etc.

4829:   PETSc must have been ./configure to use the external solver, using the option --download-package

4831:   Developer Note:
4832:   This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object

4834: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4835:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4836: @*/
4837: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4838: {
4839:   PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

4841:   PetscFunctionBegin;
4843:   PetscAssertPointer(flg, 4);

4845:   *flg = PETSC_FALSE;
4846:   if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);

4848:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4849:   MatCheckPreallocated(mat, 1);

4851:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4852:   *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4853:   PetscFunctionReturn(PETSC_SUCCESS);
4854: }

4856: /*@
4857:   MatDuplicate - Duplicates a matrix including the non-zero structure.

4859:   Collective

4861:   Input Parameters:
4862: + mat - the matrix
4863: - op  - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4864:         See the manual page for `MatDuplicateOption()` for an explanation of these options.

4866:   Output Parameter:
4867: . M - pointer to place new matrix

4869:   Level: intermediate

4871:   Notes:
4872:   You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.

4874:   If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.

4876:   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.

4878:   When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4879:   is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4880:   User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.

4882: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4883: @*/
4884: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4885: {
4886:   Mat         B;
4887:   VecType     vtype;
4888:   PetscInt    i;
4889:   PetscObject dm, container_h, container_d;
4890:   void (*viewf)(void);

4892:   PetscFunctionBegin;
4895:   PetscAssertPointer(M, 3);
4896:   PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4897:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4898:   MatCheckPreallocated(mat, 1);

4900:   *M = NULL;
4901:   PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4902:   PetscUseTypeMethod(mat, duplicate, op, M);
4903:   PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4904:   B = *M;

4906:   PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4907:   if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4908:   PetscCall(MatGetVecType(mat, &vtype));
4909:   PetscCall(MatSetVecType(B, vtype));

4911:   B->stencil.dim = mat->stencil.dim;
4912:   B->stencil.noc = mat->stencil.noc;
4913:   for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4914:     B->stencil.dims[i]   = mat->stencil.dims[i];
4915:     B->stencil.starts[i] = mat->stencil.starts[i];
4916:   }

4918:   B->nooffproczerorows = mat->nooffproczerorows;
4919:   B->nooffprocentries  = mat->nooffprocentries;

4921:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4922:   if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4923:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
4924:   if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
4925:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
4926:   if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
4927:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4928:   PetscFunctionReturn(PETSC_SUCCESS);
4929: }

4931: /*@
4932:   MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`

4934:   Logically Collective

4936:   Input Parameter:
4937: . mat - the matrix

4939:   Output Parameter:
4940: . v - the diagonal of the matrix

4942:   Level: intermediate

4944:   Note:
4945:   If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
4946:   of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
4947:   is larger than `ndiag`, the values of the remaining entries are unspecified.

4949:   Currently only correct in parallel for square matrices.

4951: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
4952: @*/
4953: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
4954: {
4955:   PetscFunctionBegin;
4959:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4960:   MatCheckPreallocated(mat, 1);
4961:   if (PetscDefined(USE_DEBUG)) {
4962:     PetscInt nv, row, col, ndiag;

4964:     PetscCall(VecGetLocalSize(v, &nv));
4965:     PetscCall(MatGetLocalSize(mat, &row, &col));
4966:     ndiag = PetscMin(row, col);
4967:     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);
4968:   }

4970:   PetscUseTypeMethod(mat, getdiagonal, v);
4971:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
4972:   PetscFunctionReturn(PETSC_SUCCESS);
4973: }

4975: /*@C
4976:   MatGetRowMin - Gets the minimum value (of the real part) of each
4977:   row of the matrix

4979:   Logically Collective

4981:   Input Parameter:
4982: . mat - the matrix

4984:   Output Parameters:
4985: + v   - the vector for storing the maximums
4986: - idx - the indices of the column found for each row (optional)

4988:   Level: intermediate

4990:   Note:
4991:   The result of this call are the same as if one converted the matrix to dense format
4992:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4994:   This code is only implemented for a couple of matrix formats.

4996: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
4997:           `MatGetRowMax()`
4998: @*/
4999: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5000: {
5001:   PetscFunctionBegin;
5005:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5007:   if (!mat->cmap->N) {
5008:     PetscCall(VecSet(v, PETSC_MAX_REAL));
5009:     if (idx) {
5010:       PetscInt i, m = mat->rmap->n;
5011:       for (i = 0; i < m; i++) idx[i] = -1;
5012:     }
5013:   } else {
5014:     MatCheckPreallocated(mat, 1);
5015:   }
5016:   PetscUseTypeMethod(mat, getrowmin, v, idx);
5017:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5018:   PetscFunctionReturn(PETSC_SUCCESS);
5019: }

5021: /*@C
5022:   MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5023:   row of the matrix

5025:   Logically Collective

5027:   Input Parameter:
5028: . mat - the matrix

5030:   Output Parameters:
5031: + v   - the vector for storing the minimums
5032: - idx - the indices of the column found for each row (or `NULL` if not needed)

5034:   Level: intermediate

5036:   Notes:
5037:   if a row is completely empty or has only 0.0 values then the `idx` value for that
5038:   row is 0 (the first column).

5040:   This code is only implemented for a couple of matrix formats.

5042: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5043: @*/
5044: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5045: {
5046:   PetscFunctionBegin;
5050:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5051:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

5053:   if (!mat->cmap->N) {
5054:     PetscCall(VecSet(v, 0.0));
5055:     if (idx) {
5056:       PetscInt i, m = mat->rmap->n;
5057:       for (i = 0; i < m; i++) idx[i] = -1;
5058:     }
5059:   } else {
5060:     MatCheckPreallocated(mat, 1);
5061:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5062:     PetscUseTypeMethod(mat, getrowminabs, v, idx);
5063:   }
5064:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5065:   PetscFunctionReturn(PETSC_SUCCESS);
5066: }

5068: /*@C
5069:   MatGetRowMax - Gets the maximum value (of the real part) of each
5070:   row of the matrix

5072:   Logically Collective

5074:   Input Parameter:
5075: . mat - the matrix

5077:   Output Parameters:
5078: + v   - the vector for storing the maximums
5079: - idx - the indices of the column found for each row (optional)

5081:   Level: intermediate

5083:   Notes:
5084:   The result of this call are the same as if one converted the matrix to dense format
5085:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5087:   This code is only implemented for a couple of matrix formats.

5089: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5090: @*/
5091: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5092: {
5093:   PetscFunctionBegin;
5097:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5099:   if (!mat->cmap->N) {
5100:     PetscCall(VecSet(v, PETSC_MIN_REAL));
5101:     if (idx) {
5102:       PetscInt i, m = mat->rmap->n;
5103:       for (i = 0; i < m; i++) idx[i] = -1;
5104:     }
5105:   } else {
5106:     MatCheckPreallocated(mat, 1);
5107:     PetscUseTypeMethod(mat, getrowmax, v, idx);
5108:   }
5109:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5110:   PetscFunctionReturn(PETSC_SUCCESS);
5111: }

5113: /*@C
5114:   MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5115:   row of the matrix

5117:   Logically Collective

5119:   Input Parameter:
5120: . mat - the matrix

5122:   Output Parameters:
5123: + v   - the vector for storing the maximums
5124: - idx - the indices of the column found for each row (or `NULL` if not needed)

5126:   Level: intermediate

5128:   Notes:
5129:   if a row is completely empty or has only 0.0 values then the `idx` value for that
5130:   row is 0 (the first column).

5132:   This code is only implemented for a couple of matrix formats.

5134: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5135: @*/
5136: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5137: {
5138:   PetscFunctionBegin;
5142:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5144:   if (!mat->cmap->N) {
5145:     PetscCall(VecSet(v, 0.0));
5146:     if (idx) {
5147:       PetscInt i, m = mat->rmap->n;
5148:       for (i = 0; i < m; i++) idx[i] = -1;
5149:     }
5150:   } else {
5151:     MatCheckPreallocated(mat, 1);
5152:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5153:     PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5154:   }
5155:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5156:   PetscFunctionReturn(PETSC_SUCCESS);
5157: }

5159: /*@
5160:   MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix

5162:   Logically Collective

5164:   Input Parameter:
5165: . mat - the matrix

5167:   Output Parameter:
5168: . v - the vector for storing the sum

5170:   Level: intermediate

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

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

5184:   if (!mat->cmap->N) {
5185:     PetscCall(VecSet(v, 0.0));
5186:   } else {
5187:     MatCheckPreallocated(mat, 1);
5188:     PetscUseTypeMethod(mat, getrowsumabs, v);
5189:   }
5190:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5191:   PetscFunctionReturn(PETSC_SUCCESS);
5192: }

5194: /*@
5195:   MatGetRowSum - Gets the sum of each row of the matrix

5197:   Logically or Neighborhood Collective

5199:   Input Parameter:
5200: . mat - the matrix

5202:   Output Parameter:
5203: . v - the vector for storing the sum of rows

5205:   Level: intermediate

5207:   Note:
5208:   This code is slow since it is not currently specialized for different formats

5210: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5211: @*/
5212: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5213: {
5214:   Vec ones;

5216:   PetscFunctionBegin;
5220:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5221:   MatCheckPreallocated(mat, 1);
5222:   PetscCall(MatCreateVecs(mat, &ones, NULL));
5223:   PetscCall(VecSet(ones, 1.));
5224:   PetscCall(MatMult(mat, ones, v));
5225:   PetscCall(VecDestroy(&ones));
5226:   PetscFunctionReturn(PETSC_SUCCESS);
5227: }

5229: /*@
5230:   MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5231:   when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)

5233:   Collective

5235:   Input Parameter:
5236: . mat - the matrix to provide the transpose

5238:   Output Parameter:
5239: . 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

5241:   Level: advanced

5243:   Note:
5244:   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
5245:   routine allows bypassing that call.

5247: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5248: @*/
5249: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5250: {
5251:   PetscContainer  rB = NULL;
5252:   MatParentState *rb = NULL;

5254:   PetscFunctionBegin;
5255:   PetscCall(PetscNew(&rb));
5256:   rb->id    = ((PetscObject)mat)->id;
5257:   rb->state = 0;
5258:   PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5259:   PetscCall(PetscContainerCreate(PetscObjectComm((PetscObject)B), &rB));
5260:   PetscCall(PetscContainerSetPointer(rB, rb));
5261:   PetscCall(PetscContainerSetUserDestroy(rB, PetscContainerUserDestroyDefault));
5262:   PetscCall(PetscObjectCompose((PetscObject)B, "MatTransposeParent", (PetscObject)rB));
5263:   PetscCall(PetscObjectDereference((PetscObject)rB));
5264:   PetscFunctionReturn(PETSC_SUCCESS);
5265: }

5267: /*@
5268:   MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

5270:   Collective

5272:   Input Parameters:
5273: + mat   - the matrix to transpose
5274: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5276:   Output Parameter:
5277: . B - the transpose

5279:   Level: intermediate

5281:   Notes:
5282:   If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`

5284:   `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
5285:   transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.

5287:   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.

5289:   Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.

5291:   If mat is unchanged from the last call this function returns immediately without recomputing the result

5293:   If you only need the symbolic transpose, and not the numerical values, use `MatTransposeSymbolic()`

5295: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5296:           `MatTransposeSymbolic()`, `MatCreateTranspose()`
5297: @*/
5298: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5299: {
5300:   PetscContainer  rB = NULL;
5301:   MatParentState *rb = NULL;

5303:   PetscFunctionBegin;
5306:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5307:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5308:   PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5309:   PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5310:   MatCheckPreallocated(mat, 1);
5311:   if (reuse == MAT_REUSE_MATRIX) {
5312:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5313:     PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5314:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5315:     PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5316:     if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5317:   }

5319:   PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5320:   if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5321:     PetscUseTypeMethod(mat, transpose, reuse, B);
5322:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5323:   }
5324:   PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

5326:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5327:   if (reuse != MAT_INPLACE_MATRIX) {
5328:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5329:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5330:     rb->state        = ((PetscObject)mat)->state;
5331:     rb->nonzerostate = mat->nonzerostate;
5332:   }
5333:   PetscFunctionReturn(PETSC_SUCCESS);
5334: }

5336: /*@
5337:   MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

5339:   Collective

5341:   Input Parameter:
5342: . A - the matrix to transpose

5344:   Output Parameter:
5345: . 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
5346:       numerical portion.

5348:   Level: intermediate

5350:   Note:
5351:   This is not supported for many matrix types, use `MatTranspose()` in those cases

5353: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5354: @*/
5355: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5356: {
5357:   PetscFunctionBegin;
5360:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5361:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5362:   PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5363:   PetscUseTypeMethod(A, transposesymbolic, B);
5364:   PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

5366:   PetscCall(MatTransposeSetPrecursor(A, *B));
5367:   PetscFunctionReturn(PETSC_SUCCESS);
5368: }

5370: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5371: {
5372:   PetscContainer  rB;
5373:   MatParentState *rb;

5375:   PetscFunctionBegin;
5378:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5379:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5380:   PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5381:   PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5382:   PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5383:   PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5384:   PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5385:   PetscFunctionReturn(PETSC_SUCCESS);
5386: }

5388: /*@
5389:   MatIsTranspose - Test whether a matrix is another one's transpose,
5390:   or its own, in which case it tests symmetry.

5392:   Collective

5394:   Input Parameters:
5395: + A   - the matrix to test
5396: . B   - the matrix to test against, this can equal the first parameter
5397: - tol - tolerance, differences between entries smaller than this are counted as zero

5399:   Output Parameter:
5400: . flg - the result

5402:   Level: intermediate

5404:   Notes:
5405:   The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5406:   test involves parallel copies of the block off-diagonal parts of the matrix.

5408: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5409: @*/
5410: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5411: {
5412:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5414:   PetscFunctionBegin;
5417:   PetscAssertPointer(flg, 4);
5418:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5419:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5420:   *flg = PETSC_FALSE;
5421:   if (f && g) {
5422:     PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5423:     PetscCall((*f)(A, B, tol, flg));
5424:   } else {
5425:     MatType mattype;

5427:     PetscCall(MatGetType(f ? B : A, &mattype));
5428:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5429:   }
5430:   PetscFunctionReturn(PETSC_SUCCESS);
5431: }

5433: /*@
5434:   MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

5436:   Collective

5438:   Input Parameters:
5439: + mat   - the matrix to transpose and complex conjugate
5440: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5442:   Output Parameter:
5443: . B - the Hermitian transpose

5445:   Level: intermediate

5447: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5448: @*/
5449: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5450: {
5451:   PetscFunctionBegin;
5452:   PetscCall(MatTranspose(mat, reuse, B));
5453: #if defined(PETSC_USE_COMPLEX)
5454:   PetscCall(MatConjugate(*B));
5455: #endif
5456:   PetscFunctionReturn(PETSC_SUCCESS);
5457: }

5459: /*@
5460:   MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

5462:   Collective

5464:   Input Parameters:
5465: + A   - the matrix to test
5466: . B   - the matrix to test against, this can equal the first parameter
5467: - tol - tolerance, differences between entries smaller than this are counted as zero

5469:   Output Parameter:
5470: . flg - the result

5472:   Level: intermediate

5474:   Notes:
5475:   Only available for `MATAIJ` matrices.

5477:   The sequential algorithm
5478:   has a running time of the order of the number of nonzeros; the parallel
5479:   test involves parallel copies of the block off-diagonal parts of the matrix.

5481: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5482: @*/
5483: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5484: {
5485:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5487:   PetscFunctionBegin;
5490:   PetscAssertPointer(flg, 4);
5491:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5492:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5493:   if (f && g) {
5494:     PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5495:     PetscCall((*f)(A, B, tol, flg));
5496:   }
5497:   PetscFunctionReturn(PETSC_SUCCESS);
5498: }

5500: /*@
5501:   MatPermute - Creates a new matrix with rows and columns permuted from the
5502:   original.

5504:   Collective

5506:   Input Parameters:
5507: + mat - the matrix to permute
5508: . row - row permutation, each processor supplies only the permutation for its rows
5509: - col - column permutation, each processor supplies only the permutation for its columns

5511:   Output Parameter:
5512: . B - the permuted matrix

5514:   Level: advanced

5516:   Note:
5517:   The index sets map from row/col of permuted matrix to row/col of original matrix.
5518:   The index sets should be on the same communicator as mat and have the same local sizes.

5520:   Developer Note:
5521:   If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5522:   exploit the fact that row and col are permutations, consider implementing the
5523:   more general `MatCreateSubMatrix()` instead.

5525: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5526: @*/
5527: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5528: {
5529:   PetscFunctionBegin;
5534:   PetscAssertPointer(B, 4);
5535:   PetscCheckSameComm(mat, 1, row, 2);
5536:   if (row != col) PetscCheckSameComm(row, 2, col, 3);
5537:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5538:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5539:   PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5540:   MatCheckPreallocated(mat, 1);

5542:   if (mat->ops->permute) {
5543:     PetscUseTypeMethod(mat, permute, row, col, B);
5544:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5545:   } else {
5546:     PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5547:   }
5548:   PetscFunctionReturn(PETSC_SUCCESS);
5549: }

5551: /*@
5552:   MatEqual - Compares two matrices.

5554:   Collective

5556:   Input Parameters:
5557: + A - the first matrix
5558: - B - the second matrix

5560:   Output Parameter:
5561: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

5563:   Level: intermediate

5565: .seealso: [](ch_matrices), `Mat`
5566: @*/
5567: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5568: {
5569:   PetscFunctionBegin;
5574:   PetscAssertPointer(flg, 3);
5575:   PetscCheckSameComm(A, 1, B, 2);
5576:   MatCheckPreallocated(A, 1);
5577:   MatCheckPreallocated(B, 2);
5578:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5579:   PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5580:   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,
5581:              B->cmap->N);
5582:   if (A->ops->equal && A->ops->equal == B->ops->equal) {
5583:     PetscUseTypeMethod(A, equal, B, flg);
5584:   } else {
5585:     PetscCall(MatMultEqual(A, B, 10, flg));
5586:   }
5587:   PetscFunctionReturn(PETSC_SUCCESS);
5588: }

5590: /*@
5591:   MatDiagonalScale - Scales a matrix on the left and right by diagonal
5592:   matrices that are stored as vectors.  Either of the two scaling
5593:   matrices can be `NULL`.

5595:   Collective

5597:   Input Parameters:
5598: + mat - the matrix to be scaled
5599: . l   - the left scaling vector (or `NULL`)
5600: - r   - the right scaling vector (or `NULL`)

5602:   Level: intermediate

5604:   Note:
5605:   `MatDiagonalScale()` computes $A = LAR$, where
5606:   L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5607:   The L scales the rows of the matrix, the R scales the columns of the matrix.

5609: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5610: @*/
5611: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5612: {
5613:   PetscFunctionBegin;
5616:   if (l) {
5618:     PetscCheckSameComm(mat, 1, l, 2);
5619:   }
5620:   if (r) {
5622:     PetscCheckSameComm(mat, 1, r, 3);
5623:   }
5624:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5625:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5626:   MatCheckPreallocated(mat, 1);
5627:   if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

5629:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5630:   PetscUseTypeMethod(mat, diagonalscale, l, r);
5631:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5632:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5633:   if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5634:   PetscFunctionReturn(PETSC_SUCCESS);
5635: }

5637: /*@
5638:   MatScale - Scales all elements of a matrix by a given number.

5640:   Logically Collective

5642:   Input Parameters:
5643: + mat - the matrix to be scaled
5644: - a   - the scaling value

5646:   Level: intermediate

5648: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5649: @*/
5650: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5651: {
5652:   PetscFunctionBegin;
5655:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5656:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5658:   MatCheckPreallocated(mat, 1);

5660:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5661:   if (a != (PetscScalar)1.0) {
5662:     PetscUseTypeMethod(mat, scale, a);
5663:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5664:   }
5665:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5666:   PetscFunctionReturn(PETSC_SUCCESS);
5667: }

5669: /*@
5670:   MatNorm - Calculates various norms of a matrix.

5672:   Collective

5674:   Input Parameters:
5675: + mat  - the matrix
5676: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

5678:   Output Parameter:
5679: . nrm - the resulting norm

5681:   Level: intermediate

5683: .seealso: [](ch_matrices), `Mat`
5684: @*/
5685: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5686: {
5687:   PetscFunctionBegin;
5690:   PetscAssertPointer(nrm, 3);

5692:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5693:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5694:   MatCheckPreallocated(mat, 1);

5696:   PetscUseTypeMethod(mat, norm, type, nrm);
5697:   PetscFunctionReturn(PETSC_SUCCESS);
5698: }

5700: /*
5701:      This variable is used to prevent counting of MatAssemblyBegin() that
5702:    are called from within a MatAssemblyEnd().
5703: */
5704: static PetscInt MatAssemblyEnd_InUse = 0;
5705: /*@
5706:   MatAssemblyBegin - Begins assembling the matrix.  This routine should
5707:   be called after completing all calls to `MatSetValues()`.

5709:   Collective

5711:   Input Parameters:
5712: + mat  - the matrix
5713: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5715:   Level: beginner

5717:   Notes:
5718:   `MatSetValues()` generally caches the values that belong to other MPI processes.  The matrix is ready to
5719:   use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

5721:   Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5722:   in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5723:   using the matrix.

5725:   ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5726:   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
5727:   a global collective operation requiring all processes that share the matrix.

5729:   Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5730:   out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5731:   before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

5733: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5734: @*/
5735: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5736: {
5737:   PetscFunctionBegin;
5740:   MatCheckPreallocated(mat, 1);
5741:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5742:   if (mat->assembled) {
5743:     mat->was_assembled = PETSC_TRUE;
5744:     mat->assembled     = PETSC_FALSE;
5745:   }

5747:   if (!MatAssemblyEnd_InUse) {
5748:     PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5749:     PetscTryTypeMethod(mat, assemblybegin, type);
5750:     PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5751:   } else PetscTryTypeMethod(mat, assemblybegin, type);
5752:   PetscFunctionReturn(PETSC_SUCCESS);
5753: }

5755: /*@
5756:   MatAssembled - Indicates if a matrix has been assembled and is ready for
5757:   use; for example, in matrix-vector product.

5759:   Not Collective

5761:   Input Parameter:
5762: . mat - the matrix

5764:   Output Parameter:
5765: . assembled - `PETSC_TRUE` or `PETSC_FALSE`

5767:   Level: advanced

5769: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5770: @*/
5771: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5772: {
5773:   PetscFunctionBegin;
5775:   PetscAssertPointer(assembled, 2);
5776:   *assembled = mat->assembled;
5777:   PetscFunctionReturn(PETSC_SUCCESS);
5778: }

5780: /*@
5781:   MatAssemblyEnd - Completes assembling the matrix.  This routine should
5782:   be called after `MatAssemblyBegin()`.

5784:   Collective

5786:   Input Parameters:
5787: + mat  - the matrix
5788: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5790:   Options Database Keys:
5791: + -mat_view ::ascii_info             - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5792: . -mat_view ::ascii_info_detail      - Prints more detailed info
5793: . -mat_view                          - Prints matrix in ASCII format
5794: . -mat_view ::ascii_matlab           - Prints matrix in MATLAB format
5795: . -mat_view draw                     - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5796: . -display <name>                    - Sets display name (default is host)
5797: . -draw_pause <sec>                  - Sets number of seconds to pause after display
5798: . -mat_view socket                   - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5799: . -viewer_socket_machine <machine>   - Machine to use for socket
5800: . -viewer_socket_port <port>         - Port number to use for socket
5801: - -mat_view binary:filename[:append] - Save matrix to file in binary format

5803:   Level: beginner

5805: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5806: @*/
5807: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5808: {
5809:   static PetscInt inassm = 0;
5810:   PetscBool       flg    = PETSC_FALSE;

5812:   PetscFunctionBegin;

5816:   inassm++;
5817:   MatAssemblyEnd_InUse++;
5818:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5819:     PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5820:     PetscTryTypeMethod(mat, assemblyend, type);
5821:     PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5822:   } else PetscTryTypeMethod(mat, assemblyend, type);

5824:   /* Flush assembly is not a true assembly */
5825:   if (type != MAT_FLUSH_ASSEMBLY) {
5826:     if (mat->num_ass) {
5827:       if (!mat->symmetry_eternal) {
5828:         mat->symmetric = PETSC_BOOL3_UNKNOWN;
5829:         mat->hermitian = PETSC_BOOL3_UNKNOWN;
5830:       }
5831:       if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5832:       if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5833:     }
5834:     mat->num_ass++;
5835:     mat->assembled        = PETSC_TRUE;
5836:     mat->ass_nonzerostate = mat->nonzerostate;
5837:   }

5839:   mat->insertmode = NOT_SET_VALUES;
5840:   MatAssemblyEnd_InUse--;
5841:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5842:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5843:     PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

5845:     if (mat->checksymmetryonassembly) {
5846:       PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5847:       if (flg) {
5848:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5849:       } else {
5850:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5851:       }
5852:     }
5853:     if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5854:   }
5855:   inassm--;
5856:   PetscFunctionReturn(PETSC_SUCCESS);
5857: }

5859: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5860: /*@
5861:   MatSetOption - Sets a parameter option for a matrix. Some options
5862:   may be specific to certain storage formats.  Some options
5863:   determine how values will be inserted (or added). Sorted,
5864:   row-oriented input will generally assemble the fastest. The default
5865:   is row-oriented.

5867:   Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

5869:   Input Parameters:
5870: + mat - the matrix
5871: . op  - the option, one of those listed below (and possibly others),
5872: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5874:   Options Describing Matrix Structure:
5875: + `MAT_SPD`                         - symmetric positive definite
5876: . `MAT_SYMMETRIC`                   - symmetric in terms of both structure and value
5877: . `MAT_HERMITIAN`                   - transpose is the complex conjugation
5878: . `MAT_STRUCTURALLY_SYMMETRIC`      - symmetric nonzero structure
5879: . `MAT_SYMMETRY_ETERNAL`            - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5880: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5881: . `MAT_SPD_ETERNAL`                 - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

5883:    These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5884:    do not need to be computed (usually at a high cost)

5886:    Options For Use with `MatSetValues()`:
5887:    Insert a logically dense subblock, which can be
5888: . `MAT_ROW_ORIENTED`                - row-oriented (default)

5890:    These options reflect the data you pass in with `MatSetValues()`; it has
5891:    nothing to do with how the data is stored internally in the matrix
5892:    data structure.

5894:    When (re)assembling a matrix, we can restrict the input for
5895:    efficiency/debugging purposes.  These options include
5896: . `MAT_NEW_NONZERO_LOCATIONS`       - additional insertions will be allowed if they generate a new nonzero (slow)
5897: . `MAT_FORCE_DIAGONAL_ENTRIES`      - forces diagonal entries to be allocated
5898: . `MAT_IGNORE_OFF_PROC_ENTRIES`     - drops off-processor entries
5899: . `MAT_NEW_NONZERO_LOCATION_ERR`    - generates an error for new matrix entry
5900: . `MAT_USE_HASH_TABLE`              - uses a hash table to speed up matrix assembly
5901: . `MAT_NO_OFF_PROC_ENTRIES`         - you know each process will only set values for its own rows, will generate an error if
5902:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5903:         performance for very large process counts.
5904: - `MAT_SUBSET_OFF_PROC_ENTRIES`     - you know that the first assembly after setting this flag will set a superset
5905:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5906:         functions, instead sending only neighbor messages.

5908:   Level: intermediate

5910:   Notes:
5911:   Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

5913:   Some options are relevant only for particular matrix types and
5914:   are thus ignored by others.  Other options are not supported by
5915:   certain matrix types and will generate an error message if set.

5917:   If using Fortran to compute a matrix, one may need to
5918:   use the column-oriented option (or convert to the row-oriented
5919:   format).

5921:   `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5922:   that would generate a new entry in the nonzero structure is instead
5923:   ignored.  Thus, if memory has not already been allocated for this particular
5924:   data, then the insertion is ignored. For dense matrices, in which
5925:   the entire array is allocated, no entries are ever ignored.
5926:   Set after the first `MatAssemblyEnd()`. If this option is set then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

5928:   `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
5929:   that would generate a new entry in the nonzero structure instead produces
5930:   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

5932:   `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
5933:   that would generate a new entry that has not been preallocated will
5934:   instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
5935:   only.) This is a useful flag when debugging matrix memory preallocation.
5936:   If this option is set then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

5938:   `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
5939:   other processors should be dropped, rather than stashed.
5940:   This is useful if you know that the "owning" processor is also
5941:   always generating the correct matrix entries, so that PETSc need
5942:   not transfer duplicate entries generated on another processor.

5944:   `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
5945:   searches during matrix assembly. When this flag is set, the hash table
5946:   is created during the first matrix assembly. This hash table is
5947:   used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
5948:   to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
5949:   should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
5950:   supported by `MATMPIBAIJ` format only.

5952:   `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
5953:   are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`

5955:   `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
5956:   a zero location in the matrix

5958:   `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

5960:   `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
5961:   zero row routines and thus improves performance for very large process counts.

5963:   `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
5964:   part of the matrix (since they should match the upper triangular part).

5966:   `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
5967:   single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
5968:   with finite difference schemes with non-periodic boundary conditions.

5970:   Developer Note:
5971:   `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
5972:   places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
5973:   to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
5974:   not changed.

5976: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
5977: @*/
5978: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
5979: {
5980:   PetscFunctionBegin;
5982:   if (op > 0) {
5985:   }

5987:   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);

5989:   switch (op) {
5990:   case MAT_FORCE_DIAGONAL_ENTRIES:
5991:     mat->force_diagonals = flg;
5992:     PetscFunctionReturn(PETSC_SUCCESS);
5993:   case MAT_NO_OFF_PROC_ENTRIES:
5994:     mat->nooffprocentries = flg;
5995:     PetscFunctionReturn(PETSC_SUCCESS);
5996:   case MAT_SUBSET_OFF_PROC_ENTRIES:
5997:     mat->assembly_subset = flg;
5998:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
5999: #if !defined(PETSC_HAVE_MPIUNI)
6000:       PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6001: #endif
6002:       mat->stash.first_assembly_done = PETSC_FALSE;
6003:     }
6004:     PetscFunctionReturn(PETSC_SUCCESS);
6005:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6006:     mat->nooffproczerorows = flg;
6007:     PetscFunctionReturn(PETSC_SUCCESS);
6008:   case MAT_SPD:
6009:     if (flg) {
6010:       mat->spd                    = PETSC_BOOL3_TRUE;
6011:       mat->symmetric              = PETSC_BOOL3_TRUE;
6012:       mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6013:     } else {
6014:       mat->spd = PETSC_BOOL3_FALSE;
6015:     }
6016:     break;
6017:   case MAT_SYMMETRIC:
6018:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6019:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6020: #if !defined(PETSC_USE_COMPLEX)
6021:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6022: #endif
6023:     break;
6024:   case MAT_HERMITIAN:
6025:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6026:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6027: #if !defined(PETSC_USE_COMPLEX)
6028:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6029: #endif
6030:     break;
6031:   case MAT_STRUCTURALLY_SYMMETRIC:
6032:     mat->structurally_symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
6033:     break;
6034:   case MAT_SYMMETRY_ETERNAL:
6035:     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");
6036:     mat->symmetry_eternal = flg;
6037:     if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6038:     break;
6039:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6040:     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");
6041:     mat->structural_symmetry_eternal = flg;
6042:     break;
6043:   case MAT_SPD_ETERNAL:
6044:     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");
6045:     mat->spd_eternal = flg;
6046:     if (flg) {
6047:       mat->structural_symmetry_eternal = PETSC_TRUE;
6048:       mat->symmetry_eternal            = PETSC_TRUE;
6049:     }
6050:     break;
6051:   case MAT_STRUCTURE_ONLY:
6052:     mat->structure_only = flg;
6053:     break;
6054:   case MAT_SORTED_FULL:
6055:     mat->sortedfull = flg;
6056:     break;
6057:   default:
6058:     break;
6059:   }
6060:   PetscTryTypeMethod(mat, setoption, op, flg);
6061:   PetscFunctionReturn(PETSC_SUCCESS);
6062: }

6064: /*@
6065:   MatGetOption - Gets a parameter option that has been set for a matrix.

6067:   Logically Collective

6069:   Input Parameters:
6070: + mat - the matrix
6071: - op  - the option, this only responds to certain options, check the code for which ones

6073:   Output Parameter:
6074: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

6076:   Level: intermediate

6078:   Notes:
6079:   Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

6081:   Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6082:   `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

6084: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6085:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6086: @*/
6087: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6088: {
6089:   PetscFunctionBegin;

6093:   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);
6094:   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()");

6096:   switch (op) {
6097:   case MAT_NO_OFF_PROC_ENTRIES:
6098:     *flg = mat->nooffprocentries;
6099:     break;
6100:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6101:     *flg = mat->nooffproczerorows;
6102:     break;
6103:   case MAT_SYMMETRIC:
6104:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6105:     break;
6106:   case MAT_HERMITIAN:
6107:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6108:     break;
6109:   case MAT_STRUCTURALLY_SYMMETRIC:
6110:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6111:     break;
6112:   case MAT_SPD:
6113:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6114:     break;
6115:   case MAT_SYMMETRY_ETERNAL:
6116:     *flg = mat->symmetry_eternal;
6117:     break;
6118:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6119:     *flg = mat->symmetry_eternal;
6120:     break;
6121:   default:
6122:     break;
6123:   }
6124:   PetscFunctionReturn(PETSC_SUCCESS);
6125: }

6127: /*@
6128:   MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
6129:   this routine retains the old nonzero structure.

6131:   Logically Collective

6133:   Input Parameter:
6134: . mat - the matrix

6136:   Level: intermediate

6138:   Note:
6139:   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.
6140:   See the Performance chapter of the users manual for information on preallocating matrices.

6142: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6143: @*/
6144: PetscErrorCode MatZeroEntries(Mat mat)
6145: {
6146:   PetscFunctionBegin;
6149:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6150:   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");
6151:   MatCheckPreallocated(mat, 1);

6153:   PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6154:   PetscUseTypeMethod(mat, zeroentries);
6155:   PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6156:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6157:   PetscFunctionReturn(PETSC_SUCCESS);
6158: }

6160: /*@
6161:   MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6162:   of a set of rows and columns of a matrix.

6164:   Collective

6166:   Input Parameters:
6167: + mat     - the matrix
6168: . numRows - the number of rows/columns to zero
6169: . rows    - the global row indices
6170: . diag    - value put in the diagonal of the eliminated rows
6171: . x       - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6172: - b       - optional vector of the right-hand side, that will be adjusted by provided solution entries

6174:   Level: intermediate

6176:   Notes:
6177:   This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6179:   For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6180:   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

6182:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6183:   Krylov method to take advantage of the known solution on the zeroed rows.

6185:   For the parallel case, all processes that share the matrix (i.e.,
6186:   those in the communicator used for matrix creation) MUST call this
6187:   routine, regardless of whether any rows being zeroed are owned by
6188:   them.

6190:   Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6191:   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
6192:   missing.

6194:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6195:   list only rows local to itself).

6197:   The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

6199: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6200:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6201: @*/
6202: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6203: {
6204:   PetscFunctionBegin;
6207:   if (numRows) PetscAssertPointer(rows, 3);
6208:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6209:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6210:   MatCheckPreallocated(mat, 1);

6212:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6213:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6214:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6215:   PetscFunctionReturn(PETSC_SUCCESS);
6216: }

6218: /*@
6219:   MatZeroRowsColumnsIS - 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: . is   - the rows to zero
6227: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6228: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6229: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6231:   Level: intermediate

6233:   Note:
6234:   See `MatZeroRowsColumns()` for details on how this routine operates.

6236: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6237:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6238: @*/
6239: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6240: {
6241:   PetscInt        numRows;
6242:   const PetscInt *rows;

6244:   PetscFunctionBegin;
6249:   PetscCall(ISGetLocalSize(is, &numRows));
6250:   PetscCall(ISGetIndices(is, &rows));
6251:   PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6252:   PetscCall(ISRestoreIndices(is, &rows));
6253:   PetscFunctionReturn(PETSC_SUCCESS);
6254: }

6256: /*@
6257:   MatZeroRows - Zeros all entries (except possibly the main diagonal)
6258:   of a set of rows of a matrix.

6260:   Collective

6262:   Input Parameters:
6263: + mat     - the matrix
6264: . numRows - the number of rows to zero
6265: . rows    - the global row indices
6266: . diag    - value put in the diagonal of the zeroed rows
6267: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6268: - b       - optional vector of right-hand side, that will be adjusted by provided solution entries

6270:   Level: intermediate

6272:   Notes:
6273:   This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6275:   For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

6277:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6278:   Krylov method to take advantage of the known solution on the zeroed rows.

6280:   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)
6281:   from the matrix.

6283:   Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6284:   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
6285:   formats this does not alter the nonzero structure.

6287:   If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6288:   of the matrix is not changed the values are
6289:   merely zeroed.

6291:   The user can set a value in the diagonal entry (or for the `MATAIJ` format
6292:   formats can optionally remove the main diagonal entry from the
6293:   nonzero structure as well, by passing 0.0 as the final argument).

6295:   For the parallel case, all processes that share the matrix (i.e.,
6296:   those in the communicator used for matrix creation) MUST call this
6297:   routine, regardless of whether any rows being zeroed are owned by
6298:   them.

6300:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6301:   list only rows local to itself).

6303:   You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6304:   owns that are to be zeroed. This saves a global synchronization in the implementation.

6306: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6307:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6308: @*/
6309: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6310: {
6311:   PetscFunctionBegin;
6314:   if (numRows) PetscAssertPointer(rows, 3);
6315:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6316:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6317:   MatCheckPreallocated(mat, 1);

6319:   PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6320:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6321:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6322:   PetscFunctionReturn(PETSC_SUCCESS);
6323: }

6325: /*@
6326:   MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6327:   of a set of rows of a matrix.

6329:   Collective

6331:   Input Parameters:
6332: + mat  - the matrix
6333: . is   - index set of rows to remove (if `NULL` then no row is removed)
6334: . diag - value put in all diagonals of eliminated rows
6335: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6336: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6338:   Level: intermediate

6340:   Note:
6341:   See `MatZeroRows()` for details on how this routine operates.

6343: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6344:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6345: @*/
6346: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6347: {
6348:   PetscInt        numRows = 0;
6349:   const PetscInt *rows    = NULL;

6351:   PetscFunctionBegin;
6354:   if (is) {
6356:     PetscCall(ISGetLocalSize(is, &numRows));
6357:     PetscCall(ISGetIndices(is, &rows));
6358:   }
6359:   PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6360:   if (is) PetscCall(ISRestoreIndices(is, &rows));
6361:   PetscFunctionReturn(PETSC_SUCCESS);
6362: }

6364: /*@
6365:   MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6366:   of a set of rows of a matrix. These rows must be local to the process.

6368:   Collective

6370:   Input Parameters:
6371: + mat     - the matrix
6372: . numRows - the number of rows to remove
6373: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6374: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6375: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6376: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6378:   Level: intermediate

6380:   Notes:
6381:   See `MatZeroRows()` for details on how this routine operates.

6383:   The grid coordinates are across the entire grid, not just the local portion

6385:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6386:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6387:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6388:   `DM_BOUNDARY_PERIODIC` boundary type.

6390:   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
6391:   a single value per point) you can skip filling those indices.

6393:   Fortran Note:
6394:   `idxm` and `idxn` should be declared as
6395: $     MatStencil idxm(4, m)
6396:   and the values inserted using
6397: .vb
6398:     idxm(MatStencil_i, 1) = i
6399:     idxm(MatStencil_j, 1) = j
6400:     idxm(MatStencil_k, 1) = k
6401:     idxm(MatStencil_c, 1) = c
6402:    etc
6403: .ve

6405: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsl()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6406:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6407: @*/
6408: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6409: {
6410:   PetscInt  dim    = mat->stencil.dim;
6411:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6412:   PetscInt *dims   = mat->stencil.dims + 1;
6413:   PetscInt *starts = mat->stencil.starts;
6414:   PetscInt *dxm    = (PetscInt *)rows;
6415:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6417:   PetscFunctionBegin;
6420:   if (numRows) PetscAssertPointer(rows, 3);

6422:   PetscCall(PetscMalloc1(numRows, &jdxm));
6423:   for (i = 0; i < numRows; ++i) {
6424:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6425:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6426:     /* Local index in X dir */
6427:     tmp = *dxm++ - starts[0];
6428:     /* Loop over remaining dimensions */
6429:     for (j = 0; j < dim - 1; ++j) {
6430:       /* If nonlocal, set index to be negative */
6431:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6432:       /* Update local index */
6433:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6434:     }
6435:     /* Skip component slot if necessary */
6436:     if (mat->stencil.noc) dxm++;
6437:     /* Local row number */
6438:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6439:   }
6440:   PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6441:   PetscCall(PetscFree(jdxm));
6442:   PetscFunctionReturn(PETSC_SUCCESS);
6443: }

6445: /*@
6446:   MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6447:   of a set of rows and columns of a matrix.

6449:   Collective

6451:   Input Parameters:
6452: + mat     - the matrix
6453: . numRows - the number of rows/columns to remove
6454: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6455: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6456: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6457: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6459:   Level: intermediate

6461:   Notes:
6462:   See `MatZeroRowsColumns()` for details on how this routine operates.

6464:   The grid coordinates are across the entire grid, not just the local portion

6466:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6467:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6468:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6469:   `DM_BOUNDARY_PERIODIC` boundary type.

6471:   For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have
6472:   a single value per point) you can skip filling those indices.

6474:   Fortran Note:
6475:   `idxm` and `idxn` should be declared as
6476: $     MatStencil idxm(4, m)
6477:   and the values inserted using
6478: .vb
6479:     idxm(MatStencil_i, 1) = i
6480:     idxm(MatStencil_j, 1) = j
6481:     idxm(MatStencil_k, 1) = k
6482:     idxm(MatStencil_c, 1) = c
6483:     etc
6484: .ve

6486: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6487:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6488: @*/
6489: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6490: {
6491:   PetscInt  dim    = mat->stencil.dim;
6492:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6493:   PetscInt *dims   = mat->stencil.dims + 1;
6494:   PetscInt *starts = mat->stencil.starts;
6495:   PetscInt *dxm    = (PetscInt *)rows;
6496:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6498:   PetscFunctionBegin;
6501:   if (numRows) PetscAssertPointer(rows, 3);

6503:   PetscCall(PetscMalloc1(numRows, &jdxm));
6504:   for (i = 0; i < numRows; ++i) {
6505:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6506:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6507:     /* Local index in X dir */
6508:     tmp = *dxm++ - starts[0];
6509:     /* Loop over remaining dimensions */
6510:     for (j = 0; j < dim - 1; ++j) {
6511:       /* If nonlocal, set index to be negative */
6512:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6513:       /* Update local index */
6514:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6515:     }
6516:     /* Skip component slot if necessary */
6517:     if (mat->stencil.noc) dxm++;
6518:     /* Local row number */
6519:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6520:   }
6521:   PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6522:   PetscCall(PetscFree(jdxm));
6523:   PetscFunctionReturn(PETSC_SUCCESS);
6524: }

6526: /*@C
6527:   MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6528:   of a set of rows of a matrix; using local numbering of rows.

6530:   Collective

6532:   Input Parameters:
6533: + mat     - the matrix
6534: . numRows - the number of rows to remove
6535: . rows    - the local row indices
6536: . diag    - value put in all diagonals of eliminated rows
6537: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6538: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6540:   Level: intermediate

6542:   Notes:
6543:   Before calling `MatZeroRowsLocal()`, the user must first set the
6544:   local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

6546:   See `MatZeroRows()` for details on how this routine operates.

6548: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6549:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6550: @*/
6551: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6552: {
6553:   PetscFunctionBegin;
6556:   if (numRows) PetscAssertPointer(rows, 3);
6557:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6558:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6559:   MatCheckPreallocated(mat, 1);

6561:   if (mat->ops->zerorowslocal) {
6562:     PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6563:   } else {
6564:     IS              is, newis;
6565:     const PetscInt *newRows;

6567:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6568:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6569:     PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6570:     PetscCall(ISGetIndices(newis, &newRows));
6571:     PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6572:     PetscCall(ISRestoreIndices(newis, &newRows));
6573:     PetscCall(ISDestroy(&newis));
6574:     PetscCall(ISDestroy(&is));
6575:   }
6576:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6577:   PetscFunctionReturn(PETSC_SUCCESS);
6578: }

6580: /*@
6581:   MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6582:   of a set of rows of a matrix; using local numbering of rows.

6584:   Collective

6586:   Input Parameters:
6587: + mat  - the matrix
6588: . is   - index set of rows to remove
6589: . diag - value put in all diagonals of eliminated rows
6590: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6591: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6593:   Level: intermediate

6595:   Notes:
6596:   Before calling `MatZeroRowsLocalIS()`, the user must first set the
6597:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6599:   See `MatZeroRows()` for details on how this routine operates.

6601: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6602:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6603: @*/
6604: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6605: {
6606:   PetscInt        numRows;
6607:   const PetscInt *rows;

6609:   PetscFunctionBegin;
6613:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6614:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6615:   MatCheckPreallocated(mat, 1);

6617:   PetscCall(ISGetLocalSize(is, &numRows));
6618:   PetscCall(ISGetIndices(is, &rows));
6619:   PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6620:   PetscCall(ISRestoreIndices(is, &rows));
6621:   PetscFunctionReturn(PETSC_SUCCESS);
6622: }

6624: /*@
6625:   MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6626:   of a set of rows and columns of a matrix; using local numbering of rows.

6628:   Collective

6630:   Input Parameters:
6631: + mat     - the matrix
6632: . numRows - the number of rows to remove
6633: . rows    - the global row indices
6634: . diag    - value put in all diagonals of eliminated rows
6635: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6636: - b       - optional vector of right-hand side, that will be adjusted by provided solution

6638:   Level: intermediate

6640:   Notes:
6641:   Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6642:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6644:   See `MatZeroRowsColumns()` for details on how this routine operates.

6646: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6647:           `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6648: @*/
6649: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6650: {
6651:   IS              is, newis;
6652:   const PetscInt *newRows;

6654:   PetscFunctionBegin;
6657:   if (numRows) PetscAssertPointer(rows, 3);
6658:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6659:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6660:   MatCheckPreallocated(mat, 1);

6662:   PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6663:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6664:   PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6665:   PetscCall(ISGetIndices(newis, &newRows));
6666:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6667:   PetscCall(ISRestoreIndices(newis, &newRows));
6668:   PetscCall(ISDestroy(&newis));
6669:   PetscCall(ISDestroy(&is));
6670:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6671:   PetscFunctionReturn(PETSC_SUCCESS);
6672: }

6674: /*@
6675:   MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6676:   of a set of rows and columns of a matrix; using local numbering of rows.

6678:   Collective

6680:   Input Parameters:
6681: + mat  - the matrix
6682: . is   - index set of rows to remove
6683: . diag - value put in all diagonals of eliminated rows
6684: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6685: - b    - optional vector of right-hand side, that will be adjusted by provided solution

6687:   Level: intermediate

6689:   Notes:
6690:   Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6691:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6693:   See `MatZeroRowsColumns()` for details on how this routine operates.

6695: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6696:           `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6697: @*/
6698: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6699: {
6700:   PetscInt        numRows;
6701:   const PetscInt *rows;

6703:   PetscFunctionBegin;
6707:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6708:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6709:   MatCheckPreallocated(mat, 1);

6711:   PetscCall(ISGetLocalSize(is, &numRows));
6712:   PetscCall(ISGetIndices(is, &rows));
6713:   PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6714:   PetscCall(ISRestoreIndices(is, &rows));
6715:   PetscFunctionReturn(PETSC_SUCCESS);
6716: }

6718: /*@C
6719:   MatGetSize - Returns the numbers of rows and columns in a matrix.

6721:   Not Collective

6723:   Input Parameter:
6724: . mat - the matrix

6726:   Output Parameters:
6727: + m - the number of global rows
6728: - n - the number of global columns

6730:   Level: beginner

6732:   Note:
6733:   Both output parameters can be `NULL` on input.

6735: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6736: @*/
6737: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6738: {
6739:   PetscFunctionBegin;
6741:   if (m) *m = mat->rmap->N;
6742:   if (n) *n = mat->cmap->N;
6743:   PetscFunctionReturn(PETSC_SUCCESS);
6744: }

6746: /*@C
6747:   MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6748:   of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

6750:   Not Collective

6752:   Input Parameter:
6753: . mat - the matrix

6755:   Output Parameters:
6756: + m - the number of local rows, use `NULL` to not obtain this value
6757: - n - the number of local columns, use `NULL` to not obtain this value

6759:   Level: beginner

6761: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6762: @*/
6763: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6764: {
6765:   PetscFunctionBegin;
6767:   if (m) PetscAssertPointer(m, 2);
6768:   if (n) PetscAssertPointer(n, 3);
6769:   if (m) *m = mat->rmap->n;
6770:   if (n) *n = mat->cmap->n;
6771:   PetscFunctionReturn(PETSC_SUCCESS);
6772: }

6774: /*@C
6775:   MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6776:   vector one multiplies this matrix by that are owned by this processor.

6778:   Not Collective, unless matrix has not been allocated, then collective

6780:   Input Parameter:
6781: . mat - the matrix

6783:   Output Parameters:
6784: + m - the global index of the first local column, use `NULL` to not obtain this value
6785: - n - one more than the global index of the last local column, use `NULL` to not obtain this value

6787:   Level: developer

6789:   Note:
6790:   Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6791:   Layouts](sec_matlayout) for details on matrix layouts.

6793: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
6794: @*/
6795: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6796: {
6797:   PetscFunctionBegin;
6800:   if (m) PetscAssertPointer(m, 2);
6801:   if (n) PetscAssertPointer(n, 3);
6802:   MatCheckPreallocated(mat, 1);
6803:   if (m) *m = mat->cmap->rstart;
6804:   if (n) *n = mat->cmap->rend;
6805:   PetscFunctionReturn(PETSC_SUCCESS);
6806: }

6808: /*@C
6809:   MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6810:   this MPI process.

6812:   Not Collective

6814:   Input Parameter:
6815: . mat - the matrix

6817:   Output Parameters:
6818: + m - the global index of the first local row, use `NULL` to not obtain this value
6819: - n - one more than the global index of the last local row, use `NULL` to not obtain this value

6821:   Level: beginner

6823:   Note:
6824:   For all matrices  it returns the range of matrix rows associated with rows of a vector that
6825:   would contain the result of a matrix vector product with this matrix. See [Matrix
6826:   Layouts](sec_matlayout) for details on matrix layouts.

6828: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`,
6829:           `PetscLayout`
6830: @*/
6831: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6832: {
6833:   PetscFunctionBegin;
6836:   if (m) PetscAssertPointer(m, 2);
6837:   if (n) PetscAssertPointer(n, 3);
6838:   MatCheckPreallocated(mat, 1);
6839:   if (m) *m = mat->rmap->rstart;
6840:   if (n) *n = mat->rmap->rend;
6841:   PetscFunctionReturn(PETSC_SUCCESS);
6842: }

6844: /*@C
6845:   MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6846:   `MATSCALAPACK`, returns the range of matrix rows owned by each process.

6848:   Not Collective, unless matrix has not been allocated

6850:   Input Parameter:
6851: . mat - the matrix

6853:   Output Parameter:
6854: . ranges - start of each processors portion plus one more than the total length at the end

6856:   Level: beginner

6858:   Note:
6859:   For all matrices  it returns the ranges of matrix rows associated with rows of a vector that
6860:   would contain the result of a matrix vector product with this matrix. See [Matrix
6861:   Layouts](sec_matlayout) for details on matrix layouts.

6863: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
6864: @*/
6865: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt **ranges)
6866: {
6867:   PetscFunctionBegin;
6870:   MatCheckPreallocated(mat, 1);
6871:   PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6872:   PetscFunctionReturn(PETSC_SUCCESS);
6873: }

6875: /*@C
6876:   MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6877:   vector one multiplies this vector by that are owned by each processor.

6879:   Not Collective, unless matrix has not been allocated

6881:   Input Parameter:
6882: . mat - the matrix

6884:   Output Parameter:
6885: . ranges - start of each processors portion plus one more than the total length at the end

6887:   Level: beginner

6889:   Note:
6890:   Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
6891:   Layouts](sec_matlayout) for details on matrix layouts.

6893: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`
6894: @*/
6895: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt **ranges)
6896: {
6897:   PetscFunctionBegin;
6900:   MatCheckPreallocated(mat, 1);
6901:   PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
6902:   PetscFunctionReturn(PETSC_SUCCESS);
6903: }

6905: /*@C
6906:   MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.

6908:   Not Collective

6910:   Input Parameter:
6911: . A - matrix

6913:   Output Parameters:
6914: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
6915: - cols - columns in which this process owns elements, use `NULL` to not obtain this value

6917:   Level: intermediate

6919:   Note:
6920:   For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
6921:   returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
6922:   `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
6923:   details on matrix layouts.

6925: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatSetValues()`, ``MATELEMENTAL``, ``MATSCALAPACK``
6926: @*/
6927: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
6928: {
6929:   PetscErrorCode (*f)(Mat, IS *, IS *);

6931:   PetscFunctionBegin;
6932:   MatCheckPreallocated(A, 1);
6933:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
6934:   if (f) {
6935:     PetscCall((*f)(A, rows, cols));
6936:   } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
6937:     if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
6938:     if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
6939:   }
6940:   PetscFunctionReturn(PETSC_SUCCESS);
6941: }

6943: /*@C
6944:   MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
6945:   Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
6946:   to complete the factorization.

6948:   Collective

6950:   Input Parameters:
6951: + fact - the factorized matrix obtained with `MatGetFactor()`
6952: . mat  - the matrix
6953: . row  - row permutation
6954: . col  - column permutation
6955: - info - structure containing
6956: .vb
6957:       levels - number of levels of fill.
6958:       expected fill - as ratio of original fill.
6959:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
6960:                 missing diagonal entries)
6961: .ve

6963:   Level: developer

6965:   Notes:
6966:   See [Matrix Factorization](sec_matfactor) for additional information.

6968:   Most users should employ the `KSP` interface for linear solvers
6969:   instead of working directly with matrix algebra routines such as this.
6970:   See, e.g., `KSPCreate()`.

6972:   Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`

6974:   Developer Note:
6975:   The Fortran interface is not autogenerated as the
6976:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

6978: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
6979:           `MatGetOrdering()`, `MatFactorInfo`
6980: @*/
6981: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
6982: {
6983:   PetscFunctionBegin;
6988:   PetscAssertPointer(info, 5);
6989:   PetscAssertPointer(fact, 1);
6990:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
6991:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
6992:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6993:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6994:   MatCheckPreallocated(mat, 2);

6996:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
6997:   PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
6998:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
6999:   PetscFunctionReturn(PETSC_SUCCESS);
7000: }

7002: /*@C
7003:   MatICCFactorSymbolic - Performs symbolic incomplete
7004:   Cholesky factorization for a symmetric matrix.  Use
7005:   `MatCholeskyFactorNumeric()` to complete the factorization.

7007:   Collective

7009:   Input Parameters:
7010: + fact - the factorized matrix obtained with `MatGetFactor()`
7011: . mat  - the matrix to be factored
7012: . perm - row and column permutation
7013: - info - structure containing
7014: .vb
7015:       levels - number of levels of fill.
7016:       expected fill - as ratio of original fill.
7017: .ve

7019:   Level: developer

7021:   Notes:
7022:   Most users should employ the `KSP` interface for linear solvers
7023:   instead of working directly with matrix algebra routines such as this.
7024:   See, e.g., `KSPCreate()`.

7026:   This uses the definition of level of fill as in Y. Saad {cite}`saad2003`

7028:   Developer Note:
7029:   The Fortran interface is not autogenerated as the
7030:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

7032: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7033: @*/
7034: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7035: {
7036:   PetscFunctionBegin;
7040:   PetscAssertPointer(info, 4);
7041:   PetscAssertPointer(fact, 1);
7042:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7043:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7044:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7045:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7046:   MatCheckPreallocated(mat, 2);

7048:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7049:   PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7050:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7051:   PetscFunctionReturn(PETSC_SUCCESS);
7052: }

7054: /*@C
7055:   MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7056:   points to an array of valid matrices, they may be reused to store the new
7057:   submatrices.

7059:   Collective

7061:   Input Parameters:
7062: + mat   - the matrix
7063: . n     - the number of submatrixes to be extracted (on this processor, may be zero)
7064: . irow  - index set of rows to extract
7065: . icol  - index set of columns to extract
7066: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7068:   Output Parameter:
7069: . submat - the array of submatrices

7071:   Level: advanced

7073:   Notes:
7074:   `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7075:   (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7076:   to extract a parallel submatrix.

7078:   Some matrix types place restrictions on the row and column
7079:   indices, such as that they be sorted or that they be equal to each other.

7081:   The index sets may not have duplicate entries.

7083:   When extracting submatrices from a parallel matrix, each processor can
7084:   form a different submatrix by setting the rows and columns of its
7085:   individual index sets according to the local submatrix desired.

7087:   When finished using the submatrices, the user should destroy
7088:   them with `MatDestroySubMatrices()`.

7090:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7091:   original matrix has not changed from that last call to `MatCreateSubMatrices()`.

7093:   This routine creates the matrices in submat; you should NOT create them before
7094:   calling it. It also allocates the array of matrix pointers submat.

7096:   For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7097:   request one row/column in a block, they must request all rows/columns that are in
7098:   that block. For example, if the block size is 2 you cannot request just row 0 and
7099:   column 0.

7101:   Fortran Note:
7102:   The Fortran interface is slightly different from that given below; it
7103:   requires one to pass in as `submat` a `Mat` (integer) array of size at least n+1.

7105: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7106: @*/
7107: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7108: {
7109:   PetscInt  i;
7110:   PetscBool eq;

7112:   PetscFunctionBegin;
7115:   if (n) {
7116:     PetscAssertPointer(irow, 3);
7118:     PetscAssertPointer(icol, 4);
7120:   }
7121:   PetscAssertPointer(submat, 6);
7122:   if (n && scall == MAT_REUSE_MATRIX) {
7123:     PetscAssertPointer(*submat, 6);
7125:   }
7126:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7127:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7128:   MatCheckPreallocated(mat, 1);
7129:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7130:   PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7131:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7132:   for (i = 0; i < n; i++) {
7133:     (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7134:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7135:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7136: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7137:     if (mat->boundtocpu && mat->bindingpropagates) {
7138:       PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7139:       PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7140:     }
7141: #endif
7142:   }
7143:   PetscFunctionReturn(PETSC_SUCCESS);
7144: }

7146: /*@C
7147:   MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of `IS` that may live on subcomms).

7149:   Collective

7151:   Input Parameters:
7152: + mat   - the matrix
7153: . n     - the number of submatrixes to be extracted
7154: . irow  - index set of rows to extract
7155: . icol  - index set of columns to extract
7156: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7158:   Output Parameter:
7159: . submat - the array of submatrices

7161:   Level: advanced

7163:   Note:
7164:   This is used by `PCGASM`

7166: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7167: @*/
7168: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7169: {
7170:   PetscInt  i;
7171:   PetscBool eq;

7173:   PetscFunctionBegin;
7176:   if (n) {
7177:     PetscAssertPointer(irow, 3);
7179:     PetscAssertPointer(icol, 4);
7181:   }
7182:   PetscAssertPointer(submat, 6);
7183:   if (n && scall == MAT_REUSE_MATRIX) {
7184:     PetscAssertPointer(*submat, 6);
7186:   }
7187:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7188:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7189:   MatCheckPreallocated(mat, 1);

7191:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7192:   PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7193:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7194:   for (i = 0; i < n; i++) {
7195:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7196:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7197:   }
7198:   PetscFunctionReturn(PETSC_SUCCESS);
7199: }

7201: /*@C
7202:   MatDestroyMatrices - Destroys an array of matrices.

7204:   Collective

7206:   Input Parameters:
7207: + n   - the number of local matrices
7208: - mat - the matrices (this is a pointer to the array of matrices)

7210:   Level: advanced

7212:   Note:
7213:   Frees not only the matrices, but also the array that contains the matrices

7215:   Fortran Note:
7216:   This does not free the array.

7218: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()` `MatDestroySubMatrices()`
7219: @*/
7220: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7221: {
7222:   PetscInt i;

7224:   PetscFunctionBegin;
7225:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7226:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7227:   PetscAssertPointer(mat, 2);

7229:   for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

7231:   /* memory is allocated even if n = 0 */
7232:   PetscCall(PetscFree(*mat));
7233:   PetscFunctionReturn(PETSC_SUCCESS);
7234: }

7236: /*@C
7237:   MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

7239:   Collective

7241:   Input Parameters:
7242: + n   - the number of local matrices
7243: - mat - the matrices (this is a pointer to the array of matrices, just to match the calling
7244:                        sequence of `MatCreateSubMatrices()`)

7246:   Level: advanced

7248:   Note:
7249:   Frees not only the matrices, but also the array that contains the matrices

7251:   Fortran Note:
7252:   This does not free the array.

7254: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7255: @*/
7256: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7257: {
7258:   Mat mat0;

7260:   PetscFunctionBegin;
7261:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7262:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7263:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7264:   PetscAssertPointer(mat, 2);

7266:   mat0 = (*mat)[0];
7267:   if (mat0 && mat0->ops->destroysubmatrices) {
7268:     PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7269:   } else {
7270:     PetscCall(MatDestroyMatrices(n, mat));
7271:   }
7272:   PetscFunctionReturn(PETSC_SUCCESS);
7273: }

7275: /*@C
7276:   MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

7278:   Collective

7280:   Input Parameter:
7281: . mat - the matrix

7283:   Output Parameter:
7284: . matstruct - the sequential matrix with the nonzero structure of mat

7286:   Level: developer

7288: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7289: @*/
7290: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7291: {
7292:   PetscFunctionBegin;
7294:   PetscAssertPointer(matstruct, 2);

7297:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7298:   MatCheckPreallocated(mat, 1);

7300:   PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7301:   PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7302:   PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7303:   PetscFunctionReturn(PETSC_SUCCESS);
7304: }

7306: /*@C
7307:   MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

7309:   Collective

7311:   Input Parameter:
7312: . mat - the matrix (this is a pointer to the array of matrices, just to match the calling
7313:                        sequence of `MatGetSeqNonzeroStructure()`)

7315:   Level: advanced

7317:   Note:
7318:   Frees not only the matrices, but also the array that contains the matrices

7320: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7321: @*/
7322: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7323: {
7324:   PetscFunctionBegin;
7325:   PetscAssertPointer(mat, 1);
7326:   PetscCall(MatDestroy(mat));
7327:   PetscFunctionReturn(PETSC_SUCCESS);
7328: }

7330: /*@
7331:   MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7332:   replaces the index sets by larger ones that represent submatrices with
7333:   additional overlap.

7335:   Collective

7337:   Input Parameters:
7338: + mat - the matrix
7339: . n   - the number of index sets
7340: . is  - the array of index sets (these index sets will changed during the call)
7341: - ov  - the additional overlap requested

7343:   Options Database Key:
7344: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7346:   Level: developer

7348:   Note:
7349:   The computed overlap preserves the matrix block sizes when the blocks are square.
7350:   That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7351:   that block are included in the overlap regardless of whether each specific column would increase the overlap.

7353: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7354: @*/
7355: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7356: {
7357:   PetscInt i, bs, cbs;

7359:   PetscFunctionBegin;
7363:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7364:   if (n) {
7365:     PetscAssertPointer(is, 3);
7367:   }
7368:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7369:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7370:   MatCheckPreallocated(mat, 1);

7372:   if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7373:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7374:   PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7375:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7376:   PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7377:   if (bs == cbs) {
7378:     for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7379:   }
7380:   PetscFunctionReturn(PETSC_SUCCESS);
7381: }

7383: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

7385: /*@
7386:   MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7387:   a sub communicator, replaces the index sets by larger ones that represent submatrices with
7388:   additional overlap.

7390:   Collective

7392:   Input Parameters:
7393: + mat - the matrix
7394: . n   - the number of index sets
7395: . is  - the array of index sets (these index sets will changed during the call)
7396: - ov  - the additional overlap requested

7398:   `   Options Database Key:
7399: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7401:   Level: developer

7403: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7404: @*/
7405: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7406: {
7407:   PetscInt i;

7409:   PetscFunctionBegin;
7412:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7413:   if (n) {
7414:     PetscAssertPointer(is, 3);
7416:   }
7417:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7418:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7419:   MatCheckPreallocated(mat, 1);
7420:   if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7421:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7422:   for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7423:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7424:   PetscFunctionReturn(PETSC_SUCCESS);
7425: }

7427: /*@
7428:   MatGetBlockSize - Returns the matrix block size.

7430:   Not Collective

7432:   Input Parameter:
7433: . mat - the matrix

7435:   Output Parameter:
7436: . bs - block size

7438:   Level: intermediate

7440:   Notes:
7441:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

7443:   If the block size has not been set yet this routine returns 1.

7445: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7446: @*/
7447: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7448: {
7449:   PetscFunctionBegin;
7451:   PetscAssertPointer(bs, 2);
7452:   *bs = PetscAbs(mat->rmap->bs);
7453:   PetscFunctionReturn(PETSC_SUCCESS);
7454: }

7456: /*@
7457:   MatGetBlockSizes - Returns the matrix block row and column sizes.

7459:   Not Collective

7461:   Input Parameter:
7462: . mat - the matrix

7464:   Output Parameters:
7465: + rbs - row block size
7466: - cbs - column block size

7468:   Level: intermediate

7470:   Notes:
7471:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7472:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7474:   If a block size has not been set yet this routine returns 1.

7476: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7477: @*/
7478: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7479: {
7480:   PetscFunctionBegin;
7482:   if (rbs) PetscAssertPointer(rbs, 2);
7483:   if (cbs) PetscAssertPointer(cbs, 3);
7484:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7485:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7486:   PetscFunctionReturn(PETSC_SUCCESS);
7487: }

7489: /*@
7490:   MatSetBlockSize - Sets the matrix block size.

7492:   Logically Collective

7494:   Input Parameters:
7495: + mat - the matrix
7496: - bs  - block size

7498:   Level: intermediate

7500:   Notes:
7501:   Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7502:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7504:   For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7505:   is compatible with the matrix local sizes.

7507: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7508: @*/
7509: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7510: {
7511:   PetscFunctionBegin;
7514:   PetscCall(MatSetBlockSizes(mat, bs, bs));
7515:   PetscFunctionReturn(PETSC_SUCCESS);
7516: }

7518: typedef struct {
7519:   PetscInt         n;
7520:   IS              *is;
7521:   Mat             *mat;
7522:   PetscObjectState nonzerostate;
7523:   Mat              C;
7524: } EnvelopeData;

7526: static PetscErrorCode EnvelopeDataDestroy(void *ptr)
7527: {
7528:   EnvelopeData *edata = (EnvelopeData *)ptr;

7530:   PetscFunctionBegin;
7531:   for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7532:   PetscCall(PetscFree(edata->is));
7533:   PetscCall(PetscFree(edata));
7534:   PetscFunctionReturn(PETSC_SUCCESS);
7535: }

7537: /*@
7538:   MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7539:   the sizes of these blocks in the matrix. An individual block may lie over several processes.

7541:   Collective

7543:   Input Parameter:
7544: . mat - the matrix

7546:   Level: intermediate

7548:   Notes:
7549:   There can be zeros within the blocks

7551:   The blocks can overlap between processes, including laying on more than two processes

7553: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7554: @*/
7555: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7556: {
7557:   PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7558:   PetscInt          *diag, *odiag, sc;
7559:   VecScatter         scatter;
7560:   PetscScalar       *seqv;
7561:   const PetscScalar *parv;
7562:   const PetscInt    *ia, *ja;
7563:   PetscBool          set, flag, done;
7564:   Mat                AA = mat, A;
7565:   MPI_Comm           comm;
7566:   PetscMPIInt        rank, size, tag;
7567:   MPI_Status         status;
7568:   PetscContainer     container;
7569:   EnvelopeData      *edata;
7570:   Vec                seq, par;
7571:   IS                 isglobal;

7573:   PetscFunctionBegin;
7575:   PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7576:   if (!set || !flag) {
7577:     /* TODO: only needs nonzero structure of transpose */
7578:     PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7579:     PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7580:   }
7581:   PetscCall(MatAIJGetLocalMat(AA, &A));
7582:   PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7583:   PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

7585:   PetscCall(MatGetLocalSize(mat, &n, NULL));
7586:   PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7587:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7588:   PetscCallMPI(MPI_Comm_size(comm, &size));
7589:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

7591:   PetscCall(PetscMalloc2(n, &sizes, n, &starts));

7593:   if (rank > 0) {
7594:     PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7595:     PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7596:   }
7597:   PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7598:   for (i = 0; i < n; i++) {
7599:     env = PetscMax(env, ja[ia[i + 1] - 1]);
7600:     II  = rstart + i;
7601:     if (env == II) {
7602:       starts[lblocks]  = tbs;
7603:       sizes[lblocks++] = 1 + II - tbs;
7604:       tbs              = 1 + II;
7605:     }
7606:   }
7607:   if (rank < size - 1) {
7608:     PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7609:     PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7610:   }

7612:   PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7613:   if (!set || !flag) PetscCall(MatDestroy(&AA));
7614:   PetscCall(MatDestroy(&A));

7616:   PetscCall(PetscNew(&edata));
7617:   PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7618:   edata->n = lblocks;
7619:   /* create IS needed for extracting blocks from the original matrix */
7620:   PetscCall(PetscMalloc1(lblocks, &edata->is));
7621:   for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

7623:   /* Create the resulting inverse matrix structure with preallocation information */
7624:   PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7625:   PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7626:   PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7627:   PetscCall(MatSetType(edata->C, MATAIJ));

7629:   /* Communicate the start and end of each row, from each block to the correct rank */
7630:   /* TODO: Use PetscSF instead of VecScatter */
7631:   for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7632:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7633:   PetscCall(VecGetArrayWrite(seq, &seqv));
7634:   for (PetscInt i = 0; i < lblocks; i++) {
7635:     for (PetscInt j = 0; j < sizes[i]; j++) {
7636:       seqv[cnt]     = starts[i];
7637:       seqv[cnt + 1] = starts[i] + sizes[i];
7638:       cnt += 2;
7639:     }
7640:   }
7641:   PetscCall(VecRestoreArrayWrite(seq, &seqv));
7642:   PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7643:   sc -= cnt;
7644:   PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7645:   PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7646:   PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7647:   PetscCall(ISDestroy(&isglobal));
7648:   PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7649:   PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7650:   PetscCall(VecScatterDestroy(&scatter));
7651:   PetscCall(VecDestroy(&seq));
7652:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7653:   PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7654:   PetscCall(VecGetArrayRead(par, &parv));
7655:   cnt = 0;
7656:   PetscCall(MatGetSize(mat, NULL, &n));
7657:   for (PetscInt i = 0; i < mat->rmap->n; i++) {
7658:     PetscInt start, end, d = 0, od = 0;

7660:     start = (PetscInt)PetscRealPart(parv[cnt]);
7661:     end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
7662:     cnt += 2;

7664:     if (start < cstart) {
7665:       od += cstart - start + n - cend;
7666:       d += cend - cstart;
7667:     } else if (start < cend) {
7668:       od += n - cend;
7669:       d += cend - start;
7670:     } else od += n - start;
7671:     if (end <= cstart) {
7672:       od -= cstart - end + n - cend;
7673:       d -= cend - cstart;
7674:     } else if (end < cend) {
7675:       od -= n - cend;
7676:       d -= cend - end;
7677:     } else od -= n - end;

7679:     odiag[i] = od;
7680:     diag[i]  = d;
7681:   }
7682:   PetscCall(VecRestoreArrayRead(par, &parv));
7683:   PetscCall(VecDestroy(&par));
7684:   PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7685:   PetscCall(PetscFree2(diag, odiag));
7686:   PetscCall(PetscFree2(sizes, starts));

7688:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7689:   PetscCall(PetscContainerSetPointer(container, edata));
7690:   PetscCall(PetscContainerSetUserDestroy(container, (PetscErrorCode(*)(void *))EnvelopeDataDestroy));
7691:   PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7692:   PetscCall(PetscObjectDereference((PetscObject)container));
7693:   PetscFunctionReturn(PETSC_SUCCESS);
7694: }

7696: /*@
7697:   MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

7699:   Collective

7701:   Input Parameters:
7702: + A     - the matrix
7703: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine

7705:   Output Parameter:
7706: . C - matrix with inverted block diagonal of `A`

7708:   Level: advanced

7710:   Note:
7711:   For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

7713: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7714: @*/
7715: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7716: {
7717:   PetscContainer   container;
7718:   EnvelopeData    *edata;
7719:   PetscObjectState nonzerostate;

7721:   PetscFunctionBegin;
7722:   PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7723:   if (!container) {
7724:     PetscCall(MatComputeVariableBlockEnvelope(A));
7725:     PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7726:   }
7727:   PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7728:   PetscCall(MatGetNonzeroState(A, &nonzerostate));
7729:   PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7730:   PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

7732:   PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7733:   *C = edata->C;

7735:   for (PetscInt i = 0; i < edata->n; i++) {
7736:     Mat          D;
7737:     PetscScalar *dvalues;

7739:     PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7740:     PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7741:     PetscCall(MatSeqDenseInvert(D));
7742:     PetscCall(MatDenseGetArray(D, &dvalues));
7743:     PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7744:     PetscCall(MatDestroy(&D));
7745:   }
7746:   PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7747:   PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7748:   PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7749:   PetscFunctionReturn(PETSC_SUCCESS);
7750: }

7752: /*@
7753:   MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

7755:   Not Collective

7757:   Input Parameters:
7758: + mat     - the matrix
7759: . nblocks - the number of blocks on this process, each block can only exist on a single process
7760: - bsizes  - the block sizes

7762:   Level: intermediate

7764:   Notes:
7765:   Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

7767:   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.

7769: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7770:           `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7771: @*/
7772: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7773: {
7774:   PetscInt ncnt = 0, nlocal;

7776:   PetscFunctionBegin;
7778:   PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7779:   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);
7780:   for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7781:   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);
7782:   PetscCall(PetscFree(mat->bsizes));
7783:   mat->nblocks = nblocks;
7784:   PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7785:   PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7786:   PetscFunctionReturn(PETSC_SUCCESS);
7787: }

7789: /*@C
7790:   MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7792:   Not Collective; No Fortran Support

7794:   Input Parameter:
7795: . mat - the matrix

7797:   Output Parameters:
7798: + nblocks - the number of blocks on this process
7799: - bsizes  - the block sizes

7801:   Level: intermediate

7803: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7804: @*/
7805: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7806: {
7807:   PetscFunctionBegin;
7809:   if (nblocks) *nblocks = mat->nblocks;
7810:   if (bsizes) *bsizes = mat->bsizes;
7811:   PetscFunctionReturn(PETSC_SUCCESS);
7812: }

7814: /*@
7815:   MatSetBlockSizes - Sets the matrix block row and column sizes.

7817:   Logically Collective

7819:   Input Parameters:
7820: + mat - the matrix
7821: . rbs - row block size
7822: - cbs - column block size

7824:   Level: intermediate

7826:   Notes:
7827:   Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7828:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7829:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7831:   For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7832:   are compatible with the matrix local sizes.

7834:   The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

7836: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7837: @*/
7838: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7839: {
7840:   PetscFunctionBegin;
7844:   PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7845:   if (mat->rmap->refcnt) {
7846:     ISLocalToGlobalMapping l2g  = NULL;
7847:     PetscLayout            nmap = NULL;

7849:     PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7850:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7851:     PetscCall(PetscLayoutDestroy(&mat->rmap));
7852:     mat->rmap          = nmap;
7853:     mat->rmap->mapping = l2g;
7854:   }
7855:   if (mat->cmap->refcnt) {
7856:     ISLocalToGlobalMapping l2g  = NULL;
7857:     PetscLayout            nmap = NULL;

7859:     PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7860:     if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7861:     PetscCall(PetscLayoutDestroy(&mat->cmap));
7862:     mat->cmap          = nmap;
7863:     mat->cmap->mapping = l2g;
7864:   }
7865:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7866:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7867:   PetscFunctionReturn(PETSC_SUCCESS);
7868: }

7870: /*@
7871:   MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

7873:   Logically Collective

7875:   Input Parameters:
7876: + mat     - the matrix
7877: . fromRow - matrix from which to copy row block size
7878: - fromCol - matrix from which to copy column block size (can be same as fromRow)

7880:   Level: developer

7882: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
7883: @*/
7884: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
7885: {
7886:   PetscFunctionBegin;
7890:   if (fromRow->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
7891:   if (fromCol->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
7892:   PetscFunctionReturn(PETSC_SUCCESS);
7893: }

7895: /*@
7896:   MatResidual - Default routine to calculate the residual r = b - Ax

7898:   Collective

7900:   Input Parameters:
7901: + mat - the matrix
7902: . b   - the right-hand-side
7903: - x   - the approximate solution

7905:   Output Parameter:
7906: . r - location to store the residual

7908:   Level: developer

7910: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
7911: @*/
7912: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
7913: {
7914:   PetscFunctionBegin;
7920:   MatCheckPreallocated(mat, 1);
7921:   PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
7922:   if (!mat->ops->residual) {
7923:     PetscCall(MatMult(mat, x, r));
7924:     PetscCall(VecAYPX(r, -1.0, b));
7925:   } else {
7926:     PetscUseTypeMethod(mat, residual, b, x, r);
7927:   }
7928:   PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
7929:   PetscFunctionReturn(PETSC_SUCCESS);
7930: }

7932: /*MC
7933:     MatGetRowIJF90 - Obtains the compressed row storage i and j indices for the local rows of a sparse matrix

7935:     Synopsis:
7936:     MatGetRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

7938:     Not Collective

7940:     Input Parameters:
7941: +   A - the matrix
7942: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7943: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7944: -   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7945:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7946:                  always used.

7948:     Output Parameters:
7949: +   n - number of local rows in the (possibly compressed) matrix
7950: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7951: .   ja - the column indices
7952: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7953:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

7955:     Level: developer

7957:     Note:
7958:     Use  `MatRestoreRowIJF90()` when you no longer need access to the data

7960: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatRestoreRowIJF90()`
7961: M*/

7963: /*MC
7964:     MatRestoreRowIJF90 - restores the compressed row storage i and j indices for the local rows of a sparse matrix obtained with `MatGetRowIJF90()`

7966:     Synopsis:
7967:     MatRestoreRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

7969:     Not Collective

7971:     Input Parameters:
7972: +   A - the  matrix
7973: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7974: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7975:     inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7976:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7977:                  always used.
7978: .   n - number of local rows in the (possibly compressed) matrix
7979: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7980: .   ja - the column indices
7981: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7982:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

7984:     Level: developer

7986: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatGetRowIJF90()`
7987: M*/

7989: /*@C
7990:   MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

7992:   Collective

7994:   Input Parameters:
7995: + mat             - the matrix
7996: . shift           - 0 or 1 indicating we want the indices starting at 0 or 1
7997: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7998: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7999:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8000:                  always used.

8002:   Output Parameters:
8003: + n    - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8004: . 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
8005: . ja   - the column indices, use `NULL` if not needed
8006: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8007:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

8009:   Level: developer

8011:   Notes:
8012:   You CANNOT change any of the ia[] or ja[] values.

8014:   Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

8016:   Fortran Notes:
8017:   Use
8018: .vb
8019:     PetscInt, pointer :: ia(:),ja(:)
8020:     call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8021:     ! Access the ith and jth entries via ia(i) and ja(j)
8022: .ve

8024:   `MatGetRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatGetRowIJF90()`

8026: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetRowIJF90()`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8027: @*/
8028: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8029: {
8030:   PetscFunctionBegin;
8033:   if (n) PetscAssertPointer(n, 5);
8034:   if (ia) PetscAssertPointer(ia, 6);
8035:   if (ja) PetscAssertPointer(ja, 7);
8036:   if (done) PetscAssertPointer(done, 8);
8037:   MatCheckPreallocated(mat, 1);
8038:   if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8039:   else {
8040:     if (done) *done = PETSC_TRUE;
8041:     PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8042:     PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8043:     PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8044:   }
8045:   PetscFunctionReturn(PETSC_SUCCESS);
8046: }

8048: /*@C
8049:   MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

8051:   Collective

8053:   Input Parameters:
8054: + mat             - the matrix
8055: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8056: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8057:                 symmetrized
8058: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8059:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8060:                  always used.
8061: . n               - number of columns in the (possibly compressed) matrix
8062: . ia              - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8063: - ja              - the row indices

8065:   Output Parameter:
8066: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

8068:   Level: developer

8070: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8071: @*/
8072: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8073: {
8074:   PetscFunctionBegin;
8077:   PetscAssertPointer(n, 5);
8078:   if (ia) PetscAssertPointer(ia, 6);
8079:   if (ja) PetscAssertPointer(ja, 7);
8080:   PetscAssertPointer(done, 8);
8081:   MatCheckPreallocated(mat, 1);
8082:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8083:   else {
8084:     *done = PETSC_TRUE;
8085:     PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8086:   }
8087:   PetscFunctionReturn(PETSC_SUCCESS);
8088: }

8090: /*@C
8091:   MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

8093:   Collective

8095:   Input Parameters:
8096: + mat             - the matrix
8097: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8098: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8099: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8100:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8101:                  always used.
8102: . n               - size of (possibly compressed) matrix
8103: . ia              - the row pointers
8104: - ja              - the column indices

8106:   Output Parameter:
8107: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8109:   Level: developer

8111:   Note:
8112:   This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8113:   us of the array after it has been restored. If you pass `NULL`, it will
8114:   not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

8116:   Fortran Note:
8117:   `MatRestoreRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatRestoreRowIJF90()`

8119: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreRowIJF90()`, `MatRestoreColumnIJ()`
8120: @*/
8121: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8122: {
8123:   PetscFunctionBegin;
8126:   if (ia) PetscAssertPointer(ia, 6);
8127:   if (ja) PetscAssertPointer(ja, 7);
8128:   if (done) PetscAssertPointer(done, 8);
8129:   MatCheckPreallocated(mat, 1);

8131:   if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8132:   else {
8133:     if (done) *done = PETSC_TRUE;
8134:     PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8135:     if (n) *n = 0;
8136:     if (ia) *ia = NULL;
8137:     if (ja) *ja = NULL;
8138:   }
8139:   PetscFunctionReturn(PETSC_SUCCESS);
8140: }

8142: /*@C
8143:   MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

8145:   Collective

8147:   Input Parameters:
8148: + mat             - the matrix
8149: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8150: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8151: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8152:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8153:                  always used.

8155:   Output Parameters:
8156: + n    - size of (possibly compressed) matrix
8157: . ia   - the column pointers
8158: . ja   - the row indices
8159: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8161:   Level: developer

8163: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8164: @*/
8165: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8166: {
8167:   PetscFunctionBegin;
8170:   if (ia) PetscAssertPointer(ia, 6);
8171:   if (ja) PetscAssertPointer(ja, 7);
8172:   PetscAssertPointer(done, 8);
8173:   MatCheckPreallocated(mat, 1);

8175:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8176:   else {
8177:     *done = PETSC_TRUE;
8178:     PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8179:     if (n) *n = 0;
8180:     if (ia) *ia = NULL;
8181:     if (ja) *ja = NULL;
8182:   }
8183:   PetscFunctionReturn(PETSC_SUCCESS);
8184: }

8186: /*@C
8187:   MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8188:   `MatGetColumnIJ()`.

8190:   Collective

8192:   Input Parameters:
8193: + mat        - the matrix
8194: . ncolors    - maximum color value
8195: . n          - number of entries in colorarray
8196: - colorarray - array indicating color for each column

8198:   Output Parameter:
8199: . iscoloring - coloring generated using colorarray information

8201:   Level: developer

8203: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8204: @*/
8205: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8206: {
8207:   PetscFunctionBegin;
8210:   PetscAssertPointer(colorarray, 4);
8211:   PetscAssertPointer(iscoloring, 5);
8212:   MatCheckPreallocated(mat, 1);

8214:   if (!mat->ops->coloringpatch) {
8215:     PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8216:   } else {
8217:     PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8218:   }
8219:   PetscFunctionReturn(PETSC_SUCCESS);
8220: }

8222: /*@
8223:   MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

8225:   Logically Collective

8227:   Input Parameter:
8228: . mat - the factored matrix to be reset

8230:   Level: developer

8232:   Notes:
8233:   This routine should be used only with factored matrices formed by in-place
8234:   factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8235:   format).  This option can save memory, for example, when solving nonlinear
8236:   systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8237:   ILU(0) preconditioner.

8239:   One can specify in-place ILU(0) factorization by calling
8240: .vb
8241:      PCType(pc,PCILU);
8242:      PCFactorSeUseInPlace(pc);
8243: .ve
8244:   or by using the options -pc_type ilu -pc_factor_in_place

8246:   In-place factorization ILU(0) can also be used as a local
8247:   solver for the blocks within the block Jacobi or additive Schwarz
8248:   methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
8249:   for details on setting local solver options.

8251:   Most users should employ the `KSP` interface for linear solvers
8252:   instead of working directly with matrix algebra routines such as this.
8253:   See, e.g., `KSPCreate()`.

8255: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8256: @*/
8257: PetscErrorCode MatSetUnfactored(Mat mat)
8258: {
8259:   PetscFunctionBegin;
8262:   MatCheckPreallocated(mat, 1);
8263:   mat->factortype = MAT_FACTOR_NONE;
8264:   if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8265:   PetscUseTypeMethod(mat, setunfactored);
8266:   PetscFunctionReturn(PETSC_SUCCESS);
8267: }

8269: /*MC
8270:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran

8272:     Synopsis:
8273:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8275:     Not Collective

8277:     Input Parameter:
8278: .   x - matrix

8280:     Output Parameters:
8281: +   xx_v - the Fortran pointer to the array
8282: -   ierr - error code

8284:     Example of Usage:
8285: .vb
8286:       PetscScalar, pointer xx_v(:,:)
8287:       ....
8288:       call MatDenseGetArrayF90(x,xx_v,ierr)
8289:       a = xx_v(3)
8290:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
8291: .ve

8293:     Level: advanced

8295: .seealso: [](ch_matrices), `Mat`, `MatDenseRestoreArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJGetArrayF90()`
8296: M*/

8298: /*MC
8299:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
8300:     accessed with `MatDenseGetArrayF90()`.

8302:     Synopsis:
8303:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8305:     Not Collective

8307:     Input Parameters:
8308: +   x - matrix
8309: -   xx_v - the Fortran90 pointer to the array

8311:     Output Parameter:
8312: .   ierr - error code

8314:     Example of Usage:
8315: .vb
8316:        PetscScalar, pointer xx_v(:,:)
8317:        ....
8318:        call MatDenseGetArrayF90(x,xx_v,ierr)
8319:        a = xx_v(3)
8320:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
8321: .ve

8323:     Level: advanced

8325: .seealso: [](ch_matrices), `Mat`, `MatDenseGetArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJRestoreArrayF90()`
8326: M*/

8328: /*MC
8329:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran.

8331:     Synopsis:
8332:     MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

8334:     Not Collective

8336:     Input Parameter:
8337: .   x - matrix

8339:     Output Parameters:
8340: +   xx_v - the Fortran pointer to the array
8341: -   ierr - error code

8343:     Example of Usage:
8344: .vb
8345:       PetscScalar, pointer xx_v(:)
8346:       ....
8347:       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8348:       a = xx_v(3)
8349:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8350: .ve

8352:     Level: advanced

8354: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseGetArrayF90()`
8355: M*/

8357: /*MC
8358:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
8359:     accessed with `MatSeqAIJGetArrayF90()`.

8361:     Synopsis:
8362:     MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

8364:     Not Collective

8366:     Input Parameters:
8367: +   x - matrix
8368: -   xx_v - the Fortran90 pointer to the array

8370:     Output Parameter:
8371: .   ierr - error code

8373:     Example of Usage:
8374: .vb
8375:        PetscScalar, pointer xx_v(:)
8376:        ....
8377:        call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8378:        a = xx_v(3)
8379:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8380: .ve

8382:     Level: advanced

8384: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseRestoreArrayF90()`
8385: M*/

8387: /*@
8388:   MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8389:   as the original matrix.

8391:   Collective

8393:   Input Parameters:
8394: + mat   - the original matrix
8395: . isrow - parallel `IS` containing the rows this processor should obtain
8396: . iscol - parallel `IS` containing all columns you wish to keep. Each process should list the columns that will be in IT's "diagonal part" in the new matrix.
8397: - cll   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

8399:   Output Parameter:
8400: . newmat - the new submatrix, of the same type as the original matrix

8402:   Level: advanced

8404:   Notes:
8405:   The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

8407:   Some matrix types place restrictions on the row and column indices, such
8408:   as that they be sorted or that they be equal to each other. For `MATBAIJ` and `MATSBAIJ` matrices the indices must include all rows/columns of a block;
8409:   for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

8411:   The index sets may not have duplicate entries.

8413:   The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8414:   the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8415:   to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8416:   will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
8417:   you are finished using it.

8419:   The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8420:   the input matrix.

8422:   If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

8424:   If `isrow` and `iscol` have a nontrivial block-size then the resulting matrix has this block-size as well. This feature
8425:   is used by `PCFIELDSPLIT` to allow easy nesting of its use.

8427:   Example usage:
8428:   Consider the following 8x8 matrix with 34 non-zero values, that is
8429:   assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8430:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8431:   as follows
8432: .vb
8433:             1  2  0  |  0  3  0  |  0  4
8434:     Proc0   0  5  6  |  7  0  0  |  8  0
8435:             9  0 10  | 11  0  0  | 12  0
8436:     -------------------------------------
8437:            13  0 14  | 15 16 17  |  0  0
8438:     Proc1   0 18  0  | 19 20 21  |  0  0
8439:             0  0  0  | 22 23  0  | 24  0
8440:     -------------------------------------
8441:     Proc2  25 26 27  |  0  0 28  | 29  0
8442:            30  0  0  | 31 32 33  |  0 34
8443: .ve

8445:   Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

8447: .vb
8448:             2  0  |  0  3  0  |  0
8449:     Proc0   5  6  |  7  0  0  |  8
8450:     -------------------------------
8451:     Proc1  18  0  | 19 20 21  |  0
8452:     -------------------------------
8453:     Proc2  26 27  |  0  0 28  | 29
8454:             0  0  | 31 32 33  |  0
8455: .ve

8457: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8458: @*/
8459: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8460: {
8461:   PetscMPIInt size;
8462:   Mat        *local;
8463:   IS          iscoltmp;
8464:   PetscBool   flg;

8466:   PetscFunctionBegin;
8470:   PetscAssertPointer(newmat, 5);
8473:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8474:   PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");

8476:   MatCheckPreallocated(mat, 1);
8477:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));

8479:   if (!iscol || isrow == iscol) {
8480:     PetscBool   stride;
8481:     PetscMPIInt grabentirematrix = 0, grab;
8482:     PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8483:     if (stride) {
8484:       PetscInt first, step, n, rstart, rend;
8485:       PetscCall(ISStrideGetInfo(isrow, &first, &step));
8486:       if (step == 1) {
8487:         PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8488:         if (rstart == first) {
8489:           PetscCall(ISGetLocalSize(isrow, &n));
8490:           if (n == rend - rstart) grabentirematrix = 1;
8491:         }
8492:       }
8493:     }
8494:     PetscCall(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8495:     if (grab) {
8496:       PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8497:       if (cll == MAT_INITIAL_MATRIX) {
8498:         *newmat = mat;
8499:         PetscCall(PetscObjectReference((PetscObject)mat));
8500:       }
8501:       PetscFunctionReturn(PETSC_SUCCESS);
8502:     }
8503:   }

8505:   if (!iscol) {
8506:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8507:   } else {
8508:     iscoltmp = iscol;
8509:   }

8511:   /* if original matrix is on just one processor then use submatrix generated */
8512:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8513:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8514:     goto setproperties;
8515:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8516:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8517:     *newmat = *local;
8518:     PetscCall(PetscFree(local));
8519:     goto setproperties;
8520:   } else if (!mat->ops->createsubmatrix) {
8521:     /* Create a new matrix type that implements the operation using the full matrix */
8522:     PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8523:     switch (cll) {
8524:     case MAT_INITIAL_MATRIX:
8525:       PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8526:       break;
8527:     case MAT_REUSE_MATRIX:
8528:       PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8529:       break;
8530:     default:
8531:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8532:     }
8533:     PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8534:     goto setproperties;
8535:   }

8537:   PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8538:   PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8539:   PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));

8541: setproperties:
8542:   PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8543:   if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8544:   if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8545:   if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8546:   PetscFunctionReturn(PETSC_SUCCESS);
8547: }

8549: /*@
8550:   MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

8552:   Not Collective

8554:   Input Parameters:
8555: + A - the matrix we wish to propagate options from
8556: - B - the matrix we wish to propagate options to

8558:   Level: beginner

8560:   Note:
8561:   Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`

8563: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8564: @*/
8565: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8566: {
8567:   PetscFunctionBegin;
8570:   B->symmetry_eternal            = A->symmetry_eternal;
8571:   B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8572:   B->symmetric                   = A->symmetric;
8573:   B->structurally_symmetric      = A->structurally_symmetric;
8574:   B->spd                         = A->spd;
8575:   B->hermitian                   = A->hermitian;
8576:   PetscFunctionReturn(PETSC_SUCCESS);
8577: }

8579: /*@
8580:   MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8581:   used during the assembly process to store values that belong to
8582:   other processors.

8584:   Not Collective

8586:   Input Parameters:
8587: + mat   - the matrix
8588: <