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_DenseCopyToGPU, MAT_DenseCopyFromGPU;
 42: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
 43: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
 44: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
 45: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;

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

 49: /*@
 50:    MatSetRandom - Sets all components of a matrix to random numbers.

 52:    Logically Collective

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

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

 66:    Level: intermediate

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

 71:    for sparse matrices that already have locations it fills the locations with random numbers.

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

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

 81:   PetscFunctionBegin;
 85:   MatCheckPreallocated(x, 1);

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

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

105: /*@
106:    MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in

108:    Logically Collective

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

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

118:    Level: advanced

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

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

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

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

141: /*@
142:    MatFactorGetError - gets the error code from a factorization

144:    Logically Collective

146:    Input Parameter:
147: .  mat - the factored matrix

149:    Output Parameter:
150: .  err  - the error code

152:    Level: advanced

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

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

169: /*@
170:    MatFactorClearError - clears the error code in a factorization

172:    Logically Collective

174:    Input Parameter:
175: .  mat - the factored matrix

177:    Level: developer

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

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

195: PETSC_INTERN PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
196: {
197:   Vec                r, l;
198:   const PetscScalar *al;
199:   PetscInt           i, nz, gnz, N, n;

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

250: /*@
251:       MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix

253:   Input Parameter:
254: .    A  - the matrix

256:   Output Parameter:
257: .    keptrows - the rows that are not completely zero

259:   Level: intermediate

261:   Note:
262:     `keptrows` is set to `NULL` if all rows are nonzero.

264: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
265:  @*/
266: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
267: {
268:   PetscFunctionBegin;
272:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
273:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
274:   if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
275:   else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
276:   PetscFunctionReturn(PETSC_SUCCESS);
277: }

279: /*@
280:       MatFindZeroRows - Locate all rows that are completely zero in the matrix

282:   Input Parameter:
283: .    A  - the matrix

285:   Output Parameter:
286: .    zerorows - the rows that are completely zero

288:   Level: intermediate

290:   Note:
291:     `zerorows` is set to `NULL` if no rows are zero.

293: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
294:  @*/
295: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
296: {
297:   IS       keptrows;
298:   PetscInt m, n;

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

318: /*@
319:    MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling

321:    Not Collective

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

326:    Output Parameter:
327: .   a - the diagonal part (which is a SEQUENTIAL matrix)

329:    Level: advanced

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

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

336: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
337: @*/
338: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
339: {
340:   PetscFunctionBegin;
344:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
345:   if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
346:   else {
347:     PetscMPIInt size;

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

356: /*@
357:    MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.

359:    Collective

361:    Input Parameter:
362: .  mat - the matrix

364:    Output Parameter:
365: .   trace - the sum of the diagonal entries

367:    Level: advanced

369: .seealso: [](ch_matrices), `Mat`
370: @*/
371: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
372: {
373:   Vec diag;

375:   PetscFunctionBegin;
378:   PetscCall(MatCreateVecs(mat, &diag, NULL));
379:   PetscCall(MatGetDiagonal(mat, diag));
380:   PetscCall(VecSum(diag, trace));
381:   PetscCall(VecDestroy(&diag));
382:   PetscFunctionReturn(PETSC_SUCCESS);
383: }

385: /*@
386:    MatRealPart - Zeros out the imaginary part of the matrix

388:    Logically Collective

390:    Input Parameter:
391: .  mat - the matrix

393:    Level: advanced

395: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
396: @*/
397: PetscErrorCode MatRealPart(Mat mat)
398: {
399:   PetscFunctionBegin;
402:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
403:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
404:   MatCheckPreallocated(mat, 1);
405:   PetscUseTypeMethod(mat, realpart);
406:   PetscFunctionReturn(PETSC_SUCCESS);
407: }

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

412:    Collective

414:    Input Parameter:
415: .  mat - the matrix

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

421:    Level: advanced

423:    Note:
424:    `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()`

426: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`
427: @*/
428: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
429: {
430:   PetscFunctionBegin;
433:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
434:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
435:   if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
436:   else {
437:     if (nghosts) *nghosts = 0;
438:     if (ghosts) *ghosts = NULL;
439:   }
440:   PetscFunctionReturn(PETSC_SUCCESS);
441: }

443: /*@
444:    MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part

446:    Logically Collective

448:    Input Parameter:
449: .  mat - the matrix

451:    Level: advanced

453: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
454: @*/
455: PetscErrorCode MatImaginaryPart(Mat mat)
456: {
457:   PetscFunctionBegin;
460:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
461:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
462:   MatCheckPreallocated(mat, 1);
463:   PetscUseTypeMethod(mat, imaginarypart);
464:   PetscFunctionReturn(PETSC_SUCCESS);
465: }

467: /*@
468:    MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices)

470:    Not Collective

472:    Input Parameter:
473: .  mat - the matrix

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

479:    Level: advanced

481: .seealso: [](ch_matrices), `Mat`
482: @*/
483: PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
484: {
485:   PetscFunctionBegin;
489:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
490:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
491:   PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
492:   PetscFunctionReturn(PETSC_SUCCESS);
493: }

495: /*@C
496:    MatGetRow - Gets a row of a matrix.  You MUST call `MatRestoreRow()`
497:    for each row that you get to ensure that your application does
498:    not bleed memory.

500:    Not Collective

502:    Input Parameters:
503: +  mat - the matrix
504: -  row - the row to get

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

511:    Level: advanced

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

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

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

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

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

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

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

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

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

555: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
556: @*/
557: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
558: {
559:   PetscInt incols;

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

575: /*@
576:    MatConjugate - replaces the matrix values with their complex conjugates

578:    Logically Collective

580:    Input Parameter:
581: .  mat - the matrix

583:    Level: advanced

585: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
586: @*/
587: PetscErrorCode MatConjugate(Mat mat)
588: {
589:   PetscFunctionBegin;
591:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
592:   if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
593:     PetscUseTypeMethod(mat, conjugate);
594:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
595:   }
596:   PetscFunctionReturn(PETSC_SUCCESS);
597: }

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

602:    Not Collective

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

611:    Level: advanced

613:    Notes:
614:    This routine should be called after you have finished examining the entries.

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

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

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

635: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
636: @*/
637: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
638: {
639:   PetscFunctionBegin;
642:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
643:   if (!mat->ops->restorerow) PetscFunctionReturn(PETSC_SUCCESS);
644:   PetscUseTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
645:   if (ncols) *ncols = 0;
646:   if (cols) *cols = NULL;
647:   if (vals) *vals = NULL;
648:   PetscFunctionReturn(PETSC_SUCCESS);
649: }

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

655:    Not Collective

657:    Input Parameter:
658: .  mat - the matrix

660:    Level: advanced

662:    Note:
663:    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.

665: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
666: @*/
667: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
668: {
669:   PetscFunctionBegin;
672:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
673:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
674:   MatCheckPreallocated(mat, 1);
675:   if (!mat->ops->getrowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
676:   PetscUseTypeMethod(mat, getrowuppertriangular);
677:   PetscFunctionReturn(PETSC_SUCCESS);
678: }

680: /*@
681:    MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.

683:    Not Collective

685:    Input Parameter:
686: .  mat - the matrix

688:    Level: advanced

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

693: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
694: @*/
695: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
696: {
697:   PetscFunctionBegin;
700:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
701:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
702:   MatCheckPreallocated(mat, 1);
703:   if (!mat->ops->restorerowuppertriangular) PetscFunctionReturn(PETSC_SUCCESS);
704:   PetscUseTypeMethod(mat, restorerowuppertriangular);
705:   PetscFunctionReturn(PETSC_SUCCESS);
706: }

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

712:    Logically Collective

714:    Input Parameters:
715: +  A - the matrix
716: -  prefix - the prefix to prepend to all option names

718:    Level: advanced

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

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

728: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
729: @*/
730: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
731: {
732:   PetscFunctionBegin;
734:   PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
735:   PetscFunctionReturn(PETSC_SUCCESS);
736: }

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

742:    Logically Collective

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

748:    Level: developer

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

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

757: .seealso: [](ch_matrices), `Mat`,   [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
758: @*/
759: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
760: {
761:   PetscFunctionBegin;
763:   if (prefix) {
765:     PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
766:     if (prefix != A->factorprefix) {
767:       PetscCall(PetscFree(A->factorprefix));
768:       PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
769:     }
770:   } else PetscCall(PetscFree(A->factorprefix));
771:   PetscFunctionReturn(PETSC_SUCCESS);
772: }

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

778:    Logically Collective

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

784:    Level: developer

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

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

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

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

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

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

822:    Logically Collective

824:    Input Parameters:
825: +  A - the matrix
826: -  prefix - the prefix to prepend to all option names

828:    Level: advanced

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

834: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
835: @*/
836: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
837: {
838:   PetscFunctionBegin;
840:   PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
841:   PetscFunctionReturn(PETSC_SUCCESS);
842: }

844: /*@C
845:    MatGetOptionsPrefix - Gets the prefix used for searching for all
846:    matrix options in the database.

848:    Not Collective

850:    Input Parameter:
851: .  A - the matrix

853:    Output Parameter:
854: .  prefix - pointer to the prefix string used

856:    Level: advanced

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

862: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
863: @*/
864: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
865: {
866:   PetscFunctionBegin;
869:   PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
870:   PetscFunctionReturn(PETSC_SUCCESS);
871: }

873: /*@
874:    MatResetPreallocation - Reset matrix to use the original nonzero pattern provided by users.

876:    Collective

878:    Input Parameter:
879: .  A - the matrix

881:    Level: beginner

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

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

888:    Currently only supported for  `MATAIJ` matrices.

890: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
891: @*/
892: PetscErrorCode MatResetPreallocation(Mat A)
893: {
894:   PetscFunctionBegin;
897:   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()");
898:   if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
899:   PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
900:   PetscFunctionReturn(PETSC_SUCCESS);
901: }

903: /*@
904:    MatSetUp - Sets up the internal matrix data structures for later use.

906:    Collective

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

911:    Level: intermediate

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

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

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

921: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
922: @*/
923: PetscErrorCode MatSetUp(Mat A)
924: {
925:   PetscFunctionBegin;
927:   if (!((PetscObject)A)->type_name) {
928:     PetscMPIInt size;

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

940: #if defined(PETSC_HAVE_SAWS)
941: #include <petscviewersaws.h>
942: #endif

944: /*
945:    If threadsafety is on extraneous matrices may be printed

947:    This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
948: */
949: #if !defined(PETSC_HAVE_THREADSAFETY)
950: static PetscInt insidematview = 0;
951: #endif

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

956:    Collective

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

963:   Options Database Key:
964: .  -mat_view [viewertype]:... - the viewer and its options

966:    Level: intermediate

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

980: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
981: @*/
982: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
983: {
984:   PetscFunctionBegin;
986: #if !defined(PETSC_HAVE_THREADSAFETY)
987:   if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
988: #endif
989:   PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
990:   PetscFunctionReturn(PETSC_SUCCESS);
991: }

993: /*@C
994:    MatView - display information about a matrix in a variety ways

996:    Collective

998:    Input Parameters:
999: +  mat - the matrix
1000: -  viewer - visualization context

1002:    Options Database Keys:
1003: +  -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1004: .  -mat_view ::ascii_info_detail - Prints more detailed info
1005: .  -mat_view - Prints matrix in ASCII format
1006: .  -mat_view ::ascii_matlab - Prints matrix in Matlab format
1007: .  -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1008: .  -display <name> - Sets display name (default is host)
1009: .  -draw_pause <sec> - Sets number of seconds to pause after display
1010: .  -mat_view socket - Sends matrix to socket, can be accessed from Matlab (see Users-Manual: ch_matlab for details)
1011: .  -viewer_socket_machine <machine> -
1012: .  -viewer_socket_port <port> -
1013: .  -mat_view binary - save matrix to file in binary format
1014: -  -viewer_binary_filename <name> -

1016:    Level: beginner

1018:   Notes:
1019:   The available visualization contexts include
1020: +    `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1021: .    `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1022: .    `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1023: -     `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure

1025:    The user can open alternative visualization contexts with
1026: +    `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1027: .    `PetscViewerBinaryOpen()` - Outputs matrix in binary to a
1028:          specified file; corresponding input uses MatLoad()
1029: .    `PetscViewerDrawOpen()` - Outputs nonzero matrix structure to
1030:          an X window display
1031: -    `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer.
1032:          Currently only the sequential dense and AIJ
1033:          matrix types support the Socket viewer.

1035:    The user can call `PetscViewerPushFormat()` to specify the output
1036:    format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1037:    `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`).  Available formats include
1038: +    `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1039: .    `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in Matlab format
1040: .    `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1041: .    `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse
1042:          format common among all matrix types
1043: .    `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific
1044:          format (which is in many cases the same as the default)
1045: .    `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix
1046:          size and structure (not the matrix entries)
1047: -    `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about
1048:          the matrix structure

1050:     The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1051:     the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.

1053:     In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).

1055:     See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1056:       viewer is used.

1058:       See share/petsc/matlab/PetscBinaryRead.m for a Matlab code that can read in the binary file when the binary
1059:       viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.

1061:       One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1062:       and then use the following mouse functions.
1063: .vb
1064:   left mouse: zoom in
1065:   middle mouse: zoom out
1066:   right mouse: continue with the simulation
1067: .ve

1069: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1070:           `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1071: @*/
1072: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1073: {
1074:   PetscInt          rows, cols, rbs, cbs;
1075:   PetscBool         isascii, isstring, issaws;
1076:   PetscViewerFormat format;
1077:   PetscMPIInt       size;

1079:   PetscFunctionBegin;
1082:   if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1084:   PetscCheckSameComm(mat, 1, viewer, 2);

1086:   PetscCall(PetscViewerGetFormat(viewer, &format));
1087:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
1088:   if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);

1090: #if !defined(PETSC_HAVE_THREADSAFETY)
1091:   insidematview++;
1092: #endif
1093:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1094:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1095:   PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1096:   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");

1098:   PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1099:   if (isascii) {
1100:     if (!mat->preallocated) {
1101:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1102: #if !defined(PETSC_HAVE_THREADSAFETY)
1103:       insidematview--;
1104: #endif
1105:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1106:       PetscFunctionReturn(PETSC_SUCCESS);
1107:     }
1108:     if (!mat->assembled) {
1109:       PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1110: #if !defined(PETSC_HAVE_THREADSAFETY)
1111:       insidematview--;
1112: #endif
1113:       PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1114:       PetscFunctionReturn(PETSC_SUCCESS);
1115:     }
1116:     PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1117:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1118:       MatNullSpace nullsp, transnullsp;

1120:       PetscCall(PetscViewerASCIIPushTab(viewer));
1121:       PetscCall(MatGetSize(mat, &rows, &cols));
1122:       PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1123:       if (rbs != 1 || cbs != 1) {
1124:         if (rbs != cbs) PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", rbs=%" PetscInt_FMT ", cbs=%" PetscInt_FMT "\n", rows, cols, rbs, cbs));
1125:         else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "\n", rows, cols, rbs));
1126:       } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1127:       if (mat->factortype) {
1128:         MatSolverType solver;
1129:         PetscCall(MatFactorGetSolverType(mat, &solver));
1130:         PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1131:       }
1132:       if (mat->ops->getinfo) {
1133:         MatInfo info;
1134:         PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1135:         PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1136:         if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1137:       }
1138:       PetscCall(MatGetNullSpace(mat, &nullsp));
1139:       PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1140:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached null space\n"));
1141:       if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached transposed null space\n"));
1142:       PetscCall(MatGetNearNullSpace(mat, &nullsp));
1143:       if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, "  has attached near null space\n"));
1144:       PetscCall(PetscViewerASCIIPushTab(viewer));
1145:       PetscCall(MatProductView(mat, viewer));
1146:       PetscCall(PetscViewerASCIIPopTab(viewer));
1147:     }
1148:   } else if (issaws) {
1149: #if defined(PETSC_HAVE_SAWS)
1150:     PetscMPIInt rank;

1152:     PetscCall(PetscObjectName((PetscObject)mat));
1153:     PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1154:     if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1155: #endif
1156:   } else if (isstring) {
1157:     const char *type;
1158:     PetscCall(MatGetType(mat, &type));
1159:     PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1160:     PetscTryTypeMethod(mat, view, viewer);
1161:   }
1162:   if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1163:     PetscCall(PetscViewerASCIIPushTab(viewer));
1164:     PetscUseTypeMethod(mat, viewnative, viewer);
1165:     PetscCall(PetscViewerASCIIPopTab(viewer));
1166:   } else if (mat->ops->view) {
1167:     PetscCall(PetscViewerASCIIPushTab(viewer));
1168:     PetscUseTypeMethod(mat, view, viewer);
1169:     PetscCall(PetscViewerASCIIPopTab(viewer));
1170:   }
1171:   if (isascii) {
1172:     PetscCall(PetscViewerGetFormat(viewer, &format));
1173:     if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1174:   }
1175:   PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1176: #if !defined(PETSC_HAVE_THREADSAFETY)
1177:   insidematview--;
1178: #endif
1179:   PetscFunctionReturn(PETSC_SUCCESS);
1180: }

1182: #if defined(PETSC_USE_DEBUG)
1183: #include <../src/sys/totalview/tv_data_display.h>
1184: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1185: {
1186:   TV_add_row("Local rows", "int", &mat->rmap->n);
1187:   TV_add_row("Local columns", "int", &mat->cmap->n);
1188:   TV_add_row("Global rows", "int", &mat->rmap->N);
1189:   TV_add_row("Global columns", "int", &mat->cmap->N);
1190:   TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1191:   return TV_format_OK;
1192: }
1193: #endif

1195: /*@C
1196:    MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1197:    with `MatView()`.  The matrix format is determined from the options database.
1198:    Generates a parallel MPI matrix if the communicator has more than one
1199:    processor.  The default matrix type is `MATAIJ`.

1201:    Collective

1203:    Input Parameters:
1204: +  mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1205:             or some related function before a call to `MatLoad()`
1206: -  viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer

1208:    Options Database Keys:
1209:    Used with block matrix formats (`MATSEQBAIJ`,  ...) to specify
1210:    block size
1211: .    -matload_block_size <bs> - set block size

1213:    Level: beginner

1215:    Notes:
1216:    If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1217:    `Mat` before calling this routine if you wish to set it from the options database.

1219:    `MatLoad()` automatically loads into the options database any options
1220:    given in the file filename.info where filename is the name of the file
1221:    that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1222:    file will be ignored if you use the -viewer_binary_skip_info option.

1224:    If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1225:    sets the default matrix type AIJ and sets the local and global sizes.
1226:    If type and/or size is already set, then the same are used.

1228:    In parallel, each processor can load a subset of rows (or the
1229:    entire matrix).  This routine is especially useful when a large
1230:    matrix is stored on disk and only part of it is desired on each
1231:    processor.  For example, a parallel solver may access only some of
1232:    the rows from each processor.  The algorithm used here reads
1233:    relatively small blocks of data rather than reading the entire
1234:    matrix and then subsetting it.

1236:    Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1237:    Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1238:    or the sequence like
1239: .vb
1240:     `PetscViewer` v;
1241:     `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1242:     `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1243:     `PetscViewerSetFromOptions`(v);
1244:     `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1245:     `PetscViewerFileSetName`(v,"datafile");
1246: .ve
1247:    The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1248: $ -viewer_type {binary, hdf5}

1250:    See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1251:    and src/mat/tutorials/ex10.c with the second approach.

1253:    In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1254:    is read onto rank 0 and then shipped to its destination rank, one after another.
1255:    Multiple objects, both matrices and vectors, can be stored within the same file.
1256:    Their PetscObject name is ignored; they are loaded in the order of their storage.

1258:    Most users should not need to know the details of the binary storage
1259:    format, since `MatLoad()` and `MatView()` completely hide these details.
1260:    But for anyone who's interested, the standard binary matrix storage
1261:    format is

1263: .vb
1264:     PetscInt    MAT_FILE_CLASSID
1265:     PetscInt    number of rows
1266:     PetscInt    number of columns
1267:     PetscInt    total number of nonzeros
1268:     PetscInt    *number nonzeros in each row
1269:     PetscInt    *column indices of all nonzeros (starting index is zero)
1270:     PetscScalar *values of all nonzeros
1271: .ve

1273:    PETSc automatically does the byte swapping for
1274: machines that store the bytes reversed. Thus if you write your own binary
1275: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1276: and `PetscBinaryWrite()` to see how this may be done.

1278:    In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1279:    Each processor's chunk is loaded independently by its owning rank.
1280:    Multiple objects, both matrices and vectors, can be stored within the same file.
1281:    They are looked up by their PetscObject name.

1283:    As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1284:    by default the same structure and naming of the AIJ arrays and column count
1285:    within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1286: $    save example.mat A b -v7.3
1287:    can be directly read by this routine (see Reference 1 for details).

1289:    Depending on your MATLAB version, this format might be a default,
1290:    otherwise you can set it as default in Preferences.

1292:    Unless -nocompression flag is used to save the file in MATLAB,
1293:    PETSc must be configured with ZLIB package.

1295:    See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c

1297:    This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`

1299:    Corresponding `MatView()` is not yet implemented.

1301:    The loaded matrix is actually a transpose of the original one in MATLAB,
1302:    unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1303:    With this format, matrix is automatically transposed by PETSc,
1304:    unless the matrix is marked as SPD or symmetric
1305:    (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).

1307:    References:
1308: .  * - MATLAB(R) Documentation, manual page of save(), https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version

1310: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1311:  @*/
1312: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1313: {
1314:   PetscBool flg;

1316:   PetscFunctionBegin;

1320:   if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));

1322:   flg = PETSC_FALSE;
1323:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1324:   if (flg) {
1325:     PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1326:     PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1327:   }
1328:   flg = PETSC_FALSE;
1329:   PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1330:   if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));

1332:   PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1333:   PetscUseTypeMethod(mat, load, viewer);
1334:   PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1335:   PetscFunctionReturn(PETSC_SUCCESS);
1336: }

1338: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1339: {
1340:   Mat_Redundant *redund = *redundant;

1342:   PetscFunctionBegin;
1343:   if (redund) {
1344:     if (redund->matseq) { /* via MatCreateSubMatrices()  */
1345:       PetscCall(ISDestroy(&redund->isrow));
1346:       PetscCall(ISDestroy(&redund->iscol));
1347:       PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1348:     } else {
1349:       PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1350:       PetscCall(PetscFree(redund->sbuf_j));
1351:       PetscCall(PetscFree(redund->sbuf_a));
1352:       for (PetscInt i = 0; i < redund->nrecvs; i++) {
1353:         PetscCall(PetscFree(redund->rbuf_j[i]));
1354:         PetscCall(PetscFree(redund->rbuf_a[i]));
1355:       }
1356:       PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1357:     }

1359:     if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1360:     PetscCall(PetscFree(redund));
1361:   }
1362:   PetscFunctionReturn(PETSC_SUCCESS);
1363: }

1365: /*@C
1366:    MatDestroy - Frees space taken by a matrix.

1368:    Collective

1370:    Input Parameter:
1371: .  A - the matrix

1373:    Level: beginner

1375:    Developer Note:
1376:    Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1377:    `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1378:    `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1379:    if changes are needed here.

1381: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1382: @*/
1383: PetscErrorCode MatDestroy(Mat *A)
1384: {
1385:   PetscFunctionBegin;
1386:   if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1388:   if (--((PetscObject)(*A))->refct > 0) {
1389:     *A = NULL;
1390:     PetscFunctionReturn(PETSC_SUCCESS);
1391:   }

1393:   /* if memory was published with SAWs then destroy it */
1394:   PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1395:   PetscTryTypeMethod((*A), destroy);

1397:   PetscCall(PetscFree((*A)->factorprefix));
1398:   PetscCall(PetscFree((*A)->defaultvectype));
1399:   PetscCall(PetscFree((*A)->defaultrandtype));
1400:   PetscCall(PetscFree((*A)->bsizes));
1401:   PetscCall(PetscFree((*A)->solvertype));
1402:   for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1403:   if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1404:   PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1405:   PetscCall(MatProductClear(*A));
1406:   PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1407:   PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1408:   PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1409:   PetscCall(MatDestroy(&(*A)->schur));
1410:   PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1411:   PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1412:   PetscCall(PetscHeaderDestroy(A));
1413:   PetscFunctionReturn(PETSC_SUCCESS);
1414: }

1416: /*@C
1417:    MatSetValues - Inserts or adds a block of values into a matrix.
1418:    These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1419:    MUST be called after all calls to `MatSetValues()` have been completed.

1421:    Not Collective

1423:    Input Parameters:
1424: +  mat - the matrix
1425: .  v - a logically two-dimensional array of values
1426: .  m - the number of rows
1427: .  idxm - the global indices of the rows
1428: .  n - the number of columns
1429: .  idxn - the global indices of the columns
1430: -  addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1432:    Level: beginner

1434:    Notes:
1435:    By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1437:    Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1438:    options cannot be mixed without intervening calls to the assembly
1439:    routines.

1441:    `MatSetValues()` uses 0-based row and column numbers in Fortran
1442:    as well as in C.

1444:    Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1445:    simply ignored. This allows easily inserting element stiffness matrices
1446:    with homogeneous Dirchlet boundary conditions that you don't want represented
1447:    in the matrix.

1449:    Efficiency Alert:
1450:    The routine `MatSetValuesBlocked()` may offer much better efficiency
1451:    for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1453:    Developer Note:
1454:    This is labeled with C so does not automatically generate Fortran stubs and interfaces
1455:    because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1457: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1458:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1459: @*/
1460: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1461: {
1462:   PetscFunctionBeginHot;
1465:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1468:   MatCheckPreallocated(mat, 1);

1470:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1471:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");

1473:   if (PetscDefined(USE_DEBUG)) {
1474:     PetscInt i, j;

1476:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1477:     for (i = 0; i < m; i++) {
1478:       for (j = 0; j < n; j++) {
1479:         if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1480: #if defined(PETSC_USE_COMPLEX)
1481:           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]);
1482: #else
1483:           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]);
1484: #endif
1485:       }
1486:     }
1487:     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);
1488:     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);
1489:   }

1491:   if (mat->assembled) {
1492:     mat->was_assembled = PETSC_TRUE;
1493:     mat->assembled     = PETSC_FALSE;
1494:   }
1495:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1496:   PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1497:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1498:   PetscFunctionReturn(PETSC_SUCCESS);
1499: }

1501: /*@C
1502:    MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1503:    These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1504:    MUST be called after all calls to `MatSetValues()` have been completed.

1506:    Not Collective

1508:    Input Parameters:
1509: +  mat - the matrix
1510: .  v - a logically two-dimensional array of values
1511: .  ism - the rows to provide
1512: .  isn - the columns to provide
1513: -  addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

1515:    Level: beginner

1517:    Notes:
1518:    By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.

1520:    Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1521:    options cannot be mixed without intervening calls to the assembly
1522:    routines.

1524:    `MatSetValues()` uses 0-based row and column numbers in Fortran
1525:    as well as in C.

1527:    Negative indices may be passed in `ism` and `isn`, these rows and columns are
1528:    simply ignored. This allows easily inserting element stiffness matrices
1529:    with homogeneous Dirchlet boundary conditions that you don't want represented
1530:    in the matrix.

1532:    Efficiency Alert:
1533:    The routine `MatSetValuesBlocked()` may offer much better efficiency
1534:    for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1536:     This is currently not optimized for any particular `ISType`

1538:    Developer Notes:
1539:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
1540:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

1542: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1543:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`
1544: @*/
1545: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1546: {
1547:   PetscInt        m, n;
1548:   const PetscInt *rows, *cols;

1550:   PetscFunctionBeginHot;
1552:   PetscCall(ISGetIndices(ism, &rows));
1553:   PetscCall(ISGetIndices(isn, &cols));
1554:   PetscCall(ISGetLocalSize(ism, &m));
1555:   PetscCall(ISGetLocalSize(isn, &n));
1556:   PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1557:   PetscCall(ISRestoreIndices(ism, &rows));
1558:   PetscCall(ISRestoreIndices(isn, &cols));
1559:   PetscFunctionReturn(PETSC_SUCCESS);
1560: }

1562: /*@
1563:    MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1564:         values into a matrix

1566:    Not Collective

1568:    Input Parameters:
1569: +  mat - the matrix
1570: .  row - the (block) row to set
1571: -  v - a logically two-dimensional array of values

1573:    Level: intermediate

1575:    Notes:
1576:    The values, `v`, are column-oriented (for the block version) and sorted

1578:    All the nonzeros in the row must be provided

1580:    The matrix must have previously had its column indices set, likely by having been assembled.

1582:    The row must belong to this process

1584: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1585:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1586: @*/
1587: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1588: {
1589:   PetscInt globalrow;

1591:   PetscFunctionBegin;
1595:   PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1596:   PetscCall(MatSetValuesRow(mat, globalrow, v));
1597:   PetscFunctionReturn(PETSC_SUCCESS);
1598: }

1600: /*@
1601:    MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1602:         values into a matrix

1604:    Not Collective

1606:    Input Parameters:
1607: +  mat - the matrix
1608: .  row - the (block) row to set
1609: -  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

1611:    Level: advanced

1613:    Notes:
1614:    The values, `v`, are column-oriented for the block version.

1616:    All the nonzeros in the row must be provided

1618:    THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.

1620:    The row must belong to this process

1622: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1623:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`
1624: @*/
1625: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1626: {
1627:   PetscFunctionBeginHot;
1630:   MatCheckPreallocated(mat, 1);
1632:   PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1633:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1634:   mat->insertmode = INSERT_VALUES;

1636:   if (mat->assembled) {
1637:     mat->was_assembled = PETSC_TRUE;
1638:     mat->assembled     = PETSC_FALSE;
1639:   }
1640:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1641:   PetscUseTypeMethod(mat, setvaluesrow, row, v);
1642:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1643:   PetscFunctionReturn(PETSC_SUCCESS);
1644: }

1646: /*@
1647:    MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1648:      Using structured grid indexing

1650:    Not Collective

1652:    Input Parameters:
1653: +  mat - the matrix
1654: .  m - number of rows being entered
1655: .  idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1656: .  n - number of columns being entered
1657: .  idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1658: .  v - a logically two-dimensional array of values
1659: -  addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values

1661:    Level: beginner

1663:    Notes:
1664:    By default the values, `v`, are row-oriented.  See `MatSetOption()` for other options.

1666:    Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1667:    options cannot be mixed without intervening calls to the assembly
1668:    routines.

1670:    The grid coordinates are across the entire grid, not just the local portion

1672:    `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1673:    as well as in C.

1675:    For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1677:    In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1678:    or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1680:    The columns and rows in the stencil passed in MUST be contained within the
1681:    ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1682:    if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1683:    local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1684:    first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1686:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1687:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1688:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1689:    `DM_BOUNDARY_PERIODIC` boundary type.

1691:    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
1692:    a single value per point) you can skip filling those indices.

1694:    Inspired by the structured grid interface to the HYPRE package
1695:    (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1697:    Efficiency Alert:
1698:    The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1699:    for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).

1701:    Fortran Note:
1702:    `idxm` and `idxn` should be declared as
1703: $     MatStencil idxm(4,m),idxn(4,n)
1704:    and the values inserted using
1705: .vb
1706:     idxm(MatStencil_i,1) = i
1707:     idxm(MatStencil_j,1) = j
1708:     idxm(MatStencil_k,1) = k
1709:     idxm(MatStencil_c,1) = c
1710:     etc
1711: .ve

1713: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1714:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1715: @*/
1716: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1717: {
1718:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1719:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1720:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1722:   PetscFunctionBegin;
1723:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */

1729:   if ((m + n) <= (PetscInt)(sizeof(buf) / sizeof(PetscInt))) {
1730:     jdxm = buf;
1731:     jdxn = buf + m;
1732:   } else {
1733:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1734:     jdxm = bufm;
1735:     jdxn = bufn;
1736:   }
1737:   for (i = 0; i < m; i++) {
1738:     for (j = 0; j < 3 - sdim; j++) dxm++;
1739:     tmp = *dxm++ - starts[0];
1740:     for (j = 0; j < dim - 1; j++) {
1741:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1742:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1743:     }
1744:     if (mat->stencil.noc) dxm++;
1745:     jdxm[i] = tmp;
1746:   }
1747:   for (i = 0; i < n; i++) {
1748:     for (j = 0; j < 3 - sdim; j++) dxn++;
1749:     tmp = *dxn++ - starts[0];
1750:     for (j = 0; j < dim - 1; j++) {
1751:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1752:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1753:     }
1754:     if (mat->stencil.noc) dxn++;
1755:     jdxn[i] = tmp;
1756:   }
1757:   PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1758:   PetscCall(PetscFree2(bufm, bufn));
1759:   PetscFunctionReturn(PETSC_SUCCESS);
1760: }

1762: /*@
1763:    MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1764:      Using structured grid indexing

1766:    Not Collective

1768:    Input Parameters:
1769: +  mat - the matrix
1770: .  m - number of rows being entered
1771: .  idxm - grid coordinates for matrix rows being entered
1772: .  n - number of columns being entered
1773: .  idxn - grid coordinates for matrix columns being entered
1774: .  v - a logically two-dimensional array of values
1775: -  addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values

1777:    Level: beginner

1779:    Notes:
1780:    By default the values, `v`, are row-oriented and unsorted.
1781:    See `MatSetOption()` for other options.

1783:    Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1784:    options cannot be mixed without intervening calls to the assembly
1785:    routines.

1787:    The grid coordinates are across the entire grid, not just the local portion

1789:    `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1790:    as well as in C.

1792:    For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine

1794:    In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1795:    or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.

1797:    The columns and rows in the stencil passed in MUST be contained within the
1798:    ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1799:    if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1800:    local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1801:    first i index you can use in your column and row indices in `MatSetStencil()` is 5.

1803:    Negative indices may be passed in idxm and idxn, these rows and columns are
1804:    simply ignored. This allows easily inserting element stiffness matrices
1805:    with homogeneous Dirchlet boundary conditions that you don't want represented
1806:    in the matrix.

1808:    Inspired by the structured grid interface to the HYPRE package
1809:    (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)

1811:    Fortran Note:
1812:    `idxm` and `idxn` should be declared as
1813: $     MatStencil idxm(4,m),idxn(4,n)
1814:    and the values inserted using
1815: .vb
1816:     idxm(MatStencil_i,1) = i
1817:     idxm(MatStencil_j,1) = j
1818:     idxm(MatStencil_k,1) = k
1819:    etc
1820: .ve

1822: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1823:           `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1824:           `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1825: @*/
1826: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1827: {
1828:   PetscInt  buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1829:   PetscInt  j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1830:   PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);

1832:   PetscFunctionBegin;
1833:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */

1840:   if ((m + n) <= (PetscInt)(sizeof(buf) / sizeof(PetscInt))) {
1841:     jdxm = buf;
1842:     jdxn = buf + m;
1843:   } else {
1844:     PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1845:     jdxm = bufm;
1846:     jdxn = bufn;
1847:   }
1848:   for (i = 0; i < m; i++) {
1849:     for (j = 0; j < 3 - sdim; j++) dxm++;
1850:     tmp = *dxm++ - starts[0];
1851:     for (j = 0; j < sdim - 1; j++) {
1852:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1853:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1854:     }
1855:     dxm++;
1856:     jdxm[i] = tmp;
1857:   }
1858:   for (i = 0; i < n; i++) {
1859:     for (j = 0; j < 3 - sdim; j++) dxn++;
1860:     tmp = *dxn++ - starts[0];
1861:     for (j = 0; j < sdim - 1; j++) {
1862:       if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1863:       else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1864:     }
1865:     dxn++;
1866:     jdxn[i] = tmp;
1867:   }
1868:   PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1869:   PetscCall(PetscFree2(bufm, bufn));
1870:   PetscFunctionReturn(PETSC_SUCCESS);
1871: }

1873: /*@
1874:    MatSetStencil - Sets the grid information for setting values into a matrix via
1875:         `MatSetValuesStencil()`

1877:    Not Collective

1879:    Input Parameters:
1880: +  mat - the matrix
1881: .  dim - dimension of the grid 1, 2, or 3
1882: .  dims - number of grid points in x, y, and z direction, including ghost points on your processor
1883: .  starts - starting point of ghost nodes on your processor in x, y, and z direction
1884: -  dof - number of degrees of freedom per node

1886:    Level: beginner

1888:    Notes:
1889:    Inspired by the structured grid interface to the HYPRE package
1890:    (www.llnl.gov/CASC/hyper)

1892:    For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1893:    user.

1895: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1896:           `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1897: @*/
1898: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1899: {
1900:   PetscFunctionBegin;

1905:   mat->stencil.dim = dim + (dof > 1);
1906:   for (PetscInt i = 0; i < dim; i++) {
1907:     mat->stencil.dims[i]   = dims[dim - i - 1]; /* copy the values in backwards */
1908:     mat->stencil.starts[i] = starts[dim - i - 1];
1909:   }
1910:   mat->stencil.dims[dim]   = dof;
1911:   mat->stencil.starts[dim] = 0;
1912:   mat->stencil.noc         = (PetscBool)(dof == 1);
1913:   PetscFunctionReturn(PETSC_SUCCESS);
1914: }

1916: /*@C
1917:    MatSetValuesBlocked - Inserts or adds a block of values into a matrix.

1919:    Not Collective

1921:    Input Parameters:
1922: +  mat - the matrix
1923: .  v - a logically two-dimensional array of values
1924: .  m  - the number of block rows
1925: .  idxm - the global block indices
1926: .  n - the number of block columns
1927: .  idxn - the global block indices
1928: -  addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values

1930:    Level: intermediate

1932:    Notes:
1933:    If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
1934:    MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.

1936:    The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
1937:    NOT the total number of rows/columns; for example, if the block size is 2 and
1938:    you are passing in values for rows 2,3,4,5  then m would be 2 (not 4).
1939:    The values in idxm would be 1 2; that is the first index for each block divided by
1940:    the block size.

1942:    You must call `MatSetBlockSize()` when constructing this matrix (before
1943:    preallocating it).

1945:    By default the values, `v`, are row-oriented, so the layout of
1946:    `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.

1948:    Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
1949:    options cannot be mixed without intervening calls to the assembly
1950:    routines.

1952:    `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
1953:    as well as in C.

1955:    Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1956:    simply ignored. This allows easily inserting element stiffness matrices
1957:    with homogeneous Dirchlet boundary conditions that you don't want represented
1958:    in the matrix.

1960:    Each time an entry is set within a sparse matrix via `MatSetValues()`,
1961:    internal searching must be done to determine where to place the
1962:    data in the matrix storage space.  By instead inserting blocks of
1963:    entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
1964:    reduced.

1966:    Example:
1967: .vb
1968:    Suppose m=n=2 and block size(bs) = 2 The array is

1970:    1  2  | 3  4
1971:    5  6  | 7  8
1972:    - - - | - - -
1973:    9  10 | 11 12
1974:    13 14 | 15 16

1976:    v[] should be passed in like
1977:    v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]

1979:   If you are not using row oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
1980:    v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
1981: .ve

1983: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
1984: @*/
1985: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1986: {
1987:   PetscFunctionBeginHot;
1990:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1993:   MatCheckPreallocated(mat, 1);
1994:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1995:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1996:   if (PetscDefined(USE_DEBUG)) {
1997:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1998:     PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
1999:   }
2000:   if (PetscDefined(USE_DEBUG)) {
2001:     PetscInt rbs, cbs, M, N, i;
2002:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2003:     PetscCall(MatGetSize(mat, &M, &N));
2004:     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);
2005:     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);
2006:   }
2007:   if (mat->assembled) {
2008:     mat->was_assembled = PETSC_TRUE;
2009:     mat->assembled     = PETSC_FALSE;
2010:   }
2011:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2012:   if (mat->ops->setvaluesblocked) {
2013:     PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2014:   } else {
2015:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2016:     PetscInt i, j, bs, cbs;

2018:     PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2019:     if (m * bs + n * cbs <= (PetscInt)(sizeof(buf) / sizeof(PetscInt))) {
2020:       iidxm = buf;
2021:       iidxn = buf + m * bs;
2022:     } else {
2023:       PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2024:       iidxm = bufr;
2025:       iidxn = bufc;
2026:     }
2027:     for (i = 0; i < m; i++) {
2028:       for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2029:     }
2030:     if (m != n || bs != cbs || idxm != idxn) {
2031:       for (i = 0; i < n; i++) {
2032:         for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2033:       }
2034:     } else iidxn = iidxm;
2035:     PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2036:     PetscCall(PetscFree2(bufr, bufc));
2037:   }
2038:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2039:   PetscFunctionReturn(PETSC_SUCCESS);
2040: }

2042: /*@C
2043:    MatGetValues - Gets a block of local values from a matrix.

2045:    Not Collective; can only return values that are owned by the give process

2047:    Input Parameters:
2048: +  mat - the matrix
2049: .  v - a logically two-dimensional array for storing the values
2050: .  m  - the number of rows
2051: .  idxm - the  global indices of the rows
2052: .  n - the number of columns
2053: -  idxn - the global indices of the columns

2055:    Level: advanced

2057:    Notes:
2058:      The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2059:      The values, `v`, are then returned in a row-oriented format,
2060:      analogous to that used by default in `MatSetValues()`.

2062:      `MatGetValues()` uses 0-based row and column numbers in
2063:      Fortran as well as in C.

2065:      `MatGetValues()` requires that the matrix has been assembled
2066:      with `MatAssemblyBegin()`/`MatAssemblyEnd()`.  Thus, calls to
2067:      `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2068:      without intermediate matrix assembly.

2070:      Negative row or column indices will be ignored and those locations in `v` will be
2071:      left unchanged.

2073:      For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI rank.
2074:      That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2075:      from `MatGetOwnershipRange`(mat,&rstart,&rend).

2077: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2078: @*/
2079: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2080: {
2081:   PetscFunctionBegin;
2084:   if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2088:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2089:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2090:   MatCheckPreallocated(mat, 1);

2092:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2093:   PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2094:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2095:   PetscFunctionReturn(PETSC_SUCCESS);
2096: }

2098: /*@C
2099:    MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2100:      defined previously by `MatSetLocalToGlobalMapping()`

2102:    Not Collective

2104:    Input Parameters:
2105: +  mat - the matrix
2106: .  nrow - number of rows
2107: .  irow - the row local indices
2108: .  ncol - number of columns
2109: -  icol - the column local indices

2111:    Output Parameter:
2112: .  y -  a logically two-dimensional array of values

2114:    Level: advanced

2116:    Notes:
2117:      If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.

2119:      This routine can only return values that are owned by the requesting MPI rank. That is, for standard matrix formats, rows that, in the global numbering,
2120:      are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2121:      determine if the resulting global row associated with the local row r is owned by the requesting MPI rank by applying the `ISLocalToGlobalMapping` set
2122:      with `MatSetLocalToGlobalMapping()`.

2124:    Developer Note:
2125:       This is labelled with C so does not automatically generate Fortran stubs and interfaces
2126:       because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2128: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2129:           `MatSetValuesLocal()`, `MatGetValues()`
2130: @*/
2131: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2132: {
2133:   PetscFunctionBeginHot;
2136:   MatCheckPreallocated(mat, 1);
2137:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2140:   if (PetscDefined(USE_DEBUG)) {
2141:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2142:     PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2143:   }
2144:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2145:   PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2146:   if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2147:   else {
2148:     PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2149:     if ((nrow + ncol) <= (PetscInt)(sizeof(buf) / sizeof(PetscInt))) {
2150:       irowm = buf;
2151:       icolm = buf + nrow;
2152:     } else {
2153:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2154:       irowm = bufr;
2155:       icolm = bufc;
2156:     }
2157:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2158:     PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2159:     PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2160:     PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2161:     PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2162:     PetscCall(PetscFree2(bufr, bufc));
2163:   }
2164:   PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2165:   PetscFunctionReturn(PETSC_SUCCESS);
2166: }

2168: /*@
2169:   MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2170:   the same size. Currently, this can only be called once and creates the given matrix.

2172:   Not Collective

2174:   Input Parameters:
2175: + mat - the matrix
2176: . nb - the number of blocks
2177: . bs - the number of rows (and columns) in each block
2178: . rows - a concatenation of the rows for each block
2179: - v - a concatenation of logically two-dimensional arrays of values

2181:   Level: advanced

2183:   Note:
2184:   `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values

2186:   In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.

2188: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2189:           `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2190: @*/
2191: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2192: {
2193:   PetscFunctionBegin;
2198:   PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

2200:   PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2201:   if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2202:   else {
2203:     for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2204:   }
2205:   PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2206:   PetscFunctionReturn(PETSC_SUCCESS);
2207: }

2209: /*@
2210:    MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2211:    the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2212:    using a local (per-processor) numbering.

2214:    Not Collective

2216:    Input Parameters:
2217: +  x - the matrix
2218: .  rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2219: -  cmapping - column mapping

2221:    Level: intermediate

2223:    Note:
2224:    If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix

2226: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2227: @*/
2228: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2229: {
2230:   PetscFunctionBegin;
2235:   if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2236:   else {
2237:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2238:     PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2239:   }
2240:   PetscFunctionReturn(PETSC_SUCCESS);
2241: }

2243: /*@
2244:    MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`

2246:    Not Collective

2248:    Input Parameter:
2249: .  A - the matrix

2251:    Output Parameters:
2252: + rmapping - row mapping
2253: - cmapping - column mapping

2255:    Level: advanced

2257: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2258: @*/
2259: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2260: {
2261:   PetscFunctionBegin;
2264:   if (rmapping) {
2266:     *rmapping = A->rmap->mapping;
2267:   }
2268:   if (cmapping) {
2270:     *cmapping = A->cmap->mapping;
2271:   }
2272:   PetscFunctionReturn(PETSC_SUCCESS);
2273: }

2275: /*@
2276:    MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix

2278:    Logically Collective

2280:    Input Parameters:
2281: +  A - the matrix
2282: . rmap - row layout
2283: - cmap - column layout

2285:    Level: advanced

2287:    Note:
2288:    The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.

2290: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2291: @*/
2292: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2293: {
2294:   PetscFunctionBegin;
2296:   PetscCall(PetscLayoutReference(rmap, &A->rmap));
2297:   PetscCall(PetscLayoutReference(cmap, &A->cmap));
2298:   PetscFunctionReturn(PETSC_SUCCESS);
2299: }

2301: /*@
2302:    MatGetLayouts - Gets the `PetscLayout` objects for rows and columns

2304:    Not Collective

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

2309:    Output Parameters:
2310: + rmap - row layout
2311: - cmap - column layout

2313:    Level: advanced

2315: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2316: @*/
2317: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2318: {
2319:   PetscFunctionBegin;
2322:   if (rmap) {
2324:     *rmap = A->rmap;
2325:   }
2326:   if (cmap) {
2328:     *cmap = A->cmap;
2329:   }
2330:   PetscFunctionReturn(PETSC_SUCCESS);
2331: }

2333: /*@C
2334:    MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2335:    using a local numbering of the nodes.

2337:    Not Collective

2339:    Input Parameters:
2340: +  mat - the matrix
2341: .  nrow - number of rows
2342: .  irow - the row local indices
2343: .  ncol - number of columns
2344: .  icol - the column local indices
2345: .  y -  a logically two-dimensional array of values
2346: -  addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2348:    Level: intermediate

2350:    Notes:
2351:    If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call MatXXXXSetPreallocation() or
2352:       `MatSetUp()` before using this routine

2354:    If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine

2356:    Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2357:    options cannot be mixed without intervening calls to the assembly
2358:    routines.

2360:    These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2361:    MUST be called after all calls to `MatSetValuesLocal()` have been completed.

2363:    Developer Note:
2364:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
2365:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2367: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2368:           `MatGetValuesLocal()`
2369: @*/
2370: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2371: {
2372:   PetscFunctionBeginHot;
2375:   MatCheckPreallocated(mat, 1);
2376:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2379:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2380:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2381:   if (PetscDefined(USE_DEBUG)) {
2382:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2383:     PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2384:   }

2386:   if (mat->assembled) {
2387:     mat->was_assembled = PETSC_TRUE;
2388:     mat->assembled     = PETSC_FALSE;
2389:   }
2390:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2391:   if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2392:   else {
2393:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2394:     const PetscInt *irowm, *icolm;

2396:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)(sizeof(buf) / sizeof(PetscInt))) {
2397:       bufr  = buf;
2398:       bufc  = buf + nrow;
2399:       irowm = bufr;
2400:       icolm = bufc;
2401:     } else {
2402:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2403:       irowm = bufr;
2404:       icolm = bufc;
2405:     }
2406:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2407:     else irowm = irow;
2408:     if (mat->cmap->mapping) {
2409:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2410:         PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2411:       } else icolm = irowm;
2412:     } else icolm = icol;
2413:     PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2414:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2415:   }
2416:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2417:   PetscFunctionReturn(PETSC_SUCCESS);
2418: }

2420: /*@C
2421:    MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2422:    using a local ordering of the nodes a block at a time.

2424:    Not Collective

2426:    Input Parameters:
2427: +  x - the matrix
2428: .  nrow - number of rows
2429: .  irow - the row local indices
2430: .  ncol - number of columns
2431: .  icol - the column local indices
2432: .  y -  a logically two-dimensional array of values
2433: -  addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values

2435:    Level: intermediate

2437:    Notes:
2438:    If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call MatXXXXSetPreallocation() or
2439:       `MatSetUp()` before using this routine

2441:    If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2442:       before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the

2444:    Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2445:    options cannot be mixed without intervening calls to the assembly
2446:    routines.

2448:    These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2449:    MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.

2451:    Developer Note:
2452:     This is labeled with C so does not automatically generate Fortran stubs and interfaces
2453:                     because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.

2455: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2456:           `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2457: @*/
2458: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2459: {
2460:   PetscFunctionBeginHot;
2463:   MatCheckPreallocated(mat, 1);
2464:   if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2467:   if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2468:   else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2469:   if (PetscDefined(USE_DEBUG)) {
2470:     PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2471:     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);
2472:   }

2474:   if (mat->assembled) {
2475:     mat->was_assembled = PETSC_TRUE;
2476:     mat->assembled     = PETSC_FALSE;
2477:   }
2478:   if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2479:     PetscInt irbs, rbs;
2480:     PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2481:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2482:     PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2483:   }
2484:   if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2485:     PetscInt icbs, cbs;
2486:     PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2487:     PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2488:     PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2489:   }
2490:   PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2491:   if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2492:   else {
2493:     PetscInt        buf[8192], *bufr = NULL, *bufc = NULL;
2494:     const PetscInt *irowm, *icolm;

2496:     if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)(sizeof(buf) / sizeof(PetscInt))) {
2497:       bufr  = buf;
2498:       bufc  = buf + nrow;
2499:       irowm = bufr;
2500:       icolm = bufc;
2501:     } else {
2502:       PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2503:       irowm = bufr;
2504:       icolm = bufc;
2505:     }
2506:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2507:     else irowm = irow;
2508:     if (mat->cmap->mapping) {
2509:       if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2510:         PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2511:       } else icolm = irowm;
2512:     } else icolm = icol;
2513:     PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2514:     if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2515:   }
2516:   PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2517:   PetscFunctionReturn(PETSC_SUCCESS);
2518: }

2520: /*@
2521:    MatMultDiagonalBlock - Computes the matrix-vector product, y = Dx. Where D is defined by the inode or block structure of the diagonal

2523:    Collective

2525:    Input Parameters:
2526: +  mat - the matrix
2527: -  x   - the vector to be multiplied

2529:    Output Parameter:
2530: .  y - the result

2532:    Level: developer

2534:    Note:
2535:    The vectors `x` and `y` cannot be the same.  I.e., one cannot
2536:    call `MatMultDiagonalBlock`(A,y,y).

2538: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2539: @*/
2540: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2541: {
2542:   PetscFunctionBegin;

2548:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2549:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2550:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2551:   MatCheckPreallocated(mat, 1);

2553:   PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2554:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2555:   PetscFunctionReturn(PETSC_SUCCESS);
2556: }

2558: /*@
2559:    MatMult - Computes the matrix-vector product, y = Ax.

2561:    Neighbor-wise Collective

2563:    Input Parameters:
2564: +  mat - the matrix
2565: -  x   - the vector to be multiplied

2567:    Output Parameter:
2568: .  y - the result

2570:    Level: beginner

2572:    Note:
2573:    The vectors `x` and `y` cannot be the same.  I.e., one cannot
2574:    call `MatMult`(A,y,y).

2576: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2577: @*/
2578: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2579: {
2580:   PetscFunctionBegin;
2584:   VecCheckAssembled(x);
2586:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2587:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2588:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2589:   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);
2590:   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);
2591:   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);
2592:   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);
2593:   PetscCall(VecSetErrorIfLocked(y, 3));
2594:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2595:   MatCheckPreallocated(mat, 1);

2597:   PetscCall(VecLockReadPush(x));
2598:   PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2599:   PetscUseTypeMethod(mat, mult, x, y);
2600:   PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2601:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2602:   PetscCall(VecLockReadPop(x));
2603:   PetscFunctionReturn(PETSC_SUCCESS);
2604: }

2606: /*@
2607:    MatMultTranspose - Computes matrix transpose times a vector y = A^T * x.

2609:    Neighbor-wise Collective

2611:    Input Parameters:
2612: +  mat - the matrix
2613: -  x   - the vector to be multiplied

2615:    Output Parameter:
2616: .  y - the result

2618:    Level: beginner

2620:    Notes:
2621:    The vectors `x` and `y` cannot be the same.  I.e., one cannot
2622:    call `MatMultTranspose`(A,y,y).

2624:    For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2625:    use `MatMultHermitianTranspose()`

2627: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2628: @*/
2629: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2630: {
2631:   PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;

2633:   PetscFunctionBegin;
2637:   VecCheckAssembled(x);

2640:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2641:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2642:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2643:   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);
2644:   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);
2645:   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);
2646:   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);
2647:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2648:   MatCheckPreallocated(mat, 1);

2650:   if (!mat->ops->multtranspose) {
2651:     if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2652:     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);
2653:   } else op = mat->ops->multtranspose;
2654:   PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2655:   PetscCall(VecLockReadPush(x));
2656:   PetscCall((*op)(mat, x, y));
2657:   PetscCall(VecLockReadPop(x));
2658:   PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2659:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2660:   if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2661:   PetscFunctionReturn(PETSC_SUCCESS);
2662: }

2664: /*@
2665:    MatMultHermitianTranspose - Computes matrix Hermitian transpose times a vector.

2667:    Neighbor-wise Collective

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

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

2676:    Level: beginner

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

2682:    Also called the conjugate transpose, complex conjugate transpose, or adjoint.

2684:    For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.

2686: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2687: @*/
2688: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2689: {
2690:   PetscFunctionBegin;

2696:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2697:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2698:   PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2699:   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);
2700:   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);
2701:   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);
2702:   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);
2703:   MatCheckPreallocated(mat, 1);

2705:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2706: #if defined(PETSC_USE_COMPLEX)
2707:   if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2708:     PetscCall(VecLockReadPush(x));
2709:     if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2710:     else PetscUseTypeMethod(mat, mult, x, y);
2711:     PetscCall(VecLockReadPop(x));
2712:   } else {
2713:     Vec w;
2714:     PetscCall(VecDuplicate(x, &w));
2715:     PetscCall(VecCopy(x, w));
2716:     PetscCall(VecConjugate(w));
2717:     PetscCall(MatMultTranspose(mat, w, y));
2718:     PetscCall(VecDestroy(&w));
2719:     PetscCall(VecConjugate(y));
2720:   }
2721:   PetscCall(PetscObjectStateIncrease((PetscObject)y));
2722: #else
2723:   PetscCall(MatMultTranspose(mat, x, y));
2724: #endif
2725:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2726:   PetscFunctionReturn(PETSC_SUCCESS);
2727: }

2729: /*@
2730:     MatMultAdd -  Computes v3 = v2 + A * v1.

2732:     Neighbor-wise Collective

2734:     Input Parameters:
2735: +   mat - the matrix
2736: .   v1 - the vector to be multiplied by `mat`
2737: -   v2 - the vector to be added to the result

2739:     Output Parameter:
2740: .   v3 - the result

2742:     Level: beginner

2744:     Note:
2745:     The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2746:     call `MatMultAdd`(A,v1,v2,v1).

2748: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2749: @*/
2750: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2751: {
2752:   PetscFunctionBegin;

2759:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2760:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2761:   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);
2762:   /* 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);
2763:      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); */
2764:   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);
2765:   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);
2766:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2767:   MatCheckPreallocated(mat, 1);

2769:   PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2770:   PetscCall(VecLockReadPush(v1));
2771:   PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2772:   PetscCall(VecLockReadPop(v1));
2773:   PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2774:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2775:   PetscFunctionReturn(PETSC_SUCCESS);
2776: }

2778: /*@
2779:    MatMultTransposeAdd - Computes v3 = v2 + A' * v1.

2781:    Neighbor-wise Collective

2783:    Input Parameters:
2784: +  mat - the matrix
2785: .  v1 - the vector to be multiplied by the transpose of the matrix
2786: -  v2 - the vector to be added to the result

2788:    Output Parameter:
2789: .  v3 - the result

2791:    Level: beginner

2793:    Note:
2794:    The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2795:    call `MatMultTransposeAdd`(A,v1,v2,v1).

2797: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2798: @*/
2799: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2800: {
2801:   PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;

2803:   PetscFunctionBegin;

2810:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2811:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2812:   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);
2813:   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);
2814:   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);
2815:   PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2816:   PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2817:   MatCheckPreallocated(mat, 1);

2819:   PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2820:   PetscCall(VecLockReadPush(v1));
2821:   PetscCall((*op)(mat, v1, v2, v3));
2822:   PetscCall(VecLockReadPop(v1));
2823:   PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2824:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2825:   PetscFunctionReturn(PETSC_SUCCESS);
2826: }

2828: /*@
2829:    MatMultHermitianTransposeAdd - Computes v3 = v2 + A^H * v1.

2831:    Neighbor-wise Collective

2833:    Input Parameters:
2834: +  mat - the matrix
2835: .  v1 - the vector to be multiplied by the Hermitian transpose
2836: -  v2 - the vector to be added to the result

2838:    Output Parameter:
2839: .  v3 - the result

2841:    Level: beginner

2843:    Note:
2844:    The vectors `v1` and `v3` cannot be the same.  I.e., one cannot
2845:    call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).

2847: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2848: @*/
2849: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2850: {
2851:   PetscFunctionBegin;

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

2866:   PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2867:   PetscCall(VecLockReadPush(v1));
2868:   if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2869:   else {
2870:     Vec w, z;
2871:     PetscCall(VecDuplicate(v1, &w));
2872:     PetscCall(VecCopy(v1, w));
2873:     PetscCall(VecConjugate(w));
2874:     PetscCall(VecDuplicate(v3, &z));
2875:     PetscCall(MatMultTranspose(mat, w, z));
2876:     PetscCall(VecDestroy(&w));
2877:     PetscCall(VecConjugate(z));
2878:     if (v2 != v3) {
2879:       PetscCall(VecWAXPY(v3, 1.0, v2, z));
2880:     } else {
2881:       PetscCall(VecAXPY(v3, 1.0, z));
2882:     }
2883:     PetscCall(VecDestroy(&z));
2884:   }
2885:   PetscCall(VecLockReadPop(v1));
2886:   PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2887:   PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2888:   PetscFunctionReturn(PETSC_SUCCESS);
2889: }

2891: /*@C
2892:    MatGetFactorType - gets the type of factorization it is

2894:    Not Collective

2896:    Input Parameter:
2897: .  mat - the matrix

2899:    Output Parameter:
2900: .  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`

2902:    Level: intermediate

2904: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2905:           `MAT_FACTOR_ICC,MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2906: @*/
2907: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
2908: {
2909:   PetscFunctionBegin;
2913:   *t = mat->factortype;
2914:   PetscFunctionReturn(PETSC_SUCCESS);
2915: }

2917: /*@C
2918:    MatSetFactorType - sets the type of factorization it is

2920:    Logically Collective

2922:    Input Parameters:
2923: +  mat - the matrix
2924: -  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`

2926:    Level: intermediate

2928: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2929:           `MAT_FACTOR_ICC,MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2930: @*/
2931: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
2932: {
2933:   PetscFunctionBegin;
2936:   mat->factortype = t;
2937:   PetscFunctionReturn(PETSC_SUCCESS);
2938: }

2940: /*@C
2941:    MatGetInfo - Returns information about matrix storage (number of
2942:    nonzeros, memory, etc.).

2944:    Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag

2946:    Input Parameters:
2947: +  mat - the matrix
2948: -  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)

2950:    Output Parameter:
2951: .  info - matrix information context

2953:    Options Database Key:
2954: .  -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`

2956:    Notes:
2957:    The `MatInfo` context contains a variety of matrix data, including
2958:    number of nonzeros allocated and used, number of mallocs during
2959:    matrix assembly, etc.  Additional information for factored matrices
2960:    is provided (such as the fill ratio, number of mallocs during
2961:    factorization, etc.).

2963:    Example:
2964:    See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
2965:    data within the MatInfo context.  For example,
2966: .vb
2967:       MatInfo info;
2968:       Mat     A;
2969:       double  mal, nz_a, nz_u;

2971:       MatGetInfo(A, MAT_LOCAL, &info);
2972:       mal  = info.mallocs;
2973:       nz_a = info.nz_allocated;
2974: .ve

2976:    Fortran users should declare info as a double precision
2977:    array of dimension `MAT_INFO_SIZE`, and then extract the parameters
2978:    of interest.  See the file ${PETSC_DIR}/include/petsc/finclude/petscmat.h
2979:    a complete list of parameter names.
2980: .vb
2981:       double  precision info(MAT_INFO_SIZE)
2982:       double  precision mal, nz_a
2983:       Mat     A
2984:       integer ierr

2986:       call MatGetInfo(A, MAT_LOCAL, info, ierr)
2987:       mal = info(MAT_INFO_MALLOCS)
2988:       nz_a = info(MAT_INFO_NZ_ALLOCATED)
2989: .ve

2991:     Level: intermediate

2993:     Developer Note:
2994:     The Fortran interface is not autogenerated as the
2995:     interface definition cannot be generated correctly [due to `MatInfo` argument]

2997: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
2998: @*/
2999: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3000: {
3001:   PetscFunctionBegin;
3005:   MatCheckPreallocated(mat, 1);
3006:   PetscUseTypeMethod(mat, getinfo, flag, info);
3007:   PetscFunctionReturn(PETSC_SUCCESS);
3008: }

3010: /*
3011:    This is used by external packages where it is not easy to get the info from the actual
3012:    matrix factorization.
3013: */
3014: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3015: {
3016:   PetscFunctionBegin;
3017:   PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3018:   PetscFunctionReturn(PETSC_SUCCESS);
3019: }

3021: /*@C
3022:    MatLUFactor - Performs in-place LU factorization of matrix.

3024:    Collective

3026:    Input Parameters:
3027: +  mat - the matrix
3028: .  row - row permutation
3029: .  col - column permutation
3030: -  info - options for factorization, includes
3031: .vb
3032:           fill - expected fill as ratio of original fill.
3033:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3034:                    Run with the option -info to determine an optimal value to use
3035: .ve
3036:    Level: developer

3038:    Notes:
3039:    Most users should employ the `KSP` interface for linear solvers
3040:    instead of working directly with matrix algebra routines such as this.
3041:    See, e.g., `KSPCreate()`.

3043:    This changes the state of the matrix to a factored matrix; it cannot be used
3044:    for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3046:    This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3047:    when not using `KSP`.

3049:    Developer Note:
3050:    The Fortran interface is not autogenerated as the
3051:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

3053: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3054:           `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3055: @*/
3056: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3057: {
3058:   MatFactorInfo tinfo;

3060:   PetscFunctionBegin;
3066:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3067:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3068:   MatCheckPreallocated(mat, 1);
3069:   if (!info) {
3070:     PetscCall(MatFactorInfoInitialize(&tinfo));
3071:     info = &tinfo;
3072:   }

3074:   PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3075:   PetscUseTypeMethod(mat, lufactor, row, col, info);
3076:   PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3077:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3078:   PetscFunctionReturn(PETSC_SUCCESS);
3079: }

3081: /*@C
3082:    MatILUFactor - Performs in-place ILU factorization of matrix.

3084:    Collective

3086:    Input Parameters:
3087: +  mat - the matrix
3088: .  row - row permutation
3089: .  col - column permutation
3090: -  info - structure containing
3091: .vb
3092:       levels - number of levels of fill.
3093:       expected fill - as ratio of original fill.
3094:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3095:                 missing diagonal entries)
3096: .ve

3098:    Level: developer

3100:    Notes:
3101:    Most users should employ the `KSP` interface for linear solvers
3102:    instead of working directly with matrix algebra routines such as this.
3103:    See, e.g., `KSPCreate()`.

3105:    Probably really in-place only when level of fill is zero, otherwise allocates
3106:    new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3107:    when not using `KSP`.

3109:    Developer Note:
3110:    The Fortran interface is not autogenerated as the
3111:    interface definition cannot be generated correctly [due to MatFactorInfo]

3113: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3114: @*/
3115: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3116: {
3117:   PetscFunctionBegin;
3123:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3124:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3125:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3126:   MatCheckPreallocated(mat, 1);

3128:   PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3129:   PetscUseTypeMethod(mat, ilufactor, row, col, info);
3130:   PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3131:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3132:   PetscFunctionReturn(PETSC_SUCCESS);
3133: }

3135: /*@C
3136:    MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3137:    Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.

3139:    Collective

3141:    Input Parameters:
3142: +  fact - the factor matrix obtained with `MatGetFactor()`
3143: .  mat - the matrix
3144: .  row - the row permutation
3145: .  col - the column permutation
3146: -  info - options for factorization, includes
3147: .vb
3148:           fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3149:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3150: .ve

3152:    Level: developer

3154:    Notes:
3155:     See [Matrix Factorization](sec_matfactor) for additional information about factorizations

3157:    Most users should employ the simplified `KSP` interface for linear solvers
3158:    instead of working directly with matrix algebra routines such as this.
3159:    See, e.g., `KSPCreate()`.

3161:    Developer Note:
3162:    The Fortran interface is not autogenerated as the
3163:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

3165: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3166: @*/
3167: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3168: {
3169:   MatFactorInfo tinfo;

3171:   PetscFunctionBegin;
3179:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3180:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3181:   MatCheckPreallocated(mat, 2);
3182:   if (!info) {
3183:     PetscCall(MatFactorInfoInitialize(&tinfo));
3184:     info = &tinfo;
3185:   }

3187:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3188:   PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3189:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3190:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3191:   PetscFunctionReturn(PETSC_SUCCESS);
3192: }

3194: /*@C
3195:    MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3196:    Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.

3198:    Collective

3200:    Input Parameters:
3201: +  fact - the factor matrix obtained with `MatGetFactor()`
3202: .  mat - the matrix
3203: -  info - options for factorization

3205:    Level: developer

3207:    Notes:
3208:    See `MatLUFactor()` for in-place factorization.  See
3209:    `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.

3211:    Most users should employ the `KSP` interface for linear solvers
3212:    instead of working directly with matrix algebra routines such as this.
3213:    See, e.g., `KSPCreate()`.

3215:     Developer Note:
3216:     The Fortran interface is not autogenerated as the
3217:     interface definition cannot be generated correctly [due to `MatFactorInfo`]

3219: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3220: @*/
3221: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3222: {
3223:   MatFactorInfo tinfo;

3225:   PetscFunctionBegin;
3231:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3232:   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,
3233:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3235:   MatCheckPreallocated(mat, 2);
3236:   if (!info) {
3237:     PetscCall(MatFactorInfoInitialize(&tinfo));
3238:     info = &tinfo;
3239:   }

3241:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3242:   else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3243:   PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3244:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3245:   else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3246:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3247:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3248:   PetscFunctionReturn(PETSC_SUCCESS);
3249: }

3251: /*@C
3252:    MatCholeskyFactor - Performs in-place Cholesky factorization of a
3253:    symmetric matrix.

3255:    Collective

3257:    Input Parameters:
3258: +  mat - the matrix
3259: .  perm - row and column permutations
3260: -  f - expected fill as ratio of original fill

3262:    Level: developer

3264:    Notes:
3265:    See `MatLUFactor()` for the nonsymmetric case.  See also `MatGetFactor()`,
3266:    `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.

3268:    Most users should employ the `KSP` interface for linear solvers
3269:    instead of working directly with matrix algebra routines such as this.
3270:    See, e.g., `KSPCreate()`.

3272:    Developer Note:
3273:    The Fortran interface is not autogenerated as the
3274:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

3276: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3277:           `MatGetOrdering()`
3278: @*/
3279: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3280: {
3281:   MatFactorInfo tinfo;

3283:   PetscFunctionBegin;
3288:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3289:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3290:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3291:   MatCheckPreallocated(mat, 1);
3292:   if (!info) {
3293:     PetscCall(MatFactorInfoInitialize(&tinfo));
3294:     info = &tinfo;
3295:   }

3297:   PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3298:   PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3299:   PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3300:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3301:   PetscFunctionReturn(PETSC_SUCCESS);
3302: }

3304: /*@C
3305:    MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3306:    of a symmetric matrix.

3308:    Collective

3310:    Input Parameters:
3311: +  fact - the factor matrix obtained with `MatGetFactor()`
3312: .  mat - the matrix
3313: .  perm - row and column permutations
3314: -  info - options for factorization, includes
3315: .vb
3316:           fill - expected fill as ratio of original fill.
3317:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3318:                    Run with the option -info to determine an optimal value to use
3319: .ve

3321:    Level: developer

3323:    Notes:
3324:    See `MatLUFactorSymbolic()` for the nonsymmetric case.  See also
3325:    `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.

3327:    Most users should employ the `KSP` interface for linear solvers
3328:    instead of working directly with matrix algebra routines such as this.
3329:    See, e.g., `KSPCreate()`.

3331:    Developer Note:
3332:    The Fortran interface is not autogenerated as the
3333:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

3335: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3336:           `MatGetOrdering()`
3337: @*/
3338: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3339: {
3340:   MatFactorInfo tinfo;

3342:   PetscFunctionBegin;
3349:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3350:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3351:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3352:   MatCheckPreallocated(mat, 2);
3353:   if (!info) {
3354:     PetscCall(MatFactorInfoInitialize(&tinfo));
3355:     info = &tinfo;
3356:   }

3358:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3359:   PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3360:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3361:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3362:   PetscFunctionReturn(PETSC_SUCCESS);
3363: }

3365: /*@C
3366:    MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3367:    of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3368:    `MatCholeskyFactorSymbolic()`.

3370:    Collective

3372:    Input Parameters:
3373: +  fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3374: .  mat - the initial matrix that is to be factored
3375: -  info - options for factorization

3377:    Level: developer

3379:    Note:
3380:    Most users should employ the `KSP` interface for linear solvers
3381:    instead of working directly with matrix algebra routines such as this.
3382:    See, e.g., `KSPCreate()`.

3384:    Developer Note:
3385:    The Fortran interface is not autogenerated as the
3386:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

3388: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3389: @*/
3390: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3391: {
3392:   MatFactorInfo tinfo;

3394:   PetscFunctionBegin;
3400:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3401:   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,
3402:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3403:   MatCheckPreallocated(mat, 2);
3404:   if (!info) {
3405:     PetscCall(MatFactorInfoInitialize(&tinfo));
3406:     info = &tinfo;
3407:   }

3409:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3410:   else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3411:   PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3412:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3413:   else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3414:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3415:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3416:   PetscFunctionReturn(PETSC_SUCCESS);
3417: }

3419: /*@
3420:    MatQRFactor - Performs in-place QR factorization of matrix.

3422:    Collective

3424:    Input Parameters:
3425: +  mat - the matrix
3426: .  col - column permutation
3427: -  info - options for factorization, includes
3428: .vb
3429:           fill - expected fill as ratio of original fill.
3430:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3431:                    Run with the option -info to determine an optimal value to use
3432: .ve

3434:    Level: developer

3436:    Notes:
3437:    Most users should employ the `KSP` interface for linear solvers
3438:    instead of working directly with matrix algebra routines such as this.
3439:    See, e.g., `KSPCreate()`.

3441:    This changes the state of the matrix to a factored matrix; it cannot be used
3442:    for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.

3444:    Developer Note:
3445:    The Fortran interface is not autogenerated as the
3446:    interface definition cannot be generated correctly [due to MatFactorInfo]

3448: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3449:           `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3450: @*/
3451: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3452: {
3453:   PetscFunctionBegin;
3458:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3459:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3460:   MatCheckPreallocated(mat, 1);
3461:   PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3462:   PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3463:   PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3464:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3465:   PetscFunctionReturn(PETSC_SUCCESS);
3466: }

3468: /*@
3469:    MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3470:    Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.

3472:    Collective

3474:    Input Parameters:
3475: +  fact - the factor matrix obtained with `MatGetFactor()`
3476: .  mat - the matrix
3477: .  col - column permutation
3478: -  info - options for factorization, includes
3479: .vb
3480:           fill - expected fill as ratio of original fill.
3481:           dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3482:                    Run with the option -info to determine an optimal value to use
3483: .ve

3485:    Level: developer

3487:    Note:
3488:    Most users should employ the `KSP` interface for linear solvers
3489:    instead of working directly with matrix algebra routines such as this.
3490:    See, e.g., `KSPCreate()`.

3492:    Developer Note:
3493:    The Fortran interface is not autogenerated as the
3494:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

3496: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3497: @*/
3498: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3499: {
3500:   MatFactorInfo tinfo;

3502:   PetscFunctionBegin;
3509:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3510:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3511:   MatCheckPreallocated(mat, 2);
3512:   if (!info) {
3513:     PetscCall(MatFactorInfoInitialize(&tinfo));
3514:     info = &tinfo;
3515:   }

3517:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3518:   PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3519:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3520:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3521:   PetscFunctionReturn(PETSC_SUCCESS);
3522: }

3524: /*@
3525:    MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3526:    Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.

3528:    Collective

3530:    Input Parameters:
3531: +  fact - the factor matrix obtained with `MatGetFactor()`
3532: .  mat - the matrix
3533: -  info - options for factorization

3535:    Level: developer

3537:    Notes:
3538:    See `MatQRFactor()` for in-place factorization.

3540:    Most users should employ the `KSP` interface for linear solvers
3541:    instead of working directly with matrix algebra routines such as this.
3542:    See, e.g., `KSPCreate()`.

3544:    Developer Note:
3545:    The Fortran interface is not autogenerated as the
3546:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

3548: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3549: @*/
3550: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3551: {
3552:   MatFactorInfo tinfo;

3554:   PetscFunctionBegin;
3559:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3560:   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,
3561:              mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);

3563:   MatCheckPreallocated(mat, 2);
3564:   if (!info) {
3565:     PetscCall(MatFactorInfoInitialize(&tinfo));
3566:     info = &tinfo;
3567:   }

3569:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3570:   else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3571:   PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3572:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3573:   else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3574:   PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3575:   PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3576:   PetscFunctionReturn(PETSC_SUCCESS);
3577: }

3579: /*@
3580:    MatSolve - Solves A x = b, given a factored matrix.

3582:    Neighbor-wise Collective

3584:    Input Parameters:
3585: +  mat - the factored matrix
3586: -  b - the right-hand-side vector

3588:    Output Parameter:
3589: .  x - the result vector

3591:    Level: developer

3593:    Notes:
3594:    The vectors `b` and `x` cannot be the same.  I.e., one cannot
3595:    call `MatSolve`(A,x,x).

3597:    Most users should employ the `KSP` interface for linear solvers
3598:    instead of working directly with matrix algebra routines such as this.
3599:    See, e.g., `KSPCreate()`.

3601: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3602: @*/
3603: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3604: {
3605:   PetscFunctionBegin;
3610:   PetscCheckSameComm(mat, 1, b, 2);
3611:   PetscCheckSameComm(mat, 1, x, 3);
3612:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3613:   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);
3614:   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);
3615:   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);
3616:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3617:   MatCheckPreallocated(mat, 1);

3619:   PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3620:   if (mat->factorerrortype) {
3621:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3622:     PetscCall(VecSetInf(x));
3623:   } else PetscUseTypeMethod(mat, solve, b, x);
3624:   PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3625:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3626:   PetscFunctionReturn(PETSC_SUCCESS);
3627: }

3629: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3630: {
3631:   Vec      b, x;
3632:   PetscInt N, i;
3633:   PetscErrorCode (*f)(Mat, Vec, Vec);
3634:   PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;

3636:   PetscFunctionBegin;
3637:   if (A->factorerrortype) {
3638:     PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3639:     PetscCall(MatSetInf(X));
3640:     PetscFunctionReturn(PETSC_SUCCESS);
3641:   }
3642:   f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3643:   PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3644:   PetscCall(MatBoundToCPU(A, &Abound));
3645:   if (!Abound) {
3646:     PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3647:     PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3648:   }
3649: #if PetscDefined(HAVE_CUDA)
3650:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3651:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3652: #elif PetscDefined(HAVE_HIP)
3653:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3654:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3655: #endif
3656:   PetscCall(MatGetSize(B, NULL, &N));
3657:   for (i = 0; i < N; i++) {
3658:     PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3659:     PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3660:     PetscCall((*f)(A, b, x));
3661:     PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3662:     PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3663:   }
3664:   if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3665:   if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3666:   PetscFunctionReturn(PETSC_SUCCESS);
3667: }

3669: /*@
3670:    MatMatSolve - Solves A X = B, given a factored matrix.

3672:    Neighbor-wise Collective

3674:    Input Parameters:
3675: +  A - the factored matrix
3676: -  B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)

3678:    Output Parameter:
3679: .  X - the result matrix (dense matrix)

3681:    Level: developer

3683:    Note:
3684:    If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3685:    otherwise, `B` and `X` cannot be the same.

3687: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3688: @*/
3689: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3690: {
3691:   PetscFunctionBegin;
3696:   PetscCheckSameComm(A, 1, B, 2);
3697:   PetscCheckSameComm(A, 1, X, 3);
3698:   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);
3699:   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);
3700:   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");
3701:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3702:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3703:   MatCheckPreallocated(A, 1);

3705:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3706:   if (!A->ops->matsolve) {
3707:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3708:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3709:   } else PetscUseTypeMethod(A, matsolve, B, X);
3710:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3711:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3712:   PetscFunctionReturn(PETSC_SUCCESS);
3713: }

3715: /*@
3716:    MatMatSolveTranspose - Solves A^T X = B, given a factored matrix.

3718:    Neighbor-wise Collective

3720:    Input Parameters:
3721: +  A - the factored matrix
3722: -  B - the right-hand-side matrix  (`MATDENSE` matrix)

3724:    Output Parameter:
3725: .  X - the result matrix (dense matrix)

3727:    Level: developer

3729:    Note:
3730:    The matrices `B` and `X` cannot be the same.  I.e., one cannot
3731:    call `MatMatSolveTranspose`(A,X,X).

3733: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3734: @*/
3735: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3736: {
3737:   PetscFunctionBegin;
3742:   PetscCheckSameComm(A, 1, B, 2);
3743:   PetscCheckSameComm(A, 1, X, 3);
3744:   PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3745:   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);
3746:   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);
3747:   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);
3748:   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");
3749:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3750:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3751:   MatCheckPreallocated(A, 1);

3753:   PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3754:   if (!A->ops->matsolvetranspose) {
3755:     PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3756:     PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3757:   } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3758:   PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3759:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3760:   PetscFunctionReturn(PETSC_SUCCESS);
3761: }

3763: /*@
3764:    MatMatTransposeSolve - Solves A X = B^T, given a factored matrix.

3766:    Neighbor-wise Collective

3768:    Input Parameters:
3769: +  A - the factored matrix
3770: -  Bt - the transpose of right-hand-side matrix as a `MATDENSE`

3772:    Output Parameter:
3773: .  X - the result matrix (dense matrix)

3775:    Level: developer

3777:    Note:
3778:    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
3779:    format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.

3781: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3782: @*/
3783: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3784: {
3785:   PetscFunctionBegin;
3790:   PetscCheckSameComm(A, 1, Bt, 2);
3791:   PetscCheckSameComm(A, 1, X, 3);

3793:   PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3794:   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);
3795:   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);
3796:   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");
3797:   if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3798:   PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3799:   MatCheckPreallocated(A, 1);

3801:   PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3802:   PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3803:   PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3804:   PetscCall(PetscObjectStateIncrease((PetscObject)X));
3805:   PetscFunctionReturn(PETSC_SUCCESS);
3806: }

3808: /*@
3809:    MatForwardSolve - Solves L x = b, given a factored matrix, A = LU, or
3810:                             U^T*D^(1/2) x = b, given a factored symmetric matrix, A = U^T*D*U,

3812:    Neighbor-wise Collective

3814:    Input Parameters:
3815: +  mat - the factored matrix
3816: -  b - the right-hand-side vector

3818:    Output Parameter:
3819: .  x - the result vector

3821:    Level: developer

3823:    Notes:
3824:    `MatSolve()` should be used for most applications, as it performs
3825:    a forward solve followed by a backward solve.

3827:    The vectors `b` and `x` cannot be the same,  i.e., one cannot
3828:    call `MatForwardSolve`(A,x,x).

3830:    For matrix in `MATSEQBAIJ` format with block size larger than 1,
3831:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3832:    `MatForwardSolve()` solves U^T*D y = b, and
3833:    `MatBackwardSolve()` solves U x = y.
3834:    Thus they do not provide a symmetric preconditioner.

3836: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`, `MatBackwardSolve()`
3837: @*/
3838: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3839: {
3840:   PetscFunctionBegin;
3845:   PetscCheckSameComm(mat, 1, b, 2);
3846:   PetscCheckSameComm(mat, 1, x, 3);
3847:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3848:   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);
3849:   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);
3850:   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);
3851:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3852:   MatCheckPreallocated(mat, 1);

3854:   PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3855:   PetscUseTypeMethod(mat, forwardsolve, b, x);
3856:   PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3857:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3858:   PetscFunctionReturn(PETSC_SUCCESS);
3859: }

3861: /*@
3862:    MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU.
3863:                              D^(1/2) U x = b, given a factored symmetric matrix, A = U^T*D*U,

3865:    Neighbor-wise Collective

3867:    Input Parameters:
3868: +  mat - the factored matrix
3869: -  b - the right-hand-side vector

3871:    Output Parameter:
3872: .  x - the result vector

3874:    Level: developer

3876:    Notes:
3877:    `MatSolve()` should be used for most applications, as it performs
3878:    a forward solve followed by a backward solve.

3880:    The vectors `b` and `x` cannot be the same.  I.e., one cannot
3881:    call `MatBackwardSolve`(A,x,x).

3883:    For matrix in `MATSEQBAIJ` format with block size larger than 1,
3884:    the diagonal blocks are not implemented as D = D^(1/2) * D^(1/2) yet.
3885:    `MatForwardSolve()` solves U^T*D y = b, and
3886:    `MatBackwardSolve()` solves U x = y.
3887:    Thus they do not provide a symmetric preconditioner.

3889: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`, `MatForwardSolve()`
3890: @*/
3891: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3892: {
3893:   PetscFunctionBegin;
3898:   PetscCheckSameComm(mat, 1, b, 2);
3899:   PetscCheckSameComm(mat, 1, x, 3);
3900:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3901:   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);
3902:   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);
3903:   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);
3904:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3905:   MatCheckPreallocated(mat, 1);

3907:   PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3908:   PetscUseTypeMethod(mat, backwardsolve, b, x);
3909:   PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3910:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3911:   PetscFunctionReturn(PETSC_SUCCESS);
3912: }

3914: /*@
3915:    MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix.

3917:    Neighbor-wise Collective

3919:    Input Parameters:
3920: +  mat - the factored matrix
3921: .  b - the right-hand-side vector
3922: -  y - the vector to be added to

3924:    Output Parameter:
3925: .  x - the result vector

3927:    Level: developer

3929:    Note:
3930:    The vectors `b` and `x` cannot be the same.  I.e., one cannot
3931:    call `MatSolveAdd`(A,x,y,x).

3933: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3934: @*/
3935: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
3936: {
3937:   PetscScalar one = 1.0;
3938:   Vec         tmp;

3940:   PetscFunctionBegin;
3946:   PetscCheckSameComm(mat, 1, b, 2);
3947:   PetscCheckSameComm(mat, 1, y, 3);
3948:   PetscCheckSameComm(mat, 1, x, 4);
3949:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3950:   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);
3951:   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);
3952:   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);
3953:   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);
3954:   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);
3955:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3956:   MatCheckPreallocated(mat, 1);

3958:   PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
3959:   if (mat->factorerrortype) {
3960:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3961:     PetscCall(VecSetInf(x));
3962:   } else if (mat->ops->solveadd) {
3963:     PetscUseTypeMethod(mat, solveadd, b, y, x);
3964:   } else {
3965:     /* do the solve then the add manually */
3966:     if (x != y) {
3967:       PetscCall(MatSolve(mat, b, x));
3968:       PetscCall(VecAXPY(x, one, y));
3969:     } else {
3970:       PetscCall(VecDuplicate(x, &tmp));
3971:       PetscCall(VecCopy(x, tmp));
3972:       PetscCall(MatSolve(mat, b, x));
3973:       PetscCall(VecAXPY(x, one, tmp));
3974:       PetscCall(VecDestroy(&tmp));
3975:     }
3976:   }
3977:   PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
3978:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
3979:   PetscFunctionReturn(PETSC_SUCCESS);
3980: }

3982: /*@
3983:    MatSolveTranspose - Solves A' x = b, given a factored matrix.

3985:    Neighbor-wise Collective

3987:    Input Parameters:
3988: +  mat - the factored matrix
3989: -  b - the right-hand-side vector

3991:    Output Parameter:
3992: .  x - the result vector

3994:    Level: developer

3996:    Notes:
3997:    The vectors `b` and `x` cannot be the same.  I.e., one cannot
3998:    call `MatSolveTranspose`(A,x,x).

4000:    Most users should employ the `KSP` interface for linear solvers
4001:    instead of working directly with matrix algebra routines such as this.
4002:    See, e.g., `KSPCreate()`.

4004: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4005: @*/
4006: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4007: {
4008:   PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;

4010:   PetscFunctionBegin;
4015:   PetscCheckSameComm(mat, 1, b, 2);
4016:   PetscCheckSameComm(mat, 1, x, 3);
4017:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4018:   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);
4019:   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);
4020:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4021:   MatCheckPreallocated(mat, 1);
4022:   PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4023:   if (mat->factorerrortype) {
4024:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4025:     PetscCall(VecSetInf(x));
4026:   } else {
4027:     PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4028:     PetscCall((*f)(mat, b, x));
4029:   }
4030:   PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4031:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4032:   PetscFunctionReturn(PETSC_SUCCESS);
4033: }

4035: /*@
4036:    MatSolveTransposeAdd - Computes x = y + inv(Transpose(A)) b, given a
4037:                       factored matrix.

4039:    Neighbor-wise Collective

4041:    Input Parameters:
4042: +  mat - the factored matrix
4043: .  b - the right-hand-side vector
4044: -  y - the vector to be added to

4046:    Output Parameter:
4047: .  x - the result vector

4049:    Level: developer

4051:    Note:
4052:    The vectors `b` and `x` cannot be the same.  I.e., one cannot
4053:    call `MatSolveTransposeAdd`(A,x,y,x).

4055: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4056: @*/
4057: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4058: {
4059:   PetscScalar one = 1.0;
4060:   Vec         tmp;
4061:   PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;

4063:   PetscFunctionBegin;
4069:   PetscCheckSameComm(mat, 1, b, 2);
4070:   PetscCheckSameComm(mat, 1, y, 3);
4071:   PetscCheckSameComm(mat, 1, x, 4);
4072:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4073:   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);
4074:   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);
4075:   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);
4076:   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);
4077:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4078:   MatCheckPreallocated(mat, 1);

4080:   PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4081:   if (mat->factorerrortype) {
4082:     PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4083:     PetscCall(VecSetInf(x));
4084:   } else if (f) {
4085:     PetscCall((*f)(mat, b, y, x));
4086:   } else {
4087:     /* do the solve then the add manually */
4088:     if (x != y) {
4089:       PetscCall(MatSolveTranspose(mat, b, x));
4090:       PetscCall(VecAXPY(x, one, y));
4091:     } else {
4092:       PetscCall(VecDuplicate(x, &tmp));
4093:       PetscCall(VecCopy(x, tmp));
4094:       PetscCall(MatSolveTranspose(mat, b, x));
4095:       PetscCall(VecAXPY(x, one, tmp));
4096:       PetscCall(VecDestroy(&tmp));
4097:     }
4098:   }
4099:   PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4100:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4101:   PetscFunctionReturn(PETSC_SUCCESS);
4102: }

4104: /*@
4105:    MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.

4107:    Neighbor-wise Collective

4109:    Input Parameters:
4110: +  mat - the matrix
4111: .  b - the right hand side
4112: .  omega - the relaxation factor
4113: .  flag - flag indicating the type of SOR (see below)
4114: .  shift -  diagonal shift
4115: .  its - the number of iterations
4116: -  lits - the number of local iterations

4118:    Output Parameter:
4119: .  x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)

4121:    SOR Flags:
4122: +     `SOR_FORWARD_SWEEP` - forward SOR
4123: .     `SOR_BACKWARD_SWEEP` - backward SOR
4124: .     `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4125: .     `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4126: .     `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4127: .     `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4128: .     `SOR_EISENSTAT` - SOR with Eisenstat trick
4129: .     `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4130:          upper/lower triangular part of matrix to
4131:          vector (with omega)
4132: -     `SOR_ZERO_INITIAL_GUESS` - zero initial guess

4134:    Level: developer

4136:    Notes:
4137:    `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4138:    `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4139:    on each processor.

4141:    Application programmers will not generally use `MatSOR()` directly,
4142:    but instead will employ the `KSP`/`PC` interface.

4144:    For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing

4146:    Most users should employ the `KSP` interface for linear solvers
4147:    instead of working directly with matrix algebra routines such as this.
4148:    See, e.g., `KSPCreate()`.

4150:    Vectors `x` and `b` CANNOT be the same

4152:    The flags are implemented as bitwise inclusive or operations.
4153:    For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4154:    to specify a zero initial guess for SSOR.

4156:    Developer Note:
4157:    We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes

4159: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4160: @*/
4161: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4162: {
4163:   PetscFunctionBegin;
4168:   PetscCheckSameComm(mat, 1, b, 2);
4169:   PetscCheckSameComm(mat, 1, x, 8);
4170:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4171:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4172:   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);
4173:   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);
4174:   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);
4175:   PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4176:   PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4177:   PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");

4179:   MatCheckPreallocated(mat, 1);
4180:   PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4181:   PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4182:   PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4183:   PetscCall(PetscObjectStateIncrease((PetscObject)x));
4184:   PetscFunctionReturn(PETSC_SUCCESS);
4185: }

4187: /*
4188:       Default matrix copy routine.
4189: */
4190: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4191: {
4192:   PetscInt           i, rstart = 0, rend = 0, nz;
4193:   const PetscInt    *cwork;
4194:   const PetscScalar *vwork;

4196:   PetscFunctionBegin;
4197:   if (B->assembled) PetscCall(MatZeroEntries(B));
4198:   if (str == SAME_NONZERO_PATTERN) {
4199:     PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4200:     for (i = rstart; i < rend; i++) {
4201:       PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4202:       PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4203:       PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4204:     }
4205:   } else {
4206:     PetscCall(MatAYPX(B, 0.0, A, str));
4207:   }
4208:   PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4209:   PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4210:   PetscFunctionReturn(PETSC_SUCCESS);
4211: }

4213: /*@
4214:    MatCopy - Copies a matrix to another matrix.

4216:    Collective

4218:    Input Parameters:
4219: +  A - the matrix
4220: -  str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`

4222:    Output Parameter:
4223: .  B - where the copy is put

4225:    Level: intermediate

4227:    Notes:
4228:    If you use `SAME_NONZERO_PATTERN` then the two matrices must have the same nonzero pattern or the routine will crash.

4230:    `MatCopy()` copies the matrix entries of a matrix to another existing
4231:    matrix (after first zeroing the second matrix).  A related routine is
4232:    `MatConvert()`, which first creates a new matrix and then copies the data.

4234: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4235: @*/
4236: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4237: {
4238:   PetscInt i;

4240:   PetscFunctionBegin;
4245:   PetscCheckSameComm(A, 1, B, 2);
4246:   MatCheckPreallocated(B, 2);
4247:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4248:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4249:   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,
4250:              A->cmap->N, B->cmap->N);
4251:   MatCheckPreallocated(A, 1);
4252:   if (A == B) PetscFunctionReturn(PETSC_SUCCESS);

4254:   PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4255:   if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4256:   else PetscCall(MatCopy_Basic(A, B, str));

4258:   B->stencil.dim = A->stencil.dim;
4259:   B->stencil.noc = A->stencil.noc;
4260:   for (i = 0; i <= A->stencil.dim; i++) {
4261:     B->stencil.dims[i]   = A->stencil.dims[i];
4262:     B->stencil.starts[i] = A->stencil.starts[i];
4263:   }

4265:   PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4266:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4267:   PetscFunctionReturn(PETSC_SUCCESS);
4268: }

4270: /*@C
4271:    MatConvert - Converts a matrix to another matrix, either of the same
4272:    or different type.

4274:    Collective

4276:    Input Parameters:
4277: +  mat - the matrix
4278: .  newtype - new matrix type.  Use `MATSAME` to create a new matrix of the
4279:    same type as the original matrix.
4280: -  reuse - denotes if the destination matrix is to be created or reused.
4281:    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
4282:    `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).

4284:    Output Parameter:
4285: .  M - pointer to place new matrix

4287:    Level: intermediate

4289:    Notes:
4290:    `MatConvert()` first creates a new matrix and then copies the data from
4291:    the first matrix.  A related routine is `MatCopy()`, which copies the matrix
4292:    entries of one matrix to another already existing matrix context.

4294:    Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4295:    the MPI communicator of the generated matrix is always the same as the communicator
4296:    of the input matrix.

4298: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4299: @*/
4300: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4301: {
4302:   PetscBool  sametype, issame, flg;
4303:   PetscBool3 issymmetric, ishermitian;
4304:   char       convname[256], mtype[256];
4305:   Mat        B;

4307:   PetscFunctionBegin;
4311:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4312:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4313:   MatCheckPreallocated(mat, 1);

4315:   PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4316:   if (flg) newtype = mtype;

4318:   PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4319:   PetscCall(PetscStrcmp(newtype, "same", &issame));
4320:   PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4321:   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");

4323:   if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4324:     PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4325:     PetscFunctionReturn(PETSC_SUCCESS);
4326:   }

4328:   /* Cache Mat options because some converters use MatHeaderReplace  */
4329:   issymmetric = mat->symmetric;
4330:   ishermitian = mat->hermitian;

4332:   if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4333:     PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4334:     PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4335:   } else {
4336:     PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4337:     const char *prefix[3]                                 = {"seq", "mpi", ""};
4338:     PetscInt    i;
4339:     /*
4340:        Order of precedence:
4341:        0) See if newtype is a superclass of the current matrix.
4342:        1) See if a specialized converter is known to the current matrix.
4343:        2) See if a specialized converter is known to the desired matrix class.
4344:        3) See if a good general converter is registered for the desired class
4345:           (as of 6/27/03 only MATMPIADJ falls into this category).
4346:        4) See if a good general converter is known for the current matrix.
4347:        5) Use a really basic converter.
4348:     */

4350:     /* 0) See if newtype is a superclass of the current matrix.
4351:           i.e mat is mpiaij and newtype is aij */
4352:     for (i = 0; i < 2; i++) {
4353:       PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4354:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4355:       PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4356:       PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4357:       if (flg) {
4358:         if (reuse == MAT_INPLACE_MATRIX) {
4359:           PetscCall(PetscInfo(mat, "Early return\n"));
4360:           PetscFunctionReturn(PETSC_SUCCESS);
4361:         } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4362:           PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4363:           PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4364:           PetscFunctionReturn(PETSC_SUCCESS);
4365:         } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4366:           PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4367:           PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4368:           PetscFunctionReturn(PETSC_SUCCESS);
4369:         }
4370:       }
4371:     }
4372:     /* 1) See if a specialized converter is known to the current matrix and the desired class */
4373:     for (i = 0; i < 3; i++) {
4374:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4375:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4376:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4377:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4378:       PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4379:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4380:       PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4381:       PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4382:       if (conv) goto foundconv;
4383:     }

4385:     /* 2)  See if a specialized converter is known to the desired matrix class. */
4386:     PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4387:     PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4388:     PetscCall(MatSetType(B, newtype));
4389:     for (i = 0; i < 3; i++) {
4390:       PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4391:       PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4392:       PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4393:       PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4394:       PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4395:       PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4396:       PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4397:       PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4398:       if (conv) {
4399:         PetscCall(MatDestroy(&B));
4400:         goto foundconv;
4401:       }
4402:     }

4404:     /* 3) See if a good general converter is registered for the desired class */
4405:     conv = B->ops->convertfrom;
4406:     PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4407:     PetscCall(MatDestroy(&B));
4408:     if (conv) goto foundconv;

4410:     /* 4) See if a good general converter is known for the current matrix */
4411:     if (mat->ops->convert) conv = mat->ops->convert;
4412:     PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4413:     if (conv) goto foundconv;

4415:     /* 5) Use a really basic converter. */
4416:     PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4417:     conv = MatConvert_Basic;

4419:   foundconv:
4420:     PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4421:     PetscCall((*conv)(mat, newtype, reuse, M));
4422:     if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4423:       /* the block sizes must be same if the mappings are copied over */
4424:       (*M)->rmap->bs = mat->rmap->bs;
4425:       (*M)->cmap->bs = mat->cmap->bs;
4426:       PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4427:       PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4428:       (*M)->rmap->mapping = mat->rmap->mapping;
4429:       (*M)->cmap->mapping = mat->cmap->mapping;
4430:     }
4431:     (*M)->stencil.dim = mat->stencil.dim;
4432:     (*M)->stencil.noc = mat->stencil.noc;
4433:     for (i = 0; i <= mat->stencil.dim; i++) {
4434:       (*M)->stencil.dims[i]   = mat->stencil.dims[i];
4435:       (*M)->stencil.starts[i] = mat->stencil.starts[i];
4436:     }
4437:     PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4438:   }
4439:   PetscCall(PetscObjectStateIncrease((PetscObject)*M));

4441:   /* Copy Mat options */
4442:   if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4443:   else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4444:   if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4445:   else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4446:   PetscFunctionReturn(PETSC_SUCCESS);
4447: }

4449: /*@C
4450:    MatFactorGetSolverType - Returns name of the package providing the factorization routines

4452:    Not Collective

4454:    Input Parameter:
4455: .  mat - the matrix, must be a factored matrix

4457:    Output Parameter:
4458: .   type - the string name of the package (do not free this string)

4460:    Level: intermediate

4462:    Fortran Note:
4463:    Pass in an empty string and the package name will be copied into it. Make sure the string is long enough.

4465: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`
4466: @*/
4467: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4468: {
4469:   PetscErrorCode (*conv)(Mat, MatSolverType *);

4471:   PetscFunctionBegin;
4475:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4476:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4477:   if (conv) PetscCall((*conv)(mat, type));
4478:   else *type = MATSOLVERPETSC;
4479:   PetscFunctionReturn(PETSC_SUCCESS);
4480: }

4482: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4483: struct _MatSolverTypeForSpecifcType {
4484:   MatType mtype;
4485:   /* no entry for MAT_FACTOR_NONE */
4486:   PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4487:   MatSolverTypeForSpecifcType next;
4488: };

4490: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4491: struct _MatSolverTypeHolder {
4492:   char                       *name;
4493:   MatSolverTypeForSpecifcType handlers;
4494:   MatSolverTypeHolder         next;
4495: };

4497: static MatSolverTypeHolder MatSolverTypeHolders = NULL;

4499: /*@C
4500:    MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type

4502:    Input Parameters:
4503: +    package - name of the package, for example petsc or superlu
4504: .    mtype - the matrix type that works with this package
4505: .    ftype - the type of factorization supported by the package
4506: -    createfactor - routine that will create the factored matrix ready to be used

4508:     Level: developer

4510: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`
4511: @*/
4512: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4513: {
4514:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev = NULL;
4515:   PetscBool                   flg;
4516:   MatSolverTypeForSpecifcType inext, iprev = NULL;

4518:   PetscFunctionBegin;
4519:   PetscCall(MatInitializePackage());
4520:   if (!next) {
4521:     PetscCall(PetscNew(&MatSolverTypeHolders));
4522:     PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4523:     PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4524:     PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4525:     MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4526:     PetscFunctionReturn(PETSC_SUCCESS);
4527:   }
4528:   while (next) {
4529:     PetscCall(PetscStrcasecmp(package, next->name, &flg));
4530:     if (flg) {
4531:       PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4532:       inext = next->handlers;
4533:       while (inext) {
4534:         PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4535:         if (flg) {
4536:           inext->createfactor[(int)ftype - 1] = createfactor;
4537:           PetscFunctionReturn(PETSC_SUCCESS);
4538:         }
4539:         iprev = inext;
4540:         inext = inext->next;
4541:       }
4542:       PetscCall(PetscNew(&iprev->next));
4543:       PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4544:       iprev->next->createfactor[(int)ftype - 1] = createfactor;
4545:       PetscFunctionReturn(PETSC_SUCCESS);
4546:     }
4547:     prev = next;
4548:     next = next->next;
4549:   }
4550:   PetscCall(PetscNew(&prev->next));
4551:   PetscCall(PetscStrallocpy(package, &prev->next->name));
4552:   PetscCall(PetscNew(&prev->next->handlers));
4553:   PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4554:   prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4555:   PetscFunctionReturn(PETSC_SUCCESS);
4556: }

4558: /*@C
4559:    MatSolverTypeGet - Gets the function that creates the factor matrix if it exist

4561:    Input Parameters:
4562: +    type - name of the package, for example petsc or superlu
4563: .    ftype - the type of factorization supported by the type
4564: -    mtype - the matrix type that works with this type

4566:    Output Parameters:
4567: +   foundtype - `PETSC_TRUE` if the type was registered
4568: .   foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4569: -   createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found

4571:     Level: developer

4573: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`
4574: @*/
4575: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat, MatFactorType, Mat *))
4576: {
4577:   MatSolverTypeHolder         next = MatSolverTypeHolders;
4578:   PetscBool                   flg;
4579:   MatSolverTypeForSpecifcType inext;

4581:   PetscFunctionBegin;
4582:   if (foundtype) *foundtype = PETSC_FALSE;
4583:   if (foundmtype) *foundmtype = PETSC_FALSE;
4584:   if (createfactor) *createfactor = NULL;

4586:   if (type) {
4587:     while (next) {
4588:       PetscCall(PetscStrcasecmp(type, next->name, &flg));
4589:       if (flg) {
4590:         if (foundtype) *foundtype = PETSC_TRUE;
4591:         inext = next->handlers;
4592:         while (inext) {
4593:           PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4594:           if (flg) {
4595:             if (foundmtype) *foundmtype = PETSC_TRUE;
4596:             if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4597:             PetscFunctionReturn(PETSC_SUCCESS);
4598:           }
4599:           inext = inext->next;
4600:         }
4601:       }
4602:       next = next->next;
4603:     }
4604:   } else {
4605:     while (next) {
4606:       inext = next->handlers;
4607:       while (inext) {
4608:         PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4609:         if (flg && inext->createfactor[(int)ftype - 1]) {
4610:           if (foundtype) *foundtype = PETSC_TRUE;
4611:           if (foundmtype) *foundmtype = PETSC_TRUE;
4612:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4613:           PetscFunctionReturn(PETSC_SUCCESS);
4614:         }
4615:         inext = inext->next;
4616:       }
4617:       next = next->next;
4618:     }
4619:     /* try with base classes inext->mtype */
4620:     next = MatSolverTypeHolders;
4621:     while (next) {
4622:       inext = next->handlers;
4623:       while (inext) {
4624:         PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4625:         if (flg && inext->createfactor[(int)ftype - 1]) {
4626:           if (foundtype) *foundtype = PETSC_TRUE;
4627:           if (foundmtype) *foundmtype = PETSC_TRUE;
4628:           if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4629:           PetscFunctionReturn(PETSC_SUCCESS);
4630:         }
4631:         inext = inext->next;
4632:       }
4633:       next = next->next;
4634:     }
4635:   }
4636:   PetscFunctionReturn(PETSC_SUCCESS);
4637: }

4639: PetscErrorCode MatSolverTypeDestroy(void)
4640: {
4641:   MatSolverTypeHolder         next = MatSolverTypeHolders, prev;
4642:   MatSolverTypeForSpecifcType inext, iprev;

4644:   PetscFunctionBegin;
4645:   while (next) {
4646:     PetscCall(PetscFree(next->name));
4647:     inext = next->handlers;
4648:     while (inext) {
4649:       PetscCall(PetscFree(inext->mtype));
4650:       iprev = inext;
4651:       inext = inext->next;
4652:       PetscCall(PetscFree(iprev));
4653:     }
4654:     prev = next;
4655:     next = next->next;
4656:     PetscCall(PetscFree(prev));
4657:   }
4658:   MatSolverTypeHolders = NULL;
4659:   PetscFunctionReturn(PETSC_SUCCESS);
4660: }

4662: /*@C
4663:    MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`

4665:    Logically Collective

4667:    Input Parameter:
4668: .  mat - the matrix

4670:    Output Parameter:
4671: .  flg - `PETSC_TRUE` if uses the ordering

4673:    Level: developer

4675:    Note:
4676:    Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4677:    packages do not, thus we want to skip generating the ordering when it is not needed or used.

4679: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4680: @*/
4681: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4682: {
4683:   PetscFunctionBegin;
4684:   *flg = mat->canuseordering;
4685:   PetscFunctionReturn(PETSC_SUCCESS);
4686: }

4688: /*@C
4689:    MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object

4691:    Logically Collective

4693:    Input Parameters:
4694: +  mat - the matrix obtained with `MatGetFactor()`
4695: -  ftype - the factorization type to be used

4697:    Output Parameter:
4698: .  otype - the preferred ordering type

4700:    Level: developer

4702: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4703: @*/
4704: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4705: {
4706:   PetscFunctionBegin;
4707:   *otype = mat->preferredordering[ftype];
4708:   PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4709:   PetscFunctionReturn(PETSC_SUCCESS);
4710: }

4712: /*@C
4713:    MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic()

4715:    Collective

4717:    Input Parameters:
4718: +  mat - the matrix
4719: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4720: -  ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4722:    Output Parameter:
4723: .  f - the factor matrix used with MatXXFactorSymbolic() calls

4725:    Options Database Key:
4726: .  -mat_factor_bind_factorization <host, device> - Where to do matrix factorization? Default is device (might consume more device memory.
4727:                                   One can choose host to save device memory). Currently only supported with `MATSEQAIJCUSPARSE` matrices.

4729:    Level: intermediate

4731:    Notes:
4732:      Users usually access the factorization solvers via `KSP`

4734:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4735:      such as pastix, superlu, mumps etc.

4737:       PETSc must have been ./configure to use the external solver, using the option --download-package

4739:       Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4740:       where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4741:       call `MatSetOptionsPrefixFactor()` on the originating matrix or  `MatSetOptionsPrefix()` on the resulting factor matrix.

4743:    Developer Note:
4744:       This should actually be called `MatCreateFactor()` since it creates a new factor object

4746: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`,
4747:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4748: @*/
4749: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4750: {
4751:   PetscBool foundtype, foundmtype;
4752:   PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);

4754:   PetscFunctionBegin;

4758:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4759:   MatCheckPreallocated(mat, 1);

4761:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4762:   if (!foundtype) {
4763:     if (type) {
4764:       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],
4765:               ((PetscObject)mat)->type_name, type);
4766:     } else {
4767:       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);
4768:     }
4769:   }
4770:   PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4771:   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);

4773:   PetscCall((*conv)(mat, ftype, f));
4774:   if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4775:   PetscFunctionReturn(PETSC_SUCCESS);
4776: }

4778: /*@C
4779:    MatGetFactorAvailable - Returns a a flag if matrix supports particular type and factor type

4781:    Not Collective

4783:    Input Parameters:
4784: +  mat - the matrix
4785: .  type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4786: -  ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`

4788:    Output Parameter:
4789: .    flg - PETSC_TRUE if the factorization is available

4791:    Level: intermediate

4793:    Notes:
4794:       Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4795:      such as pastix, superlu, mumps etc.

4797:       PETSc must have been ./configure to use the external solver, using the option --download-package

4799:    Developer Note:
4800:       This should actually be called MatCreateFactorAvailable() since MatGetFactor() creates a new factor object

4802: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactor()`, `MatSolverTypeRegister()`,
4803:           `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4804: @*/
4805: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4806: {
4807:   PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);

4809:   PetscFunctionBegin;

4814:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4815:   MatCheckPreallocated(mat, 1);

4817:   PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4818:   *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4819:   PetscFunctionReturn(PETSC_SUCCESS);
4820: }

4822: /*@
4823:    MatDuplicate - Duplicates a matrix including the non-zero structure.

4825:    Collective

4827:    Input Parameters:
4828: +  mat - the matrix
4829: -  op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4830:         See the manual page for `MatDuplicateOption()` for an explanation of these options.

4832:    Output Parameter:
4833: .  M - pointer to place new matrix

4835:    Level: intermediate

4837:    Notes:
4838:     You cannot change the nonzero pattern for the parent or child matrix if you use `MAT_SHARE_NONZERO_PATTERN`.

4840:     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.

4842:     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
4843:     is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4844:     User should not use `MatDuplicate()` to create new matrix M if M is intended to be reused as the product of matrix operation.

4846: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4847: @*/
4848: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4849: {
4850:   Mat         B;
4851:   VecType     vtype;
4852:   PetscInt    i;
4853:   PetscObject dm;
4854:   void (*viewf)(void);

4856:   PetscFunctionBegin;
4860:   PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4861:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4862:   MatCheckPreallocated(mat, 1);

4864:   *M = NULL;
4865:   PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4866:   PetscUseTypeMethod(mat, duplicate, op, M);
4867:   PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4868:   B = *M;

4870:   PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4871:   if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4872:   PetscCall(MatGetVecType(mat, &vtype));
4873:   PetscCall(MatSetVecType(B, vtype));

4875:   B->stencil.dim = mat->stencil.dim;
4876:   B->stencil.noc = mat->stencil.noc;
4877:   for (i = 0; i <= mat->stencil.dim; i++) {
4878:     B->stencil.dims[i]   = mat->stencil.dims[i];
4879:     B->stencil.starts[i] = mat->stencil.starts[i];
4880:   }

4882:   B->nooffproczerorows = mat->nooffproczerorows;
4883:   B->nooffprocentries  = mat->nooffprocentries;

4885:   PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4886:   if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4887:   PetscCall(PetscObjectStateIncrease((PetscObject)B));
4888:   PetscFunctionReturn(PETSC_SUCCESS);
4889: }

4891: /*@
4892:    MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`

4894:    Logically Collective

4896:    Input Parameter:
4897: .  mat - the matrix

4899:    Output Parameter:
4900: .  v - the diagonal of the matrix

4902:    Level: intermediate

4904:    Note:
4905:    Currently only correct in parallel for square matrices.

4907: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
4908: @*/
4909: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
4910: {
4911:   PetscFunctionBegin;
4915:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4916:   MatCheckPreallocated(mat, 1);

4918:   PetscUseTypeMethod(mat, getdiagonal, v);
4919:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
4920:   PetscFunctionReturn(PETSC_SUCCESS);
4921: }

4923: /*@C
4924:    MatGetRowMin - Gets the minimum value (of the real part) of each
4925:         row of the matrix

4927:    Logically Collective

4929:    Input Parameter:
4930: .  mat - the matrix

4932:    Output Parameters:
4933: +  v - the vector for storing the maximums
4934: -  idx - the indices of the column found for each row (optional)

4936:    Level: intermediate

4938:    Note:
4939:     The result of this call are the same as if one converted the matrix to dense format
4940:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

4942:     This code is only implemented for a couple of matrix formats.

4944: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
4945:           `MatGetRowMax()`
4946: @*/
4947: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
4948: {
4949:   PetscFunctionBegin;
4953:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

4955:   if (!mat->cmap->N) {
4956:     PetscCall(VecSet(v, PETSC_MAX_REAL));
4957:     if (idx) {
4958:       PetscInt i, m = mat->rmap->n;
4959:       for (i = 0; i < m; i++) idx[i] = -1;
4960:     }
4961:   } else {
4962:     MatCheckPreallocated(mat, 1);
4963:   }
4964:   PetscUseTypeMethod(mat, getrowmin, v, idx);
4965:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
4966:   PetscFunctionReturn(PETSC_SUCCESS);
4967: }

4969: /*@C
4970:    MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
4971:         row of the matrix

4973:    Logically Collective

4975:    Input Parameter:
4976: .  mat - the matrix

4978:    Output Parameters:
4979: +  v - the vector for storing the minimums
4980: -  idx - the indices of the column found for each row (or `NULL` if not needed)

4982:    Level: intermediate

4984:    Notes:
4985:     if a row is completely empty or has only 0.0 values then the idx[] value for that
4986:     row is 0 (the first column).

4988:     This code is only implemented for a couple of matrix formats.

4990: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
4991: @*/
4992: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
4993: {
4994:   PetscFunctionBegin;
4998:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4999:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

5001:   if (!mat->cmap->N) {
5002:     PetscCall(VecSet(v, 0.0));
5003:     if (idx) {
5004:       PetscInt i, m = mat->rmap->n;
5005:       for (i = 0; i < m; i++) idx[i] = -1;
5006:     }
5007:   } else {
5008:     MatCheckPreallocated(mat, 1);
5009:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5010:     PetscUseTypeMethod(mat, getrowminabs, v, idx);
5011:   }
5012:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5013:   PetscFunctionReturn(PETSC_SUCCESS);
5014: }

5016: /*@C
5017:    MatGetRowMax - Gets the maximum value (of the real part) of each
5018:         row of the matrix

5020:    Logically Collective

5022:    Input Parameter:
5023: .  mat - the matrix

5025:    Output Parameters:
5026: +  v - the vector for storing the maximums
5027: -  idx - the indices of the column found for each row (optional)

5029:    Level: intermediate

5031:    Notes:
5032:     The result of this call are the same as if one converted the matrix to dense format
5033:       and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).

5035:     This code is only implemented for a couple of matrix formats.

5037: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5038: @*/
5039: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5040: {
5041:   PetscFunctionBegin;
5045:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");

5047:   if (!mat->cmap->N) {
5048:     PetscCall(VecSet(v, PETSC_MIN_REAL));
5049:     if (idx) {
5050:       PetscInt i, m = mat->rmap->n;
5051:       for (i = 0; i < m; i++) idx[i] = -1;
5052:     }
5053:   } else {
5054:     MatCheckPreallocated(mat, 1);
5055:     PetscUseTypeMethod(mat, getrowmax, v, idx);
5056:   }
5057:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5058:   PetscFunctionReturn(PETSC_SUCCESS);
5059: }

5061: /*@C
5062:    MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5063:         row of the matrix

5065:    Logically Collective

5067:    Input Parameter:
5068: .  mat - the matrix

5070:    Output Parameters:
5071: +  v - the vector for storing the maximums
5072: -  idx - the indices of the column found for each row (or `NULL` if not needed)

5074:    Level: intermediate

5076:    Notes:
5077:     if a row is completely empty or has only 0.0 values then the idx[] value for that
5078:     row is 0 (the first column).

5080:     This code is only implemented for a couple of matrix formats.

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

5092:   if (!mat->cmap->N) {
5093:     PetscCall(VecSet(v, 0.0));
5094:     if (idx) {
5095:       PetscInt i, m = mat->rmap->n;
5096:       for (i = 0; i < m; i++) idx[i] = -1;
5097:     }
5098:   } else {
5099:     MatCheckPreallocated(mat, 1);
5100:     if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5101:     PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5102:   }
5103:   PetscCall(PetscObjectStateIncrease((PetscObject)v));
5104:   PetscFunctionReturn(PETSC_SUCCESS);
5105: }

5107: /*@
5108:    MatGetRowSum - Gets the sum of each row of the matrix

5110:    Logically or Neighborhood Collective

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

5115:    Output Parameter:
5116: .  v - the vector for storing the sum of rows

5118:    Level: intermediate

5120:    Notes:
5121:     This code is slow since it is not currently specialized for different formats

5123: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`
5124: @*/
5125: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5126: {
5127:   Vec ones;

5129:   PetscFunctionBegin;
5133:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5134:   MatCheckPreallocated(mat, 1);
5135:   PetscCall(MatCreateVecs(mat, &ones, NULL));
5136:   PetscCall(VecSet(ones, 1.));
5137:   PetscCall(MatMult(mat, ones, v));
5138:   PetscCall(VecDestroy(&ones));
5139:   PetscFunctionReturn(PETSC_SUCCESS);
5140: }

5142: /*@
5143:    MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5144:    when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)

5146:    Collective

5148:    Input Parameter:
5149: .  mat - the matrix to provide the transpose

5151:    Output Parameter:
5152: .  mat - 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

5154:    Level: advanced

5156:    Note:
5157:    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
5158:    routine allows bypassing that call.

5160: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5161: @*/
5162: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5163: {
5164:   PetscContainer  rB = NULL;
5165:   MatParentState *rb = NULL;

5167:   PetscFunctionBegin;
5168:   PetscCall(PetscNew(&rb));
5169:   rb->id    = ((PetscObject)mat)->id;
5170:   rb->state = 0;
5171:   PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5172:   PetscCall(PetscContainerCreate(PetscObjectComm((PetscObject)B), &rB));
5173:   PetscCall(PetscContainerSetPointer(rB, rb));
5174:   PetscCall(PetscContainerSetUserDestroy(rB, PetscContainerUserDestroyDefault));
5175:   PetscCall(PetscObjectCompose((PetscObject)B, "MatTransposeParent", (PetscObject)rB));
5176:   PetscCall(PetscObjectDereference((PetscObject)rB));
5177:   PetscFunctionReturn(PETSC_SUCCESS);
5178: }

5180: /*@
5181:    MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

5183:    Collective

5185:    Input Parameters:
5186: +  mat - the matrix to transpose
5187: -  reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5189:    Output Parameter:
5190: .  B - the transpose

5192:    Level: intermediate

5194:    Notes:
5195:      If you use `MAT_INPLACE_MATRIX` then you must pass in &mat for B

5197:      `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
5198:      transpose, call `MatTransposeSetPrecursor`(mat,B) before calling this routine.

5200:      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.

5202:      Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose, but don't need the storage to be changed.

5204:      If mat is unchanged from the last call this function returns immediately without recomputing the result

5206:      If you only need the symbolic transpose, and not the numerical values, use `MatTransposeSymbolic()`

5208: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5209:           `MatTransposeSymbolic()`, `MatCreateTranspose()`
5210: @*/
5211: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5212: {
5213:   PetscContainer  rB = NULL;
5214:   MatParentState *rb = NULL;

5216:   PetscFunctionBegin;
5219:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5220:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5221:   PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5222:   PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5223:   MatCheckPreallocated(mat, 1);
5224:   if (reuse == MAT_REUSE_MATRIX) {
5225:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5226:     PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5227:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5228:     PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5229:     if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5230:   }

5232:   PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5233:   if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5234:     PetscUseTypeMethod(mat, transpose, reuse, B);
5235:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5236:   }
5237:   PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));

5239:   if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5240:   if (reuse != MAT_INPLACE_MATRIX) {
5241:     PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5242:     PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5243:     rb->state        = ((PetscObject)mat)->state;
5244:     rb->nonzerostate = mat->nonzerostate;
5245:   }
5246:   PetscFunctionReturn(PETSC_SUCCESS);
5247: }

5249: /*@
5250:    MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.

5252:    Collective

5254:    Input Parameter:
5255: .  A - the matrix to transpose

5257:    Output Parameter:
5258: .  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
5259:       numerical portion.

5261:    Level: intermediate

5263:    Note:
5264:    This is not supported for many matrix types, use `MatTranspose()` in those cases

5266: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5267: @*/
5268: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5269: {
5270:   PetscFunctionBegin;
5273:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5274:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5275:   PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5276:   PetscUseTypeMethod(A, transposesymbolic, B);
5277:   PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));

5279:   PetscCall(MatTransposeSetPrecursor(A, *B));
5280:   PetscFunctionReturn(PETSC_SUCCESS);
5281: }

5283: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5284: {
5285:   PetscContainer  rB;
5286:   MatParentState *rb;

5288:   PetscFunctionBegin;
5291:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5292:   PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5293:   PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5294:   PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5295:   PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5296:   PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5297:   PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5298:   PetscFunctionReturn(PETSC_SUCCESS);
5299: }

5301: /*@
5302:    MatIsTranspose - Test whether a matrix is another one's transpose,
5303:         or its own, in which case it tests symmetry.

5305:    Collective

5307:    Input Parameters:
5308: +  A - the matrix to test
5309: .  B - the matrix to test against, this can equal the first parameter
5310: -  tol - tolerance, differences between entries smaller than this are counted as zero

5312:    Output Parameter:
5313: .  flg - the result

5315:    Level: intermediate

5317:    Notes:
5318:    Only available for `MATAIJ` matrices.

5320:    The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5321:    test involves parallel copies of the block-offdiagonal parts of the matrix.

5323: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5324: @*/
5325: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5326: {
5327:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5329:   PetscFunctionBegin;
5333:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5334:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5335:   *flg = PETSC_FALSE;
5336:   if (f && g) {
5337:     PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5338:     PetscCall((*f)(A, B, tol, flg));
5339:   } else {
5340:     MatType mattype;

5342:     PetscCall(MatGetType(f ? B : A, &mattype));
5343:     SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5344:   }
5345:   PetscFunctionReturn(PETSC_SUCCESS);
5346: }

5348: /*@
5349:    MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.

5351:    Collective

5353:    Input Parameters:
5354: +  mat - the matrix to transpose and complex conjugate
5355: -  reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`

5357:    Output Parameter:
5358: .  B - the Hermitian transpose

5360:    Level: intermediate

5362: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5363: @*/
5364: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5365: {
5366:   PetscFunctionBegin;
5367:   PetscCall(MatTranspose(mat, reuse, B));
5368: #if defined(PETSC_USE_COMPLEX)
5369:   PetscCall(MatConjugate(*B));
5370: #endif
5371:   PetscFunctionReturn(PETSC_SUCCESS);
5372: }

5374: /*@
5375:    MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,

5377:    Collective

5379:    Input Parameters:
5380: +  A - the matrix to test
5381: .  B - the matrix to test against, this can equal the first parameter
5382: -  tol - tolerance, differences between entries smaller than this are counted as zero

5384:    Output Parameter:
5385: .  flg - the result

5387:    Level: intermediate

5389:    Notes:
5390:    Only available for `MATAIJ` matrices.

5392:    The sequential algorithm
5393:    has a running time of the order of the number of nonzeros; the parallel
5394:    test involves parallel copies of the block-offdiagonal parts of the matrix.

5396: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5397: @*/
5398: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5399: {
5400:   PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);

5402:   PetscFunctionBegin;
5406:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5407:   PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5408:   if (f && g) {
5409:     PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5410:     PetscCall((*f)(A, B, tol, flg));
5411:   }
5412:   PetscFunctionReturn(PETSC_SUCCESS);
5413: }

5415: /*@
5416:    MatPermute - Creates a new matrix with rows and columns permuted from the
5417:    original.

5419:    Collective

5421:    Input Parameters:
5422: +  mat - the matrix to permute
5423: .  row - row permutation, each processor supplies only the permutation for its rows
5424: -  col - column permutation, each processor supplies only the permutation for its columns

5426:    Output Parameter:
5427: .  B - the permuted matrix

5429:    Level: advanced

5431:    Note:
5432:    The index sets map from row/col of permuted matrix to row/col of original matrix.
5433:    The index sets should be on the same communicator as mat and have the same local sizes.

5435:    Developer Note:
5436:      If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5437:      exploit the fact that row and col are permutations, consider implementing the
5438:      more general `MatCreateSubMatrix()` instead.

5440: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5441: @*/
5442: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5443: {
5444:   PetscFunctionBegin;
5450:   PetscCheckSameComm(mat, 1, row, 2);
5451:   if (row != col) PetscCheckSameComm(row, 2, col, 3);
5452:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5453:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5454:   PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5455:   MatCheckPreallocated(mat, 1);

5457:   if (mat->ops->permute) {
5458:     PetscUseTypeMethod(mat, permute, row, col, B);
5459:     PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5460:   } else {
5461:     PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5462:   }
5463:   PetscFunctionReturn(PETSC_SUCCESS);
5464: }

5466: /*@
5467:    MatEqual - Compares two matrices.

5469:    Collective

5471:    Input Parameters:
5472: +  A - the first matrix
5473: -  B - the second matrix

5475:    Output Parameter:
5476: .  flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.

5478:    Level: intermediate

5480: .seealso: [](ch_matrices), `Mat`
5481: @*/
5482: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5483: {
5484:   PetscFunctionBegin;
5490:   PetscCheckSameComm(A, 1, B, 2);
5491:   MatCheckPreallocated(A, 1);
5492:   MatCheckPreallocated(B, 2);
5493:   PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5494:   PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5495:   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,
5496:              B->cmap->N);
5497:   if (A->ops->equal && A->ops->equal == B->ops->equal) {
5498:     PetscUseTypeMethod(A, equal, B, flg);
5499:   } else {
5500:     PetscCall(MatMultEqual(A, B, 10, flg));
5501:   }
5502:   PetscFunctionReturn(PETSC_SUCCESS);
5503: }

5505: /*@
5506:    MatDiagonalScale - Scales a matrix on the left and right by diagonal
5507:    matrices that are stored as vectors.  Either of the two scaling
5508:    matrices can be `NULL`.

5510:    Collective

5512:    Input Parameters:
5513: +  mat - the matrix to be scaled
5514: .  l - the left scaling vector (or `NULL`)
5515: -  r - the right scaling vector (or `NULL`)

5517:    Level: intermediate

5519:    Note:
5520:    `MatDiagonalScale()` computes A = LAR, where
5521:    L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5522:    The L scales the rows of the matrix, the R scales the columns of the matrix.

5524: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5525: @*/
5526: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5527: {
5528:   PetscFunctionBegin;
5531:   if (l) {
5533:     PetscCheckSameComm(mat, 1, l, 2);
5534:   }
5535:   if (r) {
5537:     PetscCheckSameComm(mat, 1, r, 3);
5538:   }
5539:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5540:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5541:   MatCheckPreallocated(mat, 1);
5542:   if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);

5544:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5545:   PetscUseTypeMethod(mat, diagonalscale, l, r);
5546:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5547:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5548:   if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5549:   PetscFunctionReturn(PETSC_SUCCESS);
5550: }

5552: /*@
5553:     MatScale - Scales all elements of a matrix by a given number.

5555:     Logically Collective

5557:     Input Parameters:
5558: +   mat - the matrix to be scaled
5559: -   a  - the scaling value

5561:     Level: intermediate

5563: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5564: @*/
5565: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5566: {
5567:   PetscFunctionBegin;
5570:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5571:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5573:   MatCheckPreallocated(mat, 1);

5575:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5576:   if (a != (PetscScalar)1.0) {
5577:     PetscUseTypeMethod(mat, scale, a);
5578:     PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5579:   }
5580:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5581:   PetscFunctionReturn(PETSC_SUCCESS);
5582: }

5584: /*@
5585:    MatNorm - Calculates various norms of a matrix.

5587:    Collective

5589:    Input Parameters:
5590: +  mat - the matrix
5591: -  type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`

5593:    Output Parameter:
5594: .  nrm - the resulting norm

5596:    Level: intermediate

5598: .seealso: [](ch_matrices), `Mat`
5599: @*/
5600: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5601: {
5602:   PetscFunctionBegin;

5607:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5608:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5609:   MatCheckPreallocated(mat, 1);

5611:   PetscUseTypeMethod(mat, norm, type, nrm);
5612:   PetscFunctionReturn(PETSC_SUCCESS);
5613: }

5615: /*
5616:      This variable is used to prevent counting of MatAssemblyBegin() that
5617:    are called from within a MatAssemblyEnd().
5618: */
5619: static PetscInt MatAssemblyEnd_InUse = 0;
5620: /*@
5621:    MatAssemblyBegin - Begins assembling the matrix.  This routine should
5622:    be called after completing all calls to `MatSetValues()`.

5624:    Collective

5626:    Input Parameters:
5627: +  mat - the matrix
5628: -  type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5630:    Level: beginner

5632:    Notes:
5633:    `MatSetValues()` generally caches the values that belong to other MPI ranks.  The matrix is ready to
5634:    use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.

5636:    Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5637:    in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5638:    using the matrix.

5640:    ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5641:    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
5642:    a global collective operation requiring all processes that share the matrix.

5644:    Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5645:    out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5646:    before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.

5648: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5649: @*/
5650: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5651: {
5652:   PetscFunctionBegin;
5655:   MatCheckPreallocated(mat, 1);
5656:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix.\nDid you forget to call MatSetUnfactored()?");
5657:   if (mat->assembled) {
5658:     mat->was_assembled = PETSC_TRUE;
5659:     mat->assembled     = PETSC_FALSE;
5660:   }

5662:   if (!MatAssemblyEnd_InUse) {
5663:     PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5664:     PetscTryTypeMethod(mat, assemblybegin, type);
5665:     PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5666:   } else PetscTryTypeMethod(mat, assemblybegin, type);
5667:   PetscFunctionReturn(PETSC_SUCCESS);
5668: }

5670: /*@
5671:    MatAssembled - Indicates if a matrix has been assembled and is ready for
5672:      use; for example, in matrix-vector product.

5674:    Not Collective

5676:    Input Parameter:
5677: .  mat - the matrix

5679:    Output Parameter:
5680: .  assembled - `PETSC_TRUE` or `PETSC_FALSE`

5682:    Level: advanced

5684: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5685: @*/
5686: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5687: {
5688:   PetscFunctionBegin;
5691:   *assembled = mat->assembled;
5692:   PetscFunctionReturn(PETSC_SUCCESS);
5693: }

5695: /*@
5696:    MatAssemblyEnd - Completes assembling the matrix.  This routine should
5697:    be called after `MatAssemblyBegin()`.

5699:    Collective

5701:    Input Parameters:
5702: +  mat - the matrix
5703: -  type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`

5705:    Options Database Keys:
5706: +  -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5707: .  -mat_view ::ascii_info_detail - Prints more detailed info
5708: .  -mat_view - Prints matrix in ASCII format
5709: .  -mat_view ::ascii_matlab - Prints matrix in Matlab format
5710: .  -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5711: .  -display <name> - Sets display name (default is host)
5712: .  -draw_pause <sec> - Sets number of seconds to pause after display
5713: .  -mat_view socket - Sends matrix to socket, can be accessed from Matlab (See [Using MATLAB with PETSc](ch_matlab))
5714: .  -viewer_socket_machine <machine> - Machine to use for socket
5715: .  -viewer_socket_port <port> - Port number to use for socket
5716: -  -mat_view binary:filename[:append] - Save matrix to file in binary format

5718:    Level: beginner

5720: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5721: @*/
5722: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5723: {
5724:   static PetscInt inassm = 0;
5725:   PetscBool       flg    = PETSC_FALSE;

5727:   PetscFunctionBegin;

5731:   inassm++;
5732:   MatAssemblyEnd_InUse++;
5733:   if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5734:     PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5735:     PetscTryTypeMethod(mat, assemblyend, type);
5736:     PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5737:   } else PetscTryTypeMethod(mat, assemblyend, type);

5739:   /* Flush assembly is not a true assembly */
5740:   if (type != MAT_FLUSH_ASSEMBLY) {
5741:     if (mat->num_ass) {
5742:       if (!mat->symmetry_eternal) {
5743:         mat->symmetric = PETSC_BOOL3_UNKNOWN;
5744:         mat->hermitian = PETSC_BOOL3_UNKNOWN;
5745:       }
5746:       if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5747:       if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5748:     }
5749:     mat->num_ass++;
5750:     mat->assembled        = PETSC_TRUE;
5751:     mat->ass_nonzerostate = mat->nonzerostate;
5752:   }

5754:   mat->insertmode = NOT_SET_VALUES;
5755:   MatAssemblyEnd_InUse--;
5756:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5757:   if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5758:     PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));

5760:     if (mat->checksymmetryonassembly) {
5761:       PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5762:       if (flg) {
5763:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5764:       } else {
5765:         PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5766:       }
5767:     }
5768:     if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5769:   }
5770:   inassm--;
5771:   PetscFunctionReturn(PETSC_SUCCESS);
5772: }

5774: /*@
5775:    MatSetOption - Sets a parameter option for a matrix. Some options
5776:    may be specific to certain storage formats.  Some options
5777:    determine how values will be inserted (or added). Sorted,
5778:    row-oriented input will generally assemble the fastest. The default
5779:    is row-oriented.

5781:    Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`

5783:    Input Parameters:
5784: +  mat - the matrix
5785: .  option - the option, one of those listed below (and possibly others),
5786: -  flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5788:   Options Describing Matrix Structure:
5789: +    `MAT_SPD` - symmetric positive definite
5790: .    `MAT_SYMMETRIC` - symmetric in terms of both structure and value
5791: .    `MAT_HERMITIAN` - transpose is the complex conjugation
5792: .    `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
5793: .    `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5794: .    `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5795: -    `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix

5797:    These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5798:    do not need to be computed (usually at a high cost)

5800:    Options For Use with `MatSetValues()`:
5801:    Insert a logically dense subblock, which can be
5802: .    `MAT_ROW_ORIENTED` - row-oriented (default)

5804:    These options reflect the data you pass in with `MatSetValues()`; it has
5805:    nothing to do with how the data is stored internally in the matrix
5806:    data structure.

5808:    When (re)assembling a matrix, we can restrict the input for
5809:    efficiency/debugging purposes.  These options include
5810: +    `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
5811: .    `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
5812: .    `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
5813: .    `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
5814: .    `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
5815: .    `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
5816:         any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5817:         performance for very large process counts.
5818: -    `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
5819:         of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5820:         functions, instead sending only neighbor messages.

5822:    Level: intermediate

5824:    Notes:
5825:    Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and  `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!

5827:    Some options are relevant only for particular matrix types and
5828:    are thus ignored by others.  Other options are not supported by
5829:    certain matrix types and will generate an error message if set.

5831:    If using Fortran to compute a matrix, one may need to
5832:    use the column-oriented option (or convert to the row-oriented
5833:    format).

5835:    `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5836:    that would generate a new entry in the nonzero structure is instead
5837:    ignored.  Thus, if memory has not already been allocated for this particular
5838:    data, then the insertion is ignored. For dense matrices, in which
5839:    the entire array is allocated, no entries are ever ignored.
5840:    Set after the first `MatAssemblyEnd()`. If this option is set then the MatAssemblyBegin/End() processes has one less global reduction

5842:    `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
5843:    that would generate a new entry in the nonzero structure instead produces
5844:    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

5846:    `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
5847:    that would generate a new entry that has not been preallocated will
5848:    instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
5849:    only.) This is a useful flag when debugging matrix memory preallocation.
5850:    If this option is set then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction

5852:    `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
5853:    other processors should be dropped, rather than stashed.
5854:    This is useful if you know that the "owning" processor is also
5855:    always generating the correct matrix entries, so that PETSc need
5856:    not transfer duplicate entries generated on another processor.

5858:    `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
5859:    searches during matrix assembly. When this flag is set, the hash table
5860:    is created during the first matrix assembly. This hash table is
5861:    used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
5862:    to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
5863:    should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
5864:    supported by `MATMPIBAIJ` format only.

5866:    `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
5867:    are kept in the nonzero structure

5869:    `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
5870:    a zero location in the matrix

5872:    `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types

5874:    `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
5875:         zero row routines and thus improves performance for very large process counts.

5877:    `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
5878:         part of the matrix (since they should match the upper triangular part).

5880:    `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
5881:                      single call to `MatSetValues()`, preallocation is perfect, row oriented, `INSERT_VALUES` is used. Common
5882:                      with finite difference schemes with non-periodic boundary conditions.

5884:    Developer Note:
5885:    `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
5886:    places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRIC` or `MAT_SPD` would need to be changed back
5887:    to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
5888:    not changed.

5890: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
5891: @*/
5892: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
5893: {
5894:   PetscFunctionBegin;
5896:   if (op > 0) {
5899:   }

5901:   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);

5903:   switch (op) {
5904:   case MAT_FORCE_DIAGONAL_ENTRIES:
5905:     mat->force_diagonals = flg;
5906:     PetscFunctionReturn(PETSC_SUCCESS);
5907:   case MAT_NO_OFF_PROC_ENTRIES:
5908:     mat->nooffprocentries = flg;
5909:     PetscFunctionReturn(PETSC_SUCCESS);
5910:   case MAT_SUBSET_OFF_PROC_ENTRIES:
5911:     mat->assembly_subset = flg;
5912:     if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
5913: #if !defined(PETSC_HAVE_MPIUNI)
5914:       PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
5915: #endif
5916:       mat->stash.first_assembly_done = PETSC_FALSE;
5917:     }
5918:     PetscFunctionReturn(PETSC_SUCCESS);
5919:   case MAT_NO_OFF_PROC_ZERO_ROWS:
5920:     mat->nooffproczerorows = flg;
5921:     PetscFunctionReturn(PETSC_SUCCESS);
5922:   case MAT_SPD:
5923:     if (flg) {
5924:       mat->spd                    = PETSC_BOOL3_TRUE;
5925:       mat->symmetric              = PETSC_BOOL3_TRUE;
5926:       mat->structurally_symmetric = PETSC_BOOL3_TRUE;
5927:     } else {
5928:       mat->spd = PETSC_BOOL3_FALSE;
5929:     }
5930:     break;
5931:   case MAT_SYMMETRIC:
5932:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5933:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
5934: #if !defined(PETSC_USE_COMPLEX)
5935:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5936: #endif
5937:     break;
5938:   case MAT_HERMITIAN:
5939:     mat->hermitian = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5940:     if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
5941: #if !defined(PETSC_USE_COMPLEX)
5942:     mat->symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5943: #endif
5944:     break;
5945:   case MAT_STRUCTURALLY_SYMMETRIC:
5946:     mat->structurally_symmetric = flg ? PETSC_BOOL3_TRUE : PETSC_BOOL3_FALSE;
5947:     break;
5948:   case MAT_SYMMETRY_ETERNAL:
5949:     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");
5950:     mat->symmetry_eternal = flg;
5951:     if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
5952:     break;
5953:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
5954:     PetscCheck(mat->structurally_symmetric != PETSC_BOOL3_UNKNOWN, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_STRUCTURAL_SYMMETRY_ETERNAL without first setting MAT_STRUCTURAL_SYMMETRIC to true or false");
5955:     mat->structural_symmetry_eternal = flg;
5956:     break;
5957:   case MAT_SPD_ETERNAL:
5958:     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");
5959:     mat->spd_eternal = flg;
5960:     if (flg) {
5961:       mat->structural_symmetry_eternal = PETSC_TRUE;
5962:       mat->symmetry_eternal            = PETSC_TRUE;
5963:     }
5964:     break;
5965:   case MAT_STRUCTURE_ONLY:
5966:     mat->structure_only = flg;
5967:     break;
5968:   case MAT_SORTED_FULL:
5969:     mat->sortedfull = flg;
5970:     break;
5971:   default:
5972:     break;
5973:   }
5974:   PetscTryTypeMethod(mat, setoption, op, flg);
5975:   PetscFunctionReturn(PETSC_SUCCESS);
5976: }

5978: /*@
5979:    MatGetOption - Gets a parameter option that has been set for a matrix.

5981:    Logically Collective

5983:    Input Parameters:
5984: +  mat - the matrix
5985: -  option - the option, this only responds to certain options, check the code for which ones

5987:    Output Parameter:
5988: .  flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)

5990:    Level: intermediate

5992:     Notes:
5993:     Can only be called after `MatSetSizes()` and `MatSetType()` have been set.

5995:     Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
5996:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`

5998: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
5999:     `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6000: @*/
6001: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6002: {
6003:   PetscFunctionBegin;

6007:   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);
6008:   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()");

6010:   switch (op) {
6011:   case MAT_NO_OFF_PROC_ENTRIES:
6012:     *flg = mat->nooffprocentries;
6013:     break;
6014:   case MAT_NO_OFF_PROC_ZERO_ROWS:
6015:     *flg = mat->nooffproczerorows;
6016:     break;
6017:   case MAT_SYMMETRIC:
6018:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6019:     break;
6020:   case MAT_HERMITIAN:
6021:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6022:     break;
6023:   case MAT_STRUCTURALLY_SYMMETRIC:
6024:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6025:     break;
6026:   case MAT_SPD:
6027:     SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6028:     break;
6029:   case MAT_SYMMETRY_ETERNAL:
6030:     *flg = mat->symmetry_eternal;
6031:     break;
6032:   case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6033:     *flg = mat->symmetry_eternal;
6034:     break;
6035:   default:
6036:     break;
6037:   }
6038:   PetscFunctionReturn(PETSC_SUCCESS);
6039: }

6041: /*@
6042:    MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
6043:    this routine retains the old nonzero structure.

6045:    Logically Collective

6047:    Input Parameter:
6048: .  mat - the matrix

6050:    Level: intermediate

6052:    Note:
6053:     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.
6054:    See the Performance chapter of the users manual for information on preallocating matrices.

6056: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6057: @*/
6058: PetscErrorCode MatZeroEntries(Mat mat)
6059: {
6060:   PetscFunctionBegin;
6063:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6064:   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");
6065:   MatCheckPreallocated(mat, 1);

6067:   PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6068:   PetscUseTypeMethod(mat, zeroentries);
6069:   PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6070:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6071:   PetscFunctionReturn(PETSC_SUCCESS);
6072: }

6074: /*@
6075:    MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6076:    of a set of rows and columns of a matrix.

6078:    Collective

6080:    Input Parameters:
6081: +  mat - the matrix
6082: .  numRows - the number of rows/columns to zero
6083: .  rows - the global row indices
6084: .  diag - value put in the diagonal of the eliminated rows
6085: .  x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6086: -  b - optional vector of the right hand side, that will be adjusted by provided solution entries

6088:    Level: intermediate

6090:    Notes:
6091:    This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6093:    For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6094:    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

6096:    If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6097:    Krylov method to take advantage of the known solution on the zeroed rows.

6099:    For the parallel case, all processes that share the matrix (i.e.,
6100:    those in the communicator used for matrix creation) MUST call this
6101:    routine, regardless of whether any rows being zeroed are owned by
6102:    them.

6104:    Unlike `MatZeroRows()` this does not change the nonzero structure of the matrix, it merely zeros those entries in the matrix.

6106:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6107:    list only rows local to itself).

6109:    The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.

6111: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6112:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6113: @*/
6114: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6115: {
6116:   PetscFunctionBegin;
6120:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6121:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6122:   MatCheckPreallocated(mat, 1);

6124:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6125:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6126:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6127:   PetscFunctionReturn(PETSC_SUCCESS);
6128: }

6130: /*@
6131:    MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6132:    of a set of rows and columns of a matrix.

6134:    Collective

6136:    Input Parameters:
6137: +  mat - the matrix
6138: .  is - the rows to zero
6139: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6140: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6141: -  b - optional vector of right hand side, that will be adjusted by provided solution

6143:    Level: intermediate

6145:    Note:
6146:    See `MatZeroRowsColumns()` for details on how this routine operates.

6148: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6149:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6150: @*/
6151: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6152: {
6153:   PetscInt        numRows;
6154:   const PetscInt *rows;

6156:   PetscFunctionBegin;
6161:   PetscCall(ISGetLocalSize(is, &numRows));
6162:   PetscCall(ISGetIndices(is, &rows));
6163:   PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6164:   PetscCall(ISRestoreIndices(is, &rows));
6165:   PetscFunctionReturn(PETSC_SUCCESS);
6166: }

6168: /*@
6169:    MatZeroRows - Zeros all entries (except possibly the main diagonal)
6170:    of a set of rows of a matrix.

6172:    Collective

6174:    Input Parameters:
6175: +  mat - the matrix
6176: .  numRows - the number of rows to zero
6177: .  rows - the global row indices
6178: .  diag - value put in the diagonal of the zeroed rows
6179: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6180: -  b - optional vector of right hand side, that will be adjusted by provided solution entries

6182:    Level: intermediate

6184:    Notes:
6185:    This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.

6187:    For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.

6189:    If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6190:    Krylov method to take advantage of the known solution on the zeroed rows.

6192:    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)
6193:    from the matrix.

6195:    Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6196:    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
6197:    formats this does not alter the nonzero structure.

6199:    If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6200:    of the matrix is not changed the values are
6201:    merely zeroed.

6203:    The user can set a value in the diagonal entry (or for the `MATAIJ` format
6204:    formats can optionally remove the main diagonal entry from the
6205:    nonzero structure as well, by passing 0.0 as the final argument).

6207:    For the parallel case, all processes that share the matrix (i.e.,
6208:    those in the communicator used for matrix creation) MUST call this
6209:    routine, regardless of whether any rows being zeroed are owned by
6210:    them.

6212:    Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6213:    list only rows local to itself).

6215:    You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6216:    owns that are to be zeroed. This saves a global synchronization in the implementation.

6218: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6219:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`
6220: @*/
6221: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6222: {
6223:   PetscFunctionBegin;
6227:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6228:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6229:   MatCheckPreallocated(mat, 1);

6231:   PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6232:   PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6233:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6234:   PetscFunctionReturn(PETSC_SUCCESS);
6235: }

6237: /*@
6238:    MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6239:    of a set of rows of a matrix.

6241:    Collective

6243:    Input Parameters:
6244: +  mat - the matrix
6245: .  is - index set of rows to remove (if `NULL` then no row is removed)
6246: .  diag - value put in all diagonals of eliminated rows
6247: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6248: -  b - optional vector of right hand side, that will be adjusted by provided solution

6250:    Level: intermediate

6252:    Note:
6253:    See `MatZeroRows()` for details on how this routine operates.

6255: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6256:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6257: @*/
6258: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6259: {
6260:   PetscInt        numRows = 0;
6261:   const PetscInt *rows    = NULL;

6263:   PetscFunctionBegin;
6266:   if (is) {
6268:     PetscCall(ISGetLocalSize(is, &numRows));
6269:     PetscCall(ISGetIndices(is, &rows));
6270:   }
6271:   PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6272:   if (is) PetscCall(ISRestoreIndices(is, &rows));
6273:   PetscFunctionReturn(PETSC_SUCCESS);
6274: }

6276: /*@
6277:    MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6278:    of a set of rows of a matrix. These rows must be local to the process.

6280:    Collective

6282:    Input Parameters:
6283: +  mat - the matrix
6284: .  numRows - the number of rows to remove
6285: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
6286: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6287: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6288: -  b - optional vector of right hand side, that will be adjusted by provided solution

6290:    Level: intermediate

6292:    Notes:
6293:    See `MatZeroRows()` for details on how this routine operates.

6295:    The grid coordinates are across the entire grid, not just the local portion

6297:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6298:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6299:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6300:    `DM_BOUNDARY_PERIODIC` boundary type.

6302:    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
6303:    a single value per point) you can skip filling those indices.

6305:    Fortran Note:
6306:    `idxm` and `idxn` should be declared as
6307: $     MatStencil idxm(4, m)
6308:    and the values inserted using
6309: .vb
6310:     idxm(MatStencil_i, 1) = i
6311:     idxm(MatStencil_j, 1) = j
6312:     idxm(MatStencil_k, 1) = k
6313:     idxm(MatStencil_c, 1) = c
6314:    etc
6315: .ve

6317: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsl()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6318:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6319: @*/
6320: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6321: {
6322:   PetscInt  dim    = mat->stencil.dim;
6323:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6324:   PetscInt *dims   = mat->stencil.dims + 1;
6325:   PetscInt *starts = mat->stencil.starts;
6326:   PetscInt *dxm    = (PetscInt *)rows;
6327:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6329:   PetscFunctionBegin;

6334:   PetscCall(PetscMalloc1(numRows, &jdxm));
6335:   for (i = 0; i < numRows; ++i) {
6336:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6337:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6338:     /* Local index in X dir */
6339:     tmp = *dxm++ - starts[0];
6340:     /* Loop over remaining dimensions */
6341:     for (j = 0; j < dim - 1; ++j) {
6342:       /* If nonlocal, set index to be negative */
6343:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6344:       /* Update local index */
6345:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6346:     }
6347:     /* Skip component slot if necessary */
6348:     if (mat->stencil.noc) dxm++;
6349:     /* Local row number */
6350:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6351:   }
6352:   PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6353:   PetscCall(PetscFree(jdxm));
6354:   PetscFunctionReturn(PETSC_SUCCESS);
6355: }

6357: /*@
6358:    MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6359:    of a set of rows and columns of a matrix.

6361:    Collective

6363:    Input Parameters:
6364: +  mat - the matrix
6365: .  numRows - the number of rows/columns to remove
6366: .  rows - the grid coordinates (and component number when dof > 1) for matrix rows
6367: .  diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6368: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6369: -  b - optional vector of right hand side, that will be adjusted by provided solution

6371:    Level: intermediate

6373:    Notes:
6374:    See `MatZeroRowsColumns()` for details on how this routine operates.

6376:    The grid coordinates are across the entire grid, not just the local portion

6378:    For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6379:    obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6380:    etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6381:    `DM_BOUNDARY_PERIODIC` boundary type.

6383:    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
6384:    a single value per point) you can skip filling those indices.

6386:    Fortran Note:
6387:    `idxm` and `idxn` should be declared as
6388: $     MatStencil idxm(4, m)
6389:    and the values inserted using
6390: .vb
6391:     idxm(MatStencil_i, 1) = i
6392:     idxm(MatStencil_j, 1) = j
6393:     idxm(MatStencil_k, 1) = k
6394:     idxm(MatStencil_c, 1) = c
6395:     etc
6396: .ve

6398: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6399:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6400: @*/
6401: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6402: {
6403:   PetscInt  dim    = mat->stencil.dim;
6404:   PetscInt  sdim   = dim - (1 - (PetscInt)mat->stencil.noc);
6405:   PetscInt *dims   = mat->stencil.dims + 1;
6406:   PetscInt *starts = mat->stencil.starts;
6407:   PetscInt *dxm    = (PetscInt *)rows;
6408:   PetscInt *jdxm, i, j, tmp, numNewRows = 0;

6410:   PetscFunctionBegin;

6415:   PetscCall(PetscMalloc1(numRows, &jdxm));
6416:   for (i = 0; i < numRows; ++i) {
6417:     /* Skip unused dimensions (they are ordered k, j, i, c) */
6418:     for (j = 0; j < 3 - sdim; ++j) dxm++;
6419:     /* Local index in X dir */
6420:     tmp = *dxm++ - starts[0];
6421:     /* Loop over remaining dimensions */
6422:     for (j = 0; j < dim - 1; ++j) {
6423:       /* If nonlocal, set index to be negative */
6424:       if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_MIN_INT;
6425:       /* Update local index */
6426:       else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6427:     }
6428:     /* Skip component slot if necessary */
6429:     if (mat->stencil.noc) dxm++;
6430:     /* Local row number */
6431:     if (tmp >= 0) jdxm[numNewRows++] = tmp;
6432:   }
6433:   PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6434:   PetscCall(PetscFree(jdxm));
6435:   PetscFunctionReturn(PETSC_SUCCESS);
6436: }

6438: /*@C
6439:    MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6440:    of a set of rows of a matrix; using local numbering of rows.

6442:    Collective

6444:    Input Parameters:
6445: +  mat - the matrix
6446: .  numRows - the number of rows to remove
6447: .  rows - the local row indices
6448: .  diag - value put in all diagonals of eliminated rows
6449: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6450: -  b - optional vector of right hand side, that will be adjusted by provided solution

6452:    Level: intermediate

6454:    Notes:
6455:    Before calling `MatZeroRowsLocal()`, the user must first set the
6456:    local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.

6458:    See `MatZeroRows()` for details on how this routine operates.

6460: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6461:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6462: @*/
6463: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6464: {
6465:   PetscFunctionBegin;
6469:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6470:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6471:   MatCheckPreallocated(mat, 1);

6473:   if (mat->ops->zerorowslocal) {
6474:     PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6475:   } else {
6476:     IS              is, newis;
6477:     const PetscInt *newRows;

6479:     PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6480:     PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6481:     PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6482:     PetscCall(ISGetIndices(newis, &newRows));
6483:     PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6484:     PetscCall(ISRestoreIndices(newis, &newRows));
6485:     PetscCall(ISDestroy(&newis));
6486:     PetscCall(ISDestroy(&is));
6487:   }
6488:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6489:   PetscFunctionReturn(PETSC_SUCCESS);
6490: }

6492: /*@
6493:    MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6494:    of a set of rows of a matrix; using local numbering of rows.

6496:    Collective

6498:    Input Parameters:
6499: +  mat - the matrix
6500: .  is - index set of rows to remove
6501: .  diag - value put in all diagonals of eliminated rows
6502: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6503: -  b - optional vector of right hand side, that will be adjusted by provided solution

6505:    Level: intermediate

6507:    Notes:
6508:    Before calling `MatZeroRowsLocalIS()`, the user must first set the
6509:    local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6511:    See `MatZeroRows()` for details on how this routine operates.

6513: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6514:           `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6515: @*/
6516: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6517: {
6518:   PetscInt        numRows;
6519:   const PetscInt *rows;

6521:   PetscFunctionBegin;
6525:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6526:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6527:   MatCheckPreallocated(mat, 1);

6529:   PetscCall(ISGetLocalSize(is, &numRows));
6530:   PetscCall(ISGetIndices(is, &rows));
6531:   PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6532:   PetscCall(ISRestoreIndices(is, &rows));
6533:   PetscFunctionReturn(PETSC_SUCCESS);
6534: }

6536: /*@
6537:    MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6538:    of a set of rows and columns of a matrix; using local numbering of rows.

6540:    Collective

6542:    Input Parameters:
6543: +  mat - the matrix
6544: .  numRows - the number of rows to remove
6545: .  rows - the global row indices
6546: .  diag - value put in all diagonals of eliminated rows
6547: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6548: -  b - optional vector of right hand side, that will be adjusted by provided solution

6550:    Level: intermediate

6552:    Notes:
6553:    Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6554:    local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6556:    See `MatZeroRowsColumns()` for details on how this routine operates.

6558: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6559:           `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6560: @*/
6561: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6562: {
6563:   IS              is, newis;
6564:   const PetscInt *newRows;

6566:   PetscFunctionBegin;
6570:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6571:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6572:   MatCheckPreallocated(mat, 1);

6574:   PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6575:   PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6576:   PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6577:   PetscCall(ISGetIndices(newis, &newRows));
6578:   PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6579:   PetscCall(ISRestoreIndices(newis, &newRows));
6580:   PetscCall(ISDestroy(&newis));
6581:   PetscCall(ISDestroy(&is));
6582:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6583:   PetscFunctionReturn(PETSC_SUCCESS);
6584: }

6586: /*@
6587:    MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6588:    of a set of rows and columns of a matrix; using local numbering of rows.

6590:    Collective

6592:    Input Parameters:
6593: +  mat - the matrix
6594: .  is - index set of rows to remove
6595: .  diag - value put in all diagonals of eliminated rows
6596: .  x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6597: -  b - optional vector of right hand side, that will be adjusted by provided solution

6599:    Level: intermediate

6601:    Notes:
6602:    Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6603:    local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.

6605:    See `MatZeroRowsColumns()` for details on how this routine operates.

6607: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6608:           `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6609: @*/
6610: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6611: {
6612:   PetscInt        numRows;
6613:   const PetscInt *rows;

6615:   PetscFunctionBegin;
6619:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6620:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6621:   MatCheckPreallocated(mat, 1);

6623:   PetscCall(ISGetLocalSize(is, &numRows));
6624:   PetscCall(ISGetIndices(is, &rows));
6625:   PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6626:   PetscCall(ISRestoreIndices(is, &rows));
6627:   PetscFunctionReturn(PETSC_SUCCESS);
6628: }

6630: /*@C
6631:    MatGetSize - Returns the numbers of rows and columns in a matrix.

6633:    Not Collective

6635:    Input Parameter:
6636: .  mat - the matrix

6638:    Output Parameters:
6639: +  m - the number of global rows
6640: -  n - the number of global columns

6642:    Level: beginner

6644:    Note:
6645:    Both output parameters can be `NULL` on input.

6647: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6648: @*/
6649: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6650: {
6651:   PetscFunctionBegin;
6653:   if (m) *m = mat->rmap->N;
6654:   if (n) *n = mat->cmap->N;
6655:   PetscFunctionReturn(PETSC_SUCCESS);
6656: }

6658: /*@C
6659:    MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6660:    of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.

6662:    Not Collective

6664:    Input Parameter:
6665: .  mat - the matrix

6667:    Output Parameters:
6668: +  m - the number of local rows, use `NULL` to not obtain this value
6669: -  n - the number of local columns, use `NULL` to not obtain this value

6671:    Level: beginner

6673: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6674: @*/
6675: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6676: {
6677:   PetscFunctionBegin;
6681:   if (m) *m = mat->rmap->n;
6682:   if (n) *n = mat->cmap->n;
6683:   PetscFunctionReturn(PETSC_SUCCESS);
6684: }

6686: /*@C
6687:    MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a vector one multiplies this matrix by that are owned by
6688:    this processor. (The columns of the "diagonal block" for most sparse matrix formats). See [Matrix Layouts](sec_matlayout) for details on matrix layouts.

6690:    Not Collective, unless matrix has not been allocated, then collective

6692:    Input Parameter:
6693: .  mat - the matrix

6695:    Output Parameters:
6696: +  m - the global index of the first local column, use `NULL` to not obtain this value
6697: -  n - one more than the global index of the last local column, use `NULL` to not obtain this value

6699:    Level: developer

6701: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
6702: @*/
6703: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6704: {
6705:   PetscFunctionBegin;
6710:   MatCheckPreallocated(mat, 1);
6711:   if (m) *m = mat->cmap->rstart;
6712:   if (n) *n = mat->cmap->rend;
6713:   PetscFunctionReturn(PETSC_SUCCESS);
6714: }

6716: /*@C
6717:    MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6718:    this MPI rank. For all matrices  it returns the range of matrix rows associated with rows of a vector that would contain the result of a matrix
6719:    vector product with this matrix. See [Matrix Layouts](sec_matlayout) for details on matrix layouts

6721:    Not Collective

6723:    Input Parameter:
6724: .  mat - the matrix

6726:    Output Parameters:
6727: +  m - the global index of the first local row, use `NULL` to not obtain this value
6728: -  n - one more than the global index of the last local row, use `NULL` to not obtain this value

6730:    Level: beginner

6732:    Note:
6733:   This function requires that the matrix be preallocated. If you have not preallocated, consider using
6734:   `PetscSplitOwnership`(`MPI_Comm` comm, `PetscInt` *n, `PetscInt` *N)
6735:   and then `MPI_Scan()` to calculate prefix sums of the local sizes.

6737: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`,
6738:           `PetscLayout`
6739: @*/
6740: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6741: {
6742:   PetscFunctionBegin;
6747:   MatCheckPreallocated(mat, 1);
6748:   if (m) *m = mat->rmap->rstart;
6749:   if (n) *n = mat->rmap->rend;
6750:   PetscFunctionReturn(PETSC_SUCCESS);
6751: }

6753: /*@C
6754:    MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6755:    each process. For all matrices  it returns the ranges of matrix rows associated with rows of a vector that would contain the result of a matrix
6756:    vector product with this matrix. See [Matrix Layouts](sec_matlayout) for details on matrix layouts

6758:    Not Collective, unless matrix has not been allocated

6760:    Input Parameter:
6761: .  mat - the matrix

6763:    Output Parameter:
6764: .  ranges - start of each processors portion plus one more than the total length at the end

6766:    Level: beginner

6768: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`
6769: @*/
6770: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt **ranges)
6771: {
6772:   PetscFunctionBegin;
6775:   MatCheckPreallocated(mat, 1);
6776:   PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6777:   PetscFunctionReturn(PETSC_SUCCESS);
6778: }

6780: /*@C
6781:    MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a vector one multiplies this vector by that are owned by
6782:    each processor. (The columns of the "diagonal blocks", for most sparse matrix formats). See [Matrix Layouts](sec_matlayout) for details on matrix layouts.

6784:    Not Collective, unless matrix has not been allocated

6786:    Input Parameter:
6787: .  mat - the matrix

6789:    Output Parameter:
6790: .  ranges - start of each processors portion plus one more then the total length at the end

6792:    Level: beginner

6794: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`
6795: @*/
6796: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt **ranges)
6797: {
6798:   PetscFunctionBegin;
6801:   MatCheckPreallocated(mat, 1);
6802:   PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
6803:   PetscFunctionReturn(PETSC_SUCCESS);
6804: }

6806: /*@C
6807:    MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets. For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this
6808:    corresponds to values returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and `MATSCALAPACK` the ownership
6809:    is more complicated. See [Matrix Layouts](sec_matlayout) for details on matrix layouts.

6811:    Not Collective

6813:    Input Parameter:
6814: .  A - matrix

6816:    Output Parameters:
6817: +  rows - rows in which this process owns elements, , use `NULL` to not obtain this value
6818: -  cols - columns in which this process owns elements, use `NULL` to not obtain this value

6820:    Level: intermediate

6822: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatSetValues()`, ``MATELEMENTAL``, ``MATSCALAPACK``
6823: @*/
6824: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
6825: {
6826:   PetscErrorCode (*f)(Mat, IS *, IS *);

6828:   PetscFunctionBegin;
6829:   MatCheckPreallocated(A, 1);
6830:   PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
6831:   if (f) {
6832:     PetscCall((*f)(A, rows, cols));
6833:   } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
6834:     if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
6835:     if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
6836:   }
6837:   PetscFunctionReturn(PETSC_SUCCESS);
6838: }

6840: /*@C
6841:    MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
6842:    Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
6843:    to complete the factorization.

6845:    Collective

6847:    Input Parameters:
6848: +  fact - the factorized matrix obtained with `MatGetFactor()`
6849: .  mat - the matrix
6850: .  row - row permutation
6851: .  col - column permutation
6852: -  info - structure containing
6853: .vb
6854:       levels - number of levels of fill.
6855:       expected fill - as ratio of original fill.
6856:       1 or 0 - indicating force fill on diagonal (improves robustness for matrices
6857:                 missing diagonal entries)
6858: .ve

6860:    Level: developer

6862:    Notes:
6863:    See [Matrix Factorization](sec_matfactor) for additional information.

6865:    Most users should employ the `KSP` interface for linear solvers
6866:    instead of working directly with matrix algebra routines such as this.
6867:    See, e.g., `KSPCreate()`.

6869:    Uses the definition of level of fill as in Y. Saad, 2003

6871:    Developer Note:
6872:    The Fortran interface is not autogenerated as the
6873:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

6875:    References:
6876: .  * - Y. Saad, Iterative methods for sparse linear systems Philadelphia: Society for Industrial and Applied Mathematics, 2003

6878: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
6879:           `MatGetOrdering()`, `MatFactorInfo`
6880: @*/
6881: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
6882: {
6883:   PetscFunctionBegin;
6890:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
6891:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
6892:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6893:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6894:   MatCheckPreallocated(mat, 2);

6896:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
6897:   PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
6898:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
6899:   PetscFunctionReturn(PETSC_SUCCESS);
6900: }

6902: /*@C
6903:    MatICCFactorSymbolic - Performs symbolic incomplete
6904:    Cholesky factorization for a symmetric matrix.  Use
6905:    `MatCholeskyFactorNumeric()` to complete the factorization.

6907:    Collective

6909:    Input Parameters:
6910: +  fact - the factorized matrix obtained with `MatGetFactor()`
6911: .  mat - the matrix to be factored
6912: .  perm - row and column permutation
6913: -  info - structure containing
6914: .vb
6915:       levels - number of levels of fill.
6916:       expected fill - as ratio of original fill.
6917: .ve

6919:    Level: developer

6921:    Notes:
6922:    Most users should employ the `KSP` interface for linear solvers
6923:    instead of working directly with matrix algebra routines such as this.
6924:    See, e.g., `KSPCreate()`.

6926:    This uses the definition of level of fill as in Y. Saad, 2003

6928:    Developer Note:
6929:    The Fortran interface is not autogenerated as the
6930:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

6932:    References:
6933: .  * - Y. Saad, Iterative methods for sparse linear systems Philadelphia: Society for Industrial and Applied Mathematics, 2003

6935: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
6936: @*/
6937: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
6938: {
6939:   PetscFunctionBegin;
6945:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6946:   PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
6947:   PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
6948:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6949:   MatCheckPreallocated(mat, 2);

6951:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
6952:   PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
6953:   if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
6954:   PetscFunctionReturn(PETSC_SUCCESS);
6955: }

6957: /*@C
6958:    MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
6959:    points to an array of valid matrices, they may be reused to store the new
6960:    submatrices.

6962:    Collective

6964:    Input Parameters:
6965: +  mat - the matrix
6966: .  n   - the number of submatrixes to be extracted (on this processor, may be zero)
6967: .  irow - index set of rows to extract
6968: .  icol - index set of columns to extract
6969: -  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

6971:    Output Parameter:
6972: .  submat - the array of submatrices

6974:    Level: advanced

6976:    Notes:
6977:    `MatCreateSubMatrices()` can extract ONLY sequential submatrices
6978:    (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
6979:    to extract a parallel submatrix.

6981:    Some matrix types place restrictions on the row and column
6982:    indices, such as that they be sorted or that they be equal to each other.

6984:    The index sets may not have duplicate entries.

6986:    When extracting submatrices from a parallel matrix, each processor can
6987:    form a different submatrix by setting the rows and columns of its
6988:    individual index sets according to the local submatrix desired.

6990:    When finished using the submatrices, the user should destroy
6991:    them with `MatDestroySubMatrices()`.

6993:    `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
6994:    original matrix has not changed from that last call to `MatCreateSubMatrices()`.

6996:    This routine creates the matrices in submat; you should NOT create them before
6997:    calling it. It also allocates the array of matrix pointers submat.

6999:    For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7000:    request one row/column in a block, they must request all rows/columns that are in
7001:    that block. For example, if the block size is 2 you cannot request just row 0 and
7002:    column 0.

7004:    Fortran Note:
7005:    The Fortran interface is slightly different from that given below; it
7006:    requires one to pass in as `submat` a `Mat` (integer) array of size at least n+1.

7008: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7009: @*/
7010: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7011: {
7012:   PetscInt  i;
7013:   PetscBool eq;

7015:   PetscFunctionBegin;
7018:   if (n) {
7023:   }
7025:   if (n && scall == MAT_REUSE_MATRIX) {
7028:   }
7029:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7030:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7031:   MatCheckPreallocated(mat, 1);
7032:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7033:   PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7034:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7035:   for (i = 0; i < n; i++) {
7036:     (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7037:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7038:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7039: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7040:     if (mat->boundtocpu && mat->bindingpropagates) {
7041:       PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7042:       PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7043:     }
7044: #endif
7045:   }
7046:   PetscFunctionReturn(PETSC_SUCCESS);
7047: }

7049: /*@C
7050:    MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of mat (by pairs of `IS` that may live on subcomms).

7052:    Collective

7054:    Input Parameters:
7055: +  mat - the matrix
7056: .  n   - the number of submatrixes to be extracted
7057: .  irow - index set of rows to extract
7058: .  icol - index set of columns to extract
7059: -  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

7061:    Output Parameter:
7062: .  submat - the array of submatrices

7064:    Level: advanced

7066:    Note:
7067:    This is used by `PCGASM`

7069: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7070: @*/
7071: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7072: {
7073:   PetscInt  i;
7074:   PetscBool eq;

7076:   PetscFunctionBegin;
7079:   if (n) {
7084:   }
7086:   if (n && scall == MAT_REUSE_MATRIX) {
7089:   }
7090:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7091:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7092:   MatCheckPreallocated(mat, 1);

7094:   PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7095:   PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7096:   PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7097:   for (i = 0; i < n; i++) {
7098:     PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7099:     if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7100:   }
7101:   PetscFunctionReturn(PETSC_SUCCESS);
7102: }

7104: /*@C
7105:    MatDestroyMatrices - Destroys an array of matrices.

7107:    Collective

7109:    Input Parameters:
7110: +  n - the number of local matrices
7111: -  mat - the matrices (this is a pointer to the array of matrices)

7113:    Level: advanced

7115:     Note:
7116:     Frees not only the matrices, but also the array that contains the matrices

7118:     Fortran Note:
7119:     This does not free the array.

7121: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()` `MatDestroySubMatrices()`
7122: @*/
7123: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7124: {
7125:   PetscInt i;

7127:   PetscFunctionBegin;
7128:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7129:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);

7132:   for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));

7134:   /* memory is allocated even if n = 0 */
7135:   PetscCall(PetscFree(*mat));
7136:   PetscFunctionReturn(PETSC_SUCCESS);
7137: }

7139: /*@C
7140:    MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.

7142:    Collective

7144:    Input Parameters:
7145: +  n - the number of local matrices
7146: -  mat - the matrices (this is a pointer to the array of matrices, just to match the calling
7147:                        sequence of `MatCreateSubMatrices()`)

7149:    Level: advanced

7151:     Note:
7152:     Frees not only the matrices, but also the array that contains the matrices

7154:     Fortran Note:
7155:     This does not free the array.

7157: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7158: @*/
7159: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7160: {
7161:   Mat mat0;

7163:   PetscFunctionBegin;
7164:   if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7165:   /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7166:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);

7169:   mat0 = (*mat)[0];
7170:   if (mat0 && mat0->ops->destroysubmatrices) {
7171:     PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7172:   } else {
7173:     PetscCall(MatDestroyMatrices(n, mat));
7174:   }
7175:   PetscFunctionReturn(PETSC_SUCCESS);
7176: }

7178: /*@C
7179:    MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process

7181:    Collective

7183:    Input Parameter:
7184: .  mat - the matrix

7186:    Output Parameter:
7187: .  matstruct - the sequential matrix with the nonzero structure of mat

7189:   Level: developer

7191: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7192: @*/
7193: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7194: {
7195:   PetscFunctionBegin;

7200:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7201:   MatCheckPreallocated(mat, 1);

7203:   PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7204:   PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7205:   PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7206:   PetscFunctionReturn(PETSC_SUCCESS);
7207: }

7209: /*@C
7210:    MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.

7212:    Collective

7214:    Input Parameter:
7215: .  mat - the matrix (this is a pointer to the array of matrices, just to match the calling
7216:                        sequence of `MatGetSeqNonzeroStructure()`)

7218:    Level: advanced

7220:     Note:
7221:     Frees not only the matrices, but also the array that contains the matrices

7223: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7224: @*/
7225: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7226: {
7227:   PetscFunctionBegin;
7229:   PetscCall(MatDestroy(mat));
7230:   PetscFunctionReturn(PETSC_SUCCESS);
7231: }

7233: /*@
7234:    MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7235:    replaces the index sets by larger ones that represent submatrices with
7236:    additional overlap.

7238:    Collective

7240:    Input Parameters:
7241: +  mat - the matrix
7242: .  n   - the number of index sets
7243: .  is  - the array of index sets (these index sets will changed during the call)
7244: -  ov  - the additional overlap requested

7246:    Options Database Key:
7247: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7249:    Level: developer

7251:    Note:
7252:    The computed overlap preserves the matrix block sizes when the blocks are square.
7253:    That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7254:    that block are included in the overlap regardless of whether each specific column would increase the overlap.

7256: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7257: @*/
7258: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7259: {
7260:   PetscInt i, bs, cbs;

7262:   PetscFunctionBegin;
7266:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7267:   if (n) {
7270:   }
7271:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7272:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7273:   MatCheckPreallocated(mat, 1);

7275:   if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7276:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7277:   PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7278:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7279:   PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7280:   if (bs == cbs) {
7281:     for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7282:   }
7283:   PetscFunctionReturn(PETSC_SUCCESS);
7284: }

7286: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);

7288: /*@
7289:    MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7290:    a sub communicator, replaces the index sets by larger ones that represent submatrices with
7291:    additional overlap.

7293:    Collective

7295:    Input Parameters:
7296: +  mat - the matrix
7297: .  n   - the number of index sets
7298: .  is  - the array of index sets (these index sets will changed during the call)
7299: -  ov  - the additional overlap requested

7301: `   Options Database Key:
7302: .  -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)

7304:    Level: developer

7306: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7307: @*/
7308: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7309: {
7310:   PetscInt i;

7312:   PetscFunctionBegin;
7315:   PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7316:   if (n) {
7319:   }
7320:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7321:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7322:   MatCheckPreallocated(mat, 1);
7323:   if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7324:   PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7325:   for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7326:   PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7327:   PetscFunctionReturn(PETSC_SUCCESS);
7328: }

7330: /*@
7331:    MatGetBlockSize - Returns the matrix block size.

7333:    Not Collective

7335:    Input Parameter:
7336: .  mat - the matrix

7338:    Output Parameter:
7339: .  bs - block size

7341:    Level: intermediate

7343:    Notes:
7344:     Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.

7346:    If the block size has not been set yet this routine returns 1.

7348: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7349: @*/
7350: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7351: {
7352:   PetscFunctionBegin;
7355:   *bs = PetscAbs(mat->rmap->bs);
7356:   PetscFunctionReturn(PETSC_SUCCESS);
7357: }

7359: /*@
7360:    MatGetBlockSizes - Returns the matrix block row and column sizes.

7362:    Not Collective

7364:    Input Parameter:
7365: .  mat - the matrix

7367:    Output Parameters:
7368: +  rbs - row block size
7369: -  cbs - column block size

7371:    Level: intermediate

7373:    Notes:
7374:     Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7375:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.

7377:    If a block size has not been set yet this routine returns 1.

7379: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7380: @*/
7381: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7382: {
7383:   PetscFunctionBegin;
7387:   if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7388:   if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7389:   PetscFunctionReturn(PETSC_SUCCESS);
7390: }

7392: /*@
7393:    MatSetBlockSize - Sets the matrix block size.

7395:    Logically Collective

7397:    Input Parameters:
7398: +  mat - the matrix
7399: -  bs - block size

7401:    Level: intermediate

7403:    Notes:
7404:     Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7405:     This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7407:     For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7408:     is compatible with the matrix local sizes.

7410: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7411: @*/
7412: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7413: {
7414:   PetscFunctionBegin;
7417:   PetscCall(MatSetBlockSizes(mat, bs, bs));
7418:   PetscFunctionReturn(PETSC_SUCCESS);
7419: }

7421: typedef struct {
7422:   PetscInt         n;
7423:   IS              *is;
7424:   Mat             *mat;
7425:   PetscObjectState nonzerostate;
7426:   Mat              C;
7427: } EnvelopeData;

7429: static PetscErrorCode EnvelopeDataDestroy(EnvelopeData *edata)
7430: {
7431:   for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7432:   PetscCall(PetscFree(edata->is));
7433:   PetscCall(PetscFree(edata));
7434:   return PETSC_SUCCESS;
7435: }

7437: /*
7438:    MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7439:          the sizes of these blocks in the matrix. An individual block may lie over several processes.

7441:    Collective

7443:    Input Parameter:
7444: .  mat - the matrix

7446:    Notes:
7447:      There can be zeros within the blocks

7449:      The blocks can overlap between processes, including laying on more than two processes

7451: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7452: */
7453: static PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7454: {
7455:   PetscInt           n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7456:   PetscInt          *diag, *odiag, sc;
7457:   VecScatter         scatter;
7458:   PetscScalar       *seqv;
7459:   const PetscScalar *parv;
7460:   const PetscInt    *ia, *ja;
7461:   PetscBool          set, flag, done;
7462:   Mat                AA = mat, A;
7463:   MPI_Comm           comm;
7464:   PetscMPIInt        rank, size, tag;
7465:   MPI_Status         status;
7466:   PetscContainer     container;
7467:   EnvelopeData      *edata;
7468:   Vec                seq, par;
7469:   IS                 isglobal;

7471:   PetscFunctionBegin;
7473:   PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7474:   if (!set || !flag) {
7475:     /* TOO: only needs nonzero structure of transpose */
7476:     PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7477:     PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7478:   }
7479:   PetscCall(MatAIJGetLocalMat(AA, &A));
7480:   PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7481:   PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");

7483:   PetscCall(MatGetLocalSize(mat, &n, NULL));
7484:   PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7485:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7486:   PetscCallMPI(MPI_Comm_size(comm, &size));
7487:   PetscCallMPI(MPI_Comm_rank(comm, &rank));

7489:   PetscCall(PetscMalloc2(n, &sizes, n, &starts));

7491:   if (rank > 0) {
7492:     PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7493:     PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7494:   }
7495:   PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7496:   for (i = 0; i < n; i++) {
7497:     env = PetscMax(env, ja[ia[i + 1] - 1]);
7498:     II  = rstart + i;
7499:     if (env == II) {
7500:       starts[lblocks]  = tbs;
7501:       sizes[lblocks++] = 1 + II - tbs;
7502:       tbs              = 1 + II;
7503:     }
7504:   }
7505:   if (rank < size - 1) {
7506:     PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7507:     PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7508:   }

7510:   PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7511:   if (!set || !flag) PetscCall(MatDestroy(&AA));
7512:   PetscCall(MatDestroy(&A));

7514:   PetscCall(PetscNew(&edata));
7515:   PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7516:   edata->n = lblocks;
7517:   /* create IS needed for extracting blocks from the original matrix */
7518:   PetscCall(PetscMalloc1(lblocks, &edata->is));
7519:   for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));

7521:   /* Create the resulting inverse matrix structure with preallocation information */
7522:   PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7523:   PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7524:   PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7525:   PetscCall(MatSetType(edata->C, MATAIJ));

7527:   /* Communicate the start and end of each row, from each block to the correct rank */
7528:   /* TODO: Use PetscSF instead of VecScatter */
7529:   for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7530:   PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7531:   PetscCall(VecGetArrayWrite(seq, &seqv));
7532:   for (PetscInt i = 0; i < lblocks; i++) {
7533:     for (PetscInt j = 0; j < sizes[i]; j++) {
7534:       seqv[cnt]     = starts[i];
7535:       seqv[cnt + 1] = starts[i] + sizes[i];
7536:       cnt += 2;
7537:     }
7538:   }
7539:   PetscCall(VecRestoreArrayWrite(seq, &seqv));
7540:   PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7541:   sc -= cnt;
7542:   PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7543:   PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7544:   PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7545:   PetscCall(ISDestroy(&isglobal));
7546:   PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7547:   PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7548:   PetscCall(VecScatterDestroy(&scatter));
7549:   PetscCall(VecDestroy(&seq));
7550:   PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7551:   PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7552:   PetscCall(VecGetArrayRead(par, &parv));
7553:   cnt = 0;
7554:   PetscCall(MatGetSize(mat, NULL, &n));
7555:   for (PetscInt i = 0; i < mat->rmap->n; i++) {
7556:     PetscInt start, end, d = 0, od = 0;

7558:     start = (PetscInt)PetscRealPart(parv[cnt]);
7559:     end   = (PetscInt)PetscRealPart(parv[cnt + 1]);
7560:     cnt += 2;

7562:     if (start < cstart) {
7563:       od += cstart - start + n - cend;
7564:       d += cend - cstart;
7565:     } else if (start < cend) {
7566:       od += n - cend;
7567:       d += cend - start;
7568:     } else od += n - start;
7569:     if (end <= cstart) {
7570:       od -= cstart - end + n - cend;
7571:       d -= cend - cstart;
7572:     } else if (end < cend) {
7573:       od -= n - cend;
7574:       d -= cend - end;
7575:     } else od -= n - end;

7577:     odiag[i] = od;
7578:     diag[i]  = d;
7579:   }
7580:   PetscCall(VecRestoreArrayRead(par, &parv));
7581:   PetscCall(VecDestroy(&par));
7582:   PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7583:   PetscCall(PetscFree2(diag, odiag));
7584:   PetscCall(PetscFree2(sizes, starts));

7586:   PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7587:   PetscCall(PetscContainerSetPointer(container, edata));
7588:   PetscCall(PetscContainerSetUserDestroy(container, (PetscErrorCode(*)(void *))EnvelopeDataDestroy));
7589:   PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7590:   PetscCall(PetscObjectDereference((PetscObject)container));
7591:   PetscFunctionReturn(PETSC_SUCCESS);
7592: }

7594: /*@
7595:   MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A

7597:   Collective

7599:   Input Parameters:
7600: + A - the matrix
7601: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine

7603:   Output Parameter:
7604: . C - matrix with inverted block diagonal of `A`

7606:   Level: advanced

7608:   Note:
7609:      For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.

7611: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7612: @*/
7613: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7614: {
7615:   PetscContainer   container;
7616:   EnvelopeData    *edata;
7617:   PetscObjectState nonzerostate;

7619:   PetscFunctionBegin;
7620:   PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7621:   if (!container) {
7622:     PetscCall(MatComputeVariableBlockEnvelope(A));
7623:     PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7624:   }
7625:   PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7626:   PetscCall(MatGetNonzeroState(A, &nonzerostate));
7627:   PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7628:   PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");

7630:   PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7631:   *C = edata->C;

7633:   for (PetscInt i = 0; i < edata->n; i++) {
7634:     Mat          D;
7635:     PetscScalar *dvalues;

7637:     PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7638:     PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7639:     PetscCall(MatSeqDenseInvert(D));
7640:     PetscCall(MatDenseGetArray(D, &dvalues));
7641:     PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7642:     PetscCall(MatDestroy(&D));
7643:   }
7644:   PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7645:   PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7646:   PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7647:   PetscFunctionReturn(PETSC_SUCCESS);
7648: }

7650: /*@
7651:    MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size

7653:    Logically Collective

7655:    Input Parameters:
7656: +  mat - the matrix
7657: .  nblocks - the number of blocks on this process, each block can only exist on a single process
7658: -  bsizes - the block sizes

7660:    Level: intermediate

7662:    Notes:
7663:     Currently used by `PCVPBJACOBI` for `MATAIJ` matrices

7665:     Each variable point-block set of degrees of freedom must live on a single MPI rank. That is a point block cannot straddle two MPI ranks.

7667: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7668:           `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7669: @*/
7670: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, PetscInt *bsizes)
7671: {
7672:   PetscInt i, ncnt = 0, nlocal;

7674:   PetscFunctionBegin;
7676:   PetscCheck(nblocks >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks must be great than or equal to zero");
7677:   PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7678:   for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
7679:   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);
7680:   PetscCall(PetscFree(mat->bsizes));
7681:   mat->nblocks = nblocks;
7682:   PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7683:   PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7684:   PetscFunctionReturn(PETSC_SUCCESS);
7685: }

7687: /*@C
7688:    MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size

7690:    Logically Collective; No Fortran Support

7692:    Input Parameter:
7693: .  mat - the matrix

7695:    Output Parameters:
7696: +  nblocks - the number of blocks on this process
7697: -  bsizes - the block sizes

7699:    Level: intermediate

7701: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7702: @*/
7703: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt **bsizes)
7704: {
7705:   PetscFunctionBegin;
7707:   *nblocks = mat->nblocks;
7708:   *bsizes  = mat->bsizes;
7709:   PetscFunctionReturn(PETSC_SUCCESS);
7710: }

7712: /*@
7713:    MatSetBlockSizes - Sets the matrix block row and column sizes.

7715:    Logically Collective

7717:    Input Parameters:
7718: +  mat - the matrix
7719: .  rbs - row block size
7720: -  cbs - column block size

7722:    Level: intermediate

7724:    Notes:
7725:     Block row formats are `MATBAIJ` and  `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7726:     If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7727:     This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.

7729:     For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7730:     are compatible with the matrix local sizes.

7732:     The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.

7734: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7735: @*/
7736: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7737: {
7738:   PetscFunctionBegin;
7742:   PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7743:   if (mat->rmap->refcnt) {
7744:     ISLocalToGlobalMapping l2g  = NULL;
7745:     PetscLayout            nmap = NULL;

7747:     PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7748:     if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7749:     PetscCall(PetscLayoutDestroy(&mat->rmap));
7750:     mat->rmap          = nmap;
7751:     mat->rmap->mapping = l2g;
7752:   }
7753:   if (mat->cmap->refcnt) {
7754:     ISLocalToGlobalMapping l2g  = NULL;
7755:     PetscLayout            nmap = NULL;

7757:     PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7758:     if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7759:     PetscCall(PetscLayoutDestroy(&mat->cmap));
7760:     mat->cmap          = nmap;
7761:     mat->cmap->mapping = l2g;
7762:   }
7763:   PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7764:   PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7765:   PetscFunctionReturn(PETSC_SUCCESS);
7766: }

7768: /*@
7769:    MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices

7771:    Logically Collective

7773:    Input Parameters:
7774: +  mat - the matrix
7775: .  fromRow - matrix from which to copy row block size
7776: -  fromCol - matrix from which to copy column block size (can be same as fromRow)

7778:    Level: developer

7780: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
7781: @*/
7782: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
7783: {
7784:   PetscFunctionBegin;
7788:   if (fromRow->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
7789:   if (fromCol->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
7790:   PetscFunctionReturn(PETSC_SUCCESS);
7791: }

7793: /*@
7794:    MatResidual - Default routine to calculate the residual r = b - Ax

7796:    Collective

7798:    Input Parameters:
7799: +  mat - the matrix
7800: .  b   - the right-hand-side
7801: -  x   - the approximate solution

7803:    Output Parameter:
7804: .  r - location to store the residual

7806:    Level: developer

7808: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
7809: @*/
7810: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
7811: {
7812:   PetscFunctionBegin;
7818:   MatCheckPreallocated(mat, 1);
7819:   PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
7820:   if (!mat->ops->residual) {
7821:     PetscCall(MatMult(mat, x, r));
7822:     PetscCall(VecAYPX(r, -1.0, b));
7823:   } else {
7824:     PetscUseTypeMethod(mat, residual, b, x, r);
7825:   }
7826:   PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
7827:   PetscFunctionReturn(PETSC_SUCCESS);
7828: }

7830: /*MC
7831:     MatGetRowIJF90 - Obtains the compressed row storage i and j indices for the local rows of a sparse matrix

7833:     Synopsis:
7834:     MatGetRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

7836:     Not Collective

7838:     Input Parameters:
7839: +   A - the matrix
7840: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7841: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7842: -   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7843:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7844:                  always used.

7846:     Output Parameters:
7847: +   n - number of local rows in the (possibly compressed) matrix
7848: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7849: .   ja - the column indices
7850: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7851:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

7853:     Level: developer

7855:     Note:
7856:     Use  `MatRestoreRowIJF90()` when you no longer need access to the data

7858: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatRestoreRowIJF90()`
7859: M*/

7861: /*MC
7862:     MatRestoreRowIJF90 - restores the compressed row storage i and j indices for the local rows of a sparse matrix obtained with `MatGetRowIJF90()`

7864:     Synopsis:
7865:     MatRestoreRowIJF90(Mat A, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt n, {PetscInt, pointer :: ia(:)}, {PetscInt, pointer :: ja(:)}, PetscBool done,integer ierr)

7867:     Not Collective

7869:     Input Parameters:
7870: +   A - the  matrix
7871: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7872: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7873:     inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7874:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7875:                  always used.
7876: .   n - number of local rows in the (possibly compressed) matrix
7877: .   ia - the row pointers; that is ia[0] = 0, ia[row] = ia[row-1] + number of elements in that row of the matrix
7878: .   ja - the column indices
7879: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7880:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

7882:     Level: developer

7884: .seealso: [](ch_matrices), [](sec_fortranarrays), `Mat`, `MATMPIAIJ`, `MatGetRowIJ()`, `MatRestoreRowIJ()`, `MatGetRowIJF90()`
7885: M*/

7887: /*@C
7888:     MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix

7890:    Collective

7892:     Input Parameters:
7893: +   mat - the matrix
7894: .   shift -  0 or 1 indicating we want the indices starting at 0 or 1
7895: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7896: -   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE`  indicating if the nonzero structure of the
7897:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7898:                  always used.

7900:     Output Parameters:
7901: +   n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
7902: .   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
7903: .   ja - the column indices, use `NULL` if not needed
7904: -   done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
7905:            are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set

7907:     Level: developer

7909:     Notes:
7910:     You CANNOT change any of the ia[] or ja[] values.

7912:     Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.

7914:     Fortran Notes:
7915:     Use
7916: .vb
7917:     PetscInt, pointer :: ia(:),ja(:)
7918:     call MatGetRowIJF90(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
7919:     ! Access the ith and jth entries via ia(i) and ja(j)
7920: .ve
7921:    `MatGetRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatGetRowIJF90()`

7923: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetRowIJF90()`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
7924: @*/
7925: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
7926: {
7927:   PetscFunctionBegin;
7934:   MatCheckPreallocated(mat, 1);
7935:   if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
7936:   else {
7937:     if (done) *done = PETSC_TRUE;
7938:     PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
7939:     PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
7940:     PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
7941:   }
7942:   PetscFunctionReturn(PETSC_SUCCESS);
7943: }

7945: /*@C
7946:     MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.

7948:     Collective

7950:     Input Parameters:
7951: +   mat - the matrix
7952: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7953: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
7954:                 symmetrized
7955: .   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
7956:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7957:                  always used.
7958: .   n - number of columns in the (possibly compressed) matrix
7959: .   ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
7960: -   ja - the row indices

7962:     Output Parameter:
7963: .   done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned

7965:     Level: developer

7967: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
7968: @*/
7969: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
7970: {
7971:   PetscFunctionBegin;
7978:   MatCheckPreallocated(mat, 1);
7979:   if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
7980:   else {
7981:     *done = PETSC_TRUE;
7982:     PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
7983:   }
7984:   PetscFunctionReturn(PETSC_SUCCESS);
7985: }

7987: /*@C
7988:     MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.

7990:     Collective

7992:     Input Parameters:
7993: +   mat - the matrix
7994: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
7995: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
7996: .   inodecompressed -  `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
7997:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
7998:                  always used.
7999: .   n - size of (possibly compressed) matrix
8000: .   ia - the row pointers
8001: -   ja - the column indices

8003:     Output Parameter:
8004: .   done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8006:     Level: developer

8008:     Note:
8009:     This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8010:     us of the array after it has been restored. If you pass `NULL`, it will
8011:     not zero the pointers.  Use of ia or ja after `MatRestoreRowIJ()` is invalid.

8013:     Fortran Note:
8014:    `MatRestoreRowIJ()` Fortran binding is deprecated (since PETSc 3.19), use `MatRestoreRowIJF90()`

8016: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreRowIJF90()`, `MatRestoreColumnIJ()`
8017: @*/
8018: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8019: {
8020:   PetscFunctionBegin;
8026:   MatCheckPreallocated(mat, 1);

8028:   if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8029:   else {
8030:     if (done) *done = PETSC_TRUE;
8031:     PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8032:     if (n) *n = 0;
8033:     if (ia) *ia = NULL;
8034:     if (ja) *ja = NULL;
8035:   }
8036:   PetscFunctionReturn(PETSC_SUCCESS);
8037: }

8039: /*@C
8040:     MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.

8042:     Collective

8044:     Input Parameters:
8045: +   mat - the matrix
8046: .   shift - 1 or zero indicating we want the indices starting at 0 or 1
8047: .   symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8048: -   inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8049:                  inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8050:                  always used.

8052:     Output Parameters:
8053: +   n - size of (possibly compressed) matrix
8054: .   ia - the column pointers
8055: .   ja - the row indices
8056: -   done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned

8058:     Level: developer

8060: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8061: @*/
8062: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8063: {
8064:   PetscFunctionBegin;
8070:   MatCheckPreallocated(mat, 1);

8072:   if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8073:   else {
8074:     *done = PETSC_TRUE;
8075:     PetscUseTypeMethod(mat, restorecolumnij, 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:     MatColoringPatch -Used inside matrix coloring routines that use `MatGetRowIJ()` and/or `MatGetColumnIJ()`.

8086:     Collective

8088:     Input Parameters:
8089: +   mat - the matrix
8090: .   ncolors - maximum color value
8091: .   n   - number of entries in colorarray
8092: -   colorarray - array indicating color for each column

8094:     Output Parameter:
8095: .   iscoloring - coloring generated using colorarray information

8097:     Level: developer

8099: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8100: @*/
8101: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8102: {
8103:   PetscFunctionBegin;
8108:   MatCheckPreallocated(mat, 1);

8110:   if (!mat->ops->coloringpatch) {
8111:     PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8112:   } else {
8113:     PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8114:   }
8115:   PetscFunctionReturn(PETSC_SUCCESS);
8116: }

8118: /*@
8119:    MatSetUnfactored - Resets a factored matrix to be treated as unfactored.

8121:    Logically Collective

8123:    Input Parameter:
8124: .  mat - the factored matrix to be reset

8126:    Level: developer

8128:    Notes:
8129:    This routine should be used only with factored matrices formed by in-place
8130:    factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8131:    format).  This option can save memory, for example, when solving nonlinear
8132:    systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8133:    ILU(0) preconditioner.

8135:    One can specify in-place ILU(0) factorization by calling
8136: .vb
8137:      PCType(pc,PCILU);
8138:      PCFactorSeUseInPlace(pc);
8139: .ve
8140:    or by using the options -pc_type ilu -pc_factor_in_place

8142:    In-place factorization ILU(0) can also be used as a local
8143:    solver for the blocks within the block Jacobi or additive Schwarz
8144:    methods (runtime option: -sub_pc_factor_in_place).  See Users-Manual: ch_pc
8145:    for details on setting local solver options.

8147:    Most users should employ the `KSP` interface for linear solvers
8148:    instead of working directly with matrix algebra routines such as this.
8149:    See, e.g., `KSPCreate()`.

8151: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8152: @*/
8153: PetscErrorCode MatSetUnfactored(Mat mat)
8154: {
8155:   PetscFunctionBegin;
8158:   MatCheckPreallocated(mat, 1);
8159:   mat->factortype = MAT_FACTOR_NONE;
8160:   if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8161:   PetscUseTypeMethod(mat, setunfactored);
8162:   PetscFunctionReturn(PETSC_SUCCESS);
8163: }

8165: /*MC
8166:     MatDenseGetArrayF90 - Accesses a matrix array from Fortran

8168:     Synopsis:
8169:     MatDenseGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8171:     Not Collective

8173:     Input Parameter:
8174: .   x - matrix

8176:     Output Parameters:
8177: +   xx_v - the Fortran pointer to the array
8178: -   ierr - error code

8180:     Example of Usage:
8181: .vb
8182:       PetscScalar, pointer xx_v(:,:)
8183:       ....
8184:       call MatDenseGetArrayF90(x,xx_v,ierr)
8185:       a = xx_v(3)
8186:       call MatDenseRestoreArrayF90(x,xx_v,ierr)
8187: .ve

8189:     Level: advanced

8191: .seealso: [](ch_matrices), `Mat`, `MatDenseRestoreArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJGetArrayF90()`
8192: M*/

8194: /*MC
8195:     MatDenseRestoreArrayF90 - Restores a matrix array that has been
8196:     accessed with `MatDenseGetArrayF90()`.

8198:     Synopsis:
8199:     MatDenseRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:,:)},integer ierr)

8201:     Not Collective

8203:     Input Parameters:
8204: +   x - matrix
8205: -   xx_v - the Fortran90 pointer to the array

8207:     Output Parameter:
8208: .   ierr - error code

8210:     Example of Usage:
8211: .vb
8212:        PetscScalar, pointer xx_v(:,:)
8213:        ....
8214:        call MatDenseGetArrayF90(x,xx_v,ierr)
8215:        a = xx_v(3)
8216:        call MatDenseRestoreArrayF90(x,xx_v,ierr)
8217: .ve

8219:     Level: advanced

8221: .seealso: [](ch_matrices), `Mat`, `MatDenseGetArrayF90()`, `MatDenseGetArray()`, `MatDenseRestoreArray()`, `MatSeqAIJRestoreArrayF90()`
8222: M*/

8224: /*MC
8225:     MatSeqAIJGetArrayF90 - Accesses a matrix array from Fortran.

8227:     Synopsis:
8228:     MatSeqAIJGetArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

8230:     Not Collective

8232:     Input Parameter:
8233: .   x - matrix

8235:     Output Parameters:
8236: +   xx_v - the Fortran pointer to the array
8237: -   ierr - error code

8239:     Example of Usage:
8240: .vb
8241:       PetscScalar, pointer xx_v(:)
8242:       ....
8243:       call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8244:       a = xx_v(3)
8245:       call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8246: .ve

8248:     Level: advanced

8250: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseGetArrayF90()`
8251: M*/

8253: /*MC
8254:     MatSeqAIJRestoreArrayF90 - Restores a matrix array that has been
8255:     accessed with `MatSeqAIJGetArrayF90()`.

8257:     Synopsis:
8258:     MatSeqAIJRestoreArrayF90(Mat x,{Scalar, pointer :: xx_v(:)},integer ierr)

8260:     Not Collective

8262:     Input Parameters:
8263: +   x - matrix
8264: -   xx_v - the Fortran90 pointer to the array

8266:     Output Parameter:
8267: .   ierr - error code

8269:     Example of Usage:
8270: .vb
8271:        PetscScalar, pointer xx_v(:)
8272:        ....
8273:        call MatSeqAIJGetArrayF90(x,xx_v,ierr)
8274:        a = xx_v(3)
8275:        call MatSeqAIJRestoreArrayF90(x,xx_v,ierr)
8276: .ve

8278:     Level: advanced

8280: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArrayF90()`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArray()`, `MatDenseRestoreArrayF90()`
8281: M*/

8283: /*@
8284:     MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8285:                       as the original matrix.

8287:     Collective

8289:     Input Parameters:
8290: +   mat - the original matrix
8291: .   isrow - parallel `IS` containing the rows this processor should obtain
8292: .   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.
8293: -   cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

8295:     Output Parameter:
8296: .   newmat - the new submatrix, of the same type as the original matrix

8298:     Level: advanced

8300:     Notes:
8301:     The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.

8303:     Some matrix types place restrictions on the row and column indices, such
8304:     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;
8305:     for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.

8307:     The index sets may not have duplicate entries.

8309:       The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8310:    the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8311:    to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8312:    will reuse the matrix generated the first time.  You should call `MatDestroy()` on `newmat` when
8313:    you are finished using it.

8315:     The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8316:     the input matrix.

8318:     If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).

8320:    Example usage:
8321:    Consider the following 8x8 matrix with 34 non-zero values, that is
8322:    assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8323:    proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8324:    as follows
8325: .vb
8326:             1  2  0  |  0  3  0  |  0  4
8327:     Proc0   0  5  6  |  7  0  0  |  8  0
8328:             9  0 10  | 11  0  0  | 12  0
8329:     -------------------------------------
8330:            13  0 14  | 15 16 17  |  0  0
8331:     Proc1   0 18  0  | 19 20 21  |  0  0
8332:             0  0  0  | 22 23  0  | 24  0
8333:     -------------------------------------
8334:     Proc2  25 26 27  |  0  0 28  | 29  0
8335:            30  0  0  | 31 32 33  |  0 34
8336: .ve

8338:     Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6].  The resulting submatrix is

8340: .vb
8341:             2  0  |  0  3  0  |  0
8342:     Proc0   5  6  |  7  0  0  |  8
8343:     -------------------------------
8344:     Proc1  18  0  | 19 20 21  |  0
8345:     -------------------------------
8346:     Proc2  26 27  |  0  0 28  | 29
8347:             0  0  | 31 32 33  |  0
8348: .ve

8350: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8351: @*/
8352: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8353: {
8354:   PetscMPIInt size;
8355:   Mat        *local;
8356:   IS          iscoltmp;
8357:   PetscBool   flg;

8359:   PetscFunctionBegin;
8366:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8367:   PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");

8369:   MatCheckPreallocated(mat, 1);
8370:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));

8372:   if (!iscol || isrow == iscol) {
8373:     PetscBool   stride;
8374:     PetscMPIInt grabentirematrix = 0, grab;
8375:     PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8376:     if (stride) {
8377:       PetscInt first, step, n, rstart, rend;
8378:       PetscCall(ISStrideGetInfo(isrow, &first, &step));
8379:       if (step == 1) {
8380:         PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8381:         if (rstart == first) {
8382:           PetscCall(ISGetLocalSize(isrow, &n));
8383:           if (n == rend - rstart) grabentirematrix = 1;
8384:         }
8385:       }
8386:     }
8387:     PetscCall(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8388:     if (grab) {
8389:       PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8390:       if (cll == MAT_INITIAL_MATRIX) {
8391:         *newmat = mat;
8392:         PetscCall(PetscObjectReference((PetscObject)mat));
8393:       }
8394:       PetscFunctionReturn(PETSC_SUCCESS);
8395:     }
8396:   }

8398:   if (!iscol) {
8399:     PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8400:   } else {
8401:     iscoltmp = iscol;
8402:   }

8404:   /* if original matrix is on just one processor then use submatrix generated */
8405:   if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8406:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8407:     goto setproperties;
8408:   } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8409:     PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8410:     *newmat = *local;
8411:     PetscCall(PetscFree(local));
8412:     goto setproperties;
8413:   } else if (!mat->ops->createsubmatrix) {
8414:     /* Create a new matrix type that implements the operation using the full matrix */
8415:     PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8416:     switch (cll) {
8417:     case MAT_INITIAL_MATRIX:
8418:       PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8419:       break;
8420:     case MAT_REUSE_MATRIX:
8421:       PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8422:       break;
8423:     default:
8424:       SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8425:     }
8426:     PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8427:     goto setproperties;
8428:   }

8430:   PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8431:   PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8432:   PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));

8434: setproperties:
8435:   PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8436:   if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8437:   if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8438:   if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8439:   PetscFunctionReturn(PETSC_SUCCESS);
8440: }

8442: /*@
8443:    MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix

8445:    Not Collective

8447:    Input Parameters:
8448: +  A - the matrix we wish to propagate options from
8449: -  B - the matrix we wish to propagate options to

8451:    Level: beginner

8453:    Note:
8454:    Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`

8456: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, MatIsStructurallySymmetricKnown()`
8457: @*/
8458: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8459: {
8460:   PetscFunctionBegin;
8463:   B->symmetry_eternal            = A->symmetry_eternal;
8464:   B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8465:   B->symmetric                   = A->symmetric;
8466:   B->structurally_symmetric      = A->structurally_symmetric;
8467:   B->spd                         = A->spd;
8468:   B->hermitian                   = A->hermitian;
8469:   PetscFunctionReturn(PETSC_SUCCESS);
8470: }

8472: /*@
8473:    MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8474:    used during the assembly process to store values that belong to
8475:    other processors.

8477:    Not Collective

8479:    Input Parameters:
8480: +  mat   - the matrix
8481: .  size  - the initial size of the stash.
8482: -  bsize - the initial size of the block-stash(if used).

8484:    Options Database Keys:
8485: +   -matstash_initial_size <size> or <size0,size1,...sizep-1>
8486: -   -matstash_block_initial_size <bsize>  or <bsize0,bsize1,...bsizep-1>

8488:    Level: intermediate

8490:    Notes:
8491:      The block-stash is used for values set with `MatSetValuesBlocked()` while
8492:      the stash is used for values set with `MatSetValues()`

8494:      Run with the option -info and look for output of the form
8495:      MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8496:      to determine the appropriate value, MM, to use for size and
8497:      MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8498:      to determine the value, BMM to use for bsize

8500: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8501: @*/
8502: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8503: {
8504:   PetscFunctionBegin;
8507:   PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8508:   PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8509:   PetscFunctionReturn(PETSC_SUCCESS);
8510: }

8512: /*@
8513:    MatInterpolateAdd - w = y + A*x or A'*x depending on the shape of
8514:      the matrix

8516:    Neighbor-wise Collective

8518:    Input Parameters:
8519: +  mat   - the matrix
8520: .  x - the vector to be multiplied by the interpolation operator
8521: -  y - the vector to be added to the result

8523:    Output Parameter:
8524: .  w - the resulting vector

8526:    Level: intermediate

8528:    Notes:
8529:     `w` may be the same vector as `y`.

8531:     This allows one to use either the restriction or interpolation (its transpose)
8532:     matrix to do the interpolation

8534: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8535: @*/
8536: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8537: {
8538:   PetscInt M, N, Ny;

8540:   PetscFunctionBegin;
8545:   PetscCall(MatGetSize(A, &M, &N));
8546:   PetscCall(VecGetSize(y, &Ny));
8547:   if (M == Ny) {
8548:     PetscCall(MatMultAdd(A, x, y, w));
8549:   } else {
8550:     PetscCall(MatMultTransposeAdd(A, x, y, w));
8551:   }
8552:   PetscFunctionReturn(PETSC_SUCCESS);
8553: }

8555: /*@
8556:    MatInterpolate - y = A*x or A'*x depending on the shape of
8557:      the matrix

8559:    Neighbor-wise Collective

8561:    Input Parameters:
8562: +  mat   - the matrix
8563: -  x - the vector to be interpolated

8565:    Output Parameter:
8566: .  y - the resulting vector

8568:    Level: intermediate

8570:    Note:
8571:     This allows one to use either the restriction or interpolation (its transpose)
8572:     matrix to do the interpolation

8574: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8575: @*/
8576: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8577: {
8578:   PetscInt M, N, Ny;

8580:   PetscFunctionBegin;
8584:   PetscCall(MatGetSize(A, &M, &N));
8585:   PetscCall(VecGetSize(y, &Ny));
8586:   if (M == Ny) {
8587:     PetscCall(MatMult(A, x, y));
8588:   } else {
8589:     PetscCall(MatMultTranspose(A, x, y));
8590:   }
8591:   PetscFunctionReturn(PETSC_SUCCESS);
8592: }

8594: /*@
8595:    MatRestrict - y = A*x or A'*x

8597:    Neighbor-wise Collective

8599:    Input Parameters:
8600: +  mat   - the matrix
8601: -  x - the vector to be restricted

8603:    Output Parameter:
8604: .  y - the resulting vector

8606:    Level: intermediate

8608:    Note:
8609:     This allows one to use either the restriction or interpolation (its transpose)
8610:     matrix to do the restriction

8612: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8613: @*/
8614: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8615: {
8616:   PetscInt M, N, Ny;

8618:   PetscFunctionBegin;
8622:   PetscCall(MatGetSize(A, &M, &N));
8623:   PetscCall(VecGetSize(y, &Ny));
8624:   if (M == Ny) {
8625:     PetscCall(MatMult(A, x, y));
8626:   } else {
8627:     PetscCall(MatMultTranspose(A, x, y));
8628:   }
8629:   PetscFunctionReturn(PETSC_SUCCESS);
8630: }

8632: /*@
8633:    MatMatInterpolateAdd - Y = W + A*X or W + A'*X

8635:    Neighbor-wise Collective

8637:    Input Parameters:
8638: +  mat   - the matrix
8639: .  x - the input dense matrix to be multiplied
8640: -  w - the input dense matrix to be added to the result

8642:    Output Parameter:
8643: .  y - the output dense matrix

8645:    Level: intermediate

8647:    Note:
8648:     This allows one to use either the restriction or interpolation (its transpose)
8649:     matrix to do the interpolation. y matrix can be reused if already created with the proper sizes,
8650:     otherwise it will be recreated. y must be initialized to `NULL` if not supplied.

8652: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8653: @*/
8654: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8655: {
8656:   PetscInt  M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8657:   PetscBool trans = PETSC_TRUE;
8658:   MatReuse  reuse = MAT_INITIAL_MATRIX;

8660:   PetscFunctionBegin;
8666:   PetscCall(MatGetSize(A, &M, &N));
8667:   PetscCall(MatGetSize(x, &Mx, &Nx));
8668:   if (N == Mx) trans = PETSC_FALSE;
8669:   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);
8670:   Mo = trans ? N : M;
8671:   if (*y) {
8672:     PetscCall(MatGetSize(*y, &My, &Ny));
8673:     if (Mo == My && Nx == Ny) {
8674:       reuse = MAT_REUSE_MATRIX;
8675:     } else {
8676:       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);
8677:       PetscCall(MatDestroy(y));
8678:     }
8679:   }

8681:   if (w && *y == w) { /* this is to minimize changes in PCMG */
8682:     PetscBool flg;

8684:     PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8685:     if (w) {
8686:       PetscInt My, Ny, Mw, Nw;

8688:       PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8689:       PetscCall(MatGetSize(*y, &My, &Ny));
8690:       PetscCall(MatGetSize(w, &Mw, &Nw));
8691:       if (!flg || My != Mw || Ny != Nw) w = NULL;
8692:     }
8693:     if (!w) {
8694:       PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8695:       PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8696:       PetscCall(PetscObjectDereference((PetscObject)w));
8697:     } else {
8698:       PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8699:     }
8700:   }
8701:   if (!trans) {
8702:     PetscCall(MatMatMult(A, x, reuse, PETSC_DEFAULT, y));
8703:   } else {
8704:     PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DEFAULT, y));
8705:   }
8706:   if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8707:   PetscFunctionReturn(PETSC_SUCCESS);
8708: }

8710: /*@
8711:    MatMatInterpolate - Y = A*X or A'*X

8713:    Neighbor-wise Collective

8715:    Input Parameters:
8716: +  mat   - the matrix
8717: -  x - the input dense matrix

8719:    Output Parameter:
8720: .  y - the output dense matrix

8722:    Level: intermediate

8724:    Note:
8725:     This allows one to use either the restriction or interpolation (its transpose)
8726:     matrix to do the interpolation. y matrix can be reused if already created with the proper sizes,
8727:     otherwise it will be recreated. y must be initialized to `NULL` if not supplied.

8729: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8730: @*/
8731: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8732: {
8733:   PetscFunctionBegin;
8734:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8735:   PetscFunctionReturn(PETSC_SUCCESS);
8736: }

8738: /*@
8739:    MatMatRestrict - Y = A*X or A'*X

8741:    Neighbor-wise Collective

8743:    Input Parameters:
8744: +  mat   - the matrix
8745: -  x - the input dense matrix

8747:    Output Parameter:
8748: .  y - the output dense matrix

8750:    Level: intermediate

8752:    Note:
8753:     This allows one to use either the restriction or interpolation (its transpose)
8754:     matrix to do the restriction. y matrix can be reused if already created with the proper sizes,
8755:     otherwise it will be recreated. y must be initialized to `NULL` if not supplied.

8757: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8758: @*/
8759: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8760: {
8761:   PetscFunctionBegin;
8762:   PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8763:   PetscFunctionReturn(PETSC_SUCCESS);
8764: }

8766: /*@
8767:    MatGetNullSpace - retrieves the null space of a matrix.

8769:    Logically Collective

8771:    Input Parameters:
8772: +  mat - the matrix
8773: -  nullsp - the null space object

8775:    Level: developer

8777: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8778: @*/
8779: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8780: {
8781:   PetscFunctionBegin;
8784:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8785:   PetscFunctionReturn(PETSC_SUCCESS);
8786: }

8788: /*@
8789:    MatSetNullSpace - attaches a null space to a matrix.

8791:    Logically Collective

8793:    Input Parameters:
8794: +  mat - the matrix
8795: -  nullsp - the null space object

8797:    Level: advanced

8799:    Notes:
8800:       This null space is used by the `KSP` linear solvers to solve singular systems.

8802:       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`

8804:       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
8805:       to zero but the linear system will still be solved in a least squares sense.

8807:       The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8808:    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).
8809:    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
8810:    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
8811:    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).
8812:    This  \hat{b} can be obtained by calling MatNullSpaceRemove() with the null space of the transpose of the matrix.

8814:     If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one as called
8815:     `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
8816:     routine also automatically calls `MatSetTransposeNullSpace()`.

8818:     The user should call `MatNullSpaceDestroy()`.

8820: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
8821:           `KSPSetPCSide()`
8822: @*/
8823: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
8824: {
8825:   PetscFunctionBegin;
8828:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8829:   PetscCall(MatNullSpaceDestroy(&mat->nullsp));
8830:   mat->nullsp = nullsp;
8831:   if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
8832:   PetscFunctionReturn(PETSC_SUCCESS);
8833: }

8835: /*@
8836:    MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.

8838:    Logically Collective

8840:    Input Parameters:
8841: +  mat - the matrix
8842: -  nullsp - the null space object

8844:    Level: developer

8846: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
8847: @*/
8848: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8849: {
8850:   PetscFunctionBegin;
8854:   *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
8855:   PetscFunctionReturn(PETSC_SUCCESS);
8856: }

8858: /*@
8859:    MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix

8861:    Logically Collective

8863:    Input Parameters:
8864: +  mat - the matrix
8865: -  nullsp - the null space object

8867:    Level: advanced

8869:    Notes:
8870:    This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.

8872:    See `MatSetNullSpace()`

8874: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
8875: @*/
8876: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
8877: {
8878:   PetscFunctionBegin;
8881:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8882:   PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
8883:   mat->transnullsp = nullsp;
8884:   PetscFunctionReturn(PETSC_SUCCESS);
8885: }

8887: /*@
8888:    MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
8889:         This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.

8891:    Logically Collective

8893:    Input Parameters:
8894: +  mat - the matrix
8895: -  nullsp - the null space object

8897:    Level: advanced

8899:    Notes:
8900:    Overwrites any previous near null space that may have been attached

8902:    You can remove the null space by calling this routine with an nullsp of `NULL`

8904: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
8905: @*/
8906: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
8907: {
8908:   PetscFunctionBegin;
8912:   MatCheckPreallocated(mat, 1);
8913:   if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8914:   PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
8915:   mat->nearnullsp = nullsp;
8916:   PetscFunctionReturn(PETSC_SUCCESS);
8917: }

8919: /*@
8920:    MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`

8922:    Not Collective

8924:    Input Parameter:
8925: .  mat - the matrix

8927:    Output Parameter:
8928: .  nullsp - the null space object, `NULL` if not set

8930:    Level: advanced

8932: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
8933: @*/
8934: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
8935: {
8936:   PetscFunctionBegin;
8940:   MatCheckPreallocated(mat, 1);
8941:   *nullsp = mat->nearnullsp;
8942:   PetscFunctionReturn(PETSC_SUCCESS);
8943: }

8945: /*@C
8946:    MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.

8948:    Collective

8950:    Input Parameters:
8951: +  mat - the matrix
8952: .  row - row/column permutation
8953: -  info - information on desired factorization process

8955:    Level: developer

8957:    Notes:
8958:    Probably really in-place only when level of fill is zero, otherwise allocates
8959:    new space to store factored matrix and deletes previous memory.

8961:    Most users should employ the `KSP` interface for linear solvers
8962:    instead of working directly with matrix algebra routines such as this.
8963:    See, e.g., `KSPCreate()`.

8965:    Developer Note:
8966:    The Fortran interface is not autogenerated as the
8967:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

8969: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
8970: @*/
8971: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
8972: {
8973:   PetscFunctionBegin;
8978:   PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
8979:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
8980:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8981:   MatCheckPreallocated(mat, 1);
8982:   PetscUseTypeMethod(mat, iccfactor, row, info);
8983:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
8984:   PetscFunctionReturn(PETSC_SUCCESS);
8985: }

8987: /*@
8988:    MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
8989:          ghosted ones.

8991:    Not Collective

8993:    Input Parameters:
8994: +  mat - the matrix
8995: -  diag - the diagonal values, including ghost ones

8997:    Level: developer

8999:    Notes:
9000:     Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices

9002:     This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`

9004: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9005: @*/
9006: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9007: {
9008:   PetscMPIInt size;

9010:   PetscFunctionBegin;

9015:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9016:   PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9017:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9018:   if (size == 1) {
9019:     PetscInt n, m;
9020:     PetscCall(VecGetSize(diag, &n));
9021:     PetscCall(MatGetSize(mat, NULL, &m));
9022:     PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9023:     PetscCall(MatDiagonalScale(mat, NULL, diag));
9024:   } else {
9025:     PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9026:   }
9027:   PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9028:   PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9029:   PetscFunctionReturn(PETSC_SUCCESS);
9030: }

9032: /*@
9033:    MatGetInertia - Gets the inertia from a factored matrix

9035:    Collective

9037:    Input Parameter:
9038: .  mat - the matrix

9040:    Output Parameters:
9041: +   nneg - number of negative eigenvalues
9042: .   nzero - number of zero eigenvalues
9043: -   npos - number of positive eigenvalues

9045:    Level: advanced

9047:    Note:
9048:     Matrix must have been factored by `MatCholeskyFactor()`

9050: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9051: @*/
9052: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9053: {
9054:   PetscFunctionBegin;
9057:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9058:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9059:   PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9060:   PetscFunctionReturn(PETSC_SUCCESS);
9061: }

9063: /*@C
9064:    MatSolves - Solves A x = b, given a factored matrix, for a collection of vectors

9066:    Neighbor-wise Collective

9068:    Input Parameters:
9069: +  mat - the factored matrix obtained with `MatGetFactor()`
9070: -  b - the right-hand-side vectors

9072:    Output Parameter:
9073: .  x - the result vectors

9075:    Level: developer

9077:    Note:
9078:    The vectors `b` and `x` cannot be the same.  I.e., one cannot
9079:    call `MatSolves`(A,x,x).

9081: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9082: @*/
9083: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9084: {
9085:   PetscFunctionBegin;
9088:   PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9089:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9090:   if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);

9092:   MatCheckPreallocated(mat, 1);
9093:   PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9094:   PetscUseTypeMethod(mat, solves, b, x);
9095:   PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9096:   PetscFunctionReturn(PETSC_SUCCESS);
9097: }

9099: /*@
9100:    MatIsSymmetric - Test whether a matrix is symmetric

9102:    Collective

9104:    Input Parameters:
9105: +  A - the matrix to test
9106: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)

9108:    Output Parameter:
9109: .  flg - the result

9111:    Level: intermediate

9113:    Notes:
9114:     For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9116:     If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`

9118:     One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9119:     after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9121: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9122:           `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`, `MatSetOption()`
9123: @*/
9124: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9125: {
9126:   PetscFunctionBegin;

9130:   if (A->symmetric == PETSC_BOOL3_TRUE) *flg = PETSC_TRUE;
9131:   else if (A->symmetric == PETSC_BOOL3_FALSE) *flg = PETSC_FALSE;
9132:   else {
9133:     PetscUseTypeMethod(A, issymmetric, tol, flg);
9134:     if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9135:   }
9136:   PetscFunctionReturn(PETSC_SUCCESS);
9137: }

9139: /*@
9140:    MatIsHermitian - Test whether a matrix is Hermitian

9142:    Collective

9144:    Input Parameters:
9145: +  A - the matrix to test
9146: -  tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)

9148:    Output Parameter:
9149: .  flg - the result

9151:    Level: intermediate

9153:    Notes:
9154:     For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results

9156:     If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`

9158:     One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9159:     after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)

9161: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9162:           `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`, `MatSetOption()`
9163: @*/
9164: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9165: {
9166:   PetscFunctionBegin;

9170:   if (A->hermitian == PETSC_BOOL3_TRUE) *flg = PETSC_TRUE;
9171:   else if (A->hermitian == PETSC_BOOL3_FALSE) *flg = PETSC_FALSE;
9172:   else {
9173:     PetscUseTypeMethod(A, ishermitian, tol, flg);
9174:     if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9175:   }
9176:   PetscFunctionReturn(PETSC_SUCCESS);
9177: }

9179: /*@
9180:    MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state

9182:    Not Collective

9184:    Input Parameter:
9185: .  A - the matrix to check

9187:    Output Parameters:
9188: +  set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9189: -  flg - the result (only valid if set is `PETSC_TRUE`)

9191:    Level: advanced

9193:    Notes:
9194:    Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9195:    if you want it explicitly checked

9197:     One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9198:     after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9200: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9201: @*/
9202: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9203: {
9204:   PetscFunctionBegin;
9208:   if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9209:     *set = PETSC_TRUE;
9210:     *flg = PetscBool3ToBool(A->symmetric);
9211:   } else {
9212:     *set = PETSC_FALSE;
9213:   }
9214:   PetscFunctionReturn(PETSC_SUCCESS);
9215: }

9217: /*@
9218:    MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state

9220:    Not Collective

9222:    Input Parameter:
9223: .  A - the matrix to check

9225:    Output Parameters:
9226: +  set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9227: -  flg - the result (only valid if set is `PETSC_TRUE`)

9229:    Level: advanced

9231:    Notes:
9232:    Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).

9234:    One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9235:    after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)

9237: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9238: @*/
9239: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9240: {
9241:   PetscFunctionBegin;
9245:   if (A->spd != PETSC_BOOL3_UNKNOWN) {
9246:     *set = PETSC_TRUE;
9247:     *flg = PetscBool3ToBool(A->spd);
9248:   } else {
9249:     *set = PETSC_FALSE;
9250:   }
9251:   PetscFunctionReturn(PETSC_SUCCESS);
9252: }

9254: /*@
9255:    MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state

9257:    Not Collective

9259:    Input Parameter:
9260: .  A - the matrix to check

9262:    Output Parameters:
9263: +  set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9264: -  flg - the result (only valid if set is `PETSC_TRUE`)

9266:    Level: advanced

9268:    Notes:
9269:    Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9270:    if you want it explicitly checked

9272:    One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9273:    after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9275: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9276: @*/
9277: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9278: {
9279:   PetscFunctionBegin;
9283:   if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9284:     *set = PETSC_TRUE;
9285:     *flg = PetscBool3ToBool(A->hermitian);
9286:   } else {
9287:     *set = PETSC_FALSE;
9288:   }
9289:   PetscFunctionReturn(PETSC_SUCCESS);
9290: }

9292: /*@
9293:    MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric

9295:    Collective

9297:    Input Parameter:
9298: .  A - the matrix to test

9300:    Output Parameter:
9301: .  flg - the result

9303:    Level: intermediate

9305:    Notes:
9306:    If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`

9308:    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
9309:    symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9311: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9312: @*/
9313: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9314: {
9315:   PetscFunctionBegin;
9318:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9319:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9320:   } else {
9321:     PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9322:     PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9323:   }
9324:   PetscFunctionReturn(PETSC_SUCCESS);
9325: }

9327: /*@
9328:    MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state

9330:    Not Collective

9332:    Input Parameter:
9333: .  A - the matrix to check

9335:    Output Parameters:
9336: +  set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9337: -  flg - the result (only valid if set is PETSC_TRUE)

9339:    Level: advanced

9341:    Notes:
9342:    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
9343:    symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)

9345:    Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)

9347: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9348: @*/
9349: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9350: {
9351:   PetscFunctionBegin;
9355:   if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9356:     *set = PETSC_TRUE;
9357:     *flg = PetscBool3ToBool(A->structurally_symmetric);
9358:   } else {
9359:     *set = PETSC_FALSE;
9360:   }
9361:   PetscFunctionReturn(PETSC_SUCCESS);
9362: }

9364: /*@
9365:    MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9366:        to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process

9368:     Not Collective

9370:    Input Parameter:
9371: .   mat - the matrix

9373:    Output Parameters:
9374: +   nstash   - the size of the stash
9375: .   reallocs - the number of additional mallocs incurred.
9376: .   bnstash   - the size of the block stash
9377: -   breallocs - the number of additional mallocs incurred.in the block stash

9379:    Level: advanced

9381: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9382: @*/
9383: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9384: {
9385:   PetscFunctionBegin;
9386:   PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9387:   PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9388:   PetscFunctionReturn(PETSC_SUCCESS);
9389: }

9391: /*@C
9392:    MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9393:    parallel layout, `PetscLayout` for rows and columns

9395:    Collective

9397:    Input Parameter:
9398: .  mat - the matrix

9400:    Output Parameters:
9401: +   right - (optional) vector that the matrix can be multiplied against
9402: -   left - (optional) vector that the matrix vector product can be stored in

9404:   Level: advanced

9406:    Notes:
9407:     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()`.

9409:     These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed

9411: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9412: @*/
9413: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9414: {
9415:   PetscFunctionBegin;
9418:   if (mat->ops->getvecs) {
9419:     PetscUseTypeMethod(mat, getvecs, right, left);
9420:   } else {
9421:     PetscInt rbs, cbs;
9422:     PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
9423:     if (right) {
9424:       PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9425:       PetscCall(VecCreate(PetscObjectComm((PetscObject)mat), right));
9426:       PetscCall(VecSetSizes(*right, mat->cmap->n, PETSC_DETERMINE));
9427:       PetscCall(VecSetBlockSize(*right, cbs));
9428:       PetscCall(VecSetType(*right, mat->defaultvectype));
9429: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9430:       if (mat->boundtocpu && mat->bindingpropagates) {
9431:         PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9432:         PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9433:       }
9434: #endif
9435:       PetscCall(PetscLayoutReference(mat->cmap, &(*right)->map));
9436:     }
9437:     if (left) {
9438:       PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9439:       PetscCall(VecCreate(PetscObjectComm((PetscObject)mat), left));
9440:       PetscCall(VecSetSizes(*left, mat->rmap->n, PETSC_DETERMINE));
9441:       PetscCall(VecSetBlockSize(*left, rbs));
9442:       PetscCall(VecSetType(*left, mat->defaultvectype));
9443: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9444:       if (mat->boundtocpu && mat->bindingpropagates) {
9445:         PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9446:         PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9447:       }
9448: #endif
9449:       PetscCall(PetscLayoutReference(mat->rmap, &(*left)->map));
9450:     }
9451:   }
9452:   PetscFunctionReturn(PETSC_SUCCESS);
9453: }

9455: /*@C
9456:    MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9457:      with default values.

9459:    Not Collective

9461:    Input Parameter:
9462: .    info - the `MatFactorInfo` data structure

9464:    Level: developer

9466:    Notes:
9467:     The solvers are generally used through the `KSP` and `PC` objects, for example
9468:           `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`

9470:     Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed

9472:    Developer Note:
9473:    The Fortran interface is not autogenerated as the
9474:    interface definition cannot be generated correctly [due to `MatFactorInfo`]

9476: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9477: @*/
9478: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9479: {
9480:   PetscFunctionBegin;
9481:   PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9482:   PetscFunctionReturn(PETSC_SUCCESS);
9483: }

9485: /*@
9486:    MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed

9488:    Collective

9490:    Input Parameters:
9491: +  mat - the factored matrix
9492: -  is - the index set defining the Schur indices (0-based)

9494:    Level: advanced

9496:    Notes:
9497:     Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.

9499:    You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.

9501:    This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9503: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9504:           `MatFactorSolveSchurComplementTranspose()`, `MatFactorSolveSchurComplement()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9505: @*/
9506: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9507: {
9508:   PetscErrorCode (*f)(Mat, IS);

9510:   PetscFunctionBegin;
9515:   PetscCheckSameComm(mat, 1, is, 2);
9516:   PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9517:   PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9518:   PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9519:   PetscCall(MatDestroy(&mat->schur));
9520:   PetscCall((*f)(mat, is));
9521:   PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9522:   PetscFunctionReturn(PETSC_SUCCESS);
9523: }

9525: /*@
9526:   MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step

9528:    Logically Collective

9530:    Input Parameters:
9531: +  F - the factored matrix obtained by calling `MatGetFactor()`
9532: .  S - location where to return the Schur complement, can be `NULL`
9533: -  status - the status of the Schur complement matrix, can be `NULL`

9535:    Level: advanced

9537:    Notes:
9538:    You must call `MatFactorSetSchurIS()` before calling this routine.

9540:    This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`

9542:    The routine provides a copy of the Schur matrix stored within the solver data structures.
9543:    The caller must destroy the object when it is no longer needed.
9544:    If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.

9546:    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)

9548:    See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9550:    Developer Note:
9551:     The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9552:    matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.

9554: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9555: @*/
9556: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9557: {
9558:   PetscFunctionBegin;
9562:   if (S) {
9563:     PetscErrorCode (*f)(Mat, Mat *);

9565:     PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9566:     if (f) {
9567:       PetscCall((*f)(F, S));
9568:     } else {
9569:       PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9570:     }
9571:   }
9572:   if (status) *status = F->schur_status;
9573:   PetscFunctionReturn(PETSC_SUCCESS);
9574: }

9576: /*@
9577:   MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix

9579:    Logically Collective

9581:    Input Parameters:
9582: +  F - the factored matrix obtained by calling `MatGetFactor()`
9583: .  *S - location where to return the Schur complement, can be `NULL`
9584: -  status - the status of the Schur complement matrix, can be `NULL`

9586:    Level: advanced

9588:    Notes:
9589:    You must call `MatFactorSetSchurIS()` before calling this routine.

9591:    Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`

9593:    The routine returns a the Schur Complement stored within the data structures of the solver.

9595:    If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.

9597:    The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.

9599:    Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix

9601:    See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.

9603: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9604: @*/
9605: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9606: {
9607:   PetscFunctionBegin;
9611:   if (S) *S = F->schur;
9612:   if (status) *status = F->schur_status;
9613:   PetscFunctionReturn(PETSC_SUCCESS);
9614: }

9616: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9617: {
9618:   Mat S = F->schur;

9620:   PetscFunctionBegin;
9621:   switch (F->schur_status) {
9622:   case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9623:   case MAT_FACTOR_SCHUR_INVERTED:
9624:     if (S) {
9625:       S->ops->solve             = NULL;
9626:       S->ops->matsolve          = NULL;
9627:       S->ops->solvetranspose    = NULL;
9628:       S->ops->matsolvetranspose = NULL;
9629:       S->ops->solveadd          = NULL;
9630:       S->ops->solvetransposeadd = NULL;
9631:       S->factortype             = MAT_FACTOR_NONE;
9632:       PetscCall(PetscFree(S->solvertype));
9633:     }
9634:   case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9635:     break;
9636:   default:
9637:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9638:   }
9639:   PetscFunctionReturn(PETSC_SUCCESS);
9640: }

9642: /*@
9643:   MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`

9645:    Logically Collective

9647:    Input Parameters:
9648: +  F - the factored matrix obtained by calling `MatGetFactor()`
9649: .  *S - location where the Schur complement is stored
9650: -  status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)

9652:    Level: advanced

9654: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9655: @*/
9656: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9657: {
9658:   PetscFunctionBegin;
9660:   if (S) {
9662:     *S = NULL;
9663:   }
9664:   F->schur_status = status;
9665:   PetscCall(MatFactorUpdateSchurStatus_Private(F));
9666:   PetscFunctionReturn(PETSC_SUCCESS);
9667: }

9669: /*@
9670:   MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step

9672:    Logically Collective

9674:    Input Parameters:
9675: +  F - the factored matrix obtained by calling `MatGetFactor()`
9676: .  rhs - location where the right hand side of the Schur complement system is stored
9677: -  sol - location where the solution of the Schur complement system has to be returned

9679:    Level: advanced

9681:    Notes:
9682:    The sizes of the vectors should match the size of the Schur complement

9684:    Must be called after `MatFactorSetSchurIS()`

9686: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9687: @*/
9688: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9689: {
9690:   PetscFunctionBegin;
9697:   PetscCheckSameComm(F, 1, rhs, 2);
9698:   PetscCheckSameComm(F, 1, sol, 3);
9699:   PetscCall(MatFactorFactorizeSchurComplement(F));
9700:   switch (F->schur_status) {
9701:   case MAT_FACTOR_SCHUR_FACTORED:
9702:     PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9703:     break;
9704:   case MAT_FACTOR_SCHUR_INVERTED:
9705:     PetscCall(MatMultTranspose(F->schur, rhs, sol));
9706:     break;
9707:   default:
9708:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9709:   }
9710:   PetscFunctionReturn(PETSC_SUCCESS);
9711: }

9713: /*@
9714:   MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step

9716:    Logically Collective

9718:    Input Parameters:
9719: +  F - the factored matrix obtained by calling `MatGetFactor()`
9720: .  rhs - location where the right hand side of the Schur complement system is stored
9721: -  sol - location where the solution of the Schur complement system has to be returned

9723:    Level: advanced

9725:    Notes:
9726:    The sizes of the vectors should match the size of the Schur complement

9728:    Must be called after `MatFactorSetSchurIS()`

9730: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9731: @*/
9732: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9733: {
9734:   PetscFunctionBegin;
9741:   PetscCheckSameComm(F, 1, rhs, 2);
9742:   PetscCheckSameComm(F, 1, sol, 3);
9743:   PetscCall(MatFactorFactorizeSchurComplement(F));
9744:   switch (F->schur_status) {
9745:   case MAT_FACTOR_SCHUR_FACTORED:
9746:     PetscCall(MatSolve(F->schur, rhs, sol));
9747:     break;
9748:   case MAT_FACTOR_SCHUR_INVERTED:
9749:     PetscCall(MatMult(F->schur, rhs, sol));
9750:     break;
9751:   default:
9752:     SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9753:   }
9754:   PetscFunctionReturn(PETSC_SUCCESS);
9755: }

9757: PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9758: #if PetscDefined(HAVE_CUDA)
9759: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9760: #endif

9762: /* Schur status updated in the interface */
9763: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9764: {
9765:   Mat S = F->schur;

9767:   PetscFunctionBegin;
9768:   if (S) {
9769:     PetscMPIInt size;
9770:     PetscBool   isdense, isdensecuda;

9772:     PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9773:     PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9774:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9775:     PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9776:     PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9777:     PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9778:     if (isdense) {
9779:       PetscCall(MatSeqDenseInvertFactors_Private(S));
9780:     } else if (isdensecuda) {
9781: #if defined(PETSC_HAVE_CUDA)
9782:       PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9783: #endif
9784:     }
9785:     // HIP??????????????
9786:     PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9787:   }
9788:   PetscFunctionReturn(PETSC_SUCCESS);
9789: }

9791: /*@
9792:   MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step

9794:    Logically Collective

9796:    Input Parameter:
9797: .  F - the factored matrix obtained by calling `MatGetFactor()`

9799:    Level: advanced

9801:    Notes:
9802:     Must be called after `MatFactorSetSchurIS()`.

9804:    Call `MatFactorGetSchurComplement()` or  `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.

9806: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9807: @*/
9808: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9809: {
9810:   PetscFunctionBegin;
9813:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9814:   PetscCall(MatFactorFactorizeSchurComplement(F));
9815:   PetscCall(MatFactorInvertSchurComplement_Private(F));
9816:   F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9817:   PetscFunctionReturn(PETSC_SUCCESS);
9818: }

9820: /*@
9821:   MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step

9823:    Logically Collective

9825:    Input Parameter:
9826: .  F - the factored matrix obtained by calling `MatGetFactor()`

9828:    Level: advanced

9830:    Note:
9831:     Must be called after `MatFactorSetSchurIS()`

9833: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
9834: @*/
9835: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9836: {
9837:   MatFactorInfo info;

9839:   PetscFunctionBegin;
9842:   if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
9843:   PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
9844:   PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
9845:   if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
9846:     PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
9847:   } else {
9848:     PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
9849:   }
9850:   PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
9851:   F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9852:   PetscFunctionReturn(PETSC_SUCCESS);
9853: }

9855: /*@
9856:    MatPtAP - Creates the matrix product C = P^T * A * P

9858:    Neighbor-wise Collective

9860:    Input Parameters:
9861: +  A - the matrix
9862: .  P - the projection matrix
9863: .  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
9864: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use `PETSC_DEFAULT` if you do not have a good estimate
9865:           if the result is a dense matrix this is irrelevant

9867:    Output Parameter:
9868: .  C - the product matrix

9870:    Level: intermediate

9872:    Notes:
9873:    C will be created and must be destroyed by the user with `MatDestroy()`.

9875:    An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

9877:    Developer Note:
9878:    For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.

9880: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
9881: @*/
9882: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
9883: {
9884:   PetscFunctionBegin;
9885:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
9886:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

9888:   if (scall == MAT_INITIAL_MATRIX) {
9889:     PetscCall(MatProductCreate(A, P, NULL, C));
9890:     PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
9891:     PetscCall(MatProductSetAlgorithm(*C, "default"));
9892:     PetscCall(MatProductSetFill(*C, fill));

9894:     (*C)->product->api_user = PETSC_TRUE;
9895:     PetscCall(MatProductSetFromOptions(*C));
9896:     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);
9897:     PetscCall(MatProductSymbolic(*C));
9898:   } else { /* scall == MAT_REUSE_MATRIX */
9899:     PetscCall(MatProductReplaceMats(A, P, NULL, *C));
9900:   }

9902:   PetscCall(MatProductNumeric(*C));
9903:   (*C)->symmetric = A->symmetric;
9904:   (*C)->spd       = A->spd;
9905:   PetscFunctionReturn(PETSC_SUCCESS);
9906: }

9908: /*@
9909:    MatRARt - Creates the matrix product C = R * A * R^T

9911:    Neighbor-wise Collective

9913:    Input Parameters:
9914: +  A - the matrix
9915: .  R - the projection matrix
9916: .  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
9917: -  fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DEFAULT` if you do not have a good estimate
9918:           if the result is a dense matrix this is irrelevant

9920:    Output Parameter:
9921: .  C - the product matrix

9923:    Level: intermediate

9925:    Notes:
9926:    C will be created and must be destroyed by the user with `MatDestroy()`.

9928:    An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done

9930:    This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
9931:    which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
9932:    parallel MatRARt is implemented via explicit transpose of R, which could be very expensive.
9933:    We recommend using MatPtAP().

9935: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
9936: @*/
9937: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
9938: {
9939:   PetscFunctionBegin;
9940:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
9941:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

9943:   if (scall == MAT_INITIAL_MATRIX) {
9944:     PetscCall(MatProductCreate(A, R, NULL, C));
9945:     PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
9946:     PetscCall(MatProductSetAlgorithm(*C, "default"));
9947:     PetscCall(MatProductSetFill(*C, fill));

9949:     (*C)->product->api_user = PETSC_TRUE;
9950:     PetscCall(MatProductSetFromOptions(*C));
9951:     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);
9952:     PetscCall(MatProductSymbolic(*C));
9953:   } else { /* scall == MAT_REUSE_MATRIX */
9954:     PetscCall(MatProductReplaceMats(A, R, NULL, *C));
9955:   }

9957:   PetscCall(MatProductNumeric(*C));
9958:   if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
9959:   PetscFunctionReturn(PETSC_SUCCESS);
9960: }

9962: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
9963: {
9964:   PetscFunctionBegin;
9965:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

9967:   if (scall == MAT_INITIAL_MATRIX) {
9968:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
9969:     PetscCall(MatProductCreate(A, B, NULL, C));
9970:     PetscCall(MatProductSetType(*C, ptype));
9971:     PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
9972:     PetscCall(MatProductSetFill(*C, fill));

9974:     (*C)->product->api_user = PETSC_TRUE;
9975:     PetscCall(MatProductSetFromOptions(*C));
9976:     PetscCall(MatProductSymbolic(*C));
9977:   } else { /* scall == MAT_REUSE_MATRIX */
9978:     Mat_Product *product = (*C)->product;
9979:     PetscBool    isdense;

9981:     PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)(*C), &isdense, MATSEQDENSE, MATMPIDENSE, ""));
9982:     if (isdense && product && product->type != ptype) {
9983:       PetscCall(MatProductClear(*C));
9984:       product = NULL;
9985:     }
9986:     PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
9987:     if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
9988:       PetscCheck(isdense, PetscObjectComm((PetscObject)(*C)), PETSC_ERR_SUP, "Call MatProductCreate() first");
9989:       PetscCall(MatProductCreate_Private(A, B, NULL, *C));
9990:       product           = (*C)->product;
9991:       product->fill     = fill;
9992:       product->api_user = PETSC_TRUE;
9993:       product->clear    = PETSC_TRUE;

9995:       PetscCall(MatProductSetType(*C, ptype));
9996:       PetscCall(MatProductSetFromOptions(*C));
9997:       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);
9998:       PetscCall(MatProductSymbolic(*C));
9999:     } else { /* user may change input matrices A or B when REUSE */
10000:       PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10001:     }
10002:   }
10003:   PetscCall(MatProductNumeric(*C));
10004:   PetscFunctionReturn(PETSC_SUCCESS);
10005: }

10007: /*@
10008:    MatMatMult - Performs matrix-matrix multiplication C=A*B.

10010:    Neighbor-wise Collective

10012:    Input Parameters:
10013: +  A - the left matrix
10014: .  B - the right matrix
10015: .  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10016: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if you do not have a good estimate
10017:           if the result is a dense matrix this is irrelevant

10019:    Output Parameter:
10020: .  C - the product matrix

10022:    Notes:
10023:    Unless scall is `MAT_REUSE_MATRIX` C will be created.

10025:    `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
10026:    call to this function with `MAT_INITIAL_MATRIX`.

10028:    To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value actually needed.

10030:    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`,
10031:    rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix C is sparse.

10033:    Example of Usage:
10034: .vb
10035:      MatProductCreate(A,B,NULL,&C);
10036:      MatProductSetType(C,MATPRODUCT_AB);
10037:      MatProductSymbolic(C);
10038:      MatProductNumeric(C); // compute C=A * B
10039:      MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10040:      MatProductNumeric(C);
10041:      MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10042:      MatProductNumeric(C);
10043: .ve

10045:    Level: intermediate

10047: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10048: @*/
10049: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10050: {
10051:   PetscFunctionBegin;
10052:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10053:   PetscFunctionReturn(PETSC_SUCCESS);
10054: }

10056: /*@
10057:    MatMatTransposeMult - Performs matrix-matrix multiplication C=A*B^T.

10059:    Neighbor-wise Collective

10061:    Input Parameters:
10062: +  A - the left matrix
10063: .  B - the right matrix
10064: .  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10065: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if not known

10067:    Output Parameter:
10068: .  C - the product matrix

10070:    Level: intermediate

10072:    Notes:
10073:    C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10075:    `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call

10077:    To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10078:    actually needed.

10080:    This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10081:    and for pairs of `MATMPIDENSE` matrices.

10083:    This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`

10085:    Options Database Keys:
10086: .  -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10087:               first redundantly copies the transposed B matrix on each process and requires O(log P) communication complexity;
10088:               the second never stores more than one portion of the B matrix at a time by requires O(P) communication complexity.

10090: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductCreate()`, `MatProductAlgorithm`, `MatProductType`, `MATPRODUCT_ABt`
10091: @*/
10092: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10093: {
10094:   PetscFunctionBegin;
10095:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10096:   if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10097:   PetscFunctionReturn(PETSC_SUCCESS);
10098: }

10100: /*@
10101:    MatTransposeMatMult - Performs matrix-matrix multiplication C=A^T*B.

10103:    Neighbor-wise Collective

10105:    Input Parameters:
10106: +  A - the left matrix
10107: .  B - the right matrix
10108: .  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10109: -  fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DEFAULT` if not known

10111:    Output Parameter:
10112: .  C - the product matrix

10114:    Level: intermediate

10116:    Notes:
10117:    C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.

10119:    `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.

10121:    This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`

10123:    To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10124:    actually needed.

10126:    This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10127:    which inherit from `MATSEQAIJ`.  C will be of the same type as the input matrices.

10129: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10130: @*/
10131: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10132: {
10133:   PetscFunctionBegin;
10134:   PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10135:   PetscFunctionReturn(PETSC_SUCCESS);
10136: }

10138: /*@
10139:    MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.

10141:    Neighbor-wise Collective

10143:    Input Parameters:
10144: +  A - the left matrix
10145: .  B - the middle matrix
10146: .  C - the right matrix
10147: .  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10148: -  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
10149:           if the result is a dense matrix this is irrelevant

10151:    Output Parameter:
10152: .  D - the product matrix

10154:    Level: intermediate

10156:    Notes:
10157:    Unless scall is `MAT_REUSE_MATRIX` D will be created.

10159:    `MAT_REUSE_MATRIX` can only be used if the matrices A, B and C have the same nonzero pattern as in the previous call

10161:    This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`

10163:    To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10164:    actually needed.

10166:    If you have many matrices with the same non-zero structure to multiply, you
10167:    should use `MAT_REUSE_MATRIX` in all calls but the first

10169: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10170: @*/
10171: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10172: {
10173:   PetscFunctionBegin;
10174:   if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10175:   PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");

10177:   if (scall == MAT_INITIAL_MATRIX) {
10178:     PetscCall(MatProductCreate(A, B, C, D));
10179:     PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10180:     PetscCall(MatProductSetAlgorithm(*D, "default"));
10181:     PetscCall(MatProductSetFill(*D, fill));

10183:     (*D)->product->api_user = PETSC_TRUE;
10184:     PetscCall(MatProductSetFromOptions(*D));
10185:     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,
10186:                ((PetscObject)C)->type_name);
10187:     PetscCall(MatProductSymbolic(*D));
10188:   } else { /* user may change input matrices when REUSE */
10189:     PetscCall(MatProductReplaceMats(A, B, C, *D));
10190:   }
10191:   PetscCall(MatProductNumeric(*D));
10192:   PetscFunctionReturn(PETSC_SUCCESS);
10193: }

10195: /*@
10196:    MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.

10198:    Collective

10200:    Input Parameters:
10201: +  mat - the matrix
10202: .  nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10203: .  subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10204: -  reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10206:    Output Parameter:
10207: .  matredundant - redundant matrix

10209:    Level: advanced

10211:    Notes:
10212:    `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10213:    original matrix has not changed from that last call to MatCreateRedundantMatrix().

10215:    This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10216:    calling it.

10218:    `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.

10220: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10221: @*/
10222: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10223: {
10224:   MPI_Comm       comm;
10225:   PetscMPIInt    size;
10226:   PetscInt       mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10227:   Mat_Redundant *redund     = NULL;
10228:   PetscSubcomm   psubcomm   = NULL;
10229:   MPI_Comm       subcomm_in = subcomm;
10230:   Mat           *matseq;
10231:   IS             isrow, iscol;
10232:   PetscBool      newsubcomm = PETSC_FALSE;

10234:   PetscFunctionBegin;
10236:   if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10239:   }

10241:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10242:   if (size == 1 || nsubcomm == 1) {
10243:     if (reuse == MAT_INITIAL_MATRIX) {
10244:       PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10245:     } else {
10246:       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");
10247:       PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10248:     }
10249:     PetscFunctionReturn(PETSC_SUCCESS);
10250:   }

10252:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10253:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10254:   MatCheckPreallocated(mat, 1);

10256:   PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10257:   if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10258:     /* create psubcomm, then get subcomm */
10259:     PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10260:     PetscCallMPI(MPI_Comm_size(comm, &size));
10261:     PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);

10263:     PetscCall(PetscSubcommCreate(comm, &psubcomm));
10264:     PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10265:     PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10266:     PetscCall(PetscSubcommSetFromOptions(psubcomm));
10267:     PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10268:     newsubcomm = PETSC_TRUE;
10269:     PetscCall(PetscSubcommDestroy(&psubcomm));
10270:   }

10272:   /* get isrow, iscol and a local sequential matrix matseq[0] */
10273:   if (reuse == MAT_INITIAL_MATRIX) {
10274:     mloc_sub = PETSC_DECIDE;
10275:     nloc_sub = PETSC_DECIDE;
10276:     if (bs < 1) {
10277:       PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10278:       PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10279:     } else {
10280:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10281:       PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10282:     }
10283:     PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10284:     rstart = rend - mloc_sub;
10285:     PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10286:     PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10287:   } else { /* reuse == MAT_REUSE_MATRIX */
10288:     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");
10289:     /* retrieve subcomm */
10290:     PetscCall(PetscObjectGetComm((PetscObject)(*matredundant), &subcomm));
10291:     redund = (*matredundant)->redundant;
10292:     isrow  = redund->isrow;
10293:     iscol  = redund->iscol;
10294:     matseq = redund->matseq;
10295:   }
10296:   PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));

10298:   /* get matredundant over subcomm */
10299:   if (reuse == MAT_INITIAL_MATRIX) {
10300:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));

10302:     /* create a supporting struct and attach it to C for reuse */
10303:     PetscCall(PetscNew(&redund));
10304:     (*matredundant)->redundant = redund;
10305:     redund->isrow              = isrow;
10306:     redund->iscol              = iscol;
10307:     redund->matseq             = matseq;
10308:     if (newsubcomm) {
10309:       redund->subcomm = subcomm;
10310:     } else {
10311:       redund->subcomm = MPI_COMM_NULL;
10312:     }
10313:   } else {
10314:     PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10315:   }
10316: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10317:   if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10318:     PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10319:     PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10320:   }
10321: #endif
10322:   PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10323:   PetscFunctionReturn(PETSC_SUCCESS);
10324: }

10326: /*@C
10327:    MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10328:    a given `Mat`. Each submatrix can span multiple procs.

10330:    Collective

10332:    Input Parameters:
10333: +  mat - the matrix
10334: .  subcomm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10335: -  scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10337:    Output Parameter:
10338: .  subMat - parallel sub-matrices each spanning a given `subcomm`

10340:   Level: advanced

10342:   Notes:
10343:   The submatrix partition across processors is dictated by `subComm` a
10344:   communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10345:   is not restricted to be grouped with consecutive original ranks.

10347:   Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10348:   map directly to the layout of the original matrix [wrt the local
10349:   row,col partitioning]. So the original 'DiagonalMat' naturally maps
10350:   into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10351:   the `subMat`. However the offDiagMat looses some columns - and this is
10352:   reconstructed with `MatSetValues()`

10354:   This is used by `PCBJACOBI` when a single block spans multiple MPI ranks

10356: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10357: @*/
10358: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10359: {
10360:   PetscMPIInt commsize, subCommSize;

10362:   PetscFunctionBegin;
10363:   PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10364:   PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10365:   PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);

10367:   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");
10368:   PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10369:   PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10370:   PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10371:   PetscFunctionReturn(PETSC_SUCCESS);
10372: }

10374: /*@
10375:    MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering

10377:    Not Collective

10379:    Input Parameters:
10380: +  mat - matrix to extract local submatrix from
10381: .  isrow - local row indices for submatrix
10382: -  iscol - local column indices for submatrix

10384:    Output Parameter:
10385: .  submat - the submatrix

10387:    Level: intermediate

10389:    Notes:
10390:    `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.

10392:    Depending on the format of `mat`, the returned submat may not implement `MatMult()`.  Its communicator may be
10393:    the same as mat, it may be `PETSC_COMM_SELF`, or some other subcomm of `mat`'s.

10395:    `submat` always implements `MatSetValuesLocal()`.  If `isrow` and `iscol` have the same block size, then
10396:    `MatSetValuesBlockedLocal()` will also be implemented.

10398:    `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10399:    Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.

10401: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10402: @*/
10403: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10404: {
10405:   PetscFunctionBegin;
10409:   PetscCheckSameComm(isrow, 2, iscol, 3);
10411:   PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");

10413:   if (mat->ops->getlocalsubmatrix) {
10414:     PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10415:   } else {
10416:     PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10417:   }
10418:   PetscFunctionReturn(PETSC_SUCCESS);
10419: }

10421: /*@
10422:    MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`

10424:    Not Collective

10426:    Input Parameters:
10427: +  mat - matrix to extract local submatrix from
10428: .  isrow - local row indices for submatrix
10429: .  iscol - local column indices for submatrix
10430: -  submat - the submatrix

10432:    Level: intermediate

10434: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10435: @*/
10436: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10437: {
10438:   PetscFunctionBegin;
10442:   PetscCheckSameComm(isrow, 2, iscol, 3);

10446:   if (mat->ops->restorelocalsubmatrix) {
10447:     PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10448:   } else {
10449:     PetscCall(MatDestroy(submat));
10450:   }
10451:   *submat = NULL;
10452:   PetscFunctionReturn(PETSC_SUCCESS);
10453: }

10455: /*@
10456:    MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix

10458:    Collective

10460:    Input Parameter:
10461: .  mat - the matrix

10463:    Output Parameter:
10464: .  is - if any rows have zero diagonals this contains the list of them

10466:    Level: developer

10468: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10469: @*/
10470: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10471: {
10472:   PetscFunctionBegin;
10475:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10476:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10478:   if (!mat->ops->findzerodiagonals) {
10479:     Vec                diag;
10480:     const PetscScalar *a;
10481:     PetscInt          *rows;
10482:     PetscInt           rStart, rEnd, r, nrow = 0;

10484:     PetscCall(MatCreateVecs(mat, &diag, NULL));
10485:     PetscCall(MatGetDiagonal(mat, diag));
10486:     PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10487:     PetscCall(VecGetArrayRead(diag, &a));
10488:     for (r = 0; r < rEnd - rStart; ++r)
10489:       if (a[r] == 0.0) ++nrow;
10490:     PetscCall(PetscMalloc1(nrow, &rows));
10491:     nrow = 0;
10492:     for (r = 0; r < rEnd - rStart; ++r)
10493:       if (a[r] == 0.0) rows[nrow++] = r + rStart;
10494:     PetscCall(VecRestoreArrayRead(diag, &a));
10495:     PetscCall(VecDestroy(&diag));
10496:     PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10497:   } else {
10498:     PetscUseTypeMethod(mat, findzerodiagonals, is);
10499:   }
10500:   PetscFunctionReturn(PETSC_SUCCESS);
10501: }

10503: /*@
10504:    MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)

10506:    Collective

10508:    Input Parameter:
10509: .  mat - the matrix

10511:    Output Parameter:
10512: .  is - contains the list of rows with off block diagonal entries

10514:    Level: developer

10516: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10517: @*/
10518: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10519: {
10520:   PetscFunctionBegin;
10523:   PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10524:   PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");

10526:   PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10527:   PetscFunctionReturn(PETSC_SUCCESS);
10528: }

10530: /*@C
10531:   MatInvertBlockDiagonal - Inverts the block diagonal entries.

10533:   Collective; No Fortran Support

10535:   Input Parameter:
10536: . mat - the matrix

10538:   Output Parameter:
10539: . values - the block inverses in column major order (FORTRAN-like)

10541:   Level: advanced

10543:    Notes:
10544:    The size of the blocks is determined by the block size of the matrix.

10546:    The blocks never overlap between two MPI ranks, use `MatInvertVariableBlockEnvelope()` for that case

10548:    The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size

10550: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10551: @*/
10552: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar **values)
10553: {
10554:   PetscFunctionBegin;
10556:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10557:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10558:   PetscUseTypeMethod(mat, invertblockdiagonal, values);
10559:   PetscFunctionReturn(PETSC_SUCCESS);
10560: }

10562: /*@C
10563:   MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.

10565:   Collective; No Fortran Support

10567:   Input Parameters:
10568: + mat - the matrix
10569: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10570: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`

10572:   Output Parameter:
10573: . values - the block inverses in column major order (FORTRAN-like)

10575:   Level: advanced

10577:   Notes:
10578:   Use `MatInvertBlockDiagonal()` if all blocks have the same size

10580:   The blocks never overlap between two MPI ranks, use `MatInvertVariableBlockEnvelope()` for that case

10582: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10583: @*/
10584: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *values)
10585: {
10586:   PetscFunctionBegin;
10588:   PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10589:   PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10590:   PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10591:   PetscFunctionReturn(PETSC_SUCCESS);
10592: }

10594: /*@
10595:   MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A

10597:   Collective

10599:   Input Parameters:
10600: + A - the matrix
10601: - C - matrix with inverted block diagonal of `A`.  This matrix should be created and may have its type set.

10603:   Level: advanced

10605:   Note:
10606:   The blocksize of the matrix is used to determine the blocks on the diagonal of `C`

10608: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10609: @*/
10610: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10611: {
10612:   const PetscScalar *vals;
10613:   PetscInt          *dnnz;
10614:   PetscInt           m, rstart, rend, bs, i, j;

10616:   PetscFunctionBegin;
10617:   PetscCall(MatInvertBlockDiagonal(A, &vals));
10618:   PetscCall(MatGetBlockSize(A, &bs));
10619:   PetscCall(MatGetLocalSize(A, &m, NULL));
10620:   PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10621:   PetscCall(PetscMalloc1(m / bs, &dnnz));
10622:   for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10623:   PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10624:   PetscCall(PetscFree(dnnz));
10625:   PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10626:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10627:   for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10628:   PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10629:   PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10630:   PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10631:   PetscFunctionReturn(PETSC_SUCCESS);
10632: }

10634: /*@C
10635:     MatTransposeColoringDestroy - Destroys a coloring context for matrix product C=A*B^T that was created
10636:     via `MatTransposeColoringCreate()`.

10638:     Collective

10640:     Input Parameter:
10641: .   c - coloring context

10643:     Level: intermediate

10645: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10646: @*/
10647: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10648: {
10649:   MatTransposeColoring matcolor = *c;

10651:   PetscFunctionBegin;
10652:   if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10653:   if (--((PetscObject)matcolor)->refct > 0) {
10654:     matcolor = NULL;
10655:     PetscFunctionReturn(PETSC_SUCCESS);
10656:   }

10658:   PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10659:   PetscCall(PetscFree(matcolor->rows));
10660:   PetscCall(PetscFree(matcolor->den2sp));
10661:   PetscCall(PetscFree(matcolor->colorforcol));
10662:   PetscCall(PetscFree(matcolor->columns));
10663:   if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10664:   PetscCall(PetscHeaderDestroy(c));
10665:   PetscFunctionReturn(PETSC_SUCCESS);
10666: }

10668: /*@C
10669:     MatTransColoringApplySpToDen - Given a symbolic matrix product C=A*B^T for which
10670:     a `MatTransposeColoring` context has been created, computes a dense B^T by applying
10671:     `MatTransposeColoring` to sparse B.

10673:     Collective

10675:     Input Parameters:
10676: +   coloring - coloring context created with `MatTransposeColoringCreate()`
10677: -   B - sparse matrix

10679:     Output Parameter:
10680: .   Btdense - dense matrix B^T

10682:     Level: developer

10684:     Note:
10685:     These are used internally for some implementations of `MatRARt()`

10687: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10688: @*/
10689: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10690: {
10691:   PetscFunctionBegin;

10696:   PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10697:   PetscFunctionReturn(PETSC_SUCCESS);
10698: }

10700: /*@C
10701:     MatTransColoringApplyDenToSp - Given a symbolic matrix product Csp=A*B^T for which
10702:     a `MatTransposeColoring` context has been created and a dense matrix Cden=A*Btdense
10703:     in which Btdens is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10704:     `Csp` from `Cden`.

10706:     Collective

10708:     Input Parameters:
10709: +   matcoloring - coloring context created with `MatTransposeColoringCreate()`
10710: -   Cden - matrix product of a sparse matrix and a dense matrix Btdense

10712:     Output Parameter:
10713: .   Csp - sparse matrix

10715:     Level: developer

10717:     Note:
10718:     These are used internally for some implementations of `MatRARt()`

10720: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10721: @*/
10722: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10723: {
10724:   PetscFunctionBegin;

10729:   PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10730:   PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10731:   PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10732:   PetscFunctionReturn(PETSC_SUCCESS);
10733: }

10735: /*@C
10736:    MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product C=A*B^T.

10738:    Collective

10740:    Input Parameters:
10741: +  mat - the matrix product C
10742: -  iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`

10744:     Output Parameter:
10745: .   color - the new coloring context

10747:     Level: intermediate

10749: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10750:           `MatTransColoringApplyDenToSp()`
10751: @*/
10752: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10753: {
10754:   MatTransposeColoring c;
10755:   MPI_Comm             comm;

10757:   PetscFunctionBegin;
10758:   PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10759:   PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10760:   PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));

10762:   c->ctype = iscoloring->ctype;
10763:   PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);

10765:   *color = c;
10766:   PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10767:   PetscFunctionReturn(PETSC_SUCCESS);
10768: }

10770: /*@
10771:       MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10772:         matrix has had no new nonzero locations added to (or removed from) the matrix since the previous call then the value will be the
10773:         same, otherwise it will be larger

10775:      Not Collective

10777:   Input Parameter:
10778: .    A  - the matrix

10780:   Output Parameter:
10781: .    state - the current state

10783:   Level: intermediate

10785:   Notes:
10786:     You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10787:          different matrices

10789:     Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix

10791:     Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.

10793: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10794: @*/
10795: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10796: {
10797:   PetscFunctionBegin;
10799:   *state = mat->nonzerostate;
10800:   PetscFunctionReturn(PETSC_SUCCESS);
10801: }

10803: /*@
10804:       MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10805:                  matrices from each processor

10807:     Collective

10809:    Input Parameters:
10810: +    comm - the communicators the parallel matrix will live on
10811: .    seqmat - the input sequential matrices
10812: .    n - number of local columns (or `PETSC_DECIDE`)
10813: -    reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`

10815:    Output Parameter:
10816: .    mpimat - the parallel matrix generated

10818:     Level: developer

10820:    Note:
10821:     The number of columns of the matrix in EACH processor MUST be the same.

10823: .seealso: [](ch_matrices), `Mat`
10824: @*/
10825: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
10826: {
10827:   PetscMPIInt size;

10829:   PetscFunctionBegin;
10830:   PetscCallMPI(MPI_Comm_size(comm, &size));
10831:   if (size == 1) {
10832:     if (reuse == MAT_INITIAL_MATRIX) {
10833:       PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
10834:     } else {
10835:       PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
10836:     }
10837:     PetscFunctionReturn(PETSC_SUCCESS);
10838:   }

10840:   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");

10842:   PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
10843:   PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
10844:   PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
10845:   PetscFunctionReturn(PETSC_SUCCESS);
10846: }

10848: /*@
10849:      MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent ranks' ownership ranges.

10851:     Collective

10853:    Input Parameters:
10854: +    A   - the matrix to create subdomains from
10855: -    N   - requested number of subdomains

10857:    Output Parameters:
10858: +    n   - number of subdomains resulting on this rank
10859: -    iss - `IS` list with indices of subdomains on this rank

10861:     Level: advanced

10863:     Note:
10864:     The number of subdomains must be smaller than the communicator size

10866: .seealso: [](ch_matrices), `Mat`, `IS`
10867: @*/
10868: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
10869: {
10870:   MPI_Comm    comm, subcomm;
10871:   PetscMPIInt size, rank, color;
10872:   PetscInt    rstart, rend, k;

10874:   PetscFunctionBegin;
10875:   PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
10876:   PetscCallMPI(MPI_Comm_size(comm, &size));
10877:   PetscCallMPI(MPI_Comm_rank(comm, &rank));
10878:   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);
10879:   *n    = 1;
10880:   k     = ((PetscInt)size) / N + ((PetscInt)size % N > 0); /* There are up to k ranks to a color */
10881:   color = rank / k;
10882:   PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
10883:   PetscCall(PetscMalloc1(1, iss));
10884:   PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
10885:   PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
10886:   PetscCallMPI(MPI_Comm_free(&subcomm));
10887:   PetscFunctionReturn(PETSC_SUCCESS);
10888: }

10890: /*@
10891:    MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.

10893:    If the interpolation and restriction operators are the same, uses `MatPtAP()`.
10894:    If they are not the same, uses `MatMatMatMult()`.

10896:    Once the coarse grid problem is constructed, correct for interpolation operators
10897:    that are not of full rank, which can legitimately happen in the case of non-nested
10898:    geometric multigrid.

10900:    Input Parameters:
10901: +  restrct - restriction operator
10902: .  dA - fine grid matrix
10903: .  interpolate - interpolation operator
10904: .  reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10905: -  fill - expected fill, use `PETSC_DEFAULT` if you do not have a good estimate

10907:    Output Parameter:
10908: .  A - the Galerkin coarse matrix

10910:    Options Database Key:
10911: .  -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used

10913:    Level: developer

10915: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
10916: @*/
10917: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
10918: {
10919:   IS  zerorows;
10920:   Vec diag;

10922:   PetscFunctionBegin;
10923:   PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10924:   /* Construct the coarse grid matrix */
10925:   if (interpolate == restrct) {
10926:     PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
10927:   } else {
10928:     PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
10929:   }

10931:   /* If the interpolation matrix is not of full rank, A will have zero rows.
10932:      This can legitimately happen in the case of non-nested geometric multigrid.
10933:      In that event, we set the rows of the matrix to the rows of the identity,
10934:      ignoring the equations (as the RHS will also be zero). */

10936:   PetscCall(MatFindZeroRows(*A, &zerorows));

10938:   if (zerorows != NULL) { /* if there are any zero rows */
10939:     PetscCall(MatCreateVecs(*A, &diag, NULL));
10940:     PetscCall(MatGetDiagonal(*A, diag));
10941:     PetscCall(VecISSet(diag, zerorows, 1.0));
10942:     PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
10943:     PetscCall(VecDestroy(&diag));
10944:     PetscCall(ISDestroy(&zerorows));
10945:   }
10946:   PetscFunctionReturn(PETSC_SUCCESS);
10947: }

10949: /*@C
10950:     MatSetOperation - Allows user to set a matrix operation for any matrix type

10952:    Logically Collective

10954:     Input Parameters:
10955: +   mat - the matrix
10956: .   op - the name of the operation
10957: -   f - the function that provides the operation

10959:    Level: developer

10961:     Usage:
10962: .vb
10963:   extern PetscErrorCode usermult(Mat, Vec, Vec);

10965:   PetscCall(MatCreateXXX(comm, ..., &A));
10966:   PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFunction)usermult));
10967: .ve

10969:     Notes:
10970:     See the file `include/petscmat.h` for a complete list of matrix
10971:     operations, which all have the form MATOP_<OPERATION>, where
10972:     <OPERATION> is the name (in all capital letters) of the
10973:     user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

10975:     All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
10976:     sequence as the usual matrix interface routines, since they
10977:     are intended to be accessed via the usual matrix interface
10978:     routines, e.g.,
10979: .vb
10980:   MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
10981: .ve

10983:     In particular each function MUST return `PETSC_SUCCESS` on success and
10984:     nonzero on failure.

10986:     This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.

10988: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
10989: @*/
10990: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
10991: {
10992:   PetscFunctionBegin;
10994:   if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))(mat->ops->view)) mat->ops->viewnative = mat->ops->view;
10995:   (((void (**)(void))mat->ops)[op]) = f;
10996:   PetscFunctionReturn(PETSC_SUCCESS);
10997: }

10999: /*@C
11000:     MatGetOperation - Gets a matrix operation for any matrix type.

11002:     Not Collective

11004:     Input Parameters:
11005: +   mat - the matrix
11006: -   op - the name of the operation

11008:     Output Parameter:
11009: .   f - the function that provides the operation

11011:     Level: developer

11013:     Usage:
11014: .vb
11015:       PetscErrorCode (*usermult)(Mat, Vec, Vec);
11016:       MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11017: .ve

11019:     Notes:
11020:     See the file include/petscmat.h for a complete list of matrix
11021:     operations, which all have the form MATOP_<OPERATION>, where
11022:     <OPERATION> is the name (in all capital letters) of the
11023:     user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).

11025:     This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.

11027: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11028: @*/
11029: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11030: {
11031:   PetscFunctionBegin;
11033:   *f = (((void (**)(void))mat->ops)[op]);
11034:   PetscFunctionReturn(PETSC_SUCCESS);
11035: }

11037: /*@
11038:     MatHasOperation - Determines whether the given matrix supports the particular operation.

11040:    Not Collective

11042:    Input Parameters:
11043: +  mat - the matrix
11044: -  op - the operation, for example, `MATOP_GET_DIAGONAL`

11046:    Output Parameter:
11047: .  has - either `PETSC_TRUE` or `PETSC_FALSE`

11049:    Level: advanced

11051:    Note:
11052:    See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.

11054: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11055: @*/
11056: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11057: {
11058:   PetscFunctionBegin;
11061:   if (mat->ops->hasoperation) {
11062:     PetscUseTypeMethod(mat, hasoperation, op, has);
11063:   } else {
11064:     if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11065:     else {
11066:       *has = PETSC_FALSE;
11067:       if (op == MATOP_CREATE_SUBMATRIX) {
11068:         PetscMPIInt size;

11070:         PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11071:         if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11072:       }
11073:     }
11074:   }
11075:   PetscFunctionReturn(PETSC_SUCCESS);
11076: }

11078: /*@
11079:     MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent

11081:    Collective

11083:    Input Parameter:
11084: .  mat - the matrix

11086:    Output Parameter:
11087: .  cong - either `PETSC_TRUE` or `PETSC_FALSE`

11089:    Level: beginner

11091: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11092: @*/
11093: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11094: {
11095:   PetscFunctionBegin;
11099:   if (!mat->rmap || !mat->cmap) {
11100:     *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11101:     PetscFunctionReturn(PETSC_SUCCESS);
11102:   }
11103:   if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11104:     PetscCall(PetscLayoutSetUp(mat->rmap));
11105:     PetscCall(PetscLayoutSetUp(mat->cmap));
11106:     PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11107:     if (*cong) mat->congruentlayouts = 1;
11108:     else mat->congruentlayouts = 0;
11109:   } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11110:   PetscFunctionReturn(PETSC_SUCCESS);
11111: }

11113: PetscErrorCode MatSetInf(Mat A)
11114: {
11115:   PetscFunctionBegin;
11116:   PetscUseTypeMethod(A, setinf);
11117:   PetscFunctionReturn(PETSC_SUCCESS);
11118: }

11120: /*@C
11121:    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
11122:    and possibly removes small values from the graph structure.

11124:    Collective

11126:    Input Parameters:
11127: +  A - the matrix
11128: .  sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11129: .  scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11130: -  filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value

11132:    Output Parameter:
11133: .  graph - the resulting graph

11135:    Level: advanced

11137: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11138: @*/
11139: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, Mat *graph)
11140: {
11141:   PetscFunctionBegin;
11146:   PetscUseTypeMethod(A, creategraph, sym, scale, filter, graph);
11147:   PetscFunctionReturn(PETSC_SUCCESS);
11148: }

11150: /*@
11151:   MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11152:   meaning the same memory is used for the matrix, and no new memory is allocated.

11154:   Collective

11156:   Input Parameter:
11157: . A - the matrix

11159:   Level: intermediate

11161:   Developer Note:
11162:   The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11163:   of the arrays in the data structure are unneeded.

11165: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatChop()`
11166: @*/
11167: PetscErrorCode MatEliminateZeros(Mat A)
11168: {
11169:   PetscFunctionBegin;
11171:   PetscUseTypeMethod(A, eliminatezeros);
11172:   PetscFunctionReturn(PETSC_SUCCESS);
11173: }