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;
 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 locations it fills the locations with random numbers.

 74:   It generates an error if used on 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: PETSC_INTERN 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;

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

252: /*@
253:   MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix

255:   Input Parameter:
256: . mat - the matrix

258:   Output Parameter:
259: . keptrows - the rows that are not completely zero

261:   Level: intermediate

263:   Note:
264:   `keptrows` is set to `NULL` if all rows are nonzero.

266: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
267:  @*/
268: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
269: {
270:   PetscFunctionBegin;
273:   PetscAssertPointer(keptrows, 2);
274:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
275:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
276:   if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
277:   else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
278:   PetscFunctionReturn(PETSC_SUCCESS);
279: }

281: /*@
282:   MatFindZeroRows - Locate all rows that are completely zero in the matrix

284:   Input Parameter:
285: . mat - the matrix

287:   Output Parameter:
288: . zerorows - the rows that are completely zero

290:   Level: intermediate

292:   Note:
293:   `zerorows` is set to `NULL` if no rows are zero.

295: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
296:  @*/
297: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
298: {
299:   IS       keptrows;
300:   PetscInt m, n;

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

320: /*@
321:   MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling

323:   Not Collective

325:   Input Parameter:
326: . A - the matrix

328:   Output Parameter:
329: . a - the diagonal part (which is a SEQUENTIAL matrix)

331:   Level: advanced

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

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

338: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
339: @*/
340: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
341: {
342:   PetscFunctionBegin;
345:   PetscAssertPointer(a, 2);
346:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
347:   if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
348:   else {
349:     PetscMPIInt size;

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

358: /*@
359:   MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.

361:   Collective

363:   Input Parameter:
364: . mat - the matrix

366:   Output Parameter:
367: . trace - the sum of the diagonal entries

369:   Level: advanced

371: .seealso: [](ch_matrices), `Mat`
372: @*/
373: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
374: {
375:   Vec diag;

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

387: /*@
388:   MatRealPart - Zeros out the imaginary part of the matrix

390:   Logically Collective

392:   Input Parameter:
393: . mat - the matrix

395:   Level: advanced

397: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
398: @*/
399: PetscErrorCode MatRealPart(Mat mat)
400: {
401:   PetscFunctionBegin;
404:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
405:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
406:   MatCheckPreallocated(mat, 1);
407:   PetscUseTypeMethod(mat, realpart);
408:   PetscFunctionReturn(PETSC_SUCCESS);
409: }

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

414:   Collective

416:   Input Parameter:
417: . mat - the matrix

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

423:   Level: advanced

425:   Note:
426:   `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()`

428: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`
429: @*/
430: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
431: {
432:   PetscFunctionBegin;
435:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
436:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
437:   if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
438:   else {
439:     if (nghosts) *nghosts = 0;
440:     if (ghosts) *ghosts = NULL;
441:   }
442:   PetscFunctionReturn(PETSC_SUCCESS);
443: }

445: /*@
446:   MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

448:   Logically Collective

450:   Input Parameter:
451: . mat - the matrix

453:   Level: advanced

455: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
456: @*/
457: PetscErrorCode MatImaginaryPart(Mat mat)
458: {
459:   PetscFunctionBegin;
462:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
463:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
464:   MatCheckPreallocated(mat, 1);
465:   PetscUseTypeMethod(mat, imaginarypart);
466:   PetscFunctionReturn(PETSC_SUCCESS);
467: }

469: /*@
470:   MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices)

472:   Not Collective

474:   Input Parameter:
475: . mat - the matrix

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

481:   Level: advanced

483: .seealso: [](ch_matrices), `Mat`
484: @*/
485: PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
486: {
487:   PetscFunctionBegin;
490:   PetscAssertPointer(missing, 2);
491:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
492:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
493:   PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
494:   PetscFunctionReturn(PETSC_SUCCESS);
495: }

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

503:   Not Collective

505:   Input Parameters:
506: + mat - the matrix
507: - row - the row to get

509:   Output Parameters:
510: + ncols - if not `NULL`, the number of nonzeros in the row
511: . cols  - if not `NULL`, the column numbers
512: - vals  - if not `NULL`, the values

514:   Level: advanced

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

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

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

527:   The user can only examine the values extracted with `MatGetRow()`;
528:   the values cannot be altered.  To change the matrix entries, one
529:   must use `MatSetValues()`.

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

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

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

542:   Fortran Notes:
543:   The calling sequence is
544: .vb
545:    MatGetRow(matrix,row,ncols,cols,values,ierr)
546:          Mat     matrix (input)
547:          integer row    (input)
548:          integer ncols  (output)
549:          integer cols(maxcols) (output)
550:          double precision (or double complex) values(maxcols) output
551: .ve
552:   where maxcols >= maximum nonzeros in any row of the matrix.

554:   Caution:
555:   Do not try to change the contents of the output arrays (`cols` and `vals`).
556:   In some cases, this may corrupt the matrix.

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

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

578: /*@
579:   MatConjugate - replaces the matrix values with their complex conjugates

581:   Logically Collective

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

586:   Level: advanced

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

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

605:   Not Collective

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

614:   Level: advanced

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

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

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

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

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

654: /*@
655:   MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
656:   You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.

658:   Not Collective

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

663:   Level: advanced

665:   Note:
666:   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.

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

683: /*@
684:   MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.

686:   Not Collective

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

691:   Level: advanced

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

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

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

715:   Logically Collective

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

721:   Level: advanced

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

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

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

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

745:   Logically Collective

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

751:   Level: developer

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

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

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

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

781:   Logically Collective

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

787:   Level: developer

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

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

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

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

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

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

825:   Logically Collective

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

831:   Level: advanced

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

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

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

851:   Not Collective

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

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

859:   Level: advanced

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

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

876: /*@
877:   MatResetPreallocation - Reset matrix to use the original nonzero pattern provided by users.

879:   Collective

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

884:   Level: beginner

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

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

891:   Currently only supported for  `MATAIJ` matrices.

893: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
894: @*/
895: PetscErrorCode MatResetPreallocation(Mat A)
896: {
897:   PetscFunctionBegin;
900:   PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAsssemblyEnd()");
901:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
902:   PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
903:   PetscFunctionReturn(PETSC_SUCCESS);
904: }

906: /*@
907:   MatSetUp - Sets up the internal matrix data structures for later use.

909:   Collective

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

914:   Level: intermediate

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

920:   If a suitable preallocation routine is used, this function does not need to be called.

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

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

933:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
934:     PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
935:   }
936:   if (!A->preallocated) PetscTryTypeMethod(A, setup);
937:   PetscCall(PetscLayoutSetUp(A->rmap));
938:   PetscCall(PetscLayoutSetUp(A->cmap));
939:   A->preallocated = PETSC_TRUE;
940:   PetscFunctionReturn(PETSC_SUCCESS);
941: }

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

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

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

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

959:   Collective

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

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

969:   Level: intermediate

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

983: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
984: @*/
985: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
986: {
987:   PetscFunctionBegin;
989: #if !defined(PETSC_HAVE_THREADSAFETY)
990:   if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
991: #endif
992:   PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
993:   PetscFunctionReturn(PETSC_SUCCESS);
994: }

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

999:   Collective

1001:   Input Parameters:
1002: + mat    - the matrix
1003: - viewer - visualization context

1005:   Options Database Keys:
1006: + -mat_view ::ascii_info           - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1007: . -mat_view ::ascii_info_detail    - Prints more detailed info
1008: . -mat_view                        - Prints matrix in ASCII format
1009: . -mat_view ::ascii_matlab         - Prints matrix in Matlab format
1010: . -mat_view draw                   - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1011: . -display <name>                  - Sets display name (default is host)
1012: . -draw_pause <sec>                - Sets number of seconds to pause after display
1013: . -mat_view socket                 - Sends matrix to socket, can be accessed from Matlab (see Users-Manual: ch_matlab for details)
1014: . -viewer_socket_machine <machine> - -
1015: . -viewer_socket_port <port>       - -
1016: . -mat_view binary                 - save matrix to file in binary format
1017: - -viewer_binary_filename <name>   - -

1019:   Level: beginner

1021:   Notes:
1022:   The available visualization contexts include
1023: +    `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1024: .    `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1025: .    `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1026: -     `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure

1028:   The user can open alternative visualization contexts with
1029: +    `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1030: .    `PetscViewerBinaryOpen()` - Outputs matrix in binary to a
1031:   specified file; corresponding input uses `MatLoad()`
1032: .    `PetscViewerDrawOpen()` - Outputs nonzero matrix structure to
1033:   an X window display
1034: -    `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer.
1035:   Currently only the `MATSEQDENSE` and `MATAIJ`
1036:   matrix types support the Socket viewer.

1038:   The user can call `PetscViewerPushFormat()` to specify the output
1039:   format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1040:   `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`).  Available formats include
1041: +    `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1042: .    `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in Matlab format
1043: .    `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1044: .    `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse
1045:   format common among all matrix types
1046: .    `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific
1047:   format (which is in many cases the same as the default)
1048: .    `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix
1049:   size and structure (not the matrix entries)
1050: -    `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about
1051:   the matrix structure

1053:   The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1054:   the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

1056:   In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).

1058:   See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1059:   viewer is used.

1061:   See share/petsc/matlab/PetscBinaryRead.m for a Matlab code that can read in the binary file when the binary
1062:   viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.

1064:   One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1065:   and then use the following mouse functions.
1066: .vb
1067:   left mouse: zoom in
1068:   middle mouse: zoom out
1069:   right mouse: continue with the simulation
1070: .ve

1072: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1073:           `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1074: @*/
1075: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1076: {
1077:   PetscInt          rows, cols, rbs, cbs;
1078:   PetscBool         isascii, isstring, issaws;
1079:   PetscViewerFormat format;
1080:   PetscMPIInt       size;

1082:   PetscFunctionBegin;
1085:   if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1087:   PetscCheckSameComm(mat, 1, viewer, 2);

1089:   PetscCall(PetscViewerGetFormat(viewer, &format));
1090:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
1091:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);

1093: #if !defined(PETSC_HAVE_THREADSAFETY)
1094:   insidematview++;
1095: #endif
1096:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1097:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1098:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1099:   PetscCheck((isascii && (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL)) || !mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "No viewers for factored matrix except ASCII, info, or info_detail");

1101:   PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1102:   if (isascii) {
1103:     if (!mat->preallocated) {
1104:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1105: #if !defined(PETSC_HAVE_THREADSAFETY)
1106:       insidematview--;
1107: #endif
1108:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1109:       PetscFunctionReturn(PETSC_SUCCESS);
1110:     }
1111:     if (!mat->assembled) {
1112:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1113: #if !defined(PETSC_HAVE_THREADSAFETY)
1114:       insidematview--;
1115: #endif
1116:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1117:       PetscFunctionReturn(PETSC_SUCCESS);
1118:     }
1119:     PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1120:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1121:       MatNullSpace nullsp, transnullsp;

1123:       PetscCall(PetscViewerASCIIPushTab(viewer));
1124:       PetscCall(MatGetSize(mat, &rows, &cols));
1125:       PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1126:       if (rbs != 1 || cbs != 1) {
1127:         if (rbs != cbs) PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", rbs=%" PetscInt_FMT ", cbs=%" PetscInt_FMT "\n", rows, cols, rbs, cbs));
1128:         else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "\n", rows, cols, rbs));
1129:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1130:       if (mat->factortype) {
1131:         MatSolverType solver;
1132:         PetscCall(MatFactorGetSolverType(mat, &solver));
1133:         PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1134:       }
1135:       if (mat->ops->getinfo) {
1136:         MatInfo info;
1137:         PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1138:         PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1139:         if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1140:       }
1141:       PetscCall(MatGetNullSpace(mat, &nullsp));
1142:       PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1143:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached null space\n"));
1144:       if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached transposed null space\n"));
1145:       PetscCall(MatGetNearNullSpace(mat, &nullsp));
1146:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached near null space\n"));
1147:       PetscCall(PetscViewerASCIIPushTab(viewer));
1148:       PetscCall(MatProductView(mat, viewer));
1149:       PetscCall(PetscViewerASCIIPopTab(viewer));
1150:     }
1151:   } else if (issaws) {
1152: #if defined(PETSC_HAVE_SAWS)
1153:     PetscMPIInt rank;

1155:     PetscCall(PetscObjectName((PetscObject)mat));
1156:     PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1157:     if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1158: #endif
1159:   } else if (isstring) {
1160:     const char *type;
1161:     PetscCall(MatGetType(mat, &type));
1162:     PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1163:     PetscTryTypeMethod(mat, view, viewer);
1164:   }
1165:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1166:     PetscCall(PetscViewerASCIIPushTab(viewer));
1167:     PetscUseTypeMethod(mat, viewnative, viewer);
1168:     PetscCall(PetscViewerASCIIPopTab(viewer));
1169:   } else if (mat->ops->view) {
1170:     PetscCall(PetscViewerASCIIPushTab(viewer));
1171:     PetscUseTypeMethod(mat, view, viewer);
1172:     PetscCall(PetscViewerASCIIPopTab(viewer));
1173:   }
1174:   if (isascii) {
1175:     PetscCall(PetscViewerGetFormat(viewer, &format));
1176:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1177:   }
1178:   PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1179: #if !defined(PETSC_HAVE_THREADSAFETY)
1180:   insidematview--;
1181: #endif
1182:   PetscFunctionReturn(PETSC_SUCCESS);
1183: }

1185: #if defined(PETSC_USE_DEBUG)
1186: #include <../src/sys/totalview/tv_data_display.h>
1187: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1188: {
1189:   TV_add_row("Local rows", "int", &mat->rmap->n);
1190:   TV_add_row("Local columns", "int", &mat->cmap->n);
1191:   TV_add_row("Global rows", "int", &mat->rmap->N);
1192:   TV_add_row("Global columns", "int", &mat->cmap->N);
1193:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1194:   return TV_format_OK;
1195: }
1196: #endif

1198: /*@C
1199:   MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1200:   with `MatView()`.  The matrix format is determined from the options database.
1201:   Generates a parallel MPI matrix if the communicator has more than one
1202:   processor.  The default matrix type is `MATAIJ`.

1204:   Collective

1206:   Input Parameters:
1207: + mat    - the newly loaded matrix, this needs to have been created with `MatCreate()`
1208:             or some related function before a call to `MatLoad()`
1209: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer

1211:   Options Database Keys:
1212:    Used with block matrix formats (`MATSEQBAIJ`,  ...) to specify
1213:    block size
1214: . -matload_block_size <bs> - set block size

1216:   Level: beginner

1218:   Notes:
1219:   If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1220:   `Mat` before calling this routine if you wish to set it from the options database.

1222:   `MatLoad()` automatically loads into the options database any options
1223:   given in the file filename.info where filename is the name of the file
1224:   that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1225:   file will be ignored if you use the -viewer_binary_skip_info option.

1227:   If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1228:   sets the default matrix type AIJ and sets the local and global sizes.
1229:   If type and/or size is already set, then the same are used.

1231:   In parallel, each processor can load a subset of rows (or the
1232:   entire matrix).  This routine is especially useful when a large
1233:   matrix is stored on disk and only part of it is desired on each
1234:   processor.  For example, a parallel solver may access only some of
1235:   the rows from each processor.  The algorithm used here reads
1236:   relatively small blocks of data rather than reading the entire
1237:   matrix and then subsetting it.

1239:   Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1240:   Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1241:   or the sequence like
1242: .vb
1243:     `PetscViewer` v;
1244:     `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1245:     `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1246:     `PetscViewerSetFromOptions`(v);
1247:     `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1248:     `PetscViewerFileSetName`(v,"datafile");
1249: .ve
1250:   The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1251: $ -viewer_type {binary, hdf5}

1253:   See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1254:   and src/mat/tutorials/ex10.c with the second approach.

1256:   In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1257:   is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1258:   Multiple objects, both matrices and vectors, can be stored within the same file.
1259:   Their `PetscObject` name is ignored; they are loaded in the order of their storage.

1261:   Most users should not need to know the details of the binary storage
1262:   format, since `MatLoad()` and `MatView()` completely hide these details.
1263:   But for anyone who is interested, the standard binary matrix storage
1264:   format is

1266: .vb
1267:     PetscInt    MAT_FILE_CLASSID
1268:     PetscInt    number of rows
1269:     PetscInt    number of columns
1270:     PetscInt    total number of nonzeros
1271:     PetscInt    *number nonzeros in each row
1272:     PetscInt    *column indices of all nonzeros (starting index is zero)
1273:     PetscScalar *values of all nonzeros
1274: .ve
1275:   If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1276:   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
1277:   case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.

1279:   PETSc automatically does the byte swapping for
1280:   machines that store the bytes reversed. Thus if you write your own binary
1281:   read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1282:   and `PetscBinaryWrite()` to see how this may be done.

1284:   In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1285:   Each processor's chunk is loaded independently by its owning MPI process.
1286:   Multiple objects, both matrices and vectors, can be stored within the same file.
1287:   They are looked up by their PetscObject name.

1289:   As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1290:   by default the same structure and naming of the AIJ arrays and column count
1291:   within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1292: $    save example.mat A b -v7.3
1293:   can be directly read by this routine (see Reference 1 for details).

1295:   Depending on your MATLAB version, this format might be a default,
1296:   otherwise you can set it as default in Preferences.

1298:   Unless -nocompression flag is used to save the file in MATLAB,
1299:   PETSc must be configured with ZLIB package.

1301:   See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

1303:   This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`

1305:   Corresponding `MatView()` is not yet implemented.

1307:   The loaded matrix is actually a transpose of the original one in MATLAB,
1308:   unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1309:   With this format, matrix is automatically transposed by PETSc,
1310:   unless the matrix is marked as SPD or symmetric
1311:   (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).

1313:   References:
1314: .  * - MATLAB(R) Documentation, manual page of save(), https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version

1316: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1317:  @*/
1318: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1319: {
1320:   PetscBool flg;

1322:   PetscFunctionBegin;

1326:   if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));

1328:   flg = PETSC_FALSE;
1329:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1330:   if (flg) {
1331:     PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1332:     PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1333:   }
1334:   flg = PETSC_FALSE;
1335:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1336:   if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));

1338:   PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1339:   PetscUseTypeMethod(mat, load, viewer);
1340:   PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1341:   PetscFunctionReturn(PETSC_SUCCESS);
1342: }

1344: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1345: {
1346:   Mat_Redundant *redund = *redundant;

1348:   PetscFunctionBegin;
1349:   if (redund) {
1350:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1351:       PetscCall(ISDestroy(&redund->isrow));
1352:       PetscCall(ISDestroy(&redund->iscol));
1353:       PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1354:     } else {
1355:       PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1356:       PetscCall(PetscFree(redund->sbuf_j));
1357:       PetscCall(PetscFree(redund->sbuf_a));
1358:       for (PetscInt i = 0; i < redund->nrecvs; i++) {
1359:         PetscCall(PetscFree(redund->rbuf_j[i]));
1360:         PetscCall(PetscFree(redund->rbuf_a[i]));
1361:       }
1362:       PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1363:     }

1365:     if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1366:     PetscCall(PetscFree(redund));
1367:   }
1368:   PetscFunctionReturn(PETSC_SUCCESS);
1369: }

1371: /*@C
1372:   MatDestroy - Frees space taken by a matrix.

1374:   Collective

1376:   Input Parameter:
1377: . A - the matrix

1379:   Level: beginner

1381:   Developer Notes:
1382:   Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1383:   `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1384:   `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1385:   if changes are needed here.

1387: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1388: @*/
1389: PetscErrorCode MatDestroy(Mat *A)
1390: {
1391:   PetscFunctionBegin;
1392:   if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1394:   if (--((PetscObject)(*A))->refct > 0) {
1395:     *A = NULL;
1396:     PetscFunctionReturn(PETSC_SUCCESS);
1397:   }

1399:   /* if memory was published with SAWs then destroy it */
1400:   PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1401:   PetscTryTypeMethod((*A), destroy);

1403:   PetscCall(PetscFree((*A)->factorprefix));
1404:   PetscCall(PetscFree((*A)->defaultvectype));
1405:   PetscCall(PetscFree((*A)->defaultrandtype));
1406:   PetscCall(PetscFree((*A)->bsizes));
1407:   PetscCall(PetscFree((*A)->solvertype));
1408:   for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1409:   if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1410:   PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1411:   PetscCall(MatProductClear(*A));
1412:   PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1413:   PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1414:   PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1415:   PetscCall(MatDestroy(&(*A)->schur));
1416:   PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1417:   PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1418:   PetscCall(PetscHeaderDestroy(A));
1419:   PetscFunctionReturn(PETSC_SUCCESS);
1420: }

1422: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1423: /*@C
1424:   MatSetValues - Inserts or adds a block of values into a matrix.
1425:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1426:   MUST be called after all calls to `MatSetValues()` have been completed.

1428:   Not Collective

1430:   Input Parameters:
1431: + mat  - the matrix
1432: . v    - a logically two-dimensional array of values
1433: . m    - the number of rows
1434: . idxm - the global indices of the rows
1435: . n    - the number of columns
1436: . idxn - the global indices of the columns
1437: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1439:   Level: beginner

1441:   Notes:
1442:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1444:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1445:   options cannot be mixed without intervening calls to the assembly
1446:   routines.

1448:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1449:   as well as in C.

1451:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1452:   simply ignored. This allows easily inserting element stiffness matrices
1453:   with homogeneous Dirichlet boundary conditions that you don't want represented
1454:   in the matrix.

1456:   Efficiency Alert:
1457:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1458:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1460:   Developer Notes:
1461:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1462:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1464: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1465:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1466: @*/
1467: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1468: {
1469:   PetscFunctionBeginHot;
1472:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1473:   PetscAssertPointer(idxm, 3);
1474:   PetscAssertPointer(idxn, 5);
1475:   MatCheckPreallocated(mat, 1);

1477:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1478:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

1480:   if (PetscDefined(USE_DEBUG)) {
1481:     PetscInt i, j;

1483:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1484:     for (i = 0; i < m; i++) {
1485:       for (j = 0; j < n; j++) {
1486:         if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1487: #if defined(PETSC_USE_COMPLEX)
1488:           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]);
1489: #else
1490:           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]);
1491: #endif
1492:       }
1493:     }
1494:     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);
1495:     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);
1496:   }

1498:   if (mat->assembled) {
1499:     mat->was_assembled = PETSC_TRUE;
1500:     mat->assembled     = PETSC_FALSE;
1501:   }
1502:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1503:   PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1504:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1505:   PetscFunctionReturn(PETSC_SUCCESS);
1506: }

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

1514:   Not Collective

1516:   Input Parameters:
1517: + mat  - the matrix
1518: . v    - a logically two-dimensional array of values
1519: . ism  - the rows to provide
1520: . isn  - the columns to provide
1521: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1523:   Level: beginner

1525:   Notes:
1526:   By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1528:   Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1529:   options cannot be mixed without intervening calls to the assembly
1530:   routines.

1532:   `MatSetValues()` uses 0-based row and column numbers in Fortran
1533:   as well as in C.

1535:   Negative indices may be passed in `ism` and `isn`, these rows and columns are
1536:   simply ignored. This allows easily inserting element stiffness matrices
1537:   with homogeneous Dirichlet boundary conditions that you don't want represented
1538:   in the matrix.

1540:   Efficiency Alert:
1541:   The routine `MatSetValuesBlocked()` may offer much better efficiency
1542:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1544:   This is currently not optimized for any particular `ISType`

1546:   Developer Notes:
1547:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
1548:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1550: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1551:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1552: @*/
1553: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1554: {
1555:   PetscInt        m, n;
1556:   const PetscInt *rows, *cols;

1558:   PetscFunctionBeginHot;
1560:   PetscCall(ISGetIndices(ism, &rows));
1561:   PetscCall(ISGetIndices(isn, &cols));
1562:   PetscCall(ISGetLocalSize(ism, &m));
1563:   PetscCall(ISGetLocalSize(isn, &n));
1564:   PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1565:   PetscCall(ISRestoreIndices(ism, &rows));
1566:   PetscCall(ISRestoreIndices(isn, &cols));
1567:   PetscFunctionReturn(PETSC_SUCCESS);
1568: }

1570: /*@
1571:   MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1572:   values into a matrix

1574:   Not Collective

1576:   Input Parameters:
1577: + mat - the matrix
1578: . row - the (block) row to set
1579: - v   - a logically two-dimensional array of values

1581:   Level: intermediate

1583:   Notes:
1584:   The values, `v`, are column-oriented (for the block version) and sorted

1586:   All the nonzeros in the row must be provided

1588:   The matrix must have previously had its column indices set, likely by having been assembled.

1590:   The row must belong to this process

1592: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1593:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1594: @*/
1595: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1596: {
1597:   PetscInt globalrow;

1599:   PetscFunctionBegin;
1602:   PetscAssertPointer(v, 3);
1603:   PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1604:   PetscCall(MatSetValuesRow(mat, globalrow, v));
1605:   PetscFunctionReturn(PETSC_SUCCESS);
1606: }

1608: /*@
1609:   MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1610:   values into a matrix

1612:   Not Collective

1614:   Input Parameters:
1615: + mat - the matrix
1616: . row - the (block) row to set
1617: - 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

1619:   Level: advanced

1621:   Notes:
1622:   The values, `v`, are column-oriented for the block version.

1624:   All the nonzeros in the row must be provided

1626:   THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

1628:   The row must belong to this process

1630: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1631:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1632: @*/
1633: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1634: {
1635:   PetscFunctionBeginHot;
1638:   MatCheckPreallocated(mat, 1);
1639:   PetscAssertPointer(v, 3);
1640:   PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1641:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1642:   mat->insertmode = INSERT_VALUES;

1644:   if (mat->assembled) {
1645:     mat->was_assembled = PETSC_TRUE;
1646:     mat->assembled     = PETSC_FALSE;
1647:   }
1648:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1649:   PetscUseTypeMethod(mat, setvaluesrow, row, v);
1650:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1651:   PetscFunctionReturn(PETSC_SUCCESS);
1652: }

1654: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1655: /*@
1656:   MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1657:   Using structured grid indexing

1659:   Not Collective

1661:   Input Parameters:
1662: + mat  - the matrix
1663: . m    - number of rows being entered
1664: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1665: . n    - number of columns being entered
1666: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1667: . v    - a logically two-dimensional array of values
1668: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

1670:   Level: beginner

1672:   Notes:
1673:   By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

1675:   Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1676:   options cannot be mixed without intervening calls to the assembly
1677:   routines.

1679:   The grid coordinates are across the entire grid, not just the local portion

1681:   `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1682:   as well as in C.

1684:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1686:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1687:   or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1689:   The columns and rows in the stencil passed in MUST be contained within the
1690:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1691:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1692:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1693:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1695:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1696:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1697:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1698:   `DM_BOUNDARY_PERIODIC` boundary type.

1700:   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
1701:   a single value per point) you can skip filling those indices.

1703:   Inspired by the structured grid interface to the HYPRE package
1704:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1706:   Efficiency Alert:
1707:   The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1708:   for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1710:   Fortran Notes:
1711:   `idxm` and `idxn` should be declared as
1712: $     MatStencil idxm(4,m),idxn(4,n)
1713:   and the values inserted using
1714: .vb
1715:     idxm(MatStencil_i,1) = i
1716:     idxm(MatStencil_j,1) = j
1717:     idxm(MatStencil_k,1) = k
1718:     idxm(MatStencil_c,1) = c
1719:     etc
1720: .ve

1722: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1723:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1724: @*/
1725: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1726: {
1727:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1728:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1729:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1731:   PetscFunctionBegin;
1732:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1735:   PetscAssertPointer(idxm, 3);
1736:   PetscAssertPointer(idxn, 5);

1738:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1739:     jdxm = buf;
1740:     jdxn = buf + m;
1741:   } else {
1742:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1743:     jdxm = bufm;
1744:     jdxn = bufn;
1745:   }
1746:   for (i = 0; i < m; i++) {
1747:     for (j = 0; j < 3 - sdim; j++) dxm++;
1748:     tmp = *dxm++ - starts[0];
1749:     for (j = 0; j < dim - 1; j++) {
1750:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1751:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1752:     }
1753:     if (mat->stencil.noc) dxm++;
1754:     jdxm[i] = tmp;
1755:   }
1756:   for (i = 0; i < n; i++) {
1757:     for (j = 0; j < 3 - sdim; j++) dxn++;
1758:     tmp = *dxn++ - starts[0];
1759:     for (j = 0; j < dim - 1; j++) {
1760:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1761:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1762:     }
1763:     if (mat->stencil.noc) dxn++;
1764:     jdxn[i] = tmp;
1765:   }
1766:   PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1767:   PetscCall(PetscFree2(bufm, bufn));
1768:   PetscFunctionReturn(PETSC_SUCCESS);
1769: }

1771: /*@
1772:   MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1773:   Using structured grid indexing

1775:   Not Collective

1777:   Input Parameters:
1778: + mat  - the matrix
1779: . m    - number of rows being entered
1780: . idxm - grid coordinates for matrix rows being entered
1781: . n    - number of columns being entered
1782: . idxn - grid coordinates for matrix columns being entered
1783: . v    - a logically two-dimensional array of values
1784: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

1786:   Level: beginner

1788:   Notes:
1789:   By default the values, `v`, are row-oriented and unsorted.
1790:   See `MatSetOption()` for other options.

1792:   Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1793:   options cannot be mixed without intervening calls to the assembly
1794:   routines.

1796:   The grid coordinates are across the entire grid, not just the local portion

1798:   `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1799:   as well as in C.

1801:   For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1803:   In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1804:   or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1806:   The columns and rows in the stencil passed in MUST be contained within the
1807:   ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1808:   if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1809:   local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1810:   first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1812:   Negative indices may be passed in idxm and idxn, these rows and columns are
1813:   simply ignored. This allows easily inserting element stiffness matrices
1814:   with homogeneous Dirichlet boundary conditions that you don't want represented
1815:   in the matrix.

1817:   Inspired by the structured grid interface to the HYPRE package
1818:   (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1820:   Fortran Notes:
1821:   `idxm` and `idxn` should be declared as
1822: $     MatStencil idxm(4,m),idxn(4,n)
1823:   and the values inserted using
1824: .vb
1825:     idxm(MatStencil_i,1) = i
1826:     idxm(MatStencil_j,1) = j
1827:     idxm(MatStencil_k,1) = k
1828:    etc
1829: .ve

1831: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1832:           `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1833:           `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1834: @*/
1835: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1836: {
1837:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1838:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1839:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1841:   PetscFunctionBegin;
1842:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1845:   PetscAssertPointer(idxm, 3);
1846:   PetscAssertPointer(idxn, 5);
1847:   PetscAssertPointer(v, 6);

1849:   if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1850:     jdxm = buf;
1851:     jdxn = buf + m;
1852:   } else {
1853:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1854:     jdxm = bufm;
1855:     jdxn = bufn;
1856:   }
1857:   for (i = 0; i < m; i++) {
1858:     for (j = 0; j < 3 - sdim; j++) dxm++;
1859:     tmp = *dxm++ - starts[0];
1860:     for (j = 0; j < sdim - 1; j++) {
1861:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1862:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1863:     }
1864:     dxm++;
1865:     jdxm[i] = tmp;
1866:   }
1867:   for (i = 0; i < n; i++) {
1868:     for (j = 0; j < 3 - sdim; j++) dxn++;
1869:     tmp = *dxn++ - starts[0];
1870:     for (j = 0; j < sdim - 1; j++) {
1871:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1872:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1873:     }
1874:     dxn++;
1875:     jdxn[i] = tmp;
1876:   }
1877:   PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1878:   PetscCall(PetscFree2(bufm, bufn));
1879:   PetscFunctionReturn(PETSC_SUCCESS);
1880: }

1882: /*@
1883:   MatSetStencil - Sets the grid information for setting values into a matrix via
1884:   `MatSetValuesStencil()`

1886:   Not Collective

1888:   Input Parameters:
1889: + mat    - the matrix
1890: . dim    - dimension of the grid 1, 2, or 3
1891: . dims   - number of grid points in x, y, and z direction, including ghost points on your processor
1892: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1893: - dof    - number of degrees of freedom per node

1895:   Level: beginner

1897:   Notes:
1898:   Inspired by the structured grid interface to the HYPRE package
1899:   (www.llnl.gov/CASC/hyper)

1901:   For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1902:   user.

1904: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1905:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1906: @*/
1907: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1908: {
1909:   PetscFunctionBegin;
1911:   PetscAssertPointer(dims, 3);
1912:   PetscAssertPointer(starts, 4);

1914:   mat->stencil.dim = dim + (dof > 1);
1915:   for (PetscInt i = 0; i < dim; i++) {
1916:     mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
1917:     mat->stencil.starts[i] = starts[dim - i - 1];
1918:   }
1919:   mat->stencil.dims[dim]   = dof;
1920:   mat->stencil.starts[dim] = 0;
1921:   mat->stencil.noc         = (PetscBool)(dof == 1);
1922:   PetscFunctionReturn(PETSC_SUCCESS);
1923: }

1925: /*@C
1926:   MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

1928:   Not Collective

1930:   Input Parameters:
1931: + mat  - the matrix
1932: . v    - a logically two-dimensional array of values
1933: . m    - the number of block rows
1934: . idxm - the global block indices
1935: . n    - the number of block columns
1936: . idxn - the global block indices
1937: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

1939:   Level: intermediate

1941:   Notes:
1942:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
1943:   MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

1945:   The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
1946:   NOT the total number of rows/columns; for example, if the block size is 2 and
1947:   you are passing in values for rows 2,3,4,5  then m would be 2 (not 4).
1948:   The values in idxm would be 1 2; that is the first index for each block divided by
1949:   the block size.

1951:   You must call `MatSetBlockSize()` when constructing this matrix (before
1952:   preallocating it).

1954:   By default the values, `v`, are row-oriented, so the layout of
1955:   `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.

1957:   Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
1958:   options cannot be mixed without intervening calls to the assembly
1959:   routines.

1961:   `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
1962:   as well as in C.

1964:   Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1965:   simply ignored. This allows easily inserting element stiffness matrices
1966:   with homogeneous Dirichlet boundary conditions that you don't want represented
1967:   in the matrix.

1969:   Each time an entry is set within a sparse matrix via `MatSetValues()`,
1970:   internal searching must be done to determine where to place the
1971:   data in the matrix storage space.  By instead inserting blocks of
1972:   entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
1973:   reduced.

1975:   Example:
1976: .vb
1977:    Suppose m=n=2 and block size(bs) = 2 The array is

1979:    1  2  | 3  4
1980:    5  6  | 7  8
1981:    - - - | - - -
1982:    9  10 | 11 12
1983:    13 14 | 15 16

1985:    v[] should be passed in like
1986:    v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

1988:   If you are not using row oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
1989:    v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
1990: .ve

1992: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
1993: @*/
1994: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1995: {
1996:   PetscFunctionBeginHot;
1999:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2000:   PetscAssertPointer(idxm, 3);
2001:   PetscAssertPointer(idxn, 5);
2002:   MatCheckPreallocated(mat, 1);
2003:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2004:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2005:   if (PetscDefined(USE_DEBUG)) {
2006:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2007:     PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2008:   }
2009:   if (PetscDefined(USE_DEBUG)) {
2010:     PetscInt rbs, cbs, M, N, i;
2011:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2012:     PetscCall(MatGetSize(mat, &M, &N));
2013:     for (i = 0; i < m; i++) PetscCheck(idxm[i] * rbs < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row block index %" PetscInt_FMT " (index %" PetscInt_FMT ") greater than row length %" PetscInt_FMT, i, idxm[i], M);
2014:     for (i = 0; i < n; i++) PetscCheck(idxn[i] * cbs < N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column block index %" PetscInt_FMT " (index %" PetscInt_FMT ") great than column length %" PetscInt_FMT, i, idxn[i], N);
2015:   }
2016:   if (mat->assembled) {
2017:     mat->was_assembled = PETSC_TRUE;
2018:     mat->assembled     = PETSC_FALSE;
2019:   }
2020:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2021:   if (mat->ops->setvaluesblocked) {
2022:     PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2023:   } else {
2024:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2025:     PetscInt i, j, bs, cbs;

2027:     PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2028:     if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2029:       iidxm = buf;
2030:       iidxn = buf + m * bs;
2031:     } else {
2032:       PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2033:       iidxm = bufr;
2034:       iidxn = bufc;
2035:     }
2036:     for (i = 0; i < m; i++) {
2037:       for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2038:     }
2039:     if (m != n || bs != cbs || idxm != idxn) {
2040:       for (i = 0; i < n; i++) {
2041:         for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2042:       }
2043:     } else iidxn = iidxm;
2044:     PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2045:     PetscCall(PetscFree2(bufr, bufc));
2046:   }
2047:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2048:   PetscFunctionReturn(PETSC_SUCCESS);
2049: }

2051: /*@C
2052:   MatGetValues - Gets a block of local values from a matrix.

2054:   Not Collective; can only return values that are owned by the give process

2056:   Input Parameters:
2057: + mat  - the matrix
2058: . v    - a logically two-dimensional array for storing the values
2059: . m    - the number of rows
2060: . idxm - the  global indices of the rows
2061: . n    - the number of columns
2062: - idxn - the global indices of the columns

2064:   Level: advanced

2066:   Notes:
2067:   The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2068:   The values, `v`, are then returned in a row-oriented format,
2069:   analogous to that used by default in `MatSetValues()`.

2071:   `MatGetValues()` uses 0-based row and column numbers in
2072:   Fortran as well as in C.

2074:   `MatGetValues()` requires that the matrix has been assembled
2075:   with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
2076:   `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2077:   without intermediate matrix assembly.

2079:   Negative row or column indices will be ignored and those locations in `v` will be
2080:   left unchanged.

2082:   For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2083:   That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2084:   from `MatGetOwnershipRange`(mat,&rstart,&rend).

2086: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2087: @*/
2088: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2089: {
2090:   PetscFunctionBegin;
2093:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2094:   PetscAssertPointer(idxm, 3);
2095:   PetscAssertPointer(idxn, 5);
2096:   PetscAssertPointer(v, 6);
2097:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2098:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2099:   MatCheckPreallocated(mat, 1);

2101:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2102:   PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2103:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2104:   PetscFunctionReturn(PETSC_SUCCESS);
2105: }

2107: /*@C
2108:   MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2109:   defined previously by `MatSetLocalToGlobalMapping()`

2111:   Not Collective

2113:   Input Parameters:
2114: + mat  - the matrix
2115: . nrow - number of rows
2116: . irow - the row local indices
2117: . ncol - number of columns
2118: - icol - the column local indices

2120:   Output Parameter:
2121: . y - a logically two-dimensional array of values

2123:   Level: advanced

2125:   Notes:
2126:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

2128:   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,
2129:   are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2130:   determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2131:   with `MatSetLocalToGlobalMapping()`.

2133:   Developer Notes:
2134:   This is labelled with C so does not automatically generate Fortran stubs and interfaces
2135:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2137: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2138:           `MatSetValuesLocal()`, `MatGetValues()`
2139: @*/
2140: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2141: {
2142:   PetscFunctionBeginHot;
2145:   MatCheckPreallocated(mat, 1);
2146:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2147:   PetscAssertPointer(irow, 3);
2148:   PetscAssertPointer(icol, 5);
2149:   if (PetscDefined(USE_DEBUG)) {
2150:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2151:     PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2152:   }
2153:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2154:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2155:   if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2156:   else {
2157:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2158:     if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2159:       irowm = buf;
2160:       icolm = buf + nrow;
2161:     } else {
2162:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2163:       irowm = bufr;
2164:       icolm = bufc;
2165:     }
2166:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2167:     PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2168:     PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2169:     PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2170:     PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2171:     PetscCall(PetscFree2(bufr, bufc));
2172:   }
2173:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2174:   PetscFunctionReturn(PETSC_SUCCESS);
2175: }

2177: /*@
2178:   MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2179:   the same size. Currently, this can only be called once and creates the given matrix.

2181:   Not Collective

2183:   Input Parameters:
2184: + mat  - the matrix
2185: . nb   - the number of blocks
2186: . bs   - the number of rows (and columns) in each block
2187: . rows - a concatenation of the rows for each block
2188: - v    - a concatenation of logically two-dimensional arrays of values

2190:   Level: advanced

2192:   Note:
2193:   `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values

2195:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

2197: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2198:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2199: @*/
2200: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2201: {
2202:   PetscFunctionBegin;
2205:   PetscAssertPointer(rows, 4);
2206:   PetscAssertPointer(v, 5);
2207:   PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

2209:   PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2210:   if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2211:   else {
2212:     for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2213:   }
2214:   PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2215:   PetscFunctionReturn(PETSC_SUCCESS);
2216: }

2218: /*@
2219:   MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2220:   the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2221:   using a local (per-processor) numbering.

2223:   Not Collective

2225:   Input Parameters:
2226: + x        - the matrix
2227: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2228: - cmapping - column mapping

2230:   Level: intermediate

2232:   Note:
2233:   If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix

2235: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2236: @*/
2237: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2238: {
2239:   PetscFunctionBegin;
2244:   if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2245:   else {
2246:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2247:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2248:   }
2249:   PetscFunctionReturn(PETSC_SUCCESS);
2250: }

2252: /*@
2253:   MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`

2255:   Not Collective

2257:   Input Parameter:
2258: . A - the matrix

2260:   Output Parameters:
2261: + rmapping - row mapping
2262: - cmapping - column mapping

2264:   Level: advanced

2266: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2267: @*/
2268: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2269: {
2270:   PetscFunctionBegin;
2273:   if (rmapping) {
2274:     PetscAssertPointer(rmapping, 2);
2275:     *rmapping = A->rmap->mapping;
2276:   }
2277:   if (cmapping) {
2278:     PetscAssertPointer(cmapping, 3);
2279:     *cmapping = A->cmap->mapping;
2280:   }
2281:   PetscFunctionReturn(PETSC_SUCCESS);
2282: }

2284: /*@
2285:   MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix

2287:   Logically Collective

2289:   Input Parameters:
2290: + A    - the matrix
2291: . rmap - row layout
2292: - cmap - column layout

2294:   Level: advanced

2296:   Note:
2297:   The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.

2299: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2300: @*/
2301: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2302: {
2303:   PetscFunctionBegin;
2305:   PetscCall(PetscLayoutReference(rmap, &A->rmap));
2306:   PetscCall(PetscLayoutReference(cmap, &A->cmap));
2307:   PetscFunctionReturn(PETSC_SUCCESS);
2308: }

2310: /*@
2311:   MatGetLayouts - Gets the `PetscLayout` objects for rows and columns

2313:   Not Collective

2315:   Input Parameter:
2316: . A - the matrix

2318:   Output Parameters:
2319: + rmap - row layout
2320: - cmap - column layout

2322:   Level: advanced

2324: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2325: @*/
2326: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2327: {
2328:   PetscFunctionBegin;
2331:   if (rmap) {
2332:     PetscAssertPointer(rmap, 2);
2333:     *rmap = A->rmap;
2334:   }
2335:   if (cmap) {
2336:     PetscAssertPointer(cmap, 3);
2337:     *cmap = A->cmap;
2338:   }
2339:   PetscFunctionReturn(PETSC_SUCCESS);
2340: }

2342: /*@C
2343:   MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2344:   using a local numbering of the nodes.

2346:   Not Collective

2348:   Input Parameters:
2349: + mat  - the matrix
2350: . nrow - number of rows
2351: . irow - the row local indices
2352: . ncol - number of columns
2353: . icol - the column local indices
2354: . y    - a logically two-dimensional array of values
2355: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2357:   Level: intermediate

2359:   Notes:
2360:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call MatXXXXSetPreallocation() or
2361:   `MatSetUp()` before using this routine

2363:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

2365:   Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2366:   options cannot be mixed without intervening calls to the assembly
2367:   routines.

2369:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2370:   MUST be called after all calls to `MatSetValuesLocal()` have been completed.

2372:   Developer Notes:
2373:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2374:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2376: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2377:           `MatGetValuesLocal()`
2378: @*/
2379: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2380: {
2381:   PetscFunctionBeginHot;
2384:   MatCheckPreallocated(mat, 1);
2385:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2386:   PetscAssertPointer(irow, 3);
2387:   PetscAssertPointer(icol, 5);
2388:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2389:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2390:   if (PetscDefined(USE_DEBUG)) {
2391:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2392:     PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2393:   }

2395:   if (mat->assembled) {
2396:     mat->was_assembled = PETSC_TRUE;
2397:     mat->assembled     = PETSC_FALSE;
2398:   }
2399:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2400:   if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2401:   else {
2402:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2403:     const PetscInt *irowm, *icolm;

2405:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2406:       bufr  = buf;
2407:       bufc  = buf + nrow;
2408:       irowm = bufr;
2409:       icolm = bufc;
2410:     } else {
2411:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2412:       irowm = bufr;
2413:       icolm = bufc;
2414:     }
2415:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2416:     else irowm = irow;
2417:     if (mat->cmap->mapping) {
2418:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2419:         PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2420:       } else icolm = irowm;
2421:     } else icolm = icol;
2422:     PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2423:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2424:   }
2425:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2426:   PetscFunctionReturn(PETSC_SUCCESS);
2427: }

2429: /*@C
2430:   MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2431:   using a local ordering of the nodes a block at a time.

2433:   Not Collective

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

2444:   Level: intermediate

2446:   Notes:
2447:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call MatXXXXSetPreallocation() or
2448:   `MatSetUp()` before using this routine

2450:   If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2451:   before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

2453:   Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2454:   options cannot be mixed without intervening calls to the assembly
2455:   routines.

2457:   These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2458:   MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

2460:   Developer Notes:
2461:   This is labeled with C so does not automatically generate Fortran stubs and interfaces
2462:   because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2464: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2465:           `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2466: @*/
2467: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2468: {
2469:   PetscFunctionBeginHot;
2472:   MatCheckPreallocated(mat, 1);
2473:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2474:   PetscAssertPointer(irow, 3);
2475:   PetscAssertPointer(icol, 5);
2476:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2477:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2478:   if (PetscDefined(USE_DEBUG)) {
2479:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2480:     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);
2481:   }

2483:   if (mat->assembled) {
2484:     mat->was_assembled = PETSC_TRUE;
2485:     mat->assembled     = PETSC_FALSE;
2486:   }
2487:   if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2488:     PetscInt irbs, rbs;
2489:     PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2490:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2491:     PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2492:   }
2493:   if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2494:     PetscInt icbs, cbs;
2495:     PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2496:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2497:     PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2498:   }
2499:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2500:   if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2501:   else {
2502:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2503:     const PetscInt *irowm, *icolm;

2505:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2506:       bufr  = buf;
2507:       bufc  = buf + nrow;
2508:       irowm = bufr;
2509:       icolm = bufc;
2510:     } else {
2511:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2512:       irowm = bufr;
2513:       icolm = bufc;
2514:     }
2515:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2516:     else irowm = irow;
2517:     if (mat->cmap->mapping) {
2518:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2519:         PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2520:       } else icolm = irowm;
2521:     } else icolm = icol;
2522:     PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2523:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2524:   }
2525:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2526:   PetscFunctionReturn(PETSC_SUCCESS);
2527: }

2529: /*@
2530:   MatMultDiagonalBlock - Computes the matrix-vector product, y = Dx. Where D is defined by the inode or block structure of the diagonal

2532:   Collective

2534:   Input Parameters:
2535: + mat - the matrix
2536: - x   - the vector to be multiplied

2538:   Output Parameter:
2539: . y - the result

2541:   Level: developer

2543:   Note:
2544:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2545:   call `MatMultDiagonalBlock`(A,y,y).

2547: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2548: @*/
2549: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2550: {
2551:   PetscFunctionBegin;

2557:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2558:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2559:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2560:   MatCheckPreallocated(mat, 1);

2562:   PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2563:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2564:   PetscFunctionReturn(PETSC_SUCCESS);
2565: }

2567: /*@
2568:   MatMult - Computes the matrix-vector product, y = Ax.

2570:   Neighbor-wise Collective

2572:   Input Parameters:
2573: + mat - the matrix
2574: - x   - the vector to be multiplied

2576:   Output Parameter:
2577: . y - the result

2579:   Level: beginner

2581:   Note:
2582:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2583:   call `MatMult`(A,y,y).

2585: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2586: @*/
2587: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2588: {
2589:   PetscFunctionBegin;
2593:   VecCheckAssembled(x);
2595:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2596:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2597:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2598:   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);
2599:   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);
2600:   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);
2601:   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);
2602:   PetscCall(VecSetErrorIfLocked(y, 3));
2603:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2604:   MatCheckPreallocated(mat, 1);

2606:   PetscCall(VecLockReadPush(x));
2607:   PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2608:   PetscUseTypeMethod(mat, mult, x, y);
2609:   PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2610:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2611:   PetscCall(VecLockReadPop(x));
2612:   PetscFunctionReturn(PETSC_SUCCESS);
2613: }

2615: /*@
2616:   MatMultTranspose - Computes matrix transpose times a vector y = A^T * x.

2618:   Neighbor-wise Collective

2620:   Input Parameters:
2621: + mat - the matrix
2622: - x   - the vector to be multiplied

2624:   Output Parameter:
2625: . y - the result

2627:   Level: beginner

2629:   Notes:
2630:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2631:   call `MatMultTranspose`(A,y,y).

2633:   For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2634:   use `MatMultHermitianTranspose()`

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

2642:   PetscFunctionBegin;
2646:   VecCheckAssembled(x);

2649:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2650:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2651:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2652:   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);
2653:   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);
2654:   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);
2655:   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);
2656:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2657:   MatCheckPreallocated(mat, 1);

2659:   if (!mat->ops->multtranspose) {
2660:     if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2661:     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);
2662:   } else op = mat->ops->multtranspose;
2663:   PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2664:   PetscCall(VecLockReadPush(x));
2665:   PetscCall((*op)(mat, x, y));
2666:   PetscCall(VecLockReadPop(x));
2667:   PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2668:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2669:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2670:   PetscFunctionReturn(PETSC_SUCCESS);
2671: }

2673: /*@
2674:   MatMultHermitianTranspose - Computes matrix Hermitian transpose times a vector.

2676:   Neighbor-wise Collective

2678:   Input Parameters:
2679: + mat - the matrix
2680: - x   - the vector to be multiplied

2682:   Output Parameter:
2683: . y - the result

2685:   Level: beginner

2687:   Notes:
2688:   The vectors `x` and `y` cannot be the same.  I.e., one cannot
2689:   call `MatMultHermitianTranspose`(A,y,y).

2691:   Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2693:   For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

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

2705:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2706:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2707:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2708:   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);
2709:   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);
2710:   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);
2711:   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);
2712:   MatCheckPreallocated(mat, 1);

2714:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2715: #if defined(PETSC_USE_COMPLEX)
2716:   if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2717:     PetscCall(VecLockReadPush(x));
2718:     if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2719:     else PetscUseTypeMethod(mat, mult, x, y);
2720:     PetscCall(VecLockReadPop(x));
2721:   } else {
2722:     Vec w;
2723:     PetscCall(VecDuplicate(x, &w));
2724:     PetscCall(VecCopy(x, w));
2725:     PetscCall(VecConjugate(w));
2726:     PetscCall(MatMultTranspose(mat, w, y));
2727:     PetscCall(VecDestroy(&w));
2728:     PetscCall(VecConjugate(y));
2729:   }
2730:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2731: #else
2732:   PetscCall(MatMultTranspose(mat, x, y));
2733: #endif
2734:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2735:   PetscFunctionReturn(PETSC_SUCCESS);
2736: }

2738: /*@
2739:   MatMultAdd -  Computes v3 = v2 + A * v1.

2741:   Neighbor-wise Collective

2743:   Input Parameters:
2744: + mat - the matrix
2745: . v1  - the vector to be multiplied by `mat`
2746: - v2  - the vector to be added to the result

2748:   Output Parameter:
2749: . v3 - the result

2751:   Level: beginner

2753:   Note:
2754:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2755:   call `MatMultAdd`(A,v1,v2,v1).

2757: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2758: @*/
2759: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2760: {
2761:   PetscFunctionBegin;

2768:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2769:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2770:   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);
2771:   /* 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);
2772:      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); */
2773:   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);
2774:   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);
2775:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2776:   MatCheckPreallocated(mat, 1);

2778:   PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2779:   PetscCall(VecLockReadPush(v1));
2780:   PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2781:   PetscCall(VecLockReadPop(v1));
2782:   PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2783:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2784:   PetscFunctionReturn(PETSC_SUCCESS);
2785: }

2787: /*@
2788:   MatMultTransposeAdd - Computes v3 = v2 + A' * v1.

2790:   Neighbor-wise Collective

2792:   Input Parameters:
2793: + mat - the matrix
2794: . v1  - the vector to be multiplied by the transpose of the matrix
2795: - v2  - the vector to be added to the result

2797:   Output Parameter:
2798: . v3 - the result

2800:   Level: beginner

2802:   Note:
2803:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2804:   call `MatMultTransposeAdd`(A,v1,v2,v1).

2806: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2807: @*/
2808: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2809: {
2810:   PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

2812:   PetscFunctionBegin;

2819:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2820:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2821:   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);
2822:   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);
2823:   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);
2824:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2825:   PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2826:   MatCheckPreallocated(mat, 1);

2828:   PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2829:   PetscCall(VecLockReadPush(v1));
2830:   PetscCall((*op)(mat, v1, v2, v3));
2831:   PetscCall(VecLockReadPop(v1));
2832:   PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2833:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2834:   PetscFunctionReturn(PETSC_SUCCESS);
2835: }

2837: /*@
2838:   MatMultHermitianTransposeAdd - Computes v3 = v2 + A^H * v1.

2840:   Neighbor-wise Collective

2842:   Input Parameters:
2843: + mat - the matrix
2844: . v1  - the vector to be multiplied by the Hermitian transpose
2845: - v2  - the vector to be added to the result

2847:   Output Parameter:
2848: . v3 - the result

2850:   Level: beginner

2852:   Note:
2853:   The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2854:   call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

2856: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2857: @*/
2858: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2859: {
2860:   PetscFunctionBegin;

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

2875:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2876:   PetscCall(VecLockReadPush(v1));
2877:   if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2878:   else {
2879:     Vec w, z;
2880:     PetscCall(VecDuplicate(v1, &w));
2881:     PetscCall(VecCopy(v1, w));
2882:     PetscCall(VecConjugate(w));
2883:     PetscCall(VecDuplicate(v3, &z));
2884:     PetscCall(MatMultTranspose(mat, w, z));
2885:     PetscCall(VecDestroy(&w));
2886:     PetscCall(VecConjugate(z));
2887:     if (v2 != v3) {
2888:       PetscCall(VecWAXPY(v3, 1.0, v2, z));
2889:     } else {
2890:       PetscCall(VecAXPY(v3, 1.0, z));
2891:     }
2892:     PetscCall(VecDestroy(&z));
2893:   }
2894:   PetscCall(VecLockReadPop(v1));
2895:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2896:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2897:   PetscFunctionReturn(PETSC_SUCCESS);
2898: }

2900: /*@C
2901:   MatGetFactorType - gets the type of factorization it is

2903:   Not Collective

2905:   Input Parameter:
2906: . mat - the matrix

2908:   Output Parameter:
2909: . 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`

2911:   Level: intermediate

2913: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2914:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2915: @*/
2916: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
2917: {
2918:   PetscFunctionBegin;
2921:   PetscAssertPointer(t, 2);
2922:   *t = mat->factortype;
2923:   PetscFunctionReturn(PETSC_SUCCESS);
2924: }

2926: /*@C
2927:   MatSetFactorType - sets the type of factorization it is

2929:   Logically Collective

2931:   Input Parameters:
2932: + mat - the matrix
2933: - 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`

2935:   Level: intermediate

2937: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2938:           `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2939: @*/
2940: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
2941: {
2942:   PetscFunctionBegin;
2945:   mat->factortype = t;
2946:   PetscFunctionReturn(PETSC_SUCCESS);
2947: }

2949: /*@C
2950:   MatGetInfo - Returns information about matrix storage (number of
2951:   nonzeros, memory, etc.).

2953:   Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

2955:   Input Parameters:
2956: + mat  - the matrix
2957: - 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)

2959:   Output Parameter:
2960: . info - matrix information context

2962:   Options Database Key:
2963: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

2965:   Notes:
2966:   The `MatInfo` context contains a variety of matrix data, including
2967:   number of nonzeros allocated and used, number of mallocs during
2968:   matrix assembly, etc.  Additional information for factored matrices
2969:   is provided (such as the fill ratio, number of mallocs during
2970:   factorization, etc.).

2972:   Example:
2973:   See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
2974:   data within the MatInfo context.  For example,
2975: .vb
2976:       MatInfo info;
2977:       Mat     A;
2978:       double  mal, nz_a, nz_u;

2980:       MatGetInfo(A, MAT_LOCAL, &info);
2981:       mal  = info.mallocs;
2982:       nz_a = info.nz_allocated;
2983: .ve

2985:   Fortran users should declare info as a double precision
2986:   array of dimension `MAT_INFO_SIZE`, and then extract the parameters
2987:   of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
2988:   a complete list of parameter names.
2989: .vb
2990:       double  precision info(MAT_INFO_SIZE)
2991:       double  precision mal, nz_a
2992:       Mat     A
2993:       integer ierr

2995:       call MatGetInfo(A, MAT_LOCAL, info, ierr)
2996:       mal = info(MAT_INFO_MALLOCS)
2997:       nz_a = info(MAT_INFO_NZ_ALLOCATED)
2998: .ve

3000:   Level: intermediate

3002:   Developer Notes:
3003:   The Fortran interface is not autogenerated as the
3004:   interface definition cannot be generated correctly [due to `MatInfo` argument]

3006: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3007: @*/
3008: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3009: {
3010:   PetscFunctionBegin;
3013:   PetscAssertPointer(info, 3);
3014:   MatCheckPreallocated(mat, 1);
3015:   PetscUseTypeMethod(mat, getinfo, flag, info);
3016:   PetscFunctionReturn(PETSC_SUCCESS);
3017: }

3019: /*
3020:    This is used by external packages where it is not easy to get the info from the actual
3021:    matrix factorization.
3022: */
3023: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3024: {
3025:   PetscFunctionBegin;
3026:   PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3027:   PetscFunctionReturn(PETSC_SUCCESS);
3028: }

3030: /*@C
3031:   MatLUFactor - Performs in-place LU factorization of matrix.

3033:   Collective

3035:   Input Parameters:
3036: + mat  - the matrix
3037: . row  - row permutation
3038: . col  - column permutation
3039: - info - options for factorization, includes
3040: .vb
3041:           fill - expected fill as ratio of original fill.
3042:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3043:                    Run with the option -info to determine an optimal value to use
3044: .ve

3046:   Level: developer

3048:   Notes:
3049:   Most users should employ the `KSP` interface for linear solvers
3050:   instead of working directly with matrix algebra routines such as this.
3051:   See, e.g., `KSPCreate()`.

3053:   This changes the state of the matrix to a factored matrix; it cannot be used
3054:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3056:   This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3057:   when not using `KSP`.

3059:   Developer Notes:
3060:   The Fortran interface is not autogenerated as the
3061:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3063: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3064:           `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3065: @*/
3066: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3067: {
3068:   MatFactorInfo tinfo;

3070:   PetscFunctionBegin;
3074:   if (info) PetscAssertPointer(info, 4);
3076:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3077:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3078:   MatCheckPreallocated(mat, 1);
3079:   if (!info) {
3080:     PetscCall(MatFactorInfoInitialize(&tinfo));
3081:     info = &tinfo;
3082:   }

3084:   PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3085:   PetscUseTypeMethod(mat, lufactor, row, col, info);
3086:   PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3087:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3088:   PetscFunctionReturn(PETSC_SUCCESS);
3089: }

3091: /*@C
3092:   MatILUFactor - Performs in-place ILU factorization of matrix.

3094:   Collective

3096:   Input Parameters:
3097: + mat  - the matrix
3098: . row  - row permutation
3099: . col  - column permutation
3100: - info - structure containing
3101: .vb
3102:       levels - number of levels of fill.
3103:       expected fill - as ratio of original fill.
3104:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3105:                 missing diagonal entries)
3106: .ve

3108:   Level: developer

3110:   Notes:
3111:   Most users should employ the `KSP` interface for linear solvers
3112:   instead of working directly with matrix algebra routines such as this.
3113:   See, e.g., `KSPCreate()`.

3115:   Probably really in-place only when level of fill is zero, otherwise allocates
3116:   new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3117:   when not using `KSP`.

3119:   Developer Notes:
3120:   The Fortran interface is not autogenerated as the
3121:   interface definition cannot be generated correctly [due to MatFactorInfo]

3123: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3124: @*/
3125: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3126: {
3127:   PetscFunctionBegin;
3131:   PetscAssertPointer(info, 4);
3133:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3134:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3135:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3136:   MatCheckPreallocated(mat, 1);

3138:   PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3139:   PetscUseTypeMethod(mat, ilufactor, row, col, info);
3140:   PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3141:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3142:   PetscFunctionReturn(PETSC_SUCCESS);
3143: }

3145: /*@C
3146:   MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3147:   Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

3149:   Collective

3151:   Input Parameters:
3152: + fact - the factor matrix obtained with `MatGetFactor()`
3153: . mat  - the matrix
3154: . row  - the row permutation
3155: . col  - the column permutation
3156: - info - options for factorization, includes
3157: .vb
3158:           fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3159:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3160: .ve

3162:   Level: developer

3164:   Notes:
3165:   See [Matrix Factorization](sec_matfactor) for additional information about factorizations

3167:   Most users should employ the simplified `KSP` interface for linear solvers
3168:   instead of working directly with matrix algebra routines such as this.
3169:   See, e.g., `KSPCreate()`.

3171:   Developer Notes:
3172:   The Fortran interface is not autogenerated as the
3173:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

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

3181:   PetscFunctionBegin;
3186:   if (info) PetscAssertPointer(info, 5);
3189:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3190:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3191:   MatCheckPreallocated(mat, 2);
3192:   if (!info) {
3193:     PetscCall(MatFactorInfoInitialize(&tinfo));
3194:     info = &tinfo;
3195:   }

3197:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3198:   PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3199:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3200:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3201:   PetscFunctionReturn(PETSC_SUCCESS);
3202: }

3204: /*@C
3205:   MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3206:   Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

3208:   Collective

3210:   Input Parameters:
3211: + fact - the factor matrix obtained with `MatGetFactor()`
3212: . mat  - the matrix
3213: - info - options for factorization

3215:   Level: developer

3217:   Notes:
3218:   See `MatLUFactor()` for in-place factorization.  See
3219:   `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

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

3225:   Developer Notes:
3226:   The Fortran interface is not autogenerated as the
3227:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3229: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3230: @*/
3231: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3232: {
3233:   MatFactorInfo tinfo;

3235:   PetscFunctionBegin;
3240:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3241:   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,
3242:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3244:   MatCheckPreallocated(mat, 2);
3245:   if (!info) {
3246:     PetscCall(MatFactorInfoInitialize(&tinfo));
3247:     info = &tinfo;
3248:   }

3250:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3251:   else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3252:   PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3253:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3254:   else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3255:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3256:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3257:   PetscFunctionReturn(PETSC_SUCCESS);
3258: }

3260: /*@C
3261:   MatCholeskyFactor - Performs in-place Cholesky factorization of a
3262:   symmetric matrix.

3264:   Collective

3266:   Input Parameters:
3267: + mat  - the matrix
3268: . perm - row and column permutations
3269: - info - expected fill as ratio of original fill

3271:   Level: developer

3273:   Notes:
3274:   See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
3275:   `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

3277:   Most users should employ the `KSP` interface for linear solvers
3278:   instead of working directly with matrix algebra routines such as this.
3279:   See, e.g., `KSPCreate()`.

3281:   Developer Notes:
3282:   The Fortran interface is not autogenerated as the
3283:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3285: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3286:           `MatGetOrdering()`
3287: @*/
3288: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3289: {
3290:   MatFactorInfo tinfo;

3292:   PetscFunctionBegin;
3295:   if (info) PetscAssertPointer(info, 3);
3297:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3298:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3299:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3300:   MatCheckPreallocated(mat, 1);
3301:   if (!info) {
3302:     PetscCall(MatFactorInfoInitialize(&tinfo));
3303:     info = &tinfo;
3304:   }

3306:   PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3307:   PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3308:   PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3309:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3310:   PetscFunctionReturn(PETSC_SUCCESS);
3311: }

3313: /*@C
3314:   MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3315:   of a symmetric matrix.

3317:   Collective

3319:   Input Parameters:
3320: + fact - the factor matrix obtained with `MatGetFactor()`
3321: . mat  - the matrix
3322: . perm - row and column permutations
3323: - info - options for factorization, includes
3324: .vb
3325:           fill - expected fill as ratio of original fill.
3326:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3327:                    Run with the option -info to determine an optimal value to use
3328: .ve

3330:   Level: developer

3332:   Notes:
3333:   See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
3334:   `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

3336:   Most users should employ the `KSP` interface for linear solvers
3337:   instead of working directly with matrix algebra routines such as this.
3338:   See, e.g., `KSPCreate()`.

3340:   Developer Notes:
3341:   The Fortran interface is not autogenerated as the
3342:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3344: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3345:           `MatGetOrdering()`
3346: @*/
3347: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3348: {
3349:   MatFactorInfo tinfo;

3351:   PetscFunctionBegin;
3355:   if (info) PetscAssertPointer(info, 4);
3358:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3359:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3360:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3361:   MatCheckPreallocated(mat, 2);
3362:   if (!info) {
3363:     PetscCall(MatFactorInfoInitialize(&tinfo));
3364:     info = &tinfo;
3365:   }

3367:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3368:   PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3369:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3370:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3371:   PetscFunctionReturn(PETSC_SUCCESS);
3372: }

3374: /*@C
3375:   MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3376:   of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3377:   `MatCholeskyFactorSymbolic()`.

3379:   Collective

3381:   Input Parameters:
3382: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3383: . mat  - the initial matrix that is to be factored
3384: - info - options for factorization

3386:   Level: developer

3388:   Note:
3389:   Most users should employ the `KSP` interface for linear solvers
3390:   instead of working directly with matrix algebra routines such as this.
3391:   See, e.g., `KSPCreate()`.

3393:   Developer Notes:
3394:   The Fortran interface is not autogenerated as the
3395:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

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

3403:   PetscFunctionBegin;
3408:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3409:   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,
3410:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3411:   MatCheckPreallocated(mat, 2);
3412:   if (!info) {
3413:     PetscCall(MatFactorInfoInitialize(&tinfo));
3414:     info = &tinfo;
3415:   }

3417:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3418:   else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3419:   PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3420:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3421:   else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3422:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3423:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3424:   PetscFunctionReturn(PETSC_SUCCESS);
3425: }

3427: /*@
3428:   MatQRFactor - Performs in-place QR factorization of matrix.

3430:   Collective

3432:   Input Parameters:
3433: + mat  - the matrix
3434: . col  - column permutation
3435: - info - options for factorization, includes
3436: .vb
3437:           fill - expected fill as ratio of original fill.
3438:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3439:                    Run with the option -info to determine an optimal value to use
3440: .ve

3442:   Level: developer

3444:   Notes:
3445:   Most users should employ the `KSP` interface for linear solvers
3446:   instead of working directly with matrix algebra routines such as this.
3447:   See, e.g., `KSPCreate()`.

3449:   This changes the state of the matrix to a factored matrix; it cannot be used
3450:   for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3452:   Developer Notes:
3453:   The Fortran interface is not autogenerated as the
3454:   interface definition cannot be generated correctly [due to MatFactorInfo]

3456: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3457:           `MatSetUnfactored()`
3458: @*/
3459: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3460: {
3461:   PetscFunctionBegin;
3464:   if (info) PetscAssertPointer(info, 3);
3466:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3467:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3468:   MatCheckPreallocated(mat, 1);
3469:   PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3470:   PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3471:   PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3472:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3473:   PetscFunctionReturn(PETSC_SUCCESS);
3474: }

3476: /*@
3477:   MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3478:   Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

3480:   Collective

3482:   Input Parameters:
3483: + fact - the factor matrix obtained with `MatGetFactor()`
3484: . mat  - the matrix
3485: . col  - column permutation
3486: - info - options for factorization, includes
3487: .vb
3488:           fill - expected fill as ratio of original fill.
3489:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3490:                    Run with the option -info to determine an optimal value to use
3491: .ve

3493:   Level: developer

3495:   Note:
3496:   Most users should employ the `KSP` interface for linear solvers
3497:   instead of working directly with matrix algebra routines such as this.
3498:   See, e.g., `KSPCreate()`.

3500:   Developer Notes:
3501:   The Fortran interface is not autogenerated as the
3502:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3504: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3505: @*/
3506: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3507: {
3508:   MatFactorInfo tinfo;

3510:   PetscFunctionBegin;
3514:   if (info) PetscAssertPointer(info, 4);
3517:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3518:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3519:   MatCheckPreallocated(mat, 2);
3520:   if (!info) {
3521:     PetscCall(MatFactorInfoInitialize(&tinfo));
3522:     info = &tinfo;
3523:   }

3525:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3526:   PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3527:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3528:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3529:   PetscFunctionReturn(PETSC_SUCCESS);
3530: }

3532: /*@
3533:   MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3534:   Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

3536:   Collective

3538:   Input Parameters:
3539: + fact - the factor matrix obtained with `MatGetFactor()`
3540: . mat  - the matrix
3541: - info - options for factorization

3543:   Level: developer

3545:   Notes:
3546:   See `MatQRFactor()` for in-place factorization.

3548:   Most users should employ the `KSP` interface for linear solvers
3549:   instead of working directly with matrix algebra routines such as this.
3550:   See, e.g., `KSPCreate()`.

3552:   Developer Notes:
3553:   The Fortran interface is not autogenerated as the
3554:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

3556: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3557: @*/
3558: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3559: {
3560:   MatFactorInfo tinfo;

3562:   PetscFunctionBegin;
3567:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3568:   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,
3569:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3571:   MatCheckPreallocated(mat, 2);
3572:   if (!info) {
3573:     PetscCall(MatFactorInfoInitialize(&tinfo));
3574:     info = &tinfo;
3575:   }

3577:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3578:   else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3579:   PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3580:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3581:   else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3582:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3583:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3584:   PetscFunctionReturn(PETSC_SUCCESS);
3585: }

3587: /*@
3588:   MatSolve - Solves A x = b, given a factored matrix.

3590:   Neighbor-wise Collective

3592:   Input Parameters:
3593: + mat - the factored matrix
3594: - b   - the right-hand-side vector

3596:   Output Parameter:
3597: . x - the result vector

3599:   Level: developer

3601:   Notes:
3602:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3603:   call `MatSolve`(A,x,x).

3605:   Most users should employ the `KSP` interface for linear solvers
3606:   instead of working directly with matrix algebra routines such as this.
3607:   See, e.g., `KSPCreate()`.

3609: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3610: @*/
3611: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3612: {
3613:   PetscFunctionBegin;
3618:   PetscCheckSameComm(mat, 1, b, 2);
3619:   PetscCheckSameComm(mat, 1, x, 3);
3620:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3621:   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);
3622:   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);
3623:   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);
3624:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3625:   MatCheckPreallocated(mat, 1);

3627:   PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3628:   if (mat->factorerrortype) {
3629:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3630:     PetscCall(VecSetInf(x));
3631:   } else PetscUseTypeMethod(mat, solve, b, x);
3632:   PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3633:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3634:   PetscFunctionReturn(PETSC_SUCCESS);
3635: }

3637: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3638: {
3639:   Vec      b, x;
3640:   PetscInt N, i;
3641:   PetscErrorCode (*f)(Mat, Vec, Vec);
3642:   PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

3644:   PetscFunctionBegin;
3645:   if (A->factorerrortype) {
3646:     PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3647:     PetscCall(MatSetInf(X));
3648:     PetscFunctionReturn(PETSC_SUCCESS);
3649:   }
3650:   f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3651:   PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3652:   PetscCall(MatBoundToCPU(A, &Abound));
3653:   if (!Abound) {
3654:     PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3655:     PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3656:   }
3657: #if PetscDefined(HAVE_CUDA)
3658:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3659:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3660: #elif PetscDefined(HAVE_HIP)
3661:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3662:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3663: #endif
3664:   PetscCall(MatGetSize(B, NULL, &N));
3665:   for (i = 0; i < N; i++) {
3666:     PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3667:     PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3668:     PetscCall((*f)(A, b, x));
3669:     PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3670:     PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3671:   }
3672:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3673:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3674:   PetscFunctionReturn(PETSC_SUCCESS);
3675: }

3677: /*@
3678:   MatMatSolve - Solves A X = B, given a factored matrix.

3680:   Neighbor-wise Collective

3682:   Input Parameters:
3683: + A - the factored matrix
3684: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

3686:   Output Parameter:
3687: . X - the result matrix (dense matrix)

3689:   Level: developer

3691:   Note:
3692:   If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3693:   otherwise, `B` and `X` cannot be the same.

3695: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3696: @*/
3697: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3698: {
3699:   PetscFunctionBegin;
3704:   PetscCheckSameComm(A, 1, B, 2);
3705:   PetscCheckSameComm(A, 1, X, 3);
3706:   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);
3707:   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);
3708:   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");
3709:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3710:   MatCheckPreallocated(A, 1);

3712:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3713:   if (!A->ops->matsolve) {
3714:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3715:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3716:   } else PetscUseTypeMethod(A, matsolve, B, X);
3717:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3718:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3719:   PetscFunctionReturn(PETSC_SUCCESS);
3720: }

3722: /*@
3723:   MatMatSolveTranspose - Solves A^T X = B, given a factored matrix.

3725:   Neighbor-wise Collective

3727:   Input Parameters:
3728: + A - the factored matrix
3729: - B - the right-hand-side matrix  (`MATDENSE` matrix)

3731:   Output Parameter:
3732: . X - the result matrix (dense matrix)

3734:   Level: developer

3736:   Note:
3737:   The matrices `B` and `X` cannot be the same.  I.e., one cannot
3738:   call `MatMatSolveTranspose`(A,X,X).

3740: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3741: @*/
3742: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3743: {
3744:   PetscFunctionBegin;
3749:   PetscCheckSameComm(A, 1, B, 2);
3750:   PetscCheckSameComm(A, 1, X, 3);
3751:   PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3752:   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);
3753:   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);
3754:   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);
3755:   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");
3756:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3757:   MatCheckPreallocated(A, 1);

3759:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3760:   if (!A->ops->matsolvetranspose) {
3761:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3762:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3763:   } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3764:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3765:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3766:   PetscFunctionReturn(PETSC_SUCCESS);
3767: }

3769: /*@
3770:   MatMatTransposeSolve - Solves A X = B^T, given a factored matrix.

3772:   Neighbor-wise Collective

3774:   Input Parameters:
3775: + A  - the factored matrix
3776: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`

3778:   Output Parameter:
3779: . X - the result matrix (dense matrix)

3781:   Level: developer

3783:   Note:
3784:   For MUMPS, it only supports centralized sparse compressed column format on the host processor for right hand side matrix. User must create B^T in sparse compressed row
3785:   format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

3787: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3788: @*/
3789: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3790: {
3791:   PetscFunctionBegin;
3796:   PetscCheckSameComm(A, 1, Bt, 2);
3797:   PetscCheckSameComm(A, 1, X, 3);

3799:   PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3800:   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);
3801:   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);
3802:   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");
3803:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3804:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3805:   MatCheckPreallocated(A, 1);

3807:   PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3808:   PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3809:   PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3810:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3811:   PetscFunctionReturn(PETSC_SUCCESS);
3812: }

3814: /*@
3815:   MatForwardSolve - Solves L x = b, given a factored matrix, A = LU, or
3816:   U^T*D^(1/2) x = b, given a factored symmetric matrix, A = U^T*D*U,

3818:   Neighbor-wise Collective

3820:   Input Parameters:
3821: + mat - the factored matrix
3822: - b   - the right-hand-side vector

3824:   Output Parameter:
3825: . x - the result vector

3827:   Level: developer

3829:   Notes:
3830:   `MatSolve()` should be used for most applications, as it performs
3831:   a forward solve followed by a backward solve.

3833:   The vectors `b` and `x` cannot be the same,  i.e., one cannot
3834:   call `MatForwardSolve`(A,x,x).

3836:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3837:   the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3838:   `MatForwardSolve()` solves U^T*D y = b, and
3839:   `MatBackwardSolve()` solves U x = y.
3840:   Thus they do not provide a symmetric preconditioner.

3842: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3843: @*/
3844: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3845: {
3846:   PetscFunctionBegin;
3851:   PetscCheckSameComm(mat, 1, b, 2);
3852:   PetscCheckSameComm(mat, 1, x, 3);
3853:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3854:   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);
3855:   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);
3856:   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);
3857:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3858:   MatCheckPreallocated(mat, 1);

3860:   PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3861:   PetscUseTypeMethod(mat, forwardsolve, b, x);
3862:   PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3863:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3864:   PetscFunctionReturn(PETSC_SUCCESS);
3865: }

3867: /*@
3868:   MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU.
3869:   D^(1/2) U x = b, given a factored symmetric matrix, A = U^T*D*U,

3871:   Neighbor-wise Collective

3873:   Input Parameters:
3874: + mat - the factored matrix
3875: - b   - the right-hand-side vector

3877:   Output Parameter:
3878: . x - the result vector

3880:   Level: developer

3882:   Notes:
3883:   `MatSolve()` should be used for most applications, as it performs
3884:   a forward solve followed by a backward solve.

3886:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3887:   call `MatBackwardSolve`(A,x,x).

3889:   For matrix in `MATSEQBAIJ` format with block size larger than 1,
3890:   the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3891:   `MatForwardSolve()` solves U^T*D y = b, and
3892:   `MatBackwardSolve()` solves U x = y.
3893:   Thus they do not provide a symmetric preconditioner.

3895: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3896: @*/
3897: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3898: {
3899:   PetscFunctionBegin;
3904:   PetscCheckSameComm(mat, 1, b, 2);
3905:   PetscCheckSameComm(mat, 1, x, 3);
3906:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3907:   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);
3908:   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);
3909:   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);
3910:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3911:   MatCheckPreallocated(mat, 1);

3913:   PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3914:   PetscUseTypeMethod(mat, backwardsolve, b, x);
3915:   PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3916:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3917:   PetscFunctionReturn(PETSC_SUCCESS);
3918: }

3920: /*@
3921:   MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix.

3923:   Neighbor-wise Collective

3925:   Input Parameters:
3926: + mat - the factored matrix
3927: . b   - the right-hand-side vector
3928: - y   - the vector to be added to

3930:   Output Parameter:
3931: . x - the result vector

3933:   Level: developer

3935:   Note:
3936:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
3937:   call `MatSolveAdd`(A,x,y,x).

3939: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3940: @*/
3941: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
3942: {
3943:   PetscScalar one = 1.0;
3944:   Vec         tmp;

3946:   PetscFunctionBegin;
3952:   PetscCheckSameComm(mat, 1, b, 2);
3953:   PetscCheckSameComm(mat, 1, y, 3);
3954:   PetscCheckSameComm(mat, 1, x, 4);
3955:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3956:   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);
3957:   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);
3958:   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);
3959:   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);
3960:   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);
3961:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3962:   MatCheckPreallocated(mat, 1);

3964:   PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
3965:   if (mat->factorerrortype) {
3966:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3967:     PetscCall(VecSetInf(x));
3968:   } else if (mat->ops->solveadd) {
3969:     PetscUseTypeMethod(mat, solveadd, b, y, x);
3970:   } else {
3971:     /* do the solve then the add manually */
3972:     if (x != y) {
3973:       PetscCall(MatSolve(mat, b, x));
3974:       PetscCall(VecAXPY(x, one, y));
3975:     } else {
3976:       PetscCall(VecDuplicate(x, &tmp));
3977:       PetscCall(VecCopy(x, tmp));
3978:       PetscCall(MatSolve(mat, b, x));
3979:       PetscCall(VecAXPY(x, one, tmp));
3980:       PetscCall(VecDestroy(&tmp));
3981:     }
3982:   }
3983:   PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
3984:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3985:   PetscFunctionReturn(PETSC_SUCCESS);
3986: }

3988: /*@
3989:   MatSolveTranspose - Solves A' x = b, given a factored matrix.

3991:   Neighbor-wise Collective

3993:   Input Parameters:
3994: + mat - the factored matrix
3995: - b   - the right-hand-side vector

3997:   Output Parameter:
3998: . x - the result vector

4000:   Level: developer

4002:   Notes:
4003:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
4004:   call `MatSolveTranspose`(A,x,x).

4006:   Most users should employ the `KSP` interface for linear solvers
4007:   instead of working directly with matrix algebra routines such as this.
4008:   See, e.g., `KSPCreate()`.

4010: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4011: @*/
4012: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4013: {
4014:   PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

4016:   PetscFunctionBegin;
4021:   PetscCheckSameComm(mat, 1, b, 2);
4022:   PetscCheckSameComm(mat, 1, x, 3);
4023:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4024:   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);
4025:   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);
4026:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4027:   MatCheckPreallocated(mat, 1);
4028:   PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4029:   if (mat->factorerrortype) {
4030:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4031:     PetscCall(VecSetInf(x));
4032:   } else {
4033:     PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4034:     PetscCall((*f)(mat, b, x));
4035:   }
4036:   PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4037:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4038:   PetscFunctionReturn(PETSC_SUCCESS);
4039: }

4041: /*@
4042:   MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a
4043:   factored matrix.

4045:   Neighbor-wise Collective

4047:   Input Parameters:
4048: + mat - the factored matrix
4049: . b   - the right-hand-side vector
4050: - y   - the vector to be added to

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

4055:   Level: developer

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

4061: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4062: @*/
4063: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4064: {
4065:   PetscScalar one = 1.0;
4066:   Vec         tmp;
4067:   PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

4069:   PetscFunctionBegin;
4075:   PetscCheckSameComm(mat, 1, b, 2);
4076:   PetscCheckSameComm(mat, 1, y, 3);
4077:   PetscCheckSameComm(mat, 1, x, 4);
4078:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4079:   PetscCheck(mat->rmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, x->map->N);
4080:   PetscCheck(mat->cmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, b->map->N);
4081:   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);
4082:   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);
4083:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4084:   MatCheckPreallocated(mat, 1);

4086:   PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4087:   if (mat->factorerrortype) {
4088:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4089:     PetscCall(VecSetInf(x));
4090:   } else if (f) {
4091:     PetscCall((*f)(mat, b, y, x));
4092:   } else {
4093:     /* do the solve then the add manually */
4094:     if (x != y) {
4095:       PetscCall(MatSolveTranspose(mat, b, x));
4096:       PetscCall(VecAXPY(x, one, y));
4097:     } else {
4098:       PetscCall(VecDuplicate(x, &tmp));
4099:       PetscCall(VecCopy(x, tmp));
4100:       PetscCall(MatSolveTranspose(mat, b, x));
4101:       PetscCall(VecAXPY(x, one, tmp));
4102:       PetscCall(VecDestroy(&tmp));
4103:     }
4104:   }
4105:   PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4106:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4107:   PetscFunctionReturn(PETSC_SUCCESS);
4108: }

4110: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4111: /*@
4112:   MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

4114:   Neighbor-wise Collective

4116:   Input Parameters:
4117: + mat   - the matrix
4118: . b     - the right hand side
4119: . omega - the relaxation factor
4120: . flag  - flag indicating the type of SOR (see below)
4121: . shift - diagonal shift
4122: . its   - the number of iterations
4123: - lits  - the number of local iterations

4125:   Output Parameter:
4126: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

4128:   SOR Flags:
4129: +     `SOR_FORWARD_SWEEP` - forward SOR
4130: .     `SOR_BACKWARD_SWEEP` - backward SOR
4131: .     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4132: .     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4133: .     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4134: .     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4135: .     `SOR_EISENSTAT` - SOR with Eisenstat trick
4136: .     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4137:   upper/lower triangular part of matrix to
4138:   vector (with omega)
4139: -     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

4141:   Level: developer

4143:   Notes:
4144:   `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4145:   `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4146:   on each processor.

4148:   Application programmers will not generally use `MatSOR()` directly,
4149:   but instead will employ the `KSP`/`PC` interface.

4151:   For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

4153:   Most users should employ the `KSP` interface for linear solvers
4154:   instead of working directly with matrix algebra routines such as this.
4155:   See, e.g., `KSPCreate()`.

4157:   Vectors `x` and `b` CANNOT be the same

4159:   The flags are implemented as bitwise inclusive or operations.
4160:   For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4161:   to specify a zero initial guess for SSOR.

4163:   Developer Notes:
4164:   We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

4166: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4167: @*/
4168: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4169: {
4170:   PetscFunctionBegin;
4175:   PetscCheckSameComm(mat, 1, b, 2);
4176:   PetscCheckSameComm(mat, 1, x, 8);
4177:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4178:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4179:   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);
4180:   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);
4181:   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);
4182:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4183:   PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4184:   PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

4186:   MatCheckPreallocated(mat, 1);
4187:   PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4188:   PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4189:   PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4190:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4191:   PetscFunctionReturn(PETSC_SUCCESS);
4192: }

4194: /*
4195:       Default matrix copy routine.
4196: */
4197: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4198: {
4199:   PetscInt           i, rstart = 0, rend = 0, nz;
4200:   const PetscInt    *cwork;
4201:   const PetscScalar *vwork;

4203:   PetscFunctionBegin;
4204:   if (B->assembled) PetscCall(MatZeroEntries(B));
4205:   if (str == SAME_NONZERO_PATTERN) {
4206:     PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4207:     for (i = rstart; i < rend; i++) {
4208:       PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4209:       PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4210:       PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4211:     }
4212:   } else {
4213:     PetscCall(MatAYPX(B, 0.0, A, str));
4214:   }
4215:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4216:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4217:   PetscFunctionReturn(PETSC_SUCCESS);
4218: }

4220: /*@
4221:   MatCopy - Copies a matrix to another matrix.

4223:   Collective

4225:   Input Parameters:
4226: + A   - the matrix
4227: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

4229:   Output Parameter:
4230: . B - where the copy is put

4232:   Level: intermediate

4234:   Notes:
4235:   If you use `SAME_NONZERO_PATTERN` then the two matrices must have the same nonzero pattern or the routine will crash.

4237:   `MatCopy()` copies the matrix entries of a matrix to another existing
4238:   matrix (after first zeroing the second matrix).  A related routine is
4239:   `MatConvert()`, which first creates a new matrix and then copies the data.

4241: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4242: @*/
4243: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4244: {
4245:   PetscInt i;

4247:   PetscFunctionBegin;
4252:   PetscCheckSameComm(A, 1, B, 2);
4253:   MatCheckPreallocated(B, 2);
4254:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4255:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4256:   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,
4257:              A->cmap->N, B->cmap->N);
4258:   MatCheckPreallocated(A, 1);
4259:   if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

4261:   PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4262:   if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4263:   else PetscCall(MatCopy_Basic(A, B, str));

4265:   B->stencil.dim = A->stencil.dim;
4266:   B->stencil.noc = A->stencil.noc;
4267:   for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4268:     B->stencil.dims[i]   = A->stencil.dims[i];
4269:     B->stencil.starts[i] = A->stencil.starts[i];
4270:   }

4272:   PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4273:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4274:   PetscFunctionReturn(PETSC_SUCCESS);
4275: }

4277: /*@C
4278:   MatConvert - Converts a matrix to another matrix, either of the same
4279:   or different type.

4281:   Collective

4283:   Input Parameters:
4284: + mat     - the matrix
4285: . newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
4286:    same type as the original matrix.
4287: - reuse   - denotes if the destination matrix is to be created or reused.
4288:    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
4289:    `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).

4291:   Output Parameter:
4292: . M - pointer to place new matrix

4294:   Level: intermediate

4296:   Notes:
4297:   `MatConvert()` first creates a new matrix and then copies the data from
4298:   the first matrix.  A related routine is `MatCopy()`, which copies the matrix
4299:   entries of one matrix to another already existing matrix context.

4301:   Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4302:   the MPI communicator of the generated matrix is always the same as the communicator
4303:   of the input matrix.

4305: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4306: @*/
4307: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4308: {
4309:   PetscBool  sametype, issame, flg;
4310:   PetscBool3 issymmetric, ishermitian;
4311:   char       convname[256], mtype[256];
4312:   Mat        B;

4314:   PetscFunctionBegin;
4317:   PetscAssertPointer(M, 4);
4318:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4319:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4320:   MatCheckPreallocated(mat, 1);

4322:   PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4323:   if (flg) newtype = mtype;

4325:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4326:   PetscCall(PetscStrcmp(newtype, "same", &issame));
4327:   PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4328:   PetscCheck(!(reuse == MAT_REUSE_MATRIX) || !(mat == *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");

4330:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4331:     PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4332:     PetscFunctionReturn(PETSC_SUCCESS);
4333:   }

4335:   /* Cache Mat options because some converters use MatHeaderReplace  */
4336:   issymmetric = mat->symmetric;
4337:   ishermitian = mat->hermitian;

4339:   if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4340:     PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4341:     PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4342:   } else {
4343:     PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4344:     const char *prefix[3]                                 = {"seq", "mpi", ""};
4345:     PetscInt    i;
4346:     /*
4347:        Order of precedence:
4348:        0) See if newtype is a superclass of the current matrix.
4349:        1) See if a specialized converter is known to the current matrix.
4350:        2) See if a specialized converter is known to the desired matrix class.
4351:        3) See if a good general converter is registered for the desired class
4352:           (as of 6/27/03 only MATMPIADJ falls into this category).
4353:        4) See if a good general converter is known for the current matrix.
4354:        5) Use a really basic converter.
4355:     */

4357:     /* 0) See if newtype is a superclass of the current matrix.
4358:           i.e mat is mpiaij and newtype is aij */
4359:     for (i = 0; i < 2; i++) {
4360:       PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4361:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4362:       PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4363:       PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4364:       if (flg) {
4365:         if (reuse == MAT_INPLACE_MATRIX) {
4366:           PetscCall(PetscInfo(mat, "Early return\n"));
4367:           PetscFunctionReturn(PETSC_SUCCESS);
4368:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4369:           PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4370:           PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4371:           PetscFunctionReturn(PETSC_SUCCESS);
4372:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4373:           PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4374:           PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4375:           PetscFunctionReturn(PETSC_SUCCESS);
4376:         }
4377:       }
4378:     }
4379:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4380:     for (i = 0; i < 3; i++) {
4381:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4382:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4383:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4384:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4385:       PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4386:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4387:       PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4388:       PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4389:       if (conv) goto foundconv;
4390:     }

4392:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4393:     PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4394:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4395:     PetscCall(MatSetType(B, newtype));
4396:     for (i = 0; i < 3; i++) {
4397:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4398:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4399:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4400:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4401:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4402:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4403:       PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4404:       PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4405:       if (conv) {
4406:         PetscCall(MatDestroy(&B));
4407:         goto foundconv;
4408:       }
4409:     }

4411:     /* 3) See if a good general converter is registered for the desired class */
4412:     conv = B->ops->convertfrom;
4413:     PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4414:     PetscCall(MatDestroy(&B));
4415:     if (conv) goto foundconv;

4417:     /* 4) See if a good general converter is known for the current matrix */
4418:     if (mat->ops->convert) conv = mat->ops->convert;
4419:     PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4420:     if (conv) goto foundconv;

4422:     /* 5) Use a really basic converter. */
4423:     PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4424:     conv = MatConvert_Basic;

4426:   foundconv:
4427:     PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4428:     PetscCall((*conv)(mat, newtype, reuse, M));
4429:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4430:       /* the block sizes must be same if the mappings are copied over */
4431:       (*M)->rmap->bs = mat->rmap->bs;
4432:       (*M)->cmap->bs = mat->cmap->bs;
4433:       PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4434:       PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4435:       (*M)->rmap->mapping = mat->rmap->mapping;
4436:       (*M)->cmap->mapping = mat->cmap->mapping;
4437:     }
4438:     (*M)->stencil.dim = mat->stencil.dim;
4439:     (*M)->stencil.noc = mat->stencil.noc;
4440:     for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4441:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4442:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4443:     }
4444:     PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4445:   }
4446:   PetscCall(PetscObjectStateIncrease((PetscObject)*M));

4448:   /* Copy Mat options */
4449:   if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4450:   else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4451:   if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4452:   else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4453:   PetscFunctionReturn(PETSC_SUCCESS);
4454: }

4456: /*@C
4457:   MatFactorGetSolverType - Returns name of the package providing the factorization routines

4459:   Not Collective

4461:   Input Parameter:
4462: . mat - the matrix, must be a factored matrix

4464:   Output Parameter:
4465: . type - the string name of the package (do not free this string)

4467:   Level: intermediate

4469:   Fortran Notes:
4470:   Pass in an empty string and the package name will be copied into it. Make sure the string is long enough.

4472: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4473: @*/
4474: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4475: {
4476:   PetscErrorCode (*conv)(Mat, MatSolverType *);

4478:   PetscFunctionBegin;
4481:   PetscAssertPointer(type, 2);
4482:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4483:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4484:   if (conv) PetscCall((*conv)(mat, type));
4485:   else *type = MATSOLVERPETSC;
4486:   PetscFunctionReturn(PETSC_SUCCESS);
4487: }

4489: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4490: struct _MatSolverTypeForSpecifcType {
4491:   MatType mtype;
4492:   /* no entry for MAT_FACTOR_NONE */
4493:   PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4494:   MatSolverTypeForSpecifcType next;
4495: };

4497: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4498: struct _MatSolverTypeHolder {
4499:   char                       *name;
4500:   MatSolverTypeForSpecifcType handlers;
4501:   MatSolverTypeHolder         next;
4502: };

4504: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4506: /*@C
4507:   MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

4509:   Input Parameters:
4510: + package      - name of the package, for example petsc or superlu
4511: . mtype        - the matrix type that works with this package
4512: . ftype        - the type of factorization supported by the package
4513: - createfactor - routine that will create the factored matrix ready to be used

4515:   Level: developer

4517: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`
4518: @*/
4519: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4520: {
4521:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
4522:   PetscBool                   flg;
4523:   MatSolverTypeForSpecifcType inext, iprev = NULL;

4525:   PetscFunctionBegin;
4526:   PetscCall(MatInitializePackage());
4527:   if (!next) {
4528:     PetscCall(PetscNew(&MatSolverTypeHolders));
4529:     PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4530:     PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4531:     PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4532:     MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4533:     PetscFunctionReturn(PETSC_SUCCESS);
4534:   }
4535:   while (next) {
4536:     PetscCall(PetscStrcasecmp(package, next->name, &flg));
4537:     if (flg) {
4538:       PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4539:       inext = next->handlers;
4540:       while (inext) {
4541:         PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4542:         if (flg) {
4543:           inext->createfactor[(int)ftype - 1] = createfactor;
4544:           PetscFunctionReturn(PETSC_SUCCESS);
4545:         }
4546:         iprev = inext;
4547:         inext = inext->next;
4548:       }
4549:       PetscCall(PetscNew(&iprev->next));
4550:       PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4551:       iprev->next->createfactor[(int)ftype - 1] = createfactor;
4552:       PetscFunctionReturn(PETSC_SUCCESS);
4553:     }
4554:     prev = next;
4555:     next = next->next;
4556:   }
4557:   PetscCall(PetscNew(&prev->next));
4558:   PetscCall(PetscStrallocpy(package, &prev->next->name));
4559:   PetscCall(PetscNew(&prev->next->handlers));
4560:   PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4561:   prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4562:   PetscFunctionReturn(PETSC_SUCCESS);
4563: }

4565: /*@C
4566:   MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

4568:   Input Parameters:
4569: + type  - name of the package, for example petsc or superlu
4570: . ftype - the type of factorization supported by the type
4571: - mtype - the matrix type that works with this type

4573:   Output Parameters:
4574: + foundtype    - `PETSC_TRUE` if the type was registered
4575: . foundmtype   - `PETSC_TRUE` if the type supports the requested mtype
4576: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

4578:   Level: developer

4580: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`
4581: @*/
4582: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat, MatFactorType, Mat *))
4583: {
4584:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4585:   PetscBool                   flg;
4586:   MatSolverTypeForSpecifcType inext;

4588:   PetscFunctionBegin;
4589:   if (foundtype) *foundtype = PETSC_FALSE;
4590:   if (foundmtype) *foundmtype = PETSC_FALSE;
4591:   if (createfactor) *createfactor = NULL;

4593:   if (type) {
4594:     while (next) {
4595:       PetscCall(PetscStrcasecmp(type, next->name, &flg));
4596:       if (flg) {
4597:         if (foundtype) *foundtype = PETSC_TRUE;
4598:         inext = next->handlers;
4599:         while (inext) {
4600:           PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4601:           if (flg) {
4602:             if (foundmtype) *foundmtype = PETSC_TRUE;
4603:             if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4604:             PetscFunctionReturn(PETSC_SUCCESS);
4605:           }
4606:           inext = inext->next;
4607:         }
4608:       }
4609:       next = next->next;
4610:     }
4611:   } else {
4612:     while (next) {
4613:       inext = next->handlers;
4614:       while (inext) {
4615:         PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4616:         if (flg && inext->createfactor[(int)ftype - 1]) {
4617:           if (foundtype) *foundtype = PETSC_TRUE;
4618:           if (foundmtype) *foundmtype = PETSC_TRUE;
4619:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4620:           PetscFunctionReturn(PETSC_SUCCESS);
4621:         }
4622:         inext = inext->next;
4623:       }
4624:       next = next->next;
4625:     }
4626:     /* try with base classes inext->mtype */
4627:     next = MatSolverTypeHolders;
4628:     while (next) {
4629:       inext = next->handlers;
4630:       while (inext) {
4631:         PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4632:         if (flg && inext->createfactor[(int)ftype - 1]) {
4633:           if (foundtype) *foundtype = PETSC_TRUE;
4634:           if (foundmtype) *foundmtype = PETSC_TRUE;
4635:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4636:           PetscFunctionReturn(PETSC_SUCCESS);
4637:         }
4638:         inext = inext->next;
4639:       }
4640:       next = next->next;
4641:     }
4642:   }
4643:   PetscFunctionReturn(PETSC_SUCCESS);
4644: }

4646: PetscErrorCode MatSolverTypeDestroy(void)
4647: {
4648:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
4649:   MatSolverTypeForSpecifcType inext, iprev;

4651:   PetscFunctionBegin;
4652:   while (next) {
4653:     PetscCall(PetscFree(next->name));
4654:     inext = next->handlers;
4655:     while (inext) {
4656:       PetscCall(PetscFree(inext->mtype));
4657:       iprev = inext;
4658:       inext = inext->next;
4659:       PetscCall(PetscFree(iprev));
4660:     }
4661:     prev = next;
4662:     next = next->next;
4663:     PetscCall(PetscFree(prev));
4664:   }
4665:   MatSolverTypeHolders = NULL;
4666:   PetscFunctionReturn(PETSC_SUCCESS);
4667: }

4669: /*@C
4670:   MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

4672:   Logically Collective

4674:   Input Parameter:
4675: . mat - the matrix

4677:   Output Parameter:
4678: . flg - `PETSC_TRUE` if uses the ordering

4680:   Level: developer

4682:   Note:
4683:   Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4684:   packages do not, thus we want to skip generating the ordering when it is not needed or used.

4686: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4687: @*/
4688: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4689: {
4690:   PetscFunctionBegin;
4691:   *flg = mat->canuseordering;
4692:   PetscFunctionReturn(PETSC_SUCCESS);
4693: }

4695: /*@C
4696:   MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

4698:   Logically Collective

4700:   Input Parameters:
4701: + mat   - the matrix obtained with `MatGetFactor()`
4702: - ftype - the factorization type to be used

4704:   Output Parameter:
4705: . otype - the preferred ordering type

4707:   Level: developer

4709: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4710: @*/
4711: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4712: {
4713:   PetscFunctionBegin;
4714:   *otype = mat->preferredordering[ftype];
4715:   PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4716:   PetscFunctionReturn(PETSC_SUCCESS);
4717: }

4719: /*@C
4720:   MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic()

4722:   Collective

4724:   Input Parameters:
4725: + mat   - the matrix
4726: . type  - name of solver type, for example, superlu, petsc (to use PETSc's default)
4727: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4729:   Output Parameter:
4730: . f - the factor matrix used with MatXXFactorSymbolic() calls. Can be `NULL` in some cases, see notes below.

4732:   Options Database Key:
4733: . -mat_factor_bind_factorization <host, device> - Where to do matrix factorization? Default is device (might consume more device memory.
4734:                                   One can choose host to save device memory). Currently only supported with `MATSEQAIJCUSPARSE` matrices.

4736:   Level: intermediate

4738:   Notes:
4739:   The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4740:   types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.

4742:   Users usually access the factorization solvers via `KSP`

4744:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4745:   such as pastix, superlu, mumps etc.

4747:   PETSc must have been ./configure to use the external solver, using the option --download-package

4749:   Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4750:   where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4751:   call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

4753:   Developer Notes:
4754:   This should actually be called `MatCreateFactor()` since it creates a new factor object

4756: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`,
4757:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4758: @*/
4759: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4760: {
4761:   PetscBool foundtype, foundmtype;
4762:   PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

4764:   PetscFunctionBegin;

4768:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4769:   MatCheckPreallocated(mat, 1);

4771:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4772:   if (!foundtype) {
4773:     if (type) {
4774:       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],
4775:               ((PetscObject)mat)->type_name, type);
4776:     } else {
4777:       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);
4778:     }
4779:   }
4780:   PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4781:   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);

4783:   PetscCall((*conv)(mat, ftype, f));
4784:   if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4785:   PetscFunctionReturn(PETSC_SUCCESS);
4786: }

4788: /*@C
4789:   MatGetFactorAvailable - Returns a a flag if matrix supports particular type and factor type

4791:   Not Collective

4793:   Input Parameters:
4794: + mat   - the matrix
4795: . type  - name of solver type, for example, superlu, petsc (to use PETSc's default)
4796: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4798:   Output Parameter:
4799: . flg - PETSC_TRUE if the factorization is available

4801:   Level: intermediate

4803:   Notes:
4804:   Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4805:   such as pastix, superlu, mumps etc.

4807:   PETSc must have been ./configure to use the external solver, using the option --download-package

4809:   Developer Notes:
4810:   This should actually be called MatCreateFactorAvailable() since MatGetFactor() creates a new factor object

4812: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4813:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4814: @*/
4815: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4816: {
4817:   PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

4819:   PetscFunctionBegin;
4822:   PetscAssertPointer(flg, 4);

4824:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4825:   MatCheckPreallocated(mat, 1);

4827:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4828:   *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4829:   PetscFunctionReturn(PETSC_SUCCESS);
4830: }

4832: /*@
4833:   MatDuplicate - Duplicates a matrix including the non-zero structure.

4835:   Collective

4837:   Input Parameters:
4838: + mat - the matrix
4839: - op  - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4840:         See the manual page for `MatDuplicateOption()` for an explanation of these options.

4842:   Output Parameter:
4843: . M - pointer to place new matrix

4845:   Level: intermediate

4847:   Notes:
4848:   You cannot change the nonzero pattern for the parent or child matrix if you use `MAT_SHARE_NONZERO_PATTERN`.

4850:   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.

4852:   When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the simple matrix data structure of mat
4853:   is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4854:   User should not use `MatDuplicate()` to create new matrix M if M is intended to be reused as the product of matrix operation.

4856: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4857: @*/
4858: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4859: {
4860:   Mat         B;
4861:   VecType     vtype;
4862:   PetscInt    i;
4863:   PetscObject dm, container_h, container_d;
4864:   void (*viewf)(void);

4866:   PetscFunctionBegin;
4869:   PetscAssertPointer(M, 3);
4870:   PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4871:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4872:   MatCheckPreallocated(mat, 1);

4874:   *M = NULL;
4875:   PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4876:   PetscUseTypeMethod(mat, duplicate, op, M);
4877:   PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4878:   B = *M;

4880:   PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4881:   if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4882:   PetscCall(MatGetVecType(mat, &vtype));
4883:   PetscCall(MatSetVecType(B, vtype));

4885:   B->stencil.dim = mat->stencil.dim;
4886:   B->stencil.noc = mat->stencil.noc;
4887:   for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4888:     B->stencil.dims[i]   = mat->stencil.dims[i];
4889:     B->stencil.starts[i] = mat->stencil.starts[i];
4890:   }

4892:   B->nooffproczerorows = mat->nooffproczerorows;
4893:   B->nooffprocentries  = mat->nooffprocentries;

4895:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4896:   if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4897:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
4898:   if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
4899:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
4900:   if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
4901:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4902:   PetscFunctionReturn(PETSC_SUCCESS);
4903: }

4905: /*@
4906:   MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`

4908:   Logically Collective

4910:   Input Parameter:
4911: . mat - the matrix

4913:   Output Parameter:
4914: . v - the diagonal of the matrix

4916:   Level: intermediate

4918:   Note:
4919:   If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
4920:   of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
4921:   is larger than `ndiag`, the values of the remaining entries are unspecified.

4923:   Currently only correct in parallel for square matrices.

4925: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
4926: @*/
4927: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
4928: {
4929:   PetscFunctionBegin;
4933:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4934:   MatCheckPreallocated(mat, 1);
4935:   if (PetscDefined(USE_DEBUG)) {
4936:     PetscInt nv, row, col, ndiag;

4938:     PetscCall(VecGetLocalSize(v, &nv));
4939:     PetscCall(MatGetLocalSize(mat, &row, &col));
4940:     ndiag = PetscMin(row, col);
4941:     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);
4942:   }

4944:   PetscUseTypeMethod(mat, getdiagonal, v);
4945:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
4946:   PetscFunctionReturn(PETSC_SUCCESS);
4947: }

4949: /*@C
4950:   MatGetRowMin - Gets the minimum value (of the real part) of each
4951:   row of the matrix

4953:   Logically Collective

4955:   Input Parameter:
4956: . mat - the matrix

4958:   Output Parameters:
4959: + v   - the vector for storing the maximums
4960: - idx - the indices of the column found for each row (optional)

4962:   Level: intermediate

4964:   Note:
4965:   The result of this call are the same as if one converted the matrix to dense format
4966:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4968:   This code is only implemented for a couple of matrix formats.

4970: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
4971:           `MatGetRowMax()`
4972: @*/
4973: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
4974: {
4975:   PetscFunctionBegin;
4979:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

4981:   if (!mat->cmap->N) {
4982:     PetscCall(VecSet(v, PETSC_MAX_REAL));
4983:     if (idx) {
4984:       PetscInt i, m = mat->rmap->n;
4985:       for (i = 0; i < m; i++) idx[i] = -1;
4986:     }
4987:   } else {
4988:     MatCheckPreallocated(mat, 1);
4989:   }
4990:   PetscUseTypeMethod(mat, getrowmin, v, idx);
4991:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
4992:   PetscFunctionReturn(PETSC_SUCCESS);
4993: }

4995: /*@C
4996:   MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
4997:   row of the matrix

4999:   Logically Collective

5001:   Input Parameter:
5002: . mat - the matrix

5004:   Output Parameters:
5005: + v   - the vector for storing the minimums
5006: - idx - the indices of the column found for each row (or `NULL` if not needed)

5008:   Level: intermediate

5010:   Notes:
5011:   if a row is completely empty or has only 0.0 values then the idx[] value for that
5012:   row is 0 (the first column).

5014:   This code is only implemented for a couple of matrix formats.

5016: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5017: @*/
5018: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5019: {
5020:   PetscFunctionBegin;
5024:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5025:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

5027:   if (!mat->cmap->N) {
5028:     PetscCall(VecSet(v, 0.0));
5029:     if (idx) {
5030:       PetscInt i, m = mat->rmap->n;
5031:       for (i = 0; i < m; i++) idx[i] = -1;
5032:     }
5033:   } else {
5034:     MatCheckPreallocated(mat, 1);
5035:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5036:     PetscUseTypeMethod(mat, getrowminabs, v, idx);
5037:   }
5038:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5039:   PetscFunctionReturn(PETSC_SUCCESS);
5040: }

5042: /*@C
5043:   MatGetRowMax - Gets the maximum value (of the real part) of each
5044:   row of the matrix

5046:   Logically Collective

5048:   Input Parameter:
5049: . mat - the matrix

5051:   Output Parameters:
5052: + v   - the vector for storing the maximums
5053: - idx - the indices of the column found for each row (optional)

5055:   Level: intermediate

5057:   Notes:
5058:   The result of this call are the same as if one converted the matrix to dense format
5059:   and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5061:   This code is only implemented for a couple of matrix formats.

5063: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5064: @*/
5065: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5066: {
5067:   PetscFunctionBegin;
5071:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5073:   if (!mat->cmap->N) {
5074:     PetscCall(VecSet(v, PETSC_MIN_REAL));
5075:     if (idx) {
5076:       PetscInt i, m = mat->rmap->n;
5077:       for (i = 0; i < m; i++) idx[i] = -1;
5078:     }
5079:   } else {
5080:     MatCheckPreallocated(mat, 1);
5081:     PetscUseTypeMethod(mat, getrowmax, v, idx);
5082:   }
5083:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5084:   PetscFunctionReturn(PETSC_SUCCESS);
5085: }

5087: /*@C
5088:   MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5089:   row of the matrix

5091:   Logically Collective

5093:   Input Parameter:
5094: . mat - the matrix

5096:   Output Parameters:
5097: + v   - the vector for storing the maximums
5098: - idx - the indices of the column found for each row (or `NULL` if not needed)

5100:   Level: intermediate

5102:   Notes:
5103:   if a row is completely empty or has only 0.0 values then the idx[] value for that
5104:   row is 0 (the first column).

5106:   This code is only implemented for a couple of matrix formats.

5108: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5109: @*/
5110: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5111: {
5112:   PetscFunctionBegin;
5116:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5118:   if (!mat->cmap->N) {
5119:     PetscCall(VecSet(v, 0.0));
5120:     if (idx) {
5121:       PetscInt i, m = mat->rmap->n;
5122:       for (i = 0; i < m; i++) idx[i] = -1;
5123:     }
5124:   } else {
5125:     MatCheckPreallocated(mat, 1);
5126:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5127:     PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5128:   }
5129:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5130:   PetscFunctionReturn(PETSC_SUCCESS);
5131: }

5133: /*@
5134:   MatGetRowSum - Gets the sum of each row of the matrix

5136:   Logically or Neighborhood Collective

5138:   Input Parameter:
5139: . mat - the matrix

5141:   Output Parameter:
5142: . v - the vector for storing the sum of rows

5144:   Level: intermediate

5146:   Notes:
5147:   This code is slow since it is not currently specialized for different formats

5149: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`
5150: @*/
5151: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5152: {
5153:   Vec ones;

5155:   PetscFunctionBegin;
5159:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5160:   MatCheckPreallocated(mat, 1);
5161:   PetscCall(MatCreateVecs(mat, &ones, NULL));
5162:   PetscCall(VecSet(ones, 1.));
5163:   PetscCall(MatMult(mat, ones, v));
5164:   PetscCall(VecDestroy(&ones));
5165:   PetscFunctionReturn(PETSC_SUCCESS);
5166: }

5168: /*@
5169:   MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5170:   when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)

5172:   Collective

5174:   Input Parameter:
5175: . mat - the matrix to provide the transpose

5177:   Output Parameter:
5178: . 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

5180:   Level: advanced

5182:   Note:
5183:   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
5184:   routine allows bypassing that call.

5186: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5187: @*/
5188: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5189: {
5190:   PetscContainer  rB = NULL;
5191:   MatParentState *rb = NULL;

5193:   PetscFunctionBegin;
5194:   PetscCall(PetscNew(&rb));
5195:   rb->id    = ((PetscObject)mat)->id;
5196:   rb->state = 0;
5197:   PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5198:   PetscCall(PetscContainerCreate(PetscObjectComm((PetscObject)B), &rB));
5199:   PetscCall(PetscContainerSetPointer(rB, rb));
5200:   PetscCall(PetscContainerSetUserDestroy(rB, PetscContainerUserDestroyDefault));
5201:   PetscCall(PetscObjectCompose((PetscObject)B, "MatTransposeParent", (PetscObject)rB));
5202:   PetscCall(PetscObjectDereference((PetscObject)rB));
5203:   PetscFunctionReturn(PETSC_SUCCESS);
5204: }

5206: /*@
5207:   MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

5209:   Collective

5211:   Input Parameters:
5212: + mat   - the matrix to transpose
5213: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5215:   Output Parameter:
5216: . B - the transpose

5218:   Level: intermediate

5220:   Notes:
5221:   If you use `MAT_INPLACE_MATRIX` then you must pass in &mat for B

5223:   `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
5224:   transpose, call `MatTransposeSetPrecursor`(mat,B) before calling this routine.

5226:   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.

5228:   Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.

5230:   If mat is unchanged from the last call this function returns immediately without recomputing the result

5232:   If you only need the symbolic transpose, and not the numerical values, use `MatTransposeSymbolic()`

5234: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5235:           `MatTransposeSymbolic()`, `MatCreateTranspose()`
5236: @*/
5237: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5238: {
5239:   PetscContainer  rB = NULL;
5240:   MatParentState *rb = NULL;

5242:   PetscFunctionBegin;
5245:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5246:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5247:   PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5248:   PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5249:   MatCheckPreallocated(mat, 1);
5250:   if (reuse == MAT_REUSE_MATRIX) {
5251:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5252:     PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5253:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5254:     PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5255:     if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5256:   }

5258:   PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5259:   if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5260:     PetscUseTypeMethod(mat, transpose, reuse, B);
5261:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5262:   }
5263:   PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

5265:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5266:   if (reuse != MAT_INPLACE_MATRIX) {
5267:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5268:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5269:     rb->state        = ((PetscObject)mat)->state;
5270:     rb->nonzerostate = mat->nonzerostate;
5271:   }
5272:   PetscFunctionReturn(PETSC_SUCCESS);
5273: }

5275: /*@
5276:   MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

5278:   Collective

5280:   Input Parameter:
5281: . A - the matrix to transpose

5283:   Output Parameter:
5284: . 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
5285:       numerical portion.

5287:   Level: intermediate

5289:   Note:
5290:   This is not supported for many matrix types, use `MatTranspose()` in those cases

5292: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5293: @*/
5294: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5295: {
5296:   PetscFunctionBegin;
5299:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5300:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5301:   PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5302:   PetscUseTypeMethod(A, transposesymbolic, B);
5303:   PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

5305:   PetscCall(MatTransposeSetPrecursor(A, *B));
5306:   PetscFunctionReturn(PETSC_SUCCESS);
5307: }

5309: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5310: {
5311:   PetscContainer  rB;
5312:   MatParentState *rb;

5314:   PetscFunctionBegin;
5317:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5318:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5319:   PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5320:   PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5321:   PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5322:   PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5323:   PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5324:   PetscFunctionReturn(PETSC_SUCCESS);
5325: }

5327: /*@
5328:   MatIsTranspose - Test whether a matrix is another one's transpose,
5329:   or its own, in which case it tests symmetry.

5331:   Collective

5333:   Input Parameters:
5334: + A   - the matrix to test
5335: . B   - the matrix to test against, this can equal the first parameter
5336: - tol - tolerance, differences between entries smaller than this are counted as zero

5338:   Output Parameter:
5339: . flg - the result

5341:   Level: intermediate

5343:   Notes:
5344:   Only available for `MATAIJ` matrices.

5346:   The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5347:   test involves parallel copies of the block-offdiagonal parts of the matrix.

5349: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5350: @*/
5351: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5352: {
5353:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5355:   PetscFunctionBegin;
5358:   PetscAssertPointer(flg, 4);
5359:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5360:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5361:   *flg = PETSC_FALSE;
5362:   if (f && g) {
5363:     PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5364:     PetscCall((*f)(A, B, tol, flg));
5365:   } else {
5366:     MatType mattype;

5368:     PetscCall(MatGetType(f ? B : A, &mattype));
5369:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5370:   }
5371:   PetscFunctionReturn(PETSC_SUCCESS);
5372: }

5374: /*@
5375:   MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

5377:   Collective

5379:   Input Parameters:
5380: + mat   - the matrix to transpose and complex conjugate
5381: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5383:   Output Parameter:
5384: . B - the Hermitian transpose

5386:   Level: intermediate

5388: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5389: @*/
5390: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5391: {
5392:   PetscFunctionBegin;
5393:   PetscCall(MatTranspose(mat, reuse, B));
5394: #if defined(PETSC_USE_COMPLEX)
5395:   PetscCall(MatConjugate(*B));
5396: #endif
5397:   PetscFunctionReturn(PETSC_SUCCESS);
5398: }

5400: /*@
5401:   MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

5403:   Collective

5405:   Input Parameters:
5406: + A   - the matrix to test
5407: . B   - the matrix to test against, this can equal the first parameter
5408: - tol - tolerance, differences between entries smaller than this are counted as zero

5410:   Output Parameter:
5411: . flg - the result

5413:   Level: intermediate

5415:   Notes:
5416:   Only available for `MATAIJ` matrices.

5418:   The sequential algorithm
5419:   has a running time of the order of the number of nonzeros; the parallel
5420:   test involves parallel copies of the block-offdiagonal parts of the matrix.

5422: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5423: @*/
5424: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5425: {
5426:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5428:   PetscFunctionBegin;
5431:   PetscAssertPointer(flg, 4);
5432:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5433:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5434:   if (f && g) {
5435:     PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5436:     PetscCall((*f)(A, B, tol, flg));
5437:   }
5438:   PetscFunctionReturn(PETSC_SUCCESS);
5439: }

5441: /*@
5442:   MatPermute - Creates a new matrix with rows and columns permuted from the
5443:   original.

5445:   Collective

5447:   Input Parameters:
5448: + mat - the matrix to permute
5449: . row - row permutation, each processor supplies only the permutation for its rows
5450: - col - column permutation, each processor supplies only the permutation for its columns

5452:   Output Parameter:
5453: . B - the permuted matrix

5455:   Level: advanced

5457:   Note:
5458:   The index sets map from row/col of permuted matrix to row/col of original matrix.
5459:   The index sets should be on the same communicator as mat and have the same local sizes.

5461:   Developer Notes:
5462:   If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5463:   exploit the fact that row and col are permutations, consider implementing the
5464:   more general `MatCreateSubMatrix()` instead.

5466: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5467: @*/
5468: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5469: {
5470:   PetscFunctionBegin;
5475:   PetscAssertPointer(B, 4);
5476:   PetscCheckSameComm(mat, 1, row, 2);
5477:   if (row != col) PetscCheckSameComm(row, 2, col, 3);
5478:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5479:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5480:   PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5481:   MatCheckPreallocated(mat, 1);

5483:   if (mat->ops->permute) {
5484:     PetscUseTypeMethod(mat, permute, row, col, B);
5485:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5486:   } else {
5487:     PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5488:   }
5489:   PetscFunctionReturn(PETSC_SUCCESS);
5490: }

5492: /*@
5493:   MatEqual - Compares two matrices.

5495:   Collective

5497:   Input Parameters:
5498: + A - the first matrix
5499: - B - the second matrix

5501:   Output Parameter:
5502: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

5504:   Level: intermediate

5506: .seealso: [](ch_matrices), `Mat`
5507: @*/
5508: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5509: {
5510:   PetscFunctionBegin;
5515:   PetscAssertPointer(flg, 3);
5516:   PetscCheckSameComm(A, 1, B, 2);
5517:   MatCheckPreallocated(A, 1);
5518:   MatCheckPreallocated(B, 2);
5519:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5520:   PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5521:   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,
5522:              B->cmap->N);
5523:   if (A->ops->equal && A->ops->equal == B->ops->equal) {
5524:     PetscUseTypeMethod(A, equal, B, flg);
5525:   } else {
5526:     PetscCall(MatMultEqual(A, B, 10, flg));
5527:   }
5528:   PetscFunctionReturn(PETSC_SUCCESS);
5529: }

5531: /*@
5532:   MatDiagonalScale - Scales a matrix on the left and right by diagonal
5533:   matrices that are stored as vectors.  Either of the two scaling
5534:   matrices can be `NULL`.

5536:   Collective

5538:   Input Parameters:
5539: + mat - the matrix to be scaled
5540: . l   - the left scaling vector (or `NULL`)
5541: - r   - the right scaling vector (or `NULL`)

5543:   Level: intermediate

5545:   Note:
5546:   `MatDiagonalScale()` computes A = LAR, where
5547:   L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5548:   The L scales the rows of the matrix, the R scales the columns of the matrix.

5550: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5551: @*/
5552: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5553: {
5554:   PetscFunctionBegin;
5557:   if (l) {
5559:     PetscCheckSameComm(mat, 1, l, 2);
5560:   }
5561:   if (r) {
5563:     PetscCheckSameComm(mat, 1, r, 3);
5564:   }
5565:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5566:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5567:   MatCheckPreallocated(mat, 1);
5568:   if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

5570:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5571:   PetscUseTypeMethod(mat, diagonalscale, l, r);
5572:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5573:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5574:   if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5575:   PetscFunctionReturn(PETSC_SUCCESS);
5576: }

5578: /*@
5579:   MatScale - Scales all elements of a matrix by a given number.

5581:   Logically Collective

5583:   Input Parameters:
5584: + mat - the matrix to be scaled
5585: - a   - the scaling value

5587:   Level: intermediate

5589: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5590: @*/
5591: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5592: {
5593:   PetscFunctionBegin;
5596:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5597:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5599:   MatCheckPreallocated(mat, 1);

5601:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5602:   if (a != (PetscScalar)1.0) {
5603:     PetscUseTypeMethod(mat, scale, a);
5604:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5605:   }
5606:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5607:   PetscFunctionReturn(PETSC_SUCCESS);
5608: }

5610: /*@
5611:   MatNorm - Calculates various norms of a matrix.

5613:   Collective

5615:   Input Parameters:
5616: + mat  - the matrix
5617: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

5619:   Output Parameter:
5620: . nrm - the resulting norm

5622:   Level: intermediate

5624: .seealso: [](ch_matrices), `Mat`
5625: @*/
5626: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5627: {
5628:   PetscFunctionBegin;
5631:   PetscAssertPointer(nrm, 3);

5633:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5634:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5635:   MatCheckPreallocated(mat, 1);

5637:   PetscUseTypeMethod(mat, norm, type, nrm);
5638:   PetscFunctionReturn(PETSC_SUCCESS);
5639: }

5641: /*
5642:      This variable is used to prevent counting of MatAssemblyBegin() that
5643:    are called from within a MatAssemblyEnd().
5644: */
5645: static PetscInt MatAssemblyEnd_InUse = 0;
5646: /*@
5647:   MatAssemblyBegin - Begins assembling the matrix.  This routine should
5648:   be called after completing all calls to `MatSetValues()`.

5650:   Collective

5652:   Input Parameters:
5653: + mat  - the matrix
5654: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5656:   Level: beginner

5658:   Notes:
5659:   `MatSetValues()` generally caches the values that belong to other MPI processes.  The matrix is ready to
5660:   use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

5662:   Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5663:   in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5664:   using the matrix.

5666:   ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5667:   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
5668:   a global collective operation requiring all processes that share the matrix.

5670:   Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5671:   out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5672:   before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

5674: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5675: @*/
5676: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5677: {
5678:   PetscFunctionBegin;
5681:   MatCheckPreallocated(mat, 1);
5682:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix.\nDid you forget to call MatSetUnfactored()?");
5683:   if (mat->assembled) {
5684:     mat->was_assembled = PETSC_TRUE;
5685:     mat->assembled     = PETSC_FALSE;
5686:   }

5688:   if (!MatAssemblyEnd_InUse) {
5689:     PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5690:     PetscTryTypeMethod(mat, assemblybegin, type);
5691:     PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5692:   } else PetscTryTypeMethod(mat, assemblybegin, type);
5693:   PetscFunctionReturn(PETSC_SUCCESS);
5694: }

5696: /*@
5697:   MatAssembled - Indicates if a matrix has been assembled and is ready for
5698:   use; for example, in matrix-vector product.

5700:   Not Collective

5702:   Input Parameter:
5703: . mat - the matrix

5705:   Output Parameter:
5706: . assembled - `PETSC_TRUE` or `PETSC_FALSE`

5708:   Level: advanced

5710: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5711: @*/
5712: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5713: {
5714:   PetscFunctionBegin;
5716:   PetscAssertPointer(assembled, 2);
5717:   *assembled = mat->assembled;
5718:   PetscFunctionReturn(PETSC_SUCCESS);
5719: }

5721: /*@
5722:   MatAssemblyEnd - Completes assembling the matrix.  This routine should
5723:   be called after `MatAssemblyBegin()`.

5725:   Collective

5727:   Input Parameters:
5728: + mat  - the matrix
5729: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5731:   Options Database Keys:
5732: + -mat_view ::ascii_info             - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5733: . -mat_view ::ascii_info_detail      - Prints more detailed info
5734: . -mat_view                          - Prints matrix in ASCII format
5735: . -mat_view ::ascii_matlab           - Prints matrix in Matlab format
5736: . -mat_view draw                     - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5737: . -display <name>                    - Sets display name (default is host)
5738: . -draw_pause <sec>                  - Sets number of seconds to pause after display
5739: . -mat_view socket                   - Sends matrix to socket, can be accessed from Matlab (See [Using MATLAB with PETSc](ch_matlab))
5740: . -viewer_socket_machine <machine>   - Machine to use for socket
5741: . -viewer_socket_port <port>         - Port number to use for socket
5742: - -mat_view binary:filename[:append] - Save matrix to file in binary format

5744:   Level: beginner

5746: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5747: @*/
5748: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5749: {
5750:   static PetscInt inassm = 0;
5751:   PetscBool       flg    = PETSC_FALSE;

5753:   PetscFunctionBegin;

5757:   inassm++;
5758:   MatAssemblyEnd_InUse++;
5759:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5760:     PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5761:     PetscTryTypeMethod(mat, assemblyend, type);
5762:     PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5763:   } else PetscTryTypeMethod(mat, assemblyend, type);

5765:   /* Flush assembly is not a true assembly */
5766:   if (type != MAT_FLUSH_ASSEMBLY) {
5767:     if (mat->num_ass) {
5768:       if (!mat->symmetry_eternal) {
5769:         mat->symmetric = PETSC_BOOL3_UNKNOWN;
5770:         mat->hermitian = PETSC_BOOL3_UNKNOWN;
5771:       }
5772:       if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5773:       if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5774:     }
5775:     mat->num_ass++;
5776:     mat->assembled        = PETSC_TRUE;
5777:     mat->ass_nonzerostate = mat->nonzerostate;
5778:   }

5780:   mat->insertmode = NOT_SET_VALUES;
5781:   MatAssemblyEnd_InUse--;
5782:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5783:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5784:     PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

5786:     if (mat->checksymmetryonassembly) {
5787:       PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5788:       if (flg) {
5789:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5790:       } else {
5791:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5792:       }
5793:     }
5794:     if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5795:   }
5796:   inassm--;
5797:   PetscFunctionReturn(PETSC_SUCCESS);
5798: }

5800: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5801: /*@
5802:   MatSetOption - Sets a parameter option for a matrix. Some options
5803:   may be specific to certain storage formats.  Some options
5804:   determine how values will be inserted (or added). Sorted,
5805:   row-oriented input will generally assemble the fastest. The default
5806:   is row-oriented.

5808:   Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

5810:   Input Parameters:
5811: + mat - the matrix
5812: . op  - the option, one of those listed below (and possibly others),
5813: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5815:   Options Describing Matrix Structure:
5816: + `MAT_SPD`                         - symmetric positive definite
5817: . `MAT_SYMMETRIC`                   - symmetric in terms of both structure and value
5818: . `MAT_HERMITIAN`                   - transpose is the complex conjugation
5819: . `MAT_STRUCTURALLY_SYMMETRIC`      - symmetric nonzero structure
5820: . `MAT_SYMMETRY_ETERNAL`            - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5821: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5822: . `MAT_SPD_ETERNAL`                 - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

5824:    These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5825:    do not need to be computed (usually at a high cost)

5827:    Options For Use with `MatSetValues()`:
5828:    Insert a logically dense subblock, which can be
5829: . `MAT_ROW_ORIENTED`                - row-oriented (default)

5831:    These options reflect the data you pass in with `MatSetValues()`; it has
5832:    nothing to do with how the data is stored internally in the matrix
5833:    data structure.

5835:    When (re)assembling a matrix, we can restrict the input for
5836:    efficiency/debugging purposes.  These options include
5837: . `MAT_NEW_NONZERO_LOCATIONS`       - additional insertions will be allowed if they generate a new nonzero (slow)
5838: . `MAT_FORCE_DIAGONAL_ENTRIES`      - forces diagonal entries to be allocated
5839: . `MAT_IGNORE_OFF_PROC_ENTRIES`     - drops off-processor entries
5840: . `MAT_NEW_NONZERO_LOCATION_ERR`    - generates an error for new matrix entry
5841: . `MAT_USE_HASH_TABLE`              - uses a hash table to speed up matrix assembly
5842: . `MAT_NO_OFF_PROC_ENTRIES`         - you know each process will only set values for its own rows, will generate an error if
5843:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5844:         performance for very large process counts.
5845: - `MAT_SUBSET_OFF_PROC_ENTRIES`     - you know that the first assembly after setting this flag will set a superset
5846:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5847:         functions, instead sending only neighbor messages.

5849:   Level: intermediate

5851:   Notes:
5852:   Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

5854:   Some options are relevant only for particular matrix types and
5855:   are thus ignored by others.  Other options are not supported by
5856:   certain matrix types and will generate an error message if set.

5858:   If using Fortran to compute a matrix, one may need to
5859:   use the column-oriented option (or convert to the row-oriented
5860:   format).

5862:   `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5863:   that would generate a new entry in the nonzero structure is instead
5864:   ignored.  Thus, if memory has not already been allocated for this particular
5865:   data, then the insertion is ignored. For dense matrices, in which
5866:   the entire array is allocated, no entries are ever ignored.
5867:   Set after the first `MatAssemblyEnd()`. If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5869:   `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
5870:   that would generate a new entry in the nonzero structure instead produces
5871:   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

5873:   `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
5874:   that would generate a new entry that has not been preallocated will
5875:   instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
5876:   only.) This is a useful flag when debugging matrix memory preallocation.
5877:   If this option is set then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

5879:   `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
5880:   other processors should be dropped, rather than stashed.
5881:   This is useful if you know that the "owning" processor is also
5882:   always generating the correct matrix entries, so that PETSc need
5883:   not transfer duplicate entries generated on another processor.

5885:   `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
5886:   searches during matrix assembly. When this flag is set, the hash table
5887:   is created during the first matrix assembly. This hash table is
5888:   used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
5889:   to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
5890:   should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
5891:   supported by `MATMPIBAIJ` format only.

5893:   `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
5894:   are kept in the nonzero structure

5896:   `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
5897:   a zero location in the matrix

5899:   `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

5901:   `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
5902:   zero row routines and thus improves performance for very large process counts.

5904:   `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
5905:   part of the matrix (since they should match the upper triangular part).

5907:   `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
5908:   single call to `MatSetValues()`, preallocation is perfect, row oriented, `INSERT_VALUES` is used. Common
5909:   with finite difference schemes with non-periodic boundary conditions.

5911:   Developer Notes:
5912:   `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
5913:   places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
5914:   to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
5915:   not changed.

5917: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
5918: @*/
5919: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
5920: {
5921:   PetscFunctionBegin;
5923:   if (op > 0) {
5926:   }

5928:   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);

5930:   switch (op) {
5931:   case MAT_FORCE_DIAGONAL_ENTRIES:
5932:     mat->force_diagonals = flg;
5933:     PetscFunctionReturn(PETSC_SUCCESS);
5934:   case MAT_NO_OFF_PROC_ENTRIES:
5935:     mat->nooffprocentries = flg;
5936:     PetscFunctionReturn(PETSC_SUCCESS);
5937:   case MAT_SUBSET_OFF_PROC_ENTRIES:
5938:     mat->assembly_subset = flg;
5939:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
5940: #if !defined(PETSC_HAVE_MPIUNI)
5941:       PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
5942: #endif
5943:       mat->stash.first_assembly_done = PETSC_FALSE;
5944:     }
5945:     PetscFunctionReturn(PETSC_SUCCESS);
5946:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5947:     mat->nooffproczerorows = flg;
5948:     PetscFunctionReturn(PETSC_SUCCESS);
5949:   case MAT_SPD:
5950:     if (flg) {
5951:       mat->spd                    = PETSC_BOOL3_TRUE;
5952:       mat->symmetric              = PETSC_BOOL3_TRUE;
5953:       mat->structurally_symmetric = PETSC_BOOL3_TRUE;
5954:     } else {
5955:       mat->spd = PETSC_BOOL3_FALSE;
5956:     }
5957:     break;
5958:   case MAT_SYMMETRIC:
5959:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5960:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
5961: #if !defined(PETSC_USE_COMPLEX)
5962:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5963: #endif
5964:     break;
5965:   case MAT_HERMITIAN:
5966:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5967:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
5968: #if !defined(PETSC_USE_COMPLEX)
5969:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5970: #endif
5971:     break;
5972:   case MAT_STRUCTURALLY_SYMMETRIC:
5973:     mat->structurally_symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5974:     break;
5975:   case MAT_SYMMETRY_ETERNAL:
5976:     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");
5977:     mat->symmetry_eternal = flg;
5978:     if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
5979:     break;
5980:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
5981:     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");
5982:     mat->structural_symmetry_eternal = flg;
5983:     break;
5984:   case MAT_SPD_ETERNAL:
5985:     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");
5986:     mat->spd_eternal = flg;
5987:     if (flg) {
5988:       mat->structural_symmetry_eternal = PETSC_TRUE;
5989:       mat->symmetry_eternal            = PETSC_TRUE;
5990:     }
5991:     break;
5992:   case MAT_STRUCTURE_ONLY:
5993:     mat->structure_only = flg;
5994:     break;
5995:   case MAT_SORTED_FULL:
5996:     mat->sortedfull = flg;
5997:     break;
5998:   default:
5999:     break;
6000:   }
6001:   PetscTryTypeMethod(mat, setoption, op, flg);
6002:   PetscFunctionReturn(PETSC_SUCCESS);
6003: }

6005: /*@
6006:   MatGetOption - Gets a parameter option that has been set for a matrix.

6008:   Logically Collective

6010:   Input Parameters:
6011: + mat - the matrix
6012: - op  - the option, this only responds to certain options, check the code for which ones

6014:   Output Parameter:
6015: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

6017:   Level: intermediate

6019:   Notes:
6020:   Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

6022:   Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6023:   `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

6025: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6026:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6027: @*/
6028: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6029: {
6030:   PetscFunctionBegin;

6034:   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);
6035:   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()");

6037:   switch (op) {
6038:   case MAT_NO_OFF_PROC_ENTRIES:
6039:     *flg = mat->nooffprocentries;
6040:     break;
6041:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6042:     *flg = mat->nooffproczerorows;
6043:     break;
6044:   case MAT_SYMMETRIC:
6045:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6046:     break;
6047:   case MAT_HERMITIAN:
6048:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6049:     break;
6050:   case MAT_STRUCTURALLY_SYMMETRIC:
6051:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6052:     break;
6053:   case MAT_SPD:
6054:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6055:     break;
6056:   case MAT_SYMMETRY_ETERNAL:
6057:     *flg = mat->symmetry_eternal;
6058:     break;
6059:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6060:     *flg = mat->symmetry_eternal;
6061:     break;
6062:   default:
6063:     break;
6064:   }
6065:   PetscFunctionReturn(PETSC_SUCCESS);
6066: }

6068: /*@
6069:   MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
6070:   this routine retains the old nonzero structure.

6072:   Logically Collective

6074:   Input Parameter:
6075: . mat - the matrix

6077:   Level: intermediate

6079:   Note:
6080:   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.
6081:   See the Performance chapter of the users manual for information on preallocating matrices.

6083: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6084: @*/
6085: PetscErrorCode MatZeroEntries(Mat mat)
6086: {
6087:   PetscFunctionBegin;
6090:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6091:   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");
6092:   MatCheckPreallocated(mat, 1);

6094:   PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6095:   PetscUseTypeMethod(mat, zeroentries);
6096:   PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6097:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6098:   PetscFunctionReturn(PETSC_SUCCESS);
6099: }

6101: /*@
6102:   MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6103:   of a set of rows and columns of a matrix.

6105:   Collective

6107:   Input Parameters:
6108: + mat     - the matrix
6109: . numRows - the number of rows/columns to zero
6110: . rows    - the global row indices
6111: . diag    - value put in the diagonal of the eliminated rows
6112: . x       - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6113: - b       - optional vector of the right hand side, that will be adjusted by provided solution entries

6115:   Level: intermediate

6117:   Notes:
6118:   This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6120:   For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6121:   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

6123:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6124:   Krylov method to take advantage of the known solution on the zeroed rows.

6126:   For the parallel case, all processes that share the matrix (i.e.,
6127:   those in the communicator used for matrix creation) MUST call this
6128:   routine, regardless of whether any rows being zeroed are owned by
6129:   them.

6131:   Unlike `MatZeroRows()` this does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

6133:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6134:   list only rows local to itself).

6136:   The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

6138: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6139:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6140: @*/
6141: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6142: {
6143:   PetscFunctionBegin;
6146:   if (numRows) PetscAssertPointer(rows, 3);
6147:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6148:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6149:   MatCheckPreallocated(mat, 1);

6151:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6152:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6153:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6154:   PetscFunctionReturn(PETSC_SUCCESS);
6155: }

6157: /*@
6158:   MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6159:   of a set of rows and columns of a matrix.

6161:   Collective

6163:   Input Parameters:
6164: + mat  - the matrix
6165: . is   - the rows to zero
6166: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6167: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6168: - b    - optional vector of right hand side, that will be adjusted by provided solution

6170:   Level: intermediate

6172:   Note:
6173:   See `MatZeroRowsColumns()` for details on how this routine operates.

6175: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6176:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6177: @*/
6178: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6179: {
6180:   PetscInt        numRows;
6181:   const PetscInt *rows;

6183:   PetscFunctionBegin;
6188:   PetscCall(ISGetLocalSize(is, &numRows));
6189:   PetscCall(ISGetIndices(is, &rows));
6190:   PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6191:   PetscCall(ISRestoreIndices(is, &rows));
6192:   PetscFunctionReturn(PETSC_SUCCESS);
6193: }

6195: /*@
6196:   MatZeroRows - Zeros all entries (except possibly the main diagonal)
6197:   of a set of rows of a matrix.

6199:   Collective

6201:   Input Parameters:
6202: + mat     - the matrix
6203: . numRows - the number of rows to zero
6204: . rows    - the global row indices
6205: . diag    - value put in the diagonal of the zeroed rows
6206: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6207: - b       - optional vector of right hand side, that will be adjusted by provided solution entries

6209:   Level: intermediate

6211:   Notes:
6212:   This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6214:   For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

6216:   If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6217:   Krylov method to take advantage of the known solution on the zeroed rows.

6219:   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)
6220:   from the matrix.

6222:   Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6223:   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
6224:   formats this does not alter the nonzero structure.

6226:   If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6227:   of the matrix is not changed the values are
6228:   merely zeroed.

6230:   The user can set a value in the diagonal entry (or for the `MATAIJ` format
6231:   formats can optionally remove the main diagonal entry from the
6232:   nonzero structure as well, by passing 0.0 as the final argument).

6234:   For the parallel case, all processes that share the matrix (i.e.,
6235:   those in the communicator used for matrix creation) MUST call this
6236:   routine, regardless of whether any rows being zeroed are owned by
6237:   them.

6239:   Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6240:   list only rows local to itself).

6242:   You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6243:   owns that are to be zeroed. This saves a global synchronization in the implementation.

6245: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6246:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`
6247: @*/
6248: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6249: {
6250:   PetscFunctionBegin;
6253:   if (numRows) PetscAssertPointer(rows, 3);
6254:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6255:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6256:   MatCheckPreallocated(mat, 1);

6258:   PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6259:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6260:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6261:   PetscFunctionReturn(PETSC_SUCCESS);
6262: }

6264: /*@
6265:   MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6266:   of a set of rows of a matrix.

6268:   Collective

6270:   Input Parameters:
6271: + mat  - the matrix
6272: . is   - index set of rows to remove (if `NULL` then no row is removed)
6273: . diag - value put in all diagonals of eliminated rows
6274: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6275: - b    - optional vector of right hand side, that will be adjusted by provided solution

6277:   Level: intermediate

6279:   Note:
6280:   See `MatZeroRows()` for details on how this routine operates.

6282: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6283:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6284: @*/
6285: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6286: {
6287:   PetscInt        numRows = 0;
6288:   const PetscInt *rows    = NULL;

6290:   PetscFunctionBegin;
6293:   if (is) {
6295:     PetscCall(ISGetLocalSize(is, &numRows));
6296:     PetscCall(ISGetIndices(is, &rows));
6297:   }
6298:   PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6299:   if (is) PetscCall(ISRestoreIndices(is, &rows));
6300:   PetscFunctionReturn(PETSC_SUCCESS);
6301: }

6303: /*@
6304:   MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6305:   of a set of rows of a matrix. These rows must be local to the process.

6307:   Collective

6309:   Input Parameters:
6310: + mat     - the matrix
6311: . numRows - the number of rows to remove
6312: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6313: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6314: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6315: - b       - optional vector of right hand side, that will be adjusted by provided solution

6317:   Level: intermediate

6319:   Notes:
6320:   See `MatZeroRows()` for details on how this routine operates.

6322:   The grid coordinates are across the entire grid, not just the local portion

6324:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6325:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6326:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6327:   `DM_BOUNDARY_PERIODIC` boundary type.

6329:   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
6330:   a single value per point) you can skip filling those indices.

6332:   Fortran Notes:
6333:   `idxm` and `idxn` should be declared as
6334: $     MatStencil idxm(4, m)
6335:   and the values inserted using
6336: .vb
6337:     idxm(MatStencil_i, 1) = i
6338:     idxm(MatStencil_j, 1) = j
6339:     idxm(MatStencil_k, 1) = k
6340:     idxm(MatStencil_c, 1) = c
6341:    etc
6342: .ve

6344: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsl()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6345:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6346: @*/
6347: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6348: {
6349:   PetscInt  dim    = mat->stencil.dim;
6350:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6351:   PetscInt *dims   = mat->stencil.dims + 1;
6352:   PetscInt *starts = mat->stencil.starts;
6353:   PetscInt *dxm    = (PetscInt *)rows;
6354:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6356:   PetscFunctionBegin;
6359:   if (numRows) PetscAssertPointer(rows, 3);

6361:   PetscCall(PetscMalloc1(numRows, &jdxm));
6362:   for (i = 0; i < numRows; ++i) {
6363:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6364:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6365:     /* Local index in X dir */
6366:     tmp = *dxm++ - starts[0];
6367:     /* Loop over remaining dimensions */
6368:     for (j = 0; j < dim - 1; ++j) {
6369:       /* If nonlocal, set index to be negative */
6370:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6371:       /* Update local index */
6372:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6373:     }
6374:     /* Skip component slot if necessary */
6375:     if (mat->stencil.noc) dxm++;
6376:     /* Local row number */
6377:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6378:   }
6379:   PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6380:   PetscCall(PetscFree(jdxm));
6381:   PetscFunctionReturn(PETSC_SUCCESS);
6382: }

6384: /*@
6385:   MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6386:   of a set of rows and columns of a matrix.

6388:   Collective

6390:   Input Parameters:
6391: + mat     - the matrix
6392: . numRows - the number of rows/columns to remove
6393: . rows    - the grid coordinates (and component number when dof > 1) for matrix rows
6394: . diag    - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6395: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6396: - b       - optional vector of right hand side, that will be adjusted by provided solution

6398:   Level: intermediate

6400:   Notes:
6401:   See `MatZeroRowsColumns()` for details on how this routine operates.

6403:   The grid coordinates are across the entire grid, not just the local portion

6405:   For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6406:   obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6407:   etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6408:   `DM_BOUNDARY_PERIODIC` boundary type.

6410:   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
6411:   a single value per point) you can skip filling those indices.

6413:   Fortran Notes:
6414:   `idxm` and `idxn` should be declared as
6415: $     MatStencil idxm(4, m)
6416:   and the values inserted using
6417: .vb
6418:     idxm(MatStencil_i, 1) = i
6419:     idxm(MatStencil_j, 1) = j
6420:     idxm(MatStencil_k, 1) = k
6421:     idxm(MatStencil_c, 1) = c
6422:     etc
6423: .ve

6425: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6426:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6427: @*/
6428: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6429: {
6430:   PetscInt  dim    = mat->stencil.dim;
6431:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6432:   PetscInt *dims   = mat->stencil.dims + 1;
6433:   PetscInt *starts = mat->stencil.starts;
6434:   PetscInt *dxm    = (PetscInt *)rows;
6435:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6437:   PetscFunctionBegin;
6440:   if (numRows) PetscAssertPointer(rows, 3);

6442:   PetscCall(PetscMalloc1(numRows, &jdxm));
6443:   for (i = 0; i < numRows; ++i) {
6444:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6445:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6446:     /* Local index in X dir */
6447:     tmp = *dxm++ - starts[0];
6448:     /* Loop over remaining dimensions */
6449:     for (j = 0; j < dim - 1; ++j) {
6450:       /* If nonlocal, set index to be negative */
6451:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6452:       /* Update local index */
6453:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6454:     }
6455:     /* Skip component slot if necessary */
6456:     if (mat->stencil.noc) dxm++;
6457:     /* Local row number */
6458:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6459:   }
6460:   PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6461:   PetscCall(PetscFree(jdxm));
6462:   PetscFunctionReturn(PETSC_SUCCESS);
6463: }

6465: /*@C
6466:   MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6467:   of a set of rows of a matrix; using local numbering of rows.

6469:   Collective

6471:   Input Parameters:
6472: + mat     - the matrix
6473: . numRows - the number of rows to remove
6474: . rows    - the local row indices
6475: . diag    - value put in all diagonals of eliminated rows
6476: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6477: - b       - optional vector of right hand side, that will be adjusted by provided solution

6479:   Level: intermediate

6481:   Notes:
6482:   Before calling `MatZeroRowsLocal()`, the user must first set the
6483:   local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

6485:   See `MatZeroRows()` for details on how this routine operates.

6487: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6488:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6489: @*/
6490: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6491: {
6492:   PetscFunctionBegin;
6495:   if (numRows) PetscAssertPointer(rows, 3);
6496:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6497:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6498:   MatCheckPreallocated(mat, 1);

6500:   if (mat->ops->zerorowslocal) {
6501:     PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6502:   } else {
6503:     IS              is, newis;
6504:     const PetscInt *newRows;

6506:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6507:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6508:     PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6509:     PetscCall(ISGetIndices(newis, &newRows));
6510:     PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6511:     PetscCall(ISRestoreIndices(newis, &newRows));
6512:     PetscCall(ISDestroy(&newis));
6513:     PetscCall(ISDestroy(&is));
6514:   }
6515:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6516:   PetscFunctionReturn(PETSC_SUCCESS);
6517: }

6519: /*@
6520:   MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6521:   of a set of rows of a matrix; using local numbering of rows.

6523:   Collective

6525:   Input Parameters:
6526: + mat  - the matrix
6527: . is   - index set of rows to remove
6528: . diag - value put in all diagonals of eliminated rows
6529: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6530: - b    - optional vector of right hand side, that will be adjusted by provided solution

6532:   Level: intermediate

6534:   Notes:
6535:   Before calling `MatZeroRowsLocalIS()`, the user must first set the
6536:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6538:   See `MatZeroRows()` for details on how this routine operates.

6540: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6541:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6542: @*/
6543: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6544: {
6545:   PetscInt        numRows;
6546:   const PetscInt *rows;

6548:   PetscFunctionBegin;
6552:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6553:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6554:   MatCheckPreallocated(mat, 1);

6556:   PetscCall(ISGetLocalSize(is, &numRows));
6557:   PetscCall(ISGetIndices(is, &rows));
6558:   PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6559:   PetscCall(ISRestoreIndices(is, &rows));
6560:   PetscFunctionReturn(PETSC_SUCCESS);
6561: }

6563: /*@
6564:   MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6565:   of a set of rows and columns of a matrix; using local numbering of rows.

6567:   Collective

6569:   Input Parameters:
6570: + mat     - the matrix
6571: . numRows - the number of rows to remove
6572: . rows    - the global row indices
6573: . diag    - value put in all diagonals of eliminated rows
6574: . x       - optional vector of solutions for zeroed rows (other entries in vector are not used)
6575: - b       - optional vector of right hand side, that will be adjusted by provided solution

6577:   Level: intermediate

6579:   Notes:
6580:   Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6581:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6583:   See `MatZeroRowsColumns()` for details on how this routine operates.

6585: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6586:           `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6587: @*/
6588: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6589: {
6590:   IS              is, newis;
6591:   const PetscInt *newRows;

6593:   PetscFunctionBegin;
6596:   if (numRows) PetscAssertPointer(rows, 3);
6597:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6598:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6599:   MatCheckPreallocated(mat, 1);

6601:   PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6602:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6603:   PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6604:   PetscCall(ISGetIndices(newis, &newRows));
6605:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6606:   PetscCall(ISRestoreIndices(newis, &newRows));
6607:   PetscCall(ISDestroy(&newis));
6608:   PetscCall(ISDestroy(&is));
6609:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6610:   PetscFunctionReturn(PETSC_SUCCESS);
6611: }

6613: /*@
6614:   MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6615:   of a set of rows and columns of a matrix; using local numbering of rows.

6617:   Collective

6619:   Input Parameters:
6620: + mat  - the matrix
6621: . is   - index set of rows to remove
6622: . diag - value put in all diagonals of eliminated rows
6623: . x    - optional vector of solutions for zeroed rows (other entries in vector are not used)
6624: - b    - optional vector of right hand side, that will be adjusted by provided solution

6626:   Level: intermediate

6628:   Notes:
6629:   Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6630:   local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6632:   See `MatZeroRowsColumns()` for details on how this routine operates.

6634: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6635:           `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6636: @*/
6637: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6638: {
6639:   PetscInt        numRows;
6640:   const PetscInt *rows;

6642:   PetscFunctionBegin;
6646:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6647:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6648:   MatCheckPreallocated(mat, 1);

6650:   PetscCall(ISGetLocalSize(is, &numRows));
6651:   PetscCall(ISGetIndices(is, &rows));
6652:   PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6653:   PetscCall(ISRestoreIndices(is, &rows));
6654:   PetscFunctionReturn(PETSC_SUCCESS);
6655: }

6657: /*@C
6658:   MatGetSize - Returns the numbers of rows and columns in a matrix.

6660:   Not Collective

6662:   Input Parameter:
6663: . mat - the matrix

6665:   Output Parameters:
6666: + m - the number of global rows
6667: - n - the number of global columns

6669:   Level: beginner

6671:   Note:
6672:   Both output parameters can be `NULL` on input.

6674: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6675: @*/
6676: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6677: {
6678:   PetscFunctionBegin;
6680:   if (m) *m = mat->rmap->N;
6681:   if (n) *n = mat->cmap->N;
6682:   PetscFunctionReturn(PETSC_SUCCESS);
6683: }

6685: /*@C
6686:   MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6687:   of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

6689:   Not Collective

6691:   Input Parameter:
6692: . mat - the matrix

6694:   Output Parameters:
6695: + m - the number of local rows, use `NULL` to not obtain this value
6696: - n - the number of local columns, use `NULL` to not obtain this value

6698:   Level: beginner

6700: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6701: @*/
6702: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6703: {
6704:   PetscFunctionBegin;
6706:   if (m) PetscAssertPointer(m, 2);
6707:   if (n) PetscAssertPointer(n, 3);
6708:   if (m) *m = mat->rmap->n;
6709:   if (n) *n = mat->cmap->n;
6710:   PetscFunctionReturn(PETSC_SUCCESS);
6711: }

6713: /*@C
6714:   MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6715:   vector one multiplies this matrix by that are owned by this processor.

6717:   Not Collective, unless matrix has not been allocated, then collective

6719:   Input Parameter:
6720: . mat - the matrix

6722:   Output Parameters:
6723: + m - the global index of the first local column, use `NULL` to not obtain this value
6724: - n - one more than the global index of the last local column, use `NULL` to not obtain this value

6726:   Level: developer

6728:   Notes:
6729:   Retursns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6730:   Layouts](sec_matlayout) for details on matrix layouts.

6732: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
6733: @*/
6734: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6735: {
6736:   PetscFunctionBegin;
6739:   if (m) PetscAssertPointer(m, 2);
6740:   if (n) PetscAssertPointer(n, 3);
6741:   MatCheckPreallocated(mat, 1);
6742:   if (m) *m = mat->cmap->rstart;
6743:   if (n) *n = mat->cmap->rend;
6744:   PetscFunctionReturn(PETSC_SUCCESS);
6745: }

6747: /*@C
6748:   MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6749:   this MPI process.

6751:   Not Collective

6753:   Input Parameter:
6754: . mat - the matrix

6756:   Output Parameters:
6757: + m - the global index of the first local row, use `NULL` to not obtain this value
6758: - n - one more than the global index of the last local row, use `NULL` to not obtain this value

6760:   Level: beginner

6762:   Note:
6763:   For all matrices  it returns the range of matrix rows associated with rows of a vector that
6764:   would contain the result of a matrix vector product with this matrix. See [Matrix
6765:   Layouts](sec_matlayout) for details on matrix layouts.

6767: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`,
6768:           `PetscLayout`
6769: @*/
6770: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6771: {
6772:   PetscFunctionBegin;
6775:   if (m) PetscAssertPointer(m, 2);
6776:   if (n) PetscAssertPointer(n, 3);
6777:   MatCheckPreallocated(mat, 1);
6778:   if (m) *m = mat->rmap->rstart;
6779:   if (n) *n = mat->rmap->rend;
6780:   PetscFunctionReturn(PETSC_SUCCESS);
6781: }

6783: /*@C
6784:   MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6785:   `MATSCALAPACK`, returns the range of matrix rows owned by each process.

6787:   Not Collective, unless matrix has not been allocated

6789:   Input Parameter:
6790: . mat - the matrix

6792:   Output Parameter:
6793: . ranges - start of each processors portion plus one more than the total length at the end

6795:   Level: beginner

6797:   Notes:
6798:   For all matrices  it returns the ranges of matrix rows associated with rows of a vector that
6799:   would contain the result of a matrix vector product with this matrix. See [Matrix
6800:   Layouts](sec_matlayout) for details on matrix layouts.

6802: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
6803: @*/
6804: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt **ranges)
6805: {
6806:   PetscFunctionBegin;
6809:   MatCheckPreallocated(mat, 1);
6810:   PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6811:   PetscFunctionReturn(PETSC_SUCCESS);
6812: }

6814: /*@C
6815:   MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6816:   vector one multiplies this vector by that are owned by each processor.

6818:   Not Collective, unless matrix has not been allocated

6820:   Input Parameter:
6821: . mat - the matrix

6823:   Output Parameter:
6824: . ranges - start of each processors portion plus one more then the total length at the end

6826:   Level: beginner

6828:   Notes:
6829:   Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
6830:   Layouts](sec_matlayout) for details on matrix layouts.

6832: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`
6833: @*/
6834: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt **ranges)
6835: {
6836:   PetscFunctionBegin;
6839:   MatCheckPreallocated(mat, 1);
6840:   PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
6841:   PetscFunctionReturn(PETSC_SUCCESS);
6842: }

6844: /*@C
6845:   MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.

6847:   Not Collective

6849:   Input Parameter:
6850: . A - matrix

6852:   Output Parameters:
6853: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
6854: - cols - columns in which this process owns elements, use `NULL` to not obtain this value

6856:   Level: intermediate

6858:   Notes:
6859:   For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
6860:   returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
6861:   `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
6862:   details on matrix layouts.

6864: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatSetValues()`, ``MATELEMENTAL``, ``MATSCALAPACK``
6865: @*/
6866: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
6867: {
6868:   PetscErrorCode (*f)(Mat, IS *, IS *);

6870:   PetscFunctionBegin;
6871:   MatCheckPreallocated(A, 1);
6872:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
6873:   if (f) {
6874:     PetscCall((*f)(A, rows, cols));
6875:   } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
6876:     if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
6877:     if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
6878:   }
6879:   PetscFunctionReturn(PETSC_SUCCESS);
6880: }

6882: /*@C
6883:   MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
6884:   Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
6885:   to complete the factorization.

6887:   Collective

6889:   Input Parameters:
6890: + fact - the factorized matrix obtained with `MatGetFactor()`
6891: . mat  - the matrix
6892: . row  - row permutation
6893: . col  - column permutation
6894: - info - structure containing
6895: .vb
6896:       levels - number of levels of fill.
6897:       expected fill - as ratio of original fill.
6898:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
6899:                 missing diagonal entries)
6900: .ve

6902:   Level: developer

6904:   Notes:
6905:   See [Matrix Factorization](sec_matfactor) for additional information.

6907:   Most users should employ the `KSP` interface for linear solvers
6908:   instead of working directly with matrix algebra routines such as this.
6909:   See, e.g., `KSPCreate()`.

6911:   Uses the definition of level of fill as in Y. Saad, 2003

6913:   Developer Notes:
6914:   The Fortran interface is not autogenerated as the
6915:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

6917:   References:
6918: .  * - Y. Saad, Iterative methods for sparse linear systems Philadelphia: Society for Industrial and Applied Mathematics, 2003

6920: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
6921:           `MatGetOrdering()`, `MatFactorInfo`
6922: @*/
6923: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
6924: {
6925:   PetscFunctionBegin;
6930:   PetscAssertPointer(info, 5);
6931:   PetscAssertPointer(fact, 1);
6932:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
6933:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
6934:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6935:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6936:   MatCheckPreallocated(mat, 2);

6938:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
6939:   PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
6940:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
6941:   PetscFunctionReturn(PETSC_SUCCESS);
6942: }

6944: /*@C
6945:   MatICCFactorSymbolic - Performs symbolic incomplete
6946:   Cholesky factorization for a symmetric matrix.  Use
6947:   `MatCholeskyFactorNumeric()` to complete the factorization.

6949:   Collective

6951:   Input Parameters:
6952: + fact - the factorized matrix obtained with `MatGetFactor()`
6953: . mat  - the matrix to be factored
6954: . perm - row and column permutation
6955: - info - structure containing
6956: .vb
6957:       levels - number of levels of fill.
6958:       expected fill - as ratio of original fill.
6959: .ve

6961:   Level: developer

6963:   Notes:
6964:   Most users should employ the `KSP` interface for linear solvers
6965:   instead of working directly with matrix algebra routines such as this.
6966:   See, e.g., `KSPCreate()`.

6968:   This uses the definition of level of fill as in Y. Saad, 2003

6970:   Developer Notes:
6971:   The Fortran interface is not autogenerated as the
6972:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

6974:   References:
6975: .  * - Y. Saad, Iterative methods for sparse linear systems Philadelphia: Society for Industrial and Applied Mathematics, 2003

6977: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
6978: @*/
6979: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
6980: {
6981:   PetscFunctionBegin;
6985:   PetscAssertPointer(info, 4);
6986:   PetscAssertPointer(fact, 1);
6987:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6988:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
6989:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
6990:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6991:   MatCheckPreallocated(mat, 2);

6993:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
6994:   PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
6995:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
6996:   PetscFunctionReturn(PETSC_SUCCESS);
6997: }

6999: /*@C
7000:   MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7001:   points to an array of valid matrices, they may be reused to store the new
7002:   submatrices.

7004:   Collective

7006:   Input Parameters:
7007: + mat   - the matrix
7008: . n     - the number of submatrixes to be extracted (on this processor, may be zero)
7009: . irow  - index set of rows to extract
7010: . icol  - index set of columns to extract
7011: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7013:   Output Parameter:
7014: . submat - the array of submatrices

7016:   Level: advanced

7018:   Notes:
7019:   `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7020:   (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7021:   to extract a parallel submatrix.

7023:   Some matrix types place restrictions on the row and column
7024:   indices, such as that they be sorted or that they be equal to each other.

7026:   The index sets may not have duplicate entries.

7028:   When extracting submatrices from a parallel matrix, each processor can
7029:   form a different submatrix by setting the rows and columns of its
7030:   individual index sets according to the local submatrix desired.

7032:   When finished using the submatrices, the user should destroy
7033:   them with `MatDestroySubMatrices()`.

7035:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7036:   original matrix has not changed from that last call to `MatCreateSubMatrices()`.

7038:   This routine creates the matrices in submat; you should NOT create them before
7039:   calling it. It also allocates the array of matrix pointers submat.

7041:   For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7042:   request one row/column in a block, they must request all rows/columns that are in
7043:   that block. For example, if the block size is 2 you cannot request just row 0 and
7044:   column 0.

7046:   Fortran Notes:
7047:   The Fortran interface is slightly different from that given below; it
7048:   requires one to pass in as `submat` a `Mat` (integer) array of size at least n+1.

7050: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7051: @*/
7052: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7053: {
7054:   PetscInt  i;
7055:   PetscBool eq;

7057:   PetscFunctionBegin;
7060:   if (n) {
7061:     PetscAssertPointer(irow, 3);
7063:     PetscAssertPointer(icol, 4);
7065:   }
7066:   PetscAssertPointer(submat, 6);
7067:   if (n && scall == MAT_REUSE_MATRIX) {
7068:     PetscAssertPointer(*submat, 6);
7070:   }
7071:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7072:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7073:   MatCheckPreallocated(mat, 1);
7074:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7075:   PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7076:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7077:   for (i = 0; i < n; i++) {
7078:     (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7079:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7080:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7081: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7082:     if (mat->boundtocpu && mat->bindingpropagates) {
7083:       PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7084:       PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7085:     }
7086: #endif
7087:   }
7088:   PetscFunctionReturn(PETSC_SUCCESS);
7089: }

7091: /*@C
7092:   MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of `IS` that may live on subcomms).

7094:   Collective

7096:   Input Parameters:
7097: + mat   - the matrix
7098: . n     - the number of submatrixes to be extracted
7099: . irow  - index set of rows to extract
7100: . icol  - index set of columns to extract
7101: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7103:   Output Parameter:
7104: . submat - the array of submatrices

7106:   Level: advanced

7108:   Note:
7109:   This is used by `PCGASM`

7111: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7112: @*/
7113: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7114: {
7115:   PetscInt  i;
7116:   PetscBool eq;

7118:   PetscFunctionBegin;
7121:   if (n) {
7122:     PetscAssertPointer(irow, 3);
7124:     PetscAssertPointer(icol, 4);
7126:   }
7127:   PetscAssertPointer(submat, 6);
7128:   if (n && scall == MAT_REUSE_MATRIX) {
7129:     PetscAssertPointer(*submat, 6);
7131:   }
7132:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7133:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7134:   MatCheckPreallocated(mat, 1);

7136:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7137:   PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7138:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7139:   for (i = 0; i < n; i++) {
7140:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7141:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7142:   }
7143:   PetscFunctionReturn(PETSC_SUCCESS);
7144: }

7146: /*@C
7147:   MatDestroyMatrices - Destroys an array of matrices.

7149:   Collective

7151:   Input Parameters:
7152: + n   - the number of local matrices
7153: - mat - the matrices (this is a pointer to the array of matrices)

7155:   Level: advanced

7157:   Note:
7158:   Frees not only the matrices, but also the array that contains the matrices

7160:   Fortran Notes:
7161:   This does not free the array.

7163: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()` `MatDestroySubMatrices()`
7164: @*/
7165: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7166: {
7167:   PetscInt i;

7169:   PetscFunctionBegin;
7170:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7171:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7172:   PetscAssertPointer(mat, 2);

7174:   for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

7176:   /* memory is allocated even if n = 0 */
7177:   PetscCall(PetscFree(*mat));
7178:   PetscFunctionReturn(PETSC_SUCCESS);
7179: }

7181: /*@C
7182:   MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

7184:   Collective

7186:   Input Parameters:
7187: + n   - the number of local matrices
7188: - mat - the matrices (this is a pointer to the array of matrices, just to match the calling
7189:                        sequence of `MatCreateSubMatrices()`)

7191:   Level: advanced

7193:   Note:
7194:   Frees not only the matrices, but also the array that contains the matrices

7196:   Fortran Notes:
7197:   This does not free the array.

7199: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7200: @*/
7201: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7202: {
7203:   Mat mat0;

7205:   PetscFunctionBegin;
7206:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7207:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7208:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7209:   PetscAssertPointer(mat, 2);

7211:   mat0 = (*mat)[0];
7212:   if (mat0 && mat0->ops->destroysubmatrices) {
7213:     PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7214:   } else {
7215:     PetscCall(MatDestroyMatrices(n, mat));
7216:   }
7217:   PetscFunctionReturn(PETSC_SUCCESS);
7218: }

7220: /*@C
7221:   MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

7223:   Collective

7225:   Input Parameter:
7226: . mat - the matrix

7228:   Output Parameter:
7229: . matstruct - the sequential matrix with the nonzero structure of mat

7231:   Level: developer

7233: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7234: @*/
7235: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7236: {
7237:   PetscFunctionBegin;
7239:   PetscAssertPointer(matstruct, 2);

7242:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7243:   MatCheckPreallocated(mat, 1);

7245:   PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7246:   PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7247:   PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7248:   PetscFunctionReturn(PETSC_SUCCESS);
7249: }

7251: /*@C
7252:   MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

7254:   Collective

7256:   Input Parameter:
7257: . mat - the matrix (this is a pointer to the array of matrices, just to match the calling
7258:                        sequence of `MatGetSeqNonzeroStructure()`)

7260:   Level: advanced

7262:   Note:
7263:   Frees not only the matrices, but also the array that contains the matrices

7265: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7266: @*/
7267: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7268: {
7269:   PetscFunctionBegin;
7270:   PetscAssertPointer(mat, 1);
7271:   PetscCall(MatDestroy(mat));
7272:   PetscFunctionReturn(PETSC_SUCCESS);
7273: }

7275: /*@
7276:   MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7277:   replaces the index sets by larger ones that represent submatrices with
7278:   additional overlap.

7280:   Collective

7282:   Input Parameters:
7283: + mat - the matrix
7284: . n   - the number of index sets
7285: . is  - the array of index sets (these index sets will changed during the call)
7286: - ov  - the additional overlap requested

7288:   Options Database Key:
7289: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7291:   Level: developer

7293:   Note:
7294:   The computed overlap preserves the matrix block sizes when the blocks are square.
7295:   That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7296:   that block are included in the overlap regardless of whether each specific column would increase the overlap.

7298: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7299: @*/
7300: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7301: {
7302:   PetscInt i, bs, cbs;

7304:   PetscFunctionBegin;
7308:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7309:   if (n) {
7310:     PetscAssertPointer(is, 3);
7312:   }
7313:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7314:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7315:   MatCheckPreallocated(mat, 1);

7317:   if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7318:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7319:   PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7320:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7321:   PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7322:   if (bs == cbs) {
7323:     for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7324:   }
7325:   PetscFunctionReturn(PETSC_SUCCESS);
7326: }

7328: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

7330: /*@
7331:   MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7332:   a sub communicator, 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: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7349: @*/
7350: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7351: {
7352:   PetscInt i;

7354:   PetscFunctionBegin;
7357:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7358:   if (n) {
7359:     PetscAssertPointer(is, 3);
7361:   }
7362:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7363:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7364:   MatCheckPreallocated(mat, 1);
7365:   if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7366:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7367:   for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7368:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7369:   PetscFunctionReturn(PETSC_SUCCESS);
7370: }

7372: /*@
7373:   MatGetBlockSize - Returns the matrix block size.

7375:   Not Collective

7377:   Input Parameter:
7378: . mat - the matrix

7380:   Output Parameter:
7381: . bs - block size

7383:   Level: intermediate

7385:   Notes:
7386:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

7388:   If the block size has not been set yet this routine returns 1.

7390: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7391: @*/
7392: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7393: {
7394:   PetscFunctionBegin;
7396:   PetscAssertPointer(bs, 2);
7397:   *bs = PetscAbs(mat->rmap->bs);
7398:   PetscFunctionReturn(PETSC_SUCCESS);
7399: }

7401: /*@
7402:   MatGetBlockSizes - Returns the matrix block row and column sizes.

7404:   Not Collective

7406:   Input Parameter:
7407: . mat - the matrix

7409:   Output Parameters:
7410: + rbs - row block size
7411: - cbs - column block size

7413:   Level: intermediate

7415:   Notes:
7416:   Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7417:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7419:   If a block size has not been set yet this routine returns 1.

7421: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7422: @*/
7423: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7424: {
7425:   PetscFunctionBegin;
7427:   if (rbs) PetscAssertPointer(rbs, 2);
7428:   if (cbs) PetscAssertPointer(cbs, 3);
7429:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7430:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7431:   PetscFunctionReturn(PETSC_SUCCESS);
7432: }

7434: /*@
7435:   MatSetBlockSize - Sets the matrix block size.

7437:   Logically Collective

7439:   Input Parameters:
7440: + mat - the matrix
7441: - bs  - block size

7443:   Level: intermediate

7445:   Notes:
7446:   Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7447:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7449:   For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7450:   is compatible with the matrix local sizes.

7452: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7453: @*/
7454: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7455: {
7456:   PetscFunctionBegin;
7459:   PetscCall(MatSetBlockSizes(mat, bs, bs));
7460:   PetscFunctionReturn(PETSC_SUCCESS);
7461: }

7463: typedef struct {
7464:   PetscInt         n;
7465:   IS              *is;
7466:   Mat             *mat;
7467:   PetscObjectState nonzerostate;
7468:   Mat              C;
7469: } EnvelopeData;

7471: static PetscErrorCode EnvelopeDataDestroy(EnvelopeData *edata)
7472: {
7473:   for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7474:   PetscCall(PetscFree(edata->is));
7475:   PetscCall(PetscFree(edata));
7476:   return PETSC_SUCCESS;
7477: }

7479: /*@
7480:   MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7481:   the sizes of these blocks in the matrix. An individual block may lie over several processes.

7483:   Collective

7485:   Input Parameter:
7486: . mat - the matrix

7488:   Level: intermediate

7490:   Notes:
7491:   There can be zeros within the blocks

7493:   The blocks can overlap between processes, including laying on more than two processes

7495: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7496: @*/
7497: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7498: {
7499:   PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7500:   PetscInt          *diag, *odiag, sc;
7501:   VecScatter         scatter;
7502:   PetscScalar       *seqv;
7503:   const PetscScalar *parv;
7504:   const PetscInt    *ia, *ja;
7505:   PetscBool          set, flag, done;
7506:   Mat                AA = mat, A;
7507:   MPI_Comm           comm;
7508:   PetscMPIInt        rank, size, tag;
7509:   MPI_Status         status;
7510:   PetscContainer     container;
7511:   EnvelopeData      *edata;
7512:   Vec                seq, par;
7513:   IS                 isglobal;

7515:   PetscFunctionBegin;
7517:   PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7518:   if (!set || !flag) {
7519:     /* TODO: only needs nonzero structure of transpose */
7520:     PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7521:     PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7522:   }
7523:   PetscCall(MatAIJGetLocalMat(AA, &A));
7524:   PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7525:   PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

7527:   PetscCall(MatGetLocalSize(mat, &n, NULL));
7528:   PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7529:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7530:   PetscCallMPI(MPI_Comm_size(comm, &size));
7531:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

7533:   PetscCall(PetscMalloc2(n, &sizes, n, &starts));

7535:   if (rank > 0) {
7536:     PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7537:     PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7538:   }
7539:   PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7540:   for (i = 0; i < n; i++) {
7541:     env = PetscMax(env, ja[ia[i + 1] - 1]);
7542:     II  = rstart + i;
7543:     if (env == II) {
7544:       starts[lblocks]  = tbs;
7545:       sizes[lblocks++] = 1 + II - tbs;
7546:       tbs              = 1 + II;
7547:     }
7548:   }
7549:   if (rank < size - 1) {
7550:     PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7551:     PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7552:   }

7554:   PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7555:   if (!set || !flag) PetscCall(MatDestroy(&AA));
7556:   PetscCall(MatDestroy(&A));

7558:   PetscCall(PetscNew(&edata));
7559:   PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7560:   edata->n = lblocks;
7561:   /* create IS needed for extracting blocks from the original matrix */
7562:   PetscCall(PetscMalloc1(lblocks, &edata->is));
7563:   for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

7565:   /* Create the resulting inverse matrix structure with preallocation information */
7566:   PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7567:   PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7568:   PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7569:   PetscCall(MatSetType(edata->C, MATAIJ));

7571:   /* Communicate the start and end of each row, from each block to the correct rank */
7572:   /* TODO: Use PetscSF instead of VecScatter */
7573:   for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7574:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7575:   PetscCall(VecGetArrayWrite(seq, &seqv));
7576:   for (PetscInt i = 0; i < lblocks; i++) {
7577:     for (PetscInt j = 0; j < sizes[i]; j++) {
7578:       seqv[cnt]     = starts[i];
7579:       seqv[cnt + 1] = starts[i] + sizes[i];
7580:       cnt += 2;
7581:     }
7582:   }
7583:   PetscCall(VecRestoreArrayWrite(seq, &seqv));
7584:   PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7585:   sc -= cnt;
7586:   PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7587:   PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7588:   PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7589:   PetscCall(ISDestroy(&isglobal));
7590:   PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7591:   PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7592:   PetscCall(VecScatterDestroy(&scatter));
7593:   PetscCall(VecDestroy(&seq));
7594:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7595:   PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7596:   PetscCall(VecGetArrayRead(par, &parv));
7597:   cnt = 0;
7598:   PetscCall(MatGetSize(mat, NULL, &n));
7599:   for (PetscInt i = 0; i < mat->rmap->n; i++) {
7600:     PetscInt start, end, d = 0, od = 0;

7602:     start = (PetscInt)PetscRealPart(parv[cnt]);
7603:     end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
7604:     cnt += 2;

7606:     if (start < cstart) {
7607:       od += cstart - start + n - cend;
7608:       d += cend - cstart;
7609:     } else if (start < cend) {
7610:       od += n - cend;
7611:       d += cend - start;
7612:     } else od += n - start;
7613:     if (end <= cstart) {
7614:       od -= cstart - end + n - cend;
7615:       d -= cend - cstart;
7616:     } else if (end < cend) {
7617:       od -= n - cend;
7618:       d -= cend - end;
7619:     } else od -= n - end;

7621:     odiag[i] = od;
7622:     diag[i]  = d;
7623:   }
7624:   PetscCall(VecRestoreArrayRead(par, &parv));
7625:   PetscCall(VecDestroy(&par));
7626:   PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7627:   PetscCall(PetscFree2(diag, odiag));
7628:   PetscCall(PetscFree2(sizes, starts));

7630:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7631:   PetscCall(PetscContainerSetPointer(container, edata));
7632:   PetscCall(PetscContainerSetUserDestroy(container, (PetscErrorCode(*)(void *))EnvelopeDataDestroy));
7633:   PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7634:   PetscCall(PetscObjectDereference((PetscObject)container));
7635:   PetscFunctionReturn(PETSC_SUCCESS);
7636: }

7638: /*@
7639:   MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

7641:   Collective

7643:   Input Parameters:
7644: + A     - the matrix
7645: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine

7647:   Output Parameter:
7648: . C - matrix with inverted block diagonal of `A`

7650:   Level: advanced

7652:   Note:
7653:   For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

7655: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7656: @*/
7657: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7658: {
7659:   PetscContainer   container;
7660:   EnvelopeData    *edata;
7661:   PetscObjectState nonzerostate;

7663:   PetscFunctionBegin;
7664:   PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7665:   if (!container) {
7666:     PetscCall(MatComputeVariableBlockEnvelope(A));
7667:     PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7668:   }
7669:   PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7670:   PetscCall(MatGetNonzeroState(A, &nonzerostate));
7671:   PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7672:   PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

7674:   PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7675:   *C = edata->C;

7677:   for (PetscInt i = 0; i < edata->n; i++) {
7678:     Mat          D;
7679:     PetscScalar *dvalues;

7681:     PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7682:     PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7683:     PetscCall(MatSeqDenseInvert(D));
7684:     PetscCall(MatDenseGetArray(D, &dvalues));
7685:     PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7686:     PetscCall(MatDestroy(&D));
7687:   }
7688:   PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7689:   PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7690:   PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7691:   PetscFunctionReturn(PETSC_SUCCESS);
7692: }

7694: /*@
7695:   MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

7697:   Logically Collective

7699:   Input Parameters:
7700: + mat     - the matrix
7701: . nblocks - the number of blocks on this process, each block can only exist on a single process
7702: - bsizes  - the block sizes

7704:   Level: intermediate

7706:   Notes:
7707:   Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

7709:   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.

7711: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7712:           `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7713: @*/
7714: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, PetscInt *bsizes)
7715: {
7716:   PetscInt i, ncnt = 0, nlocal;

7718:   PetscFunctionBegin;
7720:   PetscCheck(nblocks >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks must be great than or equal to zero");
7721:   PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7722:   for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
7723:   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);
7724:   PetscCall(PetscFree(mat->bsizes));
7725:   mat->nblocks = nblocks;
7726:   PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7727:   PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7728:   PetscFunctionReturn(PETSC_SUCCESS);
7729: }

7731: /*@C
7732:   MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7734:   Logically Collective; No Fortran Support

7736:   Input Parameter:
7737: . mat - the matrix

7739:   Output Parameters:
7740: + nblocks - the number of blocks on this process
7741: - bsizes  - the block sizes

7743:   Level: intermediate

7745: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7746: @*/
7747: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt **bsizes)
7748: {
7749:   PetscFunctionBegin;
7751:   *nblocks = mat->nblocks;
7752:   *bsizes  = mat->bsizes;
7753:   PetscFunctionReturn(PETSC_SUCCESS);
7754: }

7756: /*@
7757:   MatSetBlockSizes - Sets the matrix block row and column sizes.

7759:   Logically Collective

7761:   Input Parameters:
7762: + mat - the matrix
7763: . rbs - row block size
7764: - cbs - column block size

7766:   Level: intermediate

7768:   Notes:
7769:   Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7770:   If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7771:   This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7773:   For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7774:   are compatible with the matrix local sizes.

7776:   The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

7778: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7779: @*/
7780: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7781: {
7782:   PetscFunctionBegin;
7786:   PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7787:   if (mat->rmap->refcnt) {
7788:     ISLocalToGlobalMapping l2g  = NULL;
7789:     PetscLayout            nmap = NULL;

7791:     PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7792:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7793:     PetscCall(PetscLayoutDestroy(&mat->rmap));
7794:     mat->rmap          = nmap;
7795:     mat->rmap->mapping = l2g;
7796:   }
7797:   if (mat->cmap->refcnt) {
7798:     ISLocalToGlobalMapping l2g  = NULL;
7799:     PetscLayout            nmap = NULL;

7801:     PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7802:     if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7803:     PetscCall(PetscLayoutDestroy(&mat->cmap));
7804:     mat->cmap          = nmap;
7805:     mat->cmap->mapping = l2g;
7806:   }
7807:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7808:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7809:   PetscFunctionReturn(PETSC_SUCCESS);
7810: }

7812: /*@
7813:   MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

7815:   Logically Collective

7817:   Input Parameters:
7818: + mat     - the matrix
7819: . fromRow - matrix from which to copy row block size
7820: - fromCol - matrix from which to copy column block size (can be same as fromRow)

7822:   Level: developer

7824: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
7825: @*/
7826: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
7827: {
7828:   PetscFunctionBegin;
7832:   if (fromRow->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
7833:   if (fromCol->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
7834:   PetscFunctionReturn(PETSC_SUCCESS);
7835: }

7837: /*@
7838:   MatResidual - Default routine to calculate the residual r = b - Ax

7840:   Collective

7842:   Input Parameters:
7843: + mat - the matrix
7844: . b   - the right-hand-side
7845: - x   - the approximate solution

7847:   Output Parameter:
7848: . r - location to store the residual

7850:   Level: developer

7852: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
7853: @*/
7854: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
7855: {
7856:   PetscFunctionBegin;
7862:   MatCheckPreallocated(mat, 1);
7863:   PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
7864:   if (!mat->ops->residual) {
7865:     PetscCall(MatMult(mat, x, r));
7866:     PetscCall(VecAYPX(r, -1.0, b));
7867:   } else {
7868:     PetscUseTypeMethod(mat, residual, b, x, r);
7869:   }
7870:   PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
7871:   PetscFunctionReturn(PETSC_SUCCESS);
7872: }

7874: /*MC
7875:     MatGetRowIJF90 - Obtains the compressed row storage i and j indices for the local rows of a sparse matrix

7877:     Synopsis:
7878:     MatGetRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

7880:     Not Collective

7882:     Input Parameters:
7883: +   A - the matrix
7884: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7885: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7886: -   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7887:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7888:                  always used.

7890:     Output Parameters:
7891: +   n - number of local rows in the (possibly compressed) matrix
7892: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7893: .   ja - the column indices
7894: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7895:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

7897:     Level: developer

7899:     Note:
7900:     Use  `MatRestoreRowIJF90()` when you no longer need access to the data

7902: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatRestoreRowIJF90()`
7903: M*/

7905: /*MC
7906:     MatRestoreRowIJF90 - restores the compressed row storage i and j indices for the local rows of a sparse matrix obtained with `MatGetRowIJF90()`

7908:     Synopsis:
7909:     MatRestoreRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

7911:     Not Collective

7913:     Input Parameters:
7914: +   A - the  matrix
7915: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7916: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7917:     inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7918:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7919:                  always used.
7920: .   n - number of local rows in the (possibly compressed) matrix
7921: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7922: .   ja - the column indices
7923: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7924:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

7926:     Level: developer

7928: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatGetRowIJF90()`
7929: M*/

7931: /*@C
7932:   MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

7934:   Collective

7936:   Input Parameters:
7937: + mat             - the matrix
7938: . shift           - 0 or 1 indicating we want the indices starting at 0 or 1
7939: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7940: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7941:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7942:                  always used.

7944:   Output Parameters:
7945: + n    - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
7946: . 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
7947: . ja   - the column indices, use `NULL` if not needed
7948: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7949:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

7951:   Level: developer

7953:   Notes:
7954:   You CANNOT change any of the ia[] or ja[] values.

7956:   Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

7958:   Fortran Notes:
7959:   Use
7960: .vb
7961:     PetscInt, pointer :: ia(:),ja(:)
7962:     call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
7963:     ! Access the ith and jth entries via ia(i) and ja(j)
7964: .ve
7965:   `MatGetRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatGetRowIJF90()`

7967: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetRowIJF90()`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
7968: @*/
7969: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
7970: {
7971:   PetscFunctionBegin;
7974:   if (n) PetscAssertPointer(n, 5);
7975:   if (ia) PetscAssertPointer(ia, 6);
7976:   if (ja) PetscAssertPointer(ja, 7);
7977:   if (done) PetscAssertPointer(done, 8);
7978:   MatCheckPreallocated(mat, 1);
7979:   if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
7980:   else {
7981:     if (done) *done = PETSC_TRUE;
7982:     PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
7983:     PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
7984:     PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
7985:   }
7986:   PetscFunctionReturn(PETSC_SUCCESS);
7987: }

7989: /*@C
7990:   MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

7992:   Collective

7994:   Input Parameters:
7995: + mat             - the matrix
7996: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
7997: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
7998:                 symmetrized
7999: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8000:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8001:                  always used.
8002: . n               - number of columns in the (possibly compressed) matrix
8003: . ia              - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8004: - ja              - the row indices

8006:   Output Parameter:
8007: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

8009:   Level: developer

8011: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8012: @*/
8013: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8014: {
8015:   PetscFunctionBegin;
8018:   PetscAssertPointer(n, 5);
8019:   if (ia) PetscAssertPointer(ia, 6);
8020:   if (ja) PetscAssertPointer(ja, 7);
8021:   PetscAssertPointer(done, 8);
8022:   MatCheckPreallocated(mat, 1);
8023:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8024:   else {
8025:     *done = PETSC_TRUE;
8026:     PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8027:   }
8028:   PetscFunctionReturn(PETSC_SUCCESS);
8029: }

8031: /*@C
8032:   MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

8034:   Collective

8036:   Input Parameters:
8037: + mat             - the matrix
8038: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8039: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8040: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8041:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8042:                  always used.
8043: . n               - size of (possibly compressed) matrix
8044: . ia              - the row pointers
8045: - ja              - the column indices

8047:   Output Parameter:
8048: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8050:   Level: developer

8052:   Note:
8053:   This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8054:   us of the array after it has been restored. If you pass `NULL`, it will
8055:   not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

8057:   Fortran Notes:
8058:   `MatRestoreRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatRestoreRowIJF90()`

8060: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreRowIJF90()`, `MatRestoreColumnIJ()`
8061: @*/
8062: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8063: {
8064:   PetscFunctionBegin;
8067:   if (ia) PetscAssertPointer(ia, 6);
8068:   if (ja) PetscAssertPointer(ja, 7);
8069:   if (done) PetscAssertPointer(done, 8);
8070:   MatCheckPreallocated(mat, 1);

8072:   if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8073:   else {
8074:     if (done) *done = PETSC_TRUE;
8075:     PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8076:     if (n) *n = 0;
8077:     if (ia) *ia = NULL;
8078:     if (ja) *ja = NULL;
8079:   }
8080:   PetscFunctionReturn(PETSC_SUCCESS);
8081: }

8083: /*@C
8084:   MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

8086:   Collective

8088:   Input Parameters:
8089: + mat             - the matrix
8090: . shift           - 1 or zero indicating we want the indices starting at 0 or 1
8091: . symmetric       - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8092: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8093:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8094:                  always used.

8096:   Output Parameters:
8097: + n    - size of (possibly compressed) matrix
8098: . ia   - the column pointers
8099: . ja   - the row indices
8100: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8102:   Level: developer

8104: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8105: @*/
8106: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8107: {
8108:   PetscFunctionBegin;
8111:   if (ia) PetscAssertPointer(ia, 6);
8112:   if (ja) PetscAssertPointer(ja, 7);
8113:   PetscAssertPointer(done, 8);
8114:   MatCheckPreallocated(mat, 1);

8116:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8117:   else {
8118:     *done = PETSC_TRUE;
8119:     PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8120:     if (n) *n = 0;
8121:     if (ia) *ia = NULL;
8122:     if (ja) *ja = NULL;
8123:   }
8124:   PetscFunctionReturn(PETSC_SUCCESS);
8125: }

8127: /*@C
8128:   MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8129:   `MatGetColumnIJ()`.

8131:   Collective

8133:   Input Parameters:
8134: + mat        - the matrix
8135: . ncolors    - maximum color value
8136: . n          - number of entries in colorarray
8137: - colorarray - array indicating color for each column

8139:   Output Parameter:
8140: . iscoloring - coloring generated using colorarray information

8142:   Level: developer

8144: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8145: @*/
8146: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8147: {
8148:   PetscFunctionBegin;
8151:   PetscAssertPointer(colorarray, 4);
8152:   PetscAssertPointer(iscoloring, 5);
8153:   MatCheckPreallocated(mat, 1);

8155:   if (!mat->ops->coloringpatch) {
8156:     PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8157:   } else {
8158:     PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8159:   }
8160:   PetscFunctionReturn(PETSC_SUCCESS);
8161: }

8163: /*@
8164:   MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

8166:   Logically Collective

8168:   Input Parameter:
8169: . mat - the factored matrix to be reset

8171:   Level: developer

8173:   Notes:
8174:   This routine should be used only with factored matrices formed by in-place
8175:   factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8176:   format).  This option can save memory, for example, when solving nonlinear
8177:   systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8178:   ILU(0) preconditioner.

8180:   One can specify in-place ILU(0) factorization by calling
8181: .vb
8182:      PCType(pc,PCILU);
8183:      PCFactorSeUseInPlace(pc);
8184: .ve
8185:   or by using the options -pc_type ilu -pc_factor_in_place

8187:   In-place factorization ILU(0) can also be used as a local
8188:   solver for the blocks within the block Jacobi or additive Schwarz
8189:   methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
8190:   for details on setting local solver options.

8192:   Most users should employ the `KSP` interface for linear solvers
8193:   instead of working directly with matrix algebra routines such as this.
8194:   See, e.g., `KSPCreate()`.

8196: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8197: @*/
8198: PetscErrorCode MatSetUnfactored(Mat mat)
8199: {
8200:   PetscFunctionBegin;
8203:   MatCheckPreallocated(mat, 1);
8204:   mat->factortype = MAT_FACTOR_NONE;
8205:   if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8206:   PetscUseTypeMethod(mat, setunfactored);
8207:   PetscFunctionReturn(PETSC_SUCCESS);
8208: }

8210: /*MC
8211:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran

8213:     Synopsis:
8214:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8216:     Not Collective

8218:     Input Parameter:
8219: .   x - matrix

8221:     Output Parameters:
8222: +   xx_v - the Fortran pointer to the array
8223: -   ierr - error code

8225:     Example of Usage:
8226: .vb
8227:       PetscScalar, pointer xx_v(:,:)
8228:       ....
8229:       call MatDenseGetArrayF90(x,xx_v,ierr)
8230:       a = xx_v(3)
8231:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
8232: .ve

8234:     Level: advanced

8236: .seealso: [](ch_matrices), `Mat`, `MatDenseRestoreArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJGetArrayF90()`
8237: M*/

8239: /*MC
8240:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
8241:     accessed with `MatDenseGetArrayF90()`.

8243:     Synopsis:
8244:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8246:     Not Collective

8248:     Input Parameters:
8249: +   x - matrix
8250: -   xx_v - the Fortran90 pointer to the array

8252:     Output Parameter:
8253: .   ierr - error code

8255:     Example of Usage:
8256: .vb
8257:        PetscScalar, pointer xx_v(:,:)
8258:        ....
8259:        call MatDenseGetArrayF90(x,xx_v,ierr)
8260:        a = xx_v(3)
8261:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
8262: .ve

8264:     Level: advanced

8266: .seealso: [](ch_matrices), `Mat`, `MatDenseGetArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJRestoreArrayF90()`
8267: M*/

8269: /*MC
8270:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran.

8272:     Synopsis:
8273:     MatSeqAIJGetArrayF90(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 MatSeqAIJGetArrayF90(x,xx_v,ierr)
8289:       a = xx_v(3)
8290:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8291: .ve

8293:     Level: advanced

8295: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseGetArrayF90()`
8296: M*/

8298: /*MC
8299:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
8300:     accessed with `MatSeqAIJGetArrayF90()`.

8302:     Synopsis:
8303:     MatSeqAIJRestoreArrayF90(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 MatSeqAIJGetArrayF90(x,xx_v,ierr)
8319:        a = xx_v(3)
8320:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8321: .ve

8323:     Level: advanced

8325: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseRestoreArrayF90()`
8326: M*/

8328: /*@
8329:   MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8330:   as the original matrix.

8332:   Collective

8334:   Input Parameters:
8335: + mat   - the original matrix
8336: . isrow - parallel `IS` containing the rows this processor should obtain
8337: . 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.
8338: - cll   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

8340:   Output Parameter:
8341: . newmat - the new submatrix, of the same type as the original matrix

8343:   Level: advanced

8345:   Notes:
8346:   The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

8348:   Some matrix types place restrictions on the row and column indices, such
8349:   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;
8350:   for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

8352:   The index sets may not have duplicate entries.

8354:   The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8355:   the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8356:   to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8357:   will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
8358:   you are finished using it.

8360:   The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8361:   the input matrix.

8363:   If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

8365:   Example usage:
8366:   Consider the following 8x8 matrix with 34 non-zero values, that is
8367:   assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8368:   proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8369:   as follows
8370: .vb
8371:             1  2  0  |  0  3  0  |  0  4
8372:     Proc0   0  5  6  |  7  0  0  |  8  0
8373:             9  0 10  | 11  0  0  | 12  0
8374:     -------------------------------------
8375:            13  0 14  | 15 16 17  |  0  0
8376:     Proc1   0 18  0  | 19 20 21  |  0  0
8377:             0  0  0  | 22 23  0  | 24  0
8378:     -------------------------------------
8379:     Proc2  25 26 27  |  0  0 28  | 29  0
8380:            30  0  0  | 31 32 33  |  0 34
8381: .ve

8383:   Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

8385: .vb
8386:             2  0  |  0  3  0  |  0
8387:     Proc0   5  6  |  7  0  0  |  8
8388:     -------------------------------
8389:     Proc1  18  0  | 19 20 21  |  0
8390:     -------------------------------
8391:     Proc2  26 27  |  0  0 28  | 29
8392:             0  0  | 31 32 33  |  0
8393: .ve

8395: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8396: @*/
8397: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8398: {
8399:   PetscMPIInt size;
8400:   Mat        *local;
8401:   IS          iscoltmp;
8402:   PetscBool   flg;

8404:   PetscFunctionBegin;
8408:   PetscAssertPointer(newmat, 5);
8411:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8412:   PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");

8414:   MatCheckPreallocated(mat, 1);
8415:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));

8417:   if (!iscol || isrow == iscol) {
8418:     PetscBool   stride;
8419:     PetscMPIInt grabentirematrix = 0, grab;
8420:     PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8421:     if (stride) {
8422:       PetscInt first, step, n, rstart, rend;
8423:       PetscCall(ISStrideGetInfo(isrow, &first, &step));
8424:       if (step == 1) {
8425:         PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8426:         if (rstart == first) {
8427:           PetscCall(ISGetLocalSize(isrow, &n));
8428:           if (n == rend - rstart) grabentirematrix = 1;
8429:         }
8430:       }
8431:     }
8432:     PetscCall(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8433:     if (grab) {
8434:       PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8435:       if (cll == MAT_INITIAL_MATRIX) {
8436:         *newmat = mat;
8437:         PetscCall(PetscObjectReference((PetscObject)mat));
8438:       }
8439:       PetscFunctionReturn(PETSC_SUCCESS);
8440:     }
8441:   }

8443:   if (!iscol) {
8444:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8445:   } else {
8446:     iscoltmp = iscol;
8447:   }

8449:   /* if original matrix is on just one processor then use submatrix generated */
8450:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8451:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8452:     goto setproperties;
8453:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8454:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8455:     *newmat = *local;
8456:     PetscCall(PetscFree(local));
8457:     goto setproperties;
8458:   } else if (!mat->ops->createsubmatrix) {
8459:     /* Create a new matrix type that implements the operation using the full matrix */
8460:     PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8461:     switch (cll) {
8462:     case MAT_INITIAL_MATRIX:
8463:       PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8464:       break;
8465:     case MAT_REUSE_MATRIX:
8466:       PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8467:       break;
8468:     default:
8469:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8470:     }
8471:     PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8472:     goto setproperties;
8473:   }

8475:   PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8476:   PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8477:   PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));

8479: setproperties:
8480:   PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8481:   if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8482:   if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8483:   if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8484:   PetscFunctionReturn(PETSC_SUCCESS);
8485: }

8487: /*@
8488:   MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

8490:   Not Collective

8492:   Input Parameters:
8493: + A - the matrix we wish to propagate options from
8494: - B - the matrix we wish to propagate options to

8496:   Level: beginner

8498:   Note:
8499:   Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`

8501: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8502: @*/
8503: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8504: {
8505:   PetscFunctionBegin;
8508:   B->symmetry_eternal            = A->symmetry_eternal;
8509:   B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8510:   B->symmetric                   = A->symmetric;
8511:   B->structurally_symmetric      = A->structurally_symmetric;
8512:   B->spd                         = A->spd;
8513:   B->hermitian                   = A->hermitian;
8514:   PetscFunctionReturn(PETSC_SUCCESS);
8515: }

8517: /*@
8518:   MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8519:   used during the assembly process to store values that belong to
8520:   other processors.

8522:   Not Collective

8524:   Input Parameters:
8525: + mat   - the matrix
8526: . size  - the initial size of the stash.
8527: - bsize - the initial size of the block-stash(if used).

8529:   Options Database Keys:
8530: + -matstash_initial_size <size> or <size0,size1,...sizep-1>            - set initial size
8531: - -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1> - set initial block size

8533:   Level: intermediate

8535:   Notes:
8536:   The block-stash is used for values set with `MatSetValuesBlocked()` while
8537:   the stash is used for values set with `MatSetValues()`

8539:   Run with the option -info and look for output of the form
8540:   MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8541:   to determine the appropriate value, MM, to use for size and
8542:   MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8543:   to determine the value, BMM to use for bsize

8545: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8546: @*/
8547: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8548: {
8549:   PetscFunctionBegin;
8552:   PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8553:   PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8554:   PetscFunctionReturn(PETSC_SUCCESS);
8555: }

8557: /*@
8558:   MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of
8559:   the matrix

8561:   Neighbor-wise Collective

8563:   Input Parameters:
8564: + A - the matrix
8565: . x - the vector to be multiplied by the interpolation operator
8566: - y - the vector to be added to the result

8568:   Output Parameter:
8569: . w - the resulting vector

8571:   Level: intermediate

8573:   Notes:
8574:   `w` may be the same vector as `y`.

8576:   This allows one to use either the restriction or interpolation (its transpose)
8577:   matrix to do the interpolation

8579: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8580: @*/
8581: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8582: {
8583:   PetscInt M, N, Ny;

8585:   PetscFunctionBegin;
8590:   PetscCall(MatGetSize(A, &M, &N));
8591:   PetscCall(VecGetSize(y, &Ny));
8592:   if (M == Ny) {
8593:     PetscCall(MatMultAdd(A, x, y, w));
8594:   } else {
8595:     PetscCall(MatMultTransposeAdd(A, x, y, w));
8596:   }
8597:   PetscFunctionReturn(PETSC_SUCCESS);
8598: }

8600: /*@
8601:   MatInterpolate - y = A*x or A'*x depending on the shape of
8602:   the matrix

8604:   Neighbor-wise Collective

8606:   Input Parameters:
8607: + A - the matrix
8608: - x - the vector to be interpolated

8610:   Output Parameter:
8611: . y - the resulting vector

8613:   Level: intermediate

8615:   Note:
8616:   This allows one to use either the restriction or interpolation (its transpose)
8617:   matrix to do the interpolation

8619: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8620: @*/
8621: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8622: {
8623:   PetscInt M, N, Ny;

8625:   PetscFunctionBegin;
8629:   PetscCall(MatGetSize(A, &M, &N));
8630:   PetscCall(VecGetSize(y, &Ny));
8631:   if (M == Ny) {
8632:     PetscCall(MatMult(A, x, y));
8633:   } else {
8634:     PetscCall(MatMultTranspose(A, x, y));
8635:   }
8636:   PetscFunctionReturn(PETSC_SUCCESS);
8637: }

8639: /*@
8640:   MatRestrict - y = A*x or A'*x

8642:   Neighbor-wise Collective

8644:   Input Parameters:
8645: + A - the matrix
8646: - x - the vector to be restricted

8648:   Output Parameter:
8649: . y - the resulting vector

8651:   Level: intermediate

8653:   Note:
8654:   This allows one to use either the restriction or interpolation (its transpose)
8655:   matrix to do the restriction

8657: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8658: @*/
8659: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8660: {
8661:   PetscInt M, N, Ny;

8663:   PetscFunctionBegin;
8667:   PetscCall(MatGetSize(A, &M, &N));
8668:   PetscCall(VecGetSize(y, &Ny));
8669:   if (M == Ny) {
8670:     PetscCall(MatMult(A, x, y));
8671:   } else {
8672:     PetscCall(MatMultTranspose(A, x, y));
8673:   }
8674:   PetscFunctionReturn(PETSC_SUCCESS);
8675: }

8677: /*@
8678:   MatMatInterpolateAdd - Y = W + A*X or W + A'*X

8680:   Neighbor-wise Collective

8682:   Input Parameters:
8683: + A - the matrix
8684: . x - the input dense matrix to be multiplied
8685: - w - the input dense matrix to be added to the result

8687:   Output Parameter:
8688: . y - the output dense matrix

8690:   Level: intermediate

8692:   Note:
8693:   This allows one to use either the restriction or interpolation (its transpose)
8694:   matrix to do the interpolation. y matrix can be reused if already created with the proper sizes,
8695:   otherwise it will be recreated. y must be initialized to `NULL` if not supplied.

8697: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8698: @*/
8699: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8700: {
8701:   PetscInt  M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8702:   PetscBool trans = PETSC_TRUE;
8703:   MatReuse  reuse = MAT_INITIAL_MATRIX;

8705:   PetscFunctionBegin;
8711:   PetscCall(MatGetSize(A, &M, &N));
8712:   PetscCall(MatGetSize(x, &Mx, &Nx));
8713:   if (N == Mx) trans = PETSC_FALSE;
8714:   else PetscCheck(M == Mx, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Size mismatch: A %" PetscInt_FMT "x%" PetscInt_FMT ", X %" PetscInt_FMT "x%" PetscInt_FMT, M, N, Mx, Nx);
8715:   Mo = trans ? N : M;
8716:   if (*y) {
8717:     PetscCall(MatGetSize(*y, &My, &Ny));
8718:     if (Mo == My && Nx == Ny) {
8719:       reuse = MAT_REUSE_MATRIX;
8720:     } else {
8721:       PetscCheck(w || *y != w, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot reuse y and w, size mismatch: A %" PetscInt_FMT "x%" PetscInt_FMT ", X %" PetscInt_FMT "x%" PetscInt_FMT ", Y %" PetscInt_FMT "x%" PetscInt_FMT, M, N, Mx, Nx, My, Ny);
8722:       PetscCall(MatDestroy(y));
8723:     }
8724:   }

8726:   if (w && *y == w) { /* this is to minimize changes in PCMG */
8727:     PetscBool flg;

8729:     PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8730:     if (w) {
8731:       PetscInt My, Ny, Mw, Nw;

8733:       PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8734:       PetscCall(MatGetSize(*y, &My, &Ny));
8735:       PetscCall(MatGetSize(w, &Mw, &Nw));
8736:       if (!flg || My != Mw || Ny != Nw) w = NULL;
8737:     }
8738:     if (!w) {
8739:       PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8740:       PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8741:       PetscCall(PetscObjectDereference((PetscObject)w));
8742:     } else {
8743:       PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8744:     }
8745:   }
8746:   if (!trans) {
8747:     PetscCall(MatMatMult(A, x, reuse, PETSC_DEFAULT, y));
8748:   } else {
8749:     PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DEFAULT, y));
8750:   }
8751:   if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8752:   PetscFunctionReturn(PETSC_SUCCESS);
8753: }

8755: /*@
8756:   MatMatInterpolate - Y = A*X or A'*X

8758:   Neighbor-wise Collective

8760:   Input Parameters:
8761: + A - the matrix
8762: - x - the input dense matrix

8764:   Output Parameter:
8765: . y - the output dense matrix

8767:   Level: intermediate

8769:   Note:
8770:   This allows one to use either the restriction or interpolation (its transpose)
8771:   matrix to do the interpolation. y matrix can be reused if already created with the proper sizes,
8772:   otherwise it will be recreated. y must be initialized to `NULL` if not supplied.

8774: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8775: @*/
8776: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8777: {
8778:   PetscFunctionBegin;
8779:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8780:   PetscFunctionReturn(PETSC_SUCCESS);
8781: }

8783: /*@
8784:   MatMatRestrict - Y = A*X or A'*X

8786:   Neighbor-wise Collective

8788:   Input Parameters:
8789: + A - the matrix
8790: - x - the input dense matrix

8792:   Output Parameter:
8793: . y - the output dense matrix

8795:   Level: intermediate

8797:   Note:
8798:   This allows one to use either the restriction or interpolation (its transpose)
8799:   matrix to do the restriction. y matrix can be reused if already created with the proper sizes,
8800:   otherwise it will be recreated. y must be initialized to `NULL` if not supplied.

8802: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8803: @*/
8804: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8805: {
8806:   PetscFunctionBegin;
8807:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8808:   PetscFunctionReturn(PETSC_SUCCESS);
8809: }

8811: /*@
8812:   MatGetNullSpace - retrieves the null space of a matrix.

8814:   Logically Collective

8816:   Input Parameters:
8817: + mat    - the matrix
8818: - nullsp - the null space object

8820:   Level: developer

8822: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8823: @*/
8824: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8825: {
8826:   PetscFunctionBegin;
8828:   PetscAssertPointer(nullsp, 2);
8829:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8830:   PetscFunctionReturn(PETSC_SUCCESS);
8831: }

8833: /*@
8834:   MatSetNullSpace - attaches a null space to a matrix.

8836:   Logically Collective

8838:   Input Parameters:
8839: + mat    - the matrix
8840: - nullsp - the null space object

8842:   Level: advanced

8844:   Notes:
8845:   This null space is used by the `KSP` linear solvers to solve singular systems.

8847:   Overwrites any previous null space that may have been attached. You can remove the null space from the matrix object by calling this routine with an nullsp of `NULL`

8849:   For inconsistent singular systems (linear systems where the right hand side is not in the range of the operator) the `KSP` residuals will not converge to
8850:   to zero but the linear system will still be solved in a least squares sense.

8852:   The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8853:   the domain of a matrix A (from R^n to R^m (m rows, n columns) R^n = the direct sum of the null space of A, n(A), + the range of A^T, R(A^T).
8854:   Similarly R^m = direct sum n(A^T) + R(A).  Hence the linear system A x = b has a solution only if b in R(A) (or correspondingly b is orthogonal to
8855:   n(A^T)) and if x is a solution then x + alpha n(A) is a solution for any alpha. The minimum norm solution is orthogonal to n(A). For problems without a solution
8856:   the solution that minimizes the norm of the residual (the least squares solution) can be obtained by solving A x = \hat{b} where \hat{b} is b orthogonalized to the n(A^T).
8857:   This  \hat{b} can be obtained by calling MatNullSpaceRemove() with the null space of the transpose of the matrix.

8859:   If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one as called
8860:   `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
8861:   routine also automatically calls `MatSetTransposeNullSpace()`.

8863:   The user should call `MatNullSpaceDestroy()`.

8865: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
8866:           `KSPSetPCSide()`
8867: @*/
8868: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
8869: {
8870:   PetscFunctionBegin;
8873:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8874:   PetscCall(MatNullSpaceDestroy(&mat->nullsp));
8875:   mat->nullsp = nullsp;
8876:   if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
8877:   PetscFunctionReturn(PETSC_SUCCESS);
8878: }

8880: /*@
8881:   MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

8883:   Logically Collective

8885:   Input Parameters:
8886: + mat    - the matrix
8887: - nullsp - the null space object

8889:   Level: developer

8891: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
8892: @*/
8893: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8894: {
8895:   PetscFunctionBegin;
8898:   PetscAssertPointer(nullsp, 2);
8899:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
8900:   PetscFunctionReturn(PETSC_SUCCESS);
8901: }

8903: /*@
8904:   MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix

8906:   Logically Collective

8908:   Input Parameters:
8909: + mat    - the matrix
8910: - nullsp - the null space object

8912:   Level: advanced

8914:   Notes:
8915:   This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.

8917:   See `MatSetNullSpace()`

8919: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
8920: @*/
8921: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
8922: {
8923:   PetscFunctionBegin;
8926:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8927:   PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
8928:   mat->transnullsp = nullsp;
8929:   PetscFunctionReturn(PETSC_SUCCESS);
8930: }

8932: /*@
8933:   MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
8934:   This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

8936:   Logically Collective

8938:   Input Parameters:
8939: + mat    - the matrix
8940: - nullsp - the null space object

8942:   Level: advanced

8944:   Notes:
8945:   Overwrites any previous near null space that may have been attached

8947:   You can remove the null space by calling this routine with an nullsp of `NULL`

8949: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
8950: @*/
8951: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
8952: {
8953:   PetscFunctionBegin;
8957:   MatCheckPreallocated(mat, 1);
8958:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8959:   PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
8960:   mat->nearnullsp = nullsp;
8961:   PetscFunctionReturn(PETSC_SUCCESS);
8962: }

8964: /*@
8965:   MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`

8967:   Not Collective

8969:   Input Parameter:
8970: . mat - the matrix

8972:   Output Parameter:
8973: . nullsp - the null space object, `NULL` if not set

8975:   Level: advanced

8977: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
8978: @*/
8979: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
8980: {
8981:   PetscFunctionBegin;
8984:   PetscAssertPointer(nullsp, 2);
8985:   MatCheckPreallocated(mat, 1);
8986:   *nullsp = mat->nearnullsp;
8987:   PetscFunctionReturn(PETSC_SUCCESS);
8988: }

8990: /*@C
8991:   MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

8993:   Collective

8995:   Input Parameters:
8996: + mat  - the matrix
8997: . row  - row/column permutation
8998: - info - information on desired factorization process

9000:   Level: developer

9002:   Notes:
9003:   Probably really in-place only when level of fill is zero, otherwise allocates
9004:   new space to store factored matrix and deletes previous memory.

9006:   Most users should employ the `KSP` interface for linear solvers
9007:   instead of working directly with matrix algebra routines such as this.
9008:   See, e.g., `KSPCreate()`.

9010:   Developer Notes:
9011:   The Fortran interface is not autogenerated as the
9012:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

9014: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9015: @*/
9016: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9017: {
9018:   PetscFunctionBegin;
9022:   PetscAssertPointer(info, 3);
9023:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9024:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9025:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9026:   MatCheckPreallocated(mat, 1);
9027:   PetscUseTypeMethod(mat, iccfactor, row, info);
9028:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9029:   PetscFunctionReturn(PETSC_SUCCESS);
9030: }

9032: /*@
9033:   MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9034:   ghosted ones.

9036:   Not Collective

9038:   Input Parameters:
9039: + mat  - the matrix
9040: - diag - the diagonal values, including ghost ones

9042:   Level: developer

9044:   Notes:
9045:   Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices

9047:   This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`

9049: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9050: @*/
9051: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9052: {
9053:   PetscMPIInt size;

9055:   PetscFunctionBegin;

9060:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9061:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9062:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9063:   if (size == 1) {
9064:     PetscInt n, m;
9065:     PetscCall(VecGetSize(diag, &n));
9066:     PetscCall(MatGetSize(mat, NULL, &m));
9067:     PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9068:     PetscCall(MatDiagonalScale(mat, NULL, diag));
9069:   } else {
9070:     PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9071:   }
9072:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9073:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9074:   PetscFunctionReturn(PETSC_SUCCESS);
9075: }

9077: /*@
9078:   MatGetInertia - Gets the inertia from a factored matrix

9080:   Collective

9082:   Input Parameter:
9083: . mat - the matrix

9085:   Output Parameters:
9086: + nneg  - number of negative eigenvalues
9087: . nzero - number of zero eigenvalues
9088: - npos  - number of positive eigenvalues

9090:   Level: advanced

9092:   Note:
9093:   Matrix must have been factored by `MatCholeskyFactor()`

9095: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9096: @*/
9097: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9098: {
9099:   PetscFunctionBegin;
9102:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9103:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9104:   PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9105:   PetscFunctionReturn(PETSC_SUCCESS);
9106: }

9108: /*@C
9109:   MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors

9111:   Neighbor-wise Collective

9113:   Input Parameters:
9114: + mat - the factored matrix obtained with `MatGetFactor()`
9115: - b   - the right-hand-side vectors

9117:   Output Parameter:
9118: . x - the result vectors

9120:   Level: developer

9122:   Note:
9123:   The vectors `b` and `x` cannot be the same.  I.e., one cannot
9124:   call `MatSolves`(A,x,x).

9126: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9127: @*/
9128: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9129: {
9130:   PetscFunctionBegin;
9133:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9134:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9135:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);

9137:   MatCheckPreallocated(mat, 1);
9138:   PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9139:   PetscUseTypeMethod(mat, solves, b, x);
9140:   PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9141:   PetscFunctionReturn(PETSC_SUCCESS);
9142: }

9144: /*@
9145:   MatIsSymmetric - Test whether a matrix is symmetric

9147:   Collective

9149:   Input Parameters:
9150: + A   - the matrix to test
9151: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

9153:   Output Parameter:
9154: . flg - the result

9156:   Level: intermediate

9158:   Notes:
9159:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9161:   If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`

9163:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9164:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9166: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9167:           `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9168: @*/
9169: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9170: {
9171:   PetscFunctionBegin;
9173:   PetscAssertPointer(flg, 3);

9175:   if (A->symmetric == PETSC_BOOL3_TRUE) *flg = PETSC_TRUE;
9176:   else if (A->symmetric == PETSC_BOOL3_FALSE) *flg = PETSC_FALSE;
9177:   else {
9178:     PetscUseTypeMethod(A, issymmetric, tol, flg);
9179:     if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9180:   }
9181:   PetscFunctionReturn(PETSC_SUCCESS);
9182: }

9184: /*@
9185:   MatIsHermitian - Test whether a matrix is Hermitian

9187:   Collective

9189:   Input Parameters:
9190: + A   - the matrix to test
9191: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

9193:   Output Parameter:
9194: . flg - the result

9196:   Level: intermediate

9198:   Notes:
9199:   For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9201:   If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`

9203:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9204:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)

9206: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9207:           `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9208: @*/
9209: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9210: {
9211:   PetscFunctionBegin;
9213:   PetscAssertPointer(flg, 3);

9215:   if (A->hermitian == PETSC_BOOL3_TRUE) *flg = PETSC_TRUE;
9216:   else if (A->hermitian == PETSC_BOOL3_FALSE) *flg = PETSC_FALSE;
9217:   else {
9218:     PetscUseTypeMethod(A, ishermitian, tol, flg);
9219:     if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9220:   }
9221:   PetscFunctionReturn(PETSC_SUCCESS);
9222: }

9224: /*@
9225:   MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state

9227:   Not Collective

9229:   Input Parameter:
9230: . A - the matrix to check

9232:   Output Parameters:
9233: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9234: - flg - the result (only valid if set is `PETSC_TRUE`)

9236:   Level: advanced

9238:   Notes:
9239:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9240:   if you want it explicitly checked

9242:   One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9243:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9245: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9246: @*/
9247: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9248: {
9249:   PetscFunctionBegin;
9251:   PetscAssertPointer(set, 2);
9252:   PetscAssertPointer(flg, 3);
9253:   if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9254:     *set = PETSC_TRUE;
9255:     *flg = PetscBool3ToBool(A->symmetric);
9256:   } else {
9257:     *set = PETSC_FALSE;
9258:   }
9259:   PetscFunctionReturn(PETSC_SUCCESS);
9260: }

9262: /*@
9263:   MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state

9265:   Not Collective

9267:   Input Parameter:
9268: . A - the matrix to check

9270:   Output Parameters:
9271: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9272: - flg - the result (only valid if set is `PETSC_TRUE`)

9274:   Level: advanced

9276:   Notes:
9277:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).

9279:   One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9280:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)

9282: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9283: @*/
9284: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9285: {
9286:   PetscFunctionBegin;
9288:   PetscAssertPointer(set, 2);
9289:   PetscAssertPointer(flg, 3);
9290:   if (A->spd != PETSC_BOOL3_UNKNOWN) {
9291:     *set = PETSC_TRUE;
9292:     *flg = PetscBool3ToBool(A->spd);
9293:   } else {
9294:     *set = PETSC_FALSE;
9295:   }
9296:   PetscFunctionReturn(PETSC_SUCCESS);
9297: }

9299: /*@
9300:   MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state

9302:   Not Collective

9304:   Input Parameter:
9305: . A - the matrix to check

9307:   Output Parameters:
9308: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9309: - flg - the result (only valid if set is `PETSC_TRUE`)

9311:   Level: advanced

9313:   Notes:
9314:   Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9315:   if you want it explicitly checked

9317:   One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9318:   after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9320: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9321: @*/
9322: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9323: {
9324:   PetscFunctionBegin;
9326:   PetscAssertPointer(set, 2);
9327:   PetscAssertPointer(flg, 3);
9328:   if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9329:     *set = PETSC_TRUE;
9330:     *flg = PetscBool3ToBool(A->hermitian);
9331:   } else {
9332:     *set = PETSC_FALSE;
9333:   }
9334:   PetscFunctionReturn(PETSC_SUCCESS);
9335: }

9337: /*@
9338:   MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

9340:   Collective

9342:   Input Parameter:
9343: . A - the matrix to test

9345:   Output Parameter:
9346: . flg - the result

9348:   Level: intermediate

9350:   Notes:
9351:   If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`

9353:   One can declare that a matrix is structurally symmetric with `MatSetOption`(mat,`MAT_STRUCTURALLY_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain structurally
9354:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9356: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9357: @*/
9358: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9359: {
9360:   PetscFunctionBegin;
9362:   PetscAssertPointer(flg, 2);
9363:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9364:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9365:   } else {
9366:     PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9367:     PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9368:   }
9369:   PetscFunctionReturn(PETSC_SUCCESS);
9370: }

9372: /*@
9373:   MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state

9375:   Not Collective

9377:   Input Parameter:
9378: . A - the matrix to check

9380:   Output Parameters:
9381: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9382: - flg - the result (only valid if set is PETSC_TRUE)

9384:   Level: advanced

9386:   Notes:
9387:   One can declare that a matrix is structurally symmetric with `MatSetOption`(mat,`MAT_STRUCTURALLY_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain structurally
9388:   symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9390:   Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)

9392: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9393: @*/
9394: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9395: {
9396:   PetscFunctionBegin;
9398:   PetscAssertPointer(set, 2);
9399:   PetscAssertPointer(flg, 3);
9400:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9401:     *set = PETSC_TRUE;
9402:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9403:   } else {
9404:     *set = PETSC_FALSE;
9405:   }
9406:   PetscFunctionReturn(PETSC_SUCCESS);
9407: }

9409: /*@
9410:   MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9411:   to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process

9413:   Not Collective

9415:   Input Parameter:
9416: . mat - the matrix

9418:   Output Parameters:
9419: + nstash    - the size of the stash
9420: . reallocs  - the number of additional mallocs incurred.
9421: . bnstash   - the size of the block stash
9422: - breallocs - the number of additional mallocs incurred.in the block stash

9424:   Level: advanced

9426: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9427: @*/
9428: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9429: {
9430:   PetscFunctionBegin;
9431:   PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9432:   PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9433:   PetscFunctionReturn(PETSC_SUCCESS);
9434: }

9436: /*@C
9437:   MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9438:   parallel layout, `PetscLayout` for rows and columns

9440:   Collective

9442:   Input Parameter:
9443: . mat - the matrix

9445:   Output Parameters:
9446: + right - (optional) vector that the matrix can be multiplied against
9447: - left  - (optional) vector that the matrix vector product can be stored in

9449:   Level: advanced

9451:   Notes:
9452:   The blocksize of the returned vectors is determined by the row and column block sizes set with `MatSetBlockSizes()` or the single blocksize (same for both) set by `MatSetBlockSize()`.

9454:   These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed

9456: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9457: @*/
9458: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9459: {
9460:   PetscFunctionBegin;
9463:   if (mat->ops->getvecs) {
9464:     PetscUseTypeMethod(mat, getvecs, right, left);
9465:   } else {
9466:     if (right) {
9467:       PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9468:       PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9469:       PetscCall(VecSetType(*right, mat->defaultvectype));
9470: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9471:       if (mat->boundtocpu && mat->bindingpropagates) {
9472:         PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9473:         PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9474:       }
9475: #endif
9476:     }
9477:     if (left) {
9478:       PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9479:       PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9480:       PetscCall(VecSetType(*left, mat->defaultvectype));
9481: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9482:       if (mat->boundtocpu && mat->bindingpropagates) {
9483:         PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9484:         PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9485:       }
9486: #endif
9487:     }
9488:   }
9489:   PetscFunctionReturn(PETSC_SUCCESS);
9490: }

9492: /*@C
9493:   MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9494:   with default values.

9496:   Not Collective

9498:   Input Parameter:
9499: . info - the `MatFactorInfo` data structure

9501:   Level: developer

9503:   Notes:
9504:   The solvers are generally used through the `KSP` and `PC` objects, for example
9505:   `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`

9507:   Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed

9509:   Developer Notes:
9510:   The Fortran interface is not autogenerated as the
9511:   interface definition cannot be generated correctly [due to `MatFactorInfo`]

9513: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9514: @*/
9515: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9516: {
9517:   PetscFunctionBegin;
9518:   PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9519:   PetscFunctionReturn(PETSC_SUCCESS);
9520: }

9522: /*@
9523:   MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

9525:   Collective

9527:   Input Parameters:
9528: + mat - the factored matrix
9529: - is  - the index set defining the Schur indices (0-based)

9531:   Level: advanced

9533:   Notes:
9534:   Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.

9536:   You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.

9538:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9540: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9541:           `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9542: @*/
9543: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9544: {
9545:   PetscErrorCode (*f)(Mat, IS);

9547:   PetscFunctionBegin;
9552:   PetscCheckSameComm(mat, 1, is, 2);
9553:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9554:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9555:   PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9556:   PetscCall(MatDestroy(&mat->schur));
9557:   PetscCall((*f)(mat, is));
9558:   PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9559:   PetscFunctionReturn(PETSC_SUCCESS);
9560: }

9562: /*@
9563:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

9565:   Logically Collective

9567:   Input Parameters:
9568: + F      - the factored matrix obtained by calling `MatGetFactor()`
9569: . S      - location where to return the Schur complement, can be `NULL`
9570: - status - the status of the Schur complement matrix, can be `NULL`

9572:   Level: advanced

9574:   Notes:
9575:   You must call `MatFactorSetSchurIS()` before calling this routine.

9577:   This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9579:   The routine provides a copy of the Schur matrix stored within the solver data structures.
9580:   The caller must destroy the object when it is no longer needed.
9581:   If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.

9583:   Use `MatFactorGetSchurComplement()` to get access to the Schur complement matrix inside the factored matrix instead of making a copy of it (which this function does)

9585:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9587:   Developer Notes:
9588:   The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9589:   matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

9591: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9592: @*/
9593: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9594: {
9595:   PetscFunctionBegin;
9597:   if (S) PetscAssertPointer(S, 2);
9598:   if (status) PetscAssertPointer(status, 3);
9599:   if (S) {
9600:     PetscErrorCode (*f)(Mat, Mat *);

9602:     PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9603:     if (f) {
9604:       PetscCall((*f)(F, S));
9605:     } else {
9606:       PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9607:     }
9608:   }
9609:   if (status) *status = F->schur_status;
9610:   PetscFunctionReturn(PETSC_SUCCESS);
9611: }

9613: /*@
9614:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

9616:   Logically Collective

9618:   Input Parameters:
9619: + F      - the factored matrix obtained by calling `MatGetFactor()`
9620: . S      - location where to return the Schur complement, can be `NULL`
9621: - status - the status of the Schur complement matrix, can be `NULL`

9623:   Level: advanced

9625:   Notes:
9626:   You must call `MatFactorSetSchurIS()` before calling this routine.

9628:   Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`

9630:   The routine returns a the Schur Complement stored within the data structures of the solver.

9632:   If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.

9634:   The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.

9636:   Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix

9638:   See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9640: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9641: @*/
9642: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9643: {
9644:   PetscFunctionBegin;
9646:   if (S) {
9647:     PetscAssertPointer(S, 2);
9648:     *S = F->schur;
9649:   }
9650:   if (status) {
9651:     PetscAssertPointer(status, 3);
9652:     *status = F->schur_status;
9653:   }
9654:   PetscFunctionReturn(PETSC_SUCCESS);
9655: }

9657: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9658: {
9659:   Mat S = F->schur;

9661:   PetscFunctionBegin;
9662:   switch (F->schur_status) {
9663:   case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9664:   case MAT_FACTOR_SCHUR_INVERTED:
9665:     if (S) {
9666:       S->ops->solve             = NULL;
9667:       S->ops->matsolve          = NULL;
9668:       S->ops->solvetranspose    = NULL;
9669:       S->ops->matsolvetranspose = NULL;
9670:       S->ops->solveadd          = NULL;
9671:       S->ops->solvetransposeadd = NULL;
9672:       S->factortype             = MAT_FACTOR_NONE;
9673:       PetscCall(PetscFree(S->solvertype));
9674:     }
9675:   case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9676:     break;
9677:   default:
9678:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9679:   }
9680:   PetscFunctionReturn(PETSC_SUCCESS);
9681: }

9683: /*@
9684:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`

9686:   Logically Collective

9688:   Input Parameters:
9689: + F      - the factored matrix obtained by calling `MatGetFactor()`
9690: . S      - location where the Schur complement is stored
9691: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)

9693:   Level: advanced

9695: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9696: @*/
9697: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9698: {
9699:   PetscFunctionBegin;
9701:   if (S) {
9703:     *S = NULL;
9704:   }
9705:   F->schur_status = status;
9706:   PetscCall(MatFactorUpdateSchurStatus_Private(F));
9707:   PetscFunctionReturn(PETSC_SUCCESS);
9708: }

9710: /*@
9711:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

9713:   Logically Collective

9715:   Input Parameters:
9716: + F   - the factored matrix obtained by calling `MatGetFactor()`
9717: . rhs - location where the right hand side of the Schur complement system is stored
9718: - sol - location where the solution of the Schur complement system has to be returned

9720:   Level: advanced

9722:   Notes:
9723:   The sizes of the vectors should match the size of the Schur complement

9725:   Must be called after `MatFactorSetSchurIS()`

9727: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9728: @*/
9729: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9730: {
9731:   PetscFunctionBegin;
9738:   PetscCheckSameComm(F, 1, rhs, 2);
9739:   PetscCheckSameComm(F, 1, sol, 3);
9740:   PetscCall(MatFactorFactorizeSchurComplement(F));
9741:   switch (F->schur_status) {
9742:   case MAT_FACTOR_SCHUR_FACTORED:
9743:     PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9744:     break;
9745:   case MAT_FACTOR_SCHUR_INVERTED:
9746:     PetscCall(MatMultTranspose(F->schur, rhs, sol));
9747:     break;
9748:   default:
9749:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9750:   }
9751:   PetscFunctionReturn(PETSC_SUCCESS);
9752: }

9754: /*@
9755:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

9757:   Logically Collective

9759:   Input Parameters:
9760: + F   - the factored matrix obtained by calling `MatGetFactor()`
9761: . rhs - location where the right hand side of the Schur complement system is stored
9762: - sol - location where the solution of the Schur complement system has to be returned

9764:   Level: advanced

9766:   Notes:
9767:   The sizes of the vectors should match the size of the Schur complement

9769:   Must be called after `MatFactorSetSchurIS()`

9771: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9772: @*/
9773: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9774: {
9775:   PetscFunctionBegin;
9782:   PetscCheckSameComm(F, 1, rhs, 2);
9783:   PetscCheckSameComm(F, 1, sol, 3);
9784:   PetscCall(MatFactorFactorizeSchurComplement(F));
9785:   switch (F->schur_status) {
9786:   case MAT_FACTOR_SCHUR_FACTORED:
9787:     PetscCall(MatSolve(F->schur, rhs, sol));
9788:     break;
9789:   case MAT_FACTOR_SCHUR_INVERTED:
9790:     PetscCall(MatMult(F->schur, rhs, sol));
9791:     break;
9792:   default:
9793:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9794:   }
9795:   PetscFunctionReturn(PETSC_SUCCESS);
9796: }

9798: PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9799: #if PetscDefined(HAVE_CUDA)
9800: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9801: #endif

9803: /* Schur status updated in the interface */
9804: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9805: {
9806:   Mat S = F->schur;

9808:   PetscFunctionBegin;
9809:   if (S) {
9810:     PetscMPIInt size;
9811:     PetscBool   isdense, isdensecuda;

9813:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9814:     PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9815:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9816:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9817:     PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9818:     PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9819:     if (isdense) {
9820:       PetscCall(MatSeqDenseInvertFactors_Private(S));
9821:     } else if (isdensecuda) {
9822: #if defined(PETSC_HAVE_CUDA)
9823:       PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9824: #endif
9825:     }
9826:     // HIP??????????????
9827:     PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9828:   }
9829:   PetscFunctionReturn(PETSC_SUCCESS);
9830: }

9832: /*@
9833:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

9835:   Logically Collective

9837:   Input Parameter:
9838: . F - the factored matrix obtained by calling `MatGetFactor()`

9840:   Level: advanced

9842:   Notes:
9843:   Must be called after `MatFactorSetSchurIS()`.

9845:   Call `MatFactorGetSchurComplement()` or  `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.

9847: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9848: @*/
9849: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9850: {
9851:   PetscFunctionBegin;
9854:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9855:   PetscCall(MatFactorFactorizeSchurComplement(F));
9856:   PetscCall(MatFactorInvertSchurComplement_Private(F));
9857:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9858:   PetscFunctionReturn(PETSC_SUCCESS);
9859: }

9861: /*@
9862:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

9864:   Logically Collective

9866:   Input Parameter:
9867: . F - the factored matrix obtained by calling `MatGetFactor()`

9869:   Level: advanced

9871:   Note:
9872:   Must be called after `MatFactorSetSchurIS()`

9874: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
9875: @*/
9876: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9877: {
9878:   MatFactorInfo info;

9880:   PetscFunctionBegin;
9883:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
9884:   PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
9885:   PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
9886:   if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
9887:     PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
9888:   } else {
9889:     PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
9890:   }
9891:   PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
9892:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9893:   PetscFunctionReturn(PETSC_SUCCESS);
9894: }

9896: /*@
9897:   MatPtAP - Creates the matrix product C = P^T * A * P

9899:   Neighbor-wise Collective

9901:   Input Parameters:
9902: + A     - the matrix
9903: . P     - the projection matrix
9904: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
9905: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use `PETSC_DEFAULT` if you do not have a good estimate
9906:           if the result is a dense matrix this is irrelevant

9908:   Output Parameter:
9909: . C - the product matrix

9911:   Level: intermediate

9913:   Notes:
9914:   C will be created and must be destroyed by the user with `MatDestroy()`.

9916:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

9918:   Developer Notes:
9919:   For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.

9921: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
9922: @*/
9923: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
9924: {
9925:   PetscFunctionBegin;
9926:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
9927:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

9929:   if (scall == MAT_INITIAL_MATRIX) {
9930:     PetscCall(MatProductCreate(A, P, NULL, C));
9931:     PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
9932:     PetscCall(MatProductSetAlgorithm(*C, "default"));
9933:     PetscCall(MatProductSetFill(*C, fill));

9935:     (*C)->product->api_user = PETSC_TRUE;
9936:     PetscCall(MatProductSetFromOptions(*C));
9937:     PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)(*C)), PETSC_ERR_SUP, "MatProduct %s not supported for A %s and P %s", MatProductTypes[MATPRODUCT_PtAP], ((PetscObject)A)->type_name, ((PetscObject)P)->type_name);
9938:     PetscCall(MatProductSymbolic(*C));
9939:   } else { /* scall == MAT_REUSE_MATRIX */
9940:     PetscCall(MatProductReplaceMats(A, P, NULL, *C));
9941:   }

9943:   PetscCall(MatProductNumeric(*C));
9944:   (*C)->symmetric = A->symmetric;
9945:   (*C)->spd       = A->spd;
9946:   PetscFunctionReturn(PETSC_SUCCESS);
9947: }

9949: /*@
9950:   MatRARt - Creates the matrix product C = R * A * R^T

9952:   Neighbor-wise Collective

9954:   Input Parameters:
9955: + A     - the matrix
9956: . R     - the projection matrix
9957: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
9958: - fill  - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DEFAULT` if you do not have a good estimate
9959:           if the result is a dense matrix this is irrelevant

9961:   Output Parameter:
9962: . C - the product matrix

9964:   Level: intermediate

9966:   Notes:
9967:   C will be created and must be destroyed by the user with `MatDestroy()`.

9969:   An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

9971:   This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
9972:   which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
9973:   parallel MatRARt is implemented via explicit transpose of R, which could be very expensive.
9974:   We recommend using MatPtAP().

9976: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
9977: @*/
9978: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
9979: {
9980:   PetscFunctionBegin;
9981:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
9982:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

9984:   if (scall == MAT_INITIAL_MATRIX) {
9985:     PetscCall(MatProductCreate(A, R, NULL, C));
9986:     PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
9987:     PetscCall(MatProductSetAlgorithm(*C, "default"));
9988:     PetscCall(MatProductSetFill(*C, fill));

9990:     (*C)->product->api_user = PETSC_TRUE;
9991:     PetscCall(MatProductSetFromOptions(*C));
9992:     PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)(*C)), PETSC_ERR_SUP, "MatProduct %s not supported for A %s and R %s", MatProductTypes[MATPRODUCT_RARt], ((PetscObject)A)->type_name, ((PetscObject)R)->type_name);
9993:     PetscCall(MatProductSymbolic(*C));
9994:   } else { /* scall == MAT_REUSE_MATRIX */
9995:     PetscCall(MatProductReplaceMats(A, R, NULL, *C));
9996:   }

9998:   PetscCall(MatProductNumeric(*C));
9999:   if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10000:   PetscFunctionReturn(PETSC_SUCCESS);
10001: }

10003: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10004: {
10005:   PetscFunctionBegin;
10006:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10008:   if (scall == MAT_INITIAL_MATRIX) {
10009:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10010:     PetscCall(MatProductCreate(A, B, NULL, C));
10011:     PetscCall(MatProductSetType(*C, ptype));
10012:     PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10013:     PetscCall(MatProductSetFill(*C, fill));

10015:     (*C)->product->api_user = PETSC_TRUE;
10016:     PetscCall(MatProductSetFromOptions(*C));
10017:     PetscCall(MatProductSymbolic(*C));
10018:   } else { /* scall == MAT_REUSE_MATRIX */
10019:     Mat_Product *product = (*C)->product;
10020:     PetscBool    isdense;

10022:     PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)(*C), &isdense, MATSEQDENSE, MATMPIDENSE, ""));
10023:     if (isdense && product && product->type != ptype) {
10024:       PetscCall(MatProductClear(*C));
10025:       product = NULL;
10026:     }
10027:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10028:     if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10029:       PetscCheck(isdense, PetscObjectComm((PetscObject)(*C)), PETSC_ERR_SUP, "Call MatProductCreate() first");
10030:       PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10031:       product           = (*C)->product;
10032:       product->fill     = fill;
10033:       product->api_user = PETSC_TRUE;
10034:       product->clear    = PETSC_TRUE;

10036:       PetscCall(MatProductSetType(*C, ptype));
10037:       PetscCall(MatProductSetFromOptions(*C));
10038:       PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)(*C)), PETSC_ERR_SUP, "MatProduct %s not supported for %s and %s", MatProductTypes[ptype], ((PetscObject)A)->type_name, ((PetscObject)B)->type_name);
10039:       PetscCall(MatProductSymbolic(*C));
10040:     } else { /* user may change input matrices A or B when REUSE */
10041:       PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10042:     }
10043:   }
10044:   PetscCall(MatProductNumeric(*C));
10045:   PetscFunctionReturn(PETSC_SUCCESS);
10046: }

10048: /*@
10049:   MatMatMult - Performs matrix-matrix multiplication C=A*B.

10051:   Neighbor-wise Collective

10053:   Input Parameters:
10054: + A     - the left matrix
10055: . B     - the right matrix
10056: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10057: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if you do not have a good estimate
10058:           if the result is a dense matrix this is irrelevant

10060:   Output Parameter:
10061: . C - the product matrix

10063:   Notes:
10064:   Unless scall is `MAT_REUSE_MATRIX` C will be created.

10066:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call and C was obtained from a previous
10067:   call to this function with `MAT_INITIAL_MATRIX`.

10069:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed.

10071:   In the special case where matrix B (and hence C) are dense you can create the correctly sized matrix C yourself and then call this routine with `MAT_REUSE_MATRIX`,
10072:   rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix C is sparse.

10074:   Example of Usage:
10075: .vb
10076:      MatProductCreate(A,B,NULL,&C);
10077:      MatProductSetType(C,MATPRODUCT_AB);
10078:      MatProductSymbolic(C);
10079:      MatProductNumeric(C); // compute C=A * B
10080:      MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10081:      MatProductNumeric(C);
10082:      MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10083:      MatProductNumeric(C);
10084: .ve

10086:   Level: intermediate

10088: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10089: @*/
10090: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10091: {
10092:   PetscFunctionBegin;
10093:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10094:   PetscFunctionReturn(PETSC_SUCCESS);
10095: }

10097: /*@
10098:   MatMatTransposeMult - Performs matrix-matrix multiplication C=A*B^T.

10100:   Neighbor-wise Collective

10102:   Input Parameters:
10103: + A     - the left matrix
10104: . B     - the right matrix
10105: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10106: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if not known

10108:   Output Parameter:
10109: . C - the product matrix

10111:   Level: intermediate

10113:   Notes:
10114:   C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10116:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call

10118:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10119:   actually needed.

10121:   This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10122:   and for pairs of `MATMPIDENSE` matrices.

10124:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`

10126:   Options Database Keys:
10127: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10128:               first redundantly copies the transposed B matrix on each process and requires O(log P) communication complexity;
10129:               the second never stores more than one portion of the B matrix at a time by requires O(P) communication complexity.

10131: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10132: @*/
10133: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10134: {
10135:   PetscFunctionBegin;
10136:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10137:   if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10138:   PetscFunctionReturn(PETSC_SUCCESS);
10139: }

10141: /*@
10142:   MatTransposeMatMult - Performs matrix-matrix multiplication C=A^T*B.

10144:   Neighbor-wise Collective

10146:   Input Parameters:
10147: + A     - the left matrix
10148: . B     - the right matrix
10149: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10150: - fill  - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if not known

10152:   Output Parameter:
10153: . C - the product matrix

10155:   Level: intermediate

10157:   Notes:
10158:   C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10160:   `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.

10162:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`

10164:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10165:   actually needed.

10167:   This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10168:   which inherit from `MATSEQAIJ`.  C will be of the same type as the input matrices.

10170: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10171: @*/
10172: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10173: {
10174:   PetscFunctionBegin;
10175:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10176:   PetscFunctionReturn(PETSC_SUCCESS);
10177: }

10179: /*@
10180:   MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.

10182:   Neighbor-wise Collective

10184:   Input Parameters:
10185: + A     - the left matrix
10186: . B     - the middle matrix
10187: . C     - the right matrix
10188: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10189: - fill  - expected fill as ratio of nnz(D)/(nnz(A) + nnz(B)+nnz(C)), use `PETSC_DEFAULT` if you do not have a good estimate
10190:           if the result is a dense matrix this is irrelevant

10192:   Output Parameter:
10193: . D - the product matrix

10195:   Level: intermediate

10197:   Notes:
10198:   Unless scall is `MAT_REUSE_MATRIX` D will be created.

10200:   `MAT_REUSE_MATRIX` can only be used if the matrices A, B and C have the same nonzero pattern as in the previous call

10202:   This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`

10204:   To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10205:   actually needed.

10207:   If you have many matrices with the same non-zero structure to multiply, you
10208:   should use `MAT_REUSE_MATRIX` in all calls but the first

10210: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10211: @*/
10212: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10213: {
10214:   PetscFunctionBegin;
10215:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10216:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10218:   if (scall == MAT_INITIAL_MATRIX) {
10219:     PetscCall(MatProductCreate(A, B, C, D));
10220:     PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10221:     PetscCall(MatProductSetAlgorithm(*D, "default"));
10222:     PetscCall(MatProductSetFill(*D, fill));

10224:     (*D)->product->api_user = PETSC_TRUE;
10225:     PetscCall(MatProductSetFromOptions(*D));
10226:     PetscCheck((*D)->ops->productsymbolic, PetscObjectComm((PetscObject)(*D)), PETSC_ERR_SUP, "MatProduct %s not supported for A %s, B %s and C %s", MatProductTypes[MATPRODUCT_ABC], ((PetscObject)A)->type_name, ((PetscObject)B)->type_name,
10227:                ((PetscObject)C)->type_name);
10228:     PetscCall(MatProductSymbolic(*D));
10229:   } else { /* user may change input matrices when REUSE */
10230:     PetscCall(MatProductReplaceMats(A, B, C, *D));
10231:   }
10232:   PetscCall(MatProductNumeric(*D));
10233:   PetscFunctionReturn(PETSC_SUCCESS);
10234: }

10236: /*@
10237:   MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.

10239:   Collective

10241:   Input Parameters:
10242: + mat      - the matrix
10243: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10244: . subcomm  - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10245: - reuse    - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10247:   Output Parameter:
10248: . matredundant - redundant matrix

10250:   Level: advanced

10252:   Notes:
10253:   `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10254:   original matrix has not changed from that last call to MatCreateRedundantMatrix().

10256:   This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10257:   calling it.

10259:   `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.

10261: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10262: @*/
10263: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10264: {
10265:   MPI_Comm       comm;
10266:   PetscMPIInt    size;
10267:   PetscInt       mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10268:   Mat_Redundant *redund     = NULL;
10269:   PetscSubcomm   psubcomm   = NULL;
10270:   MPI_Comm       subcomm_in = subcomm;
10271:   Mat           *matseq;
10272:   IS             isrow, iscol;
10273:   PetscBool      newsubcomm = PETSC_FALSE;

10275:   PetscFunctionBegin;
10277:   if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10278:     PetscAssertPointer(*matredundant, 5);
10280:   }

10282:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10283:   if (size == 1 || nsubcomm == 1) {
10284:     if (reuse == MAT_INITIAL_MATRIX) {
10285:       PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10286:     } else {
10287:       PetscCheck(*matredundant != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10288:       PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10289:     }
10290:     PetscFunctionReturn(PETSC_SUCCESS);
10291:   }

10293:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10294:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10295:   MatCheckPreallocated(mat, 1);

10297:   PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10298:   if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10299:     /* create psubcomm, then get subcomm */
10300:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10301:     PetscCallMPI(MPI_Comm_size(comm, &size));
10302:     PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);

10304:     PetscCall(PetscSubcommCreate(comm, &psubcomm));
10305:     PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10306:     PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10307:     PetscCall(PetscSubcommSetFromOptions(psubcomm));
10308:     PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10309:     newsubcomm = PETSC_TRUE;
10310:     PetscCall(PetscSubcommDestroy(&psubcomm));
10311:   }

10313:   /* get isrow, iscol and a local sequential matrix matseq[0] */
10314:   if (reuse == MAT_INITIAL_MATRIX) {
10315:     mloc_sub = PETSC_DECIDE;
10316:     nloc_sub = PETSC_DECIDE;
10317:     if (bs < 1) {
10318:       PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10319:       PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10320:     } else {
10321:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10322:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10323:     }
10324:     PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10325:     rstart = rend - mloc_sub;
10326:     PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10327:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10328:     PetscCall(ISSetIdentity(iscol));
10329:   } else { /* reuse == MAT_REUSE_MATRIX */
10330:     PetscCheck(*matredundant != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10331:     /* retrieve subcomm */
10332:     PetscCall(PetscObjectGetComm((PetscObject)(*matredundant), &subcomm));
10333:     redund = (*matredundant)->redundant;
10334:     isrow  = redund->isrow;
10335:     iscol  = redund->iscol;
10336:     matseq = redund->matseq;
10337:   }
10338:   PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));

10340:   /* get matredundant over subcomm */
10341:   if (reuse == MAT_INITIAL_MATRIX) {
10342:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));

10344:     /* create a supporting struct and attach it to C for reuse */
10345:     PetscCall(PetscNew(&redund));
10346:     (*matredundant)->redundant = redund;
10347:     redund->isrow              = isrow;
10348:     redund->iscol              = iscol;
10349:     redund->matseq             = matseq;
10350:     if (newsubcomm) {
10351:       redund->subcomm = subcomm;
10352:     } else {
10353:       redund->subcomm = MPI_COMM_NULL;
10354:     }
10355:   } else {
10356:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10357:   }
10358: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10359:   if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10360:     PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10361:     PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10362:   }
10363: #endif
10364:   PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10365:   PetscFunctionReturn(PETSC_SUCCESS);
10366: }

10368: /*@C
10369:   MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10370:   a given `Mat`. Each submatrix can span multiple procs.

10372:   Collective

10374:   Input Parameters:
10375: + mat     - the matrix
10376: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10377: - scall   - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10379:   Output Parameter:
10380: . subMat - parallel sub-matrices each spanning a given `subcomm`

10382:   Level: advanced

10384:   Notes:
10385:   The submatrix partition across processors is dictated by `subComm` a
10386:   communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10387:   is not restricted to be grouped with consecutive original ranks.

10389:   Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10390:   map directly to the layout of the original matrix [wrt the local
10391:   row,col partitioning]. So the original 'DiagonalMat' naturally maps
10392:   into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10393:   the `subMat`. However the offDiagMat looses some columns - and this is
10394:   reconstructed with `MatSetValues()`

10396:   This is used by `PCBJACOBI` when a single block spans multiple MPI processes.

10398: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10399: @*/
10400: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10401: {
10402:   PetscMPIInt commsize, subCommSize;

10404:   PetscFunctionBegin;
10405:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10406:   PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10407:   PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);

10409:   PetscCheck(scall != MAT_REUSE_MATRIX || *subMat != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10410:   PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10411:   PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10412:   PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10413:   PetscFunctionReturn(PETSC_SUCCESS);
10414: }

10416: /*@
10417:   MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering

10419:   Not Collective

10421:   Input Parameters:
10422: + mat   - matrix to extract local submatrix from
10423: . isrow - local row indices for submatrix
10424: - iscol - local column indices for submatrix

10426:   Output Parameter:
10427: . submat - the submatrix

10429:   Level: intermediate

10431:   Notes:
10432:   `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.

10434:   Depending on the format of `mat`, the returned submat may not implement `MatMult()`.  Its communicator may be
10435:   the same as mat, it may be `PETSC_COMM_SELF`, or some other subcomm of `mat`'s.

10437:   `submat` always implements `MatSetValuesLocal()`.  If `isrow` and `iscol` have the same block size, then
10438:   `MatSetValuesBlockedLocal()` will also be implemented.

10440:   `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10441:   Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.

10443: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10444: @*/
10445: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10446: {
10447:   PetscFunctionBegin;
10451:   PetscCheckSameComm(isrow, 2, iscol, 3);
10452:   PetscAssertPointer(submat, 4);
10453:   PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");

10455:   if (mat->ops->getlocalsubmatrix) {
10456:     PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10457:   } else {
10458:     PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10459:   }
10460:   PetscFunctionReturn(PETSC_SUCCESS);
10461: }

10463: /*@
10464:   MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`

10466:   Not Collective

10468:   Input Parameters:
10469: + mat    - matrix to extract local submatrix from
10470: . isrow  - local row indices for submatrix
10471: . iscol  - local column indices for submatrix
10472: - submat - the submatrix

10474:   Level: intermediate

10476: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10477: @*/
10478: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10479: {
10480:   PetscFunctionBegin;
10484:   PetscCheckSameComm(isrow, 2, iscol, 3);
10485:   PetscAssertPointer(submat, 4);

10488:   if (mat->ops->restorelocalsubmatrix) {
10489:     PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10490:   } else {
10491:     PetscCall(MatDestroy(submat));
10492:   }
10493:   *submat = NULL;
10494:   PetscFunctionReturn(PETSC_SUCCESS);
10495: }

10497: /*@
10498:   MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix

10500:   Collective

10502:   Input Parameter:
10503: . mat - the matrix

10505:   Output Parameter:
10506: . is - if any rows have zero diagonals this contains the list of them

10508:   Level: developer

10510: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10511: @*/
10512: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10513: {
10514:   PetscFunctionBegin;
10517:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10518:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10520:   if (!mat->ops->findzerodiagonals) {
10521:     Vec                diag;
10522:     const PetscScalar *a;
10523:     PetscInt          *rows;
10524:     PetscInt           rStart, rEnd, r, nrow = 0;

10526:     PetscCall(MatCreateVecs(mat, &diag, NULL));
10527:     PetscCall(MatGetDiagonal(mat, diag));
10528:     PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10529:     PetscCall(VecGetArrayRead(diag, &a));
10530:     for (r = 0; r < rEnd - rStart; ++r)
10531:       if (a[r] == 0.0) ++nrow;
10532:     PetscCall(PetscMalloc1(nrow, &rows));
10533:     nrow = 0;
10534:     for (r = 0; r < rEnd - rStart; ++r)
10535:       if (a[r] == 0.0) rows[nrow++] = r + rStart;
10536:     PetscCall(VecRestoreArrayRead(diag, &a));
10537:     PetscCall(VecDestroy(&diag));
10538:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10539:   } else {
10540:     PetscUseTypeMethod(mat, findzerodiagonals, is);
10541:   }
10542:   PetscFunctionReturn(PETSC_SUCCESS);
10543: }

10545: /*@
10546:   MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)

10548:   Collective

10550:   Input Parameter:
10551: . mat - the matrix

10553:   Output Parameter:
10554: . is - contains the list of rows with off block diagonal entries

10556:   Level: developer

10558: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10559: @*/
10560: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10561: {
10562:   PetscFunctionBegin;
10565:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10566:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10568:   PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10569:   PetscFunctionReturn(PETSC_SUCCESS);
10570: }

10572: /*@C
10573:   MatInvertBlockDiagonal - Inverts the block diagonal entries.

10575:   Collective; No Fortran Support

10577:   Input Parameter:
10578: . mat - the matrix

10580:   Output Parameter:
10581: . values - the block inverses in column major order (FORTRAN-like)

10583:   Level: advanced

10585:   Notes:
10586:   The size of the blocks is determined by the block size of the matrix.

10588:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10590:   The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size

10592: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10593: @*/
10594: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar **values)
10595: {
10596:   PetscFunctionBegin;
10598:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10599:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10600:   PetscUseTypeMethod(mat, invertblockdiagonal, values);
10601:   PetscFunctionReturn(PETSC_SUCCESS);
10602: }

10604: /*@C
10605:   MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.

10607:   Collective; No Fortran Support

10609:   Input Parameters:
10610: + mat     - the matrix
10611: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10612: - bsizes  - the size of each block on the process, set with `MatSetVariableBlockSizes()`

10614:   Output Parameter:
10615: . values - the block inverses in column major order (FORTRAN-like)

10617:   Level: advanced

10619:   Notes:
10620:   Use `MatInvertBlockDiagonal()` if all blocks have the same size

10622:   The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case

10624: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10625: @*/
10626: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *values)
10627: {
10628:   PetscFunctionBegin;
10630:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10631:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10632:   PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10633:   PetscFunctionReturn(PETSC_SUCCESS);
10634: }

10636: /*@
10637:   MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A

10639:   Collective

10641:   Input Parameters:
10642: + A - the matrix
10643: - C - matrix with inverted block diagonal of `A`.  This matrix should be created and may have its type set.

10645:   Level: advanced

10647:   Note:
10648:   The blocksize of the matrix is used to determine the blocks on the diagonal of `C`

10650: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10651: @*/
10652: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10653: {
10654:   const PetscScalar *vals;
10655:   PetscInt          *dnnz;
10656:   PetscInt           m, rstart, rend, bs, i, j;

10658:   PetscFunctionBegin;
10659:   PetscCall(MatInvertBlockDiagonal(A, &vals));
10660:   PetscCall(MatGetBlockSize(A, &bs));
10661:   PetscCall(MatGetLocalSize(A, &m, NULL));
10662:   PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10663:   PetscCall(PetscMalloc1(m / bs, &dnnz));
10664:   for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10665:   PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10666:   PetscCall(PetscFree(dnnz));
10667:   PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10668:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10669:   for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10670:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10671:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10672:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10673:   PetscFunctionReturn(PETSC_SUCCESS);
10674: }

10676: /*@C
10677:   MatTransposeColoringDestroy - Destroys a coloring context for matrix product C=A*B^T that was created
10678:   via `MatTransposeColoringCreate()`.

10680:   Collective

10682:   Input Parameter:
10683: . c - coloring context

10685:   Level: intermediate

10687: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10688: @*/
10689: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10690: {
10691:   MatTransposeColoring matcolor = *c;

10693:   PetscFunctionBegin;
10694:   if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10695:   if (--((PetscObject)matcolor)->refct > 0) {
10696:     matcolor = NULL;
10697:     PetscFunctionReturn(PETSC_SUCCESS);
10698:   }

10700:   PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10701:   PetscCall(PetscFree(matcolor->rows));
10702:   PetscCall(PetscFree(matcolor->den2sp));
10703:   PetscCall(PetscFree(matcolor->colorforcol));
10704:   PetscCall(PetscFree(matcolor->columns));
10705:   if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10706:   PetscCall(PetscHeaderDestroy(c));
10707:   PetscFunctionReturn(PETSC_SUCCESS);
10708: }

10710: /*@C
10711:   MatTransColoringApplySpToDen - Given a symbolic matrix product C=A*B^T for which
10712:   a `MatTransposeColoring` context has been created, computes a dense B^T by applying
10713:   `MatTransposeColoring` to sparse B.

10715:   Collective

10717:   Input Parameters:
10718: + coloring - coloring context created with `MatTransposeColoringCreate()`
10719: - B        - sparse matrix

10721:   Output Parameter:
10722: . Btdense - dense matrix B^T

10724:   Level: developer

10726:   Note:
10727:   These are used internally for some implementations of `MatRARt()`

10729: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10730: @*/
10731: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10732: {
10733:   PetscFunctionBegin;

10738:   PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10739:   PetscFunctionReturn(PETSC_SUCCESS);
10740: }

10742: /*@C
10743:   MatTransColoringApplyDenToSp - Given a symbolic matrix product Csp=A*B^T for which
10744:   a `MatTransposeColoring` context has been created and a dense matrix Cden=A*Btdense
10745:   in which Btdens is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10746:   `Csp` from `Cden`.

10748:   Collective

10750:   Input Parameters:
10751: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10752: - Cden        - matrix product of a sparse matrix and a dense matrix Btdense

10754:   Output Parameter:
10755: . Csp - sparse matrix

10757:   Level: developer

10759:   Note:
10760:   These are used internally for some implementations of `MatRARt()`

10762: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10763: @*/
10764: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10765: {
10766:   PetscFunctionBegin;

10771:   PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10772:   PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10773:   PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10774:   PetscFunctionReturn(PETSC_SUCCESS);
10775: }

10777: /*@C
10778:   MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product C=A*B^T.

10780:   Collective

10782:   Input Parameters:
10783: + mat        - the matrix product C
10784: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`

10786:   Output Parameter:
10787: . color - the new coloring context

10789:   Level: intermediate

10791: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10792:           `MatTransColoringApplyDenToSp()`
10793: @*/
10794: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10795: {
10796:   MatTransposeColoring c;
10797:   MPI_Comm             comm;

10799:   PetscFunctionBegin;
10800:   PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10801:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10802:   PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));

10804:   c->ctype = iscoloring->ctype;
10805:   PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);

10807:   *color = c;
10808:   PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10809:   PetscFunctionReturn(PETSC_SUCCESS);
10810: }

10812: /*@
10813:   MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10814:   matrix has had no new nonzero locations added to (or removed from) the matrix since the previous call then the value will be the
10815:   same, otherwise it will be larger

10817:   Not Collective

10819:   Input Parameter:
10820: . mat - the matrix

10822:   Output Parameter:
10823: . state - the current state

10825:   Level: intermediate

10827:   Notes:
10828:   You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10829:   different matrices

10831:   Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix

10833:   Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.

10835: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10836: @*/
10837: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10838: {
10839:   PetscFunctionBegin;
10841:   *state = mat->nonzerostate;
10842:   PetscFunctionReturn(PETSC_SUCCESS);
10843: }

10845: /*@
10846:   MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10847:   matrices from each processor

10849:   Collective

10851:   Input Parameters:
10852: + comm   - the communicators the parallel matrix will live on
10853: . seqmat - the input sequential matrices
10854: . n      - number of local columns (or `PETSC_DECIDE`)
10855: - reuse  - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10857:   Output Parameter:
10858: . mpimat - the parallel matrix generated

10860:   Level: developer

10862:   Note:
10863:   The number of columns of the matrix in EACH processor MUST be the same.

10865: .seealso: [](ch_matrices), `Mat`
10866: @*/
10867: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
10868: {
10869:   PetscMPIInt size;

10871:   PetscFunctionBegin;
10872:   PetscCallMPI(MPI_Comm_size(comm, &size));
10873:   if (size == 1) {
10874:     if (reuse == MAT_INITIAL_MATRIX) {
10875:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
10876:     } else {
10877:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
10878:     }
10879:     PetscFunctionReturn(PETSC_SUCCESS);
10880:   }

10882:   PetscCheck(reuse != MAT_REUSE_MATRIX || seqmat != *mpimat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");

10884:   PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
10885:   PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
10886:   PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
10887:   PetscFunctionReturn(PETSC_SUCCESS);
10888: }

10890: /*@
10891:   MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI ranks' ownership ranges.

10893:   Collective

10895:   Input Parameters:
10896: + A - the matrix to create subdomains from
10897: - N - requested number of subdomains

10899:   Output Parameters:
10900: + n   - number of subdomains resulting on this MPI process
10901: - iss - `IS` list with indices of subdomains on this MPI process

10903:   Level: advanced

10905:   Note:
10906:   The number of subdomains must be smaller than the communicator size

10908: .seealso: [](ch_matrices), `Mat`, `IS`
10909: @*/
10910: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
10911: {
10912:   MPI_Comm    comm, subcomm;
10913:   PetscMPIInt size, rank, color;
10914:   PetscInt    rstart, rend, k;

10916:   PetscFunctionBegin;
10917:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
10918:   PetscCallMPI(MPI_Comm_size(comm, &size));
10919:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
10920:   PetscCheck(N >= 1 && N < (PetscInt)size, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "number of subdomains must be > 0 and < %d, got N = %" PetscInt_FMT, size, N);
10921:   *n    = 1;
10922:   k     = ((PetscInt)size) / N + ((PetscInt)size % N > 0); /* There are up to k ranks to a color */
10923:   color = rank / k;
10924:   PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
10925:   PetscCall(PetscMalloc1(1, iss));
10926:   PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
10927:   PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
10928:   PetscCallMPI(MPI_Comm_free(&subcomm));
10929:   PetscFunctionReturn(PETSC_SUCCESS);
10930: }

10932: /*@
10933:   MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.

10935:   If the interpolation and restriction operators are the same, uses `MatPtAP()`.
10936:   If they are not the same, uses `MatMatMatMult()`.

10938:   Once the coarse grid problem is constructed, correct for interpolation operators
10939:   that are not of full rank, which can legitimately happen in the case of non-nested
10940:   geometric multigrid.

10942:   Input Parameters:
10943: + restrct     - restriction operator
10944: . dA          - fine grid matrix
10945: . interpolate - interpolation operator
10946: . reuse       - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10947: - fill        - expected fill, use `PETSC_DEFAULT` if you do not have a good estimate

10949:   Output Parameter:
10950: . A - the Galerkin coarse matrix

10952:   Options Database Key:
10953: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used

10955:   Level: developer

10957: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
10958: @*/
10959: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
10960: {
10961:   IS  zerorows;
10962:   Vec diag;

10964:   PetscFunctionBegin;
10965:   PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10966:   /* Construct the coarse grid matrix */
10967:   if (interpolate == restrct) {
10968:     PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
10969:   } else {
10970:     PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
10971:   }

10973:   /* If the interpolation matrix is not of full rank, A will have zero rows.
10974:      This can legitimately happen in the case of non-nested geometric multigrid.
10975:      In that event, we set the rows of the matrix to the rows of the identity,
10976:      ignoring the equations (as the RHS will also be zero). */

10978:   PetscCall(MatFindZeroRows(*A, &zerorows));

10980:   if (zerorows != NULL) { /* if there are any zero rows */
10981:     PetscCall(MatCreateVecs(*A, &diag, NULL));
10982:     PetscCall(MatGetDiagonal(*A, diag));
10983:     PetscCall(VecISSet(diag, zerorows, 1.0));
10984:     PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
10985:     PetscCall(VecDestroy(&diag));
10986:     PetscCall(ISDestroy(&zerorows));
10987:   }
10988:   PetscFunctionReturn(PETSC_SUCCESS);
10989: }

10991: /*@C
10992:   MatSetOperation - Allows user to set a matrix operation for any matrix type

10994:   Logically Collective

10996:   Input Parameters:
10997: + mat - the matrix
10998: . op  - the name of the operation
10999: - f   - the function that provides the operation

11001:   Level: developer

11003:   Example Usage:
11004: .vb
11005:   extern PetscErrorCode usermult(Mat, Vec, Vec);

11007:   PetscCall(MatCreateXXX(comm, ..., &A));
11008:   PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFunction)usermult));
11009: .ve

11011:   Notes:
11012:   See the file `include/petscmat.h` for a complete list of matrix
11013:   operations, which all have the form MATOP_<OPERATION>, where
11014:   <OPERATION> is the name (in all capital letters) of the
11015:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11017:   All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11018:   sequence as the usual matrix interface routines, since they
11019:   are intended to be accessed via the usual matrix interface
11020:   routines, e.g.,
11021: .vb
11022:   MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11023: .ve

11025:   In particular each function MUST return `PETSC_SUCCESS` on success and
11026:   nonzero on failure.

11028:   This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.

11030: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11031: @*/
11032: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11033: {
11034:   PetscFunctionBegin;
11036:   if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))(mat->ops->view)) mat->ops->viewnative = mat->ops->view;
11037:   (((void (**)(void))mat->ops)[op]) = f;
11038:   PetscFunctionReturn(PETSC_SUCCESS);
11039: }

11041: /*@C
11042:   MatGetOperation - Gets a matrix operation for any matrix type.

11044:   Not Collective

11046:   Input Parameters:
11047: + mat - the matrix
11048: - op  - the name of the operation

11050:   Output Parameter:
11051: . f - the function that provides the operation

11053:   Level: developer

11055:   Example Usage:
11056: .vb
11057:   PetscErrorCode (*usermult)(Mat, Vec, Vec);

11059:   MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11060: .ve

11062:   Notes:
11063:   See the file include/petscmat.h for a complete list of matrix
11064:   operations, which all have the form MATOP_<OPERATION>, where
11065:   <OPERATION> is the name (in all capital letters) of the
11066:   user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11068:   This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.

11070: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11071: @*/
11072: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11073: {
11074:   PetscFunctionBegin;
11076:   *f = (((void (**)(void))mat->ops)[op]);
11077:   PetscFunctionReturn(PETSC_SUCCESS);
11078: }

11080: /*@
11081:   MatHasOperation - Determines whether the given matrix supports the particular operation.

11083:   Not Collective

11085:   Input Parameters:
11086: + mat - the matrix
11087: - op  - the operation, for example, `MATOP_GET_DIAGONAL`

11089:   Output Parameter:
11090: . has - either `PETSC_TRUE` or `PETSC_FALSE`

11092:   Level: advanced

11094:   Note:
11095:   See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.

11097: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11098: @*/
11099: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11100: {
11101:   PetscFunctionBegin;
11103:   PetscAssertPointer(has, 3);
11104:   if (mat->ops->hasoperation) {
11105:     PetscUseTypeMethod(mat, hasoperation, op, has);
11106:   } else {
11107:     if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11108:     else {
11109:       *has = PETSC_FALSE;
11110:       if (op == MATOP_CREATE_SUBMATRIX) {
11111:         PetscMPIInt size;

11113:         PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11114:         if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11115:       }
11116:     }
11117:   }
11118:   PetscFunctionReturn(PETSC_SUCCESS);
11119: }

11121: /*@
11122:   MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent

11124:   Collective

11126:   Input Parameter:
11127: . mat - the matrix

11129:   Output Parameter:
11130: . cong - either `PETSC_TRUE` or `PETSC_FALSE`

11132:   Level: beginner

11134: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11135: @*/
11136: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11137: {
11138:   PetscFunctionBegin;
11141:   PetscAssertPointer(cong, 2);
11142:   if (!mat->rmap || !mat->cmap) {
11143:     *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11144:     PetscFunctionReturn(PETSC_SUCCESS);
11145:   }
11146:   if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11147:     PetscCall(PetscLayoutSetUp(mat->rmap));
11148:     PetscCall(PetscLayoutSetUp(mat->cmap));
11149:     PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11150:     if (*cong) mat->congruentlayouts = 1;
11151:     else mat->congruentlayouts = 0;
11152:   } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11153:   PetscFunctionReturn(PETSC_SUCCESS);
11154: }

11156: PetscErrorCode MatSetInf(Mat A)
11157: {
11158:   PetscFunctionBegin;
11159:   PetscUseTypeMethod(A, setinf);
11160:   PetscFunctionReturn(PETSC_SUCCESS);
11161: }

11163: /*@C
11164:   MatCreateGraph - create a scalar matrix (that is a matrix with one vertex for each block vertex in the original matrix), for use in graph algorithms
11165:   and possibly removes small values from the graph structure.

11167:   Collective

11169:   Input Parameters:
11170: + A      - the matrix
11171: . sym    - `PETSC_TRUE` indicates that the graph should be symmetrized
11172: . scale  - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11173: - filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value

11175:   Output Parameter:
11176: . graph - the resulting graph

11178:   Level: advanced

11180: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11181: @*/
11182: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, Mat *graph)
11183: {
11184:   PetscFunctionBegin;
11188:   PetscAssertPointer(graph, 5);
11189:   PetscUseTypeMethod(A, creategraph, sym, scale, filter, graph);
11190:   PetscFunctionReturn(PETSC_SUCCESS);
11191: }

11193: /*@
11194:   MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11195:   meaning the same memory is used for the matrix, and no new memory is allocated.

11197:   Collective

11199:   Input Parameters:
11200: + A    - the matrix
11201: - keep - if for a given row of `A`, the diagonal coefficient is zero, indicates whether it should be left in the structure or eliminated as well

11203:   Level: intermediate

11205:   Developer Notes:
11206:   The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11207:   of the arrays in the data structure are unneeded.

11209: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11210: @*/
11211: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11212: {
11213:   PetscFunctionBegin;
11215:   PetscUseTypeMethod(A, eliminatezeros, keep);
11216:   PetscFunctionReturn(PETSC_SUCCESS);
11217: }