Actual source code: matrix.c
1: /*
2: This is where the abstract matrix operations are defined
3: Portions of this code are under:
4: Copyright (c) 2022 Advanced Micro Devices, Inc. All rights reserved.
5: */
7: #include <petsc/private/matimpl.h>
8: #include <petsc/private/isimpl.h>
9: #include <petsc/private/vecimpl.h>
11: /* Logging support */
12: PetscClassId MAT_CLASSID;
13: PetscClassId MAT_COLORING_CLASSID;
14: PetscClassId MAT_FDCOLORING_CLASSID;
15: PetscClassId MAT_TRANSPOSECOLORING_CLASSID;
17: PetscLogEvent MAT_Mult, MAT_MultAdd, MAT_MultTranspose;
18: PetscLogEvent MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve, MAT_MatTrSolve;
19: PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
20: PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
21: PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
22: PetscLogEvent MAT_QRFactorNumeric, MAT_QRFactorSymbolic, MAT_QRFactor;
23: PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
24: PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_PartitioningND, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
25: PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply, MAT_Transpose, MAT_FDColoringFunction, MAT_CreateSubMat;
26: PetscLogEvent MAT_TransposeColoringCreate;
27: PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
28: PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric, MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
29: PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
30: PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
31: PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
32: PetscLogEvent MAT_MultHermitianTranspose, MAT_MultHermitianTransposeAdd;
33: PetscLogEvent MAT_Getsymtransreduced, MAT_GetBrowsOfAcols;
34: PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
35: PetscLogEvent MAT_GetMultiProcBlock;
36: PetscLogEvent MAT_CUSPARSECopyToGPU, MAT_CUSPARSECopyFromGPU, MAT_CUSPARSEGenerateTranspose, MAT_CUSPARSESolveAnalysis;
37: PetscLogEvent MAT_HIPSPARSECopyToGPU, MAT_HIPSPARSECopyFromGPU, MAT_HIPSPARSEGenerateTranspose, MAT_HIPSPARSESolveAnalysis;
38: PetscLogEvent MAT_PreallCOO, MAT_SetVCOO;
39: PetscLogEvent MAT_CreateGraph;
40: PetscLogEvent MAT_SetValuesBatch;
41: PetscLogEvent MAT_ViennaCLCopyToGPU;
42: PetscLogEvent MAT_CUDACopyToGPU, MAT_HIPCopyToGPU;
43: PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
44: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
45: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
46: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
47: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;
49: const char *const MatFactorTypes[] = {"NONE", "LU", "CHOLESKY", "ILU", "ICC", "ILUDT", "QR", "MatFactorType", "MAT_FACTOR_", NULL};
51: /*@
52: MatSetRandom - Sets all components of a matrix to random numbers.
54: Logically Collective
56: Input Parameters:
57: + x - the matrix
58: - rctx - the `PetscRandom` object, formed by `PetscRandomCreate()`, or `NULL` and
59: it will create one internally.
61: Example:
62: .vb
63: PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
64: MatSetRandom(x,rctx);
65: PetscRandomDestroy(rctx);
66: .ve
68: Level: intermediate
70: Notes:
71: For sparse matrices that have been preallocated but not been assembled, it randomly selects appropriate locations,
73: for sparse matrices that already have nonzero locations, it fills the locations with random numbers.
75: It generates an error if used on unassembled sparse matrices that have not been preallocated.
77: .seealso: [](ch_matrices), `Mat`, `PetscRandom`, `PetscRandomCreate()`, `MatZeroEntries()`, `MatSetValues()`, `PetscRandomDestroy()`
78: @*/
79: PetscErrorCode MatSetRandom(Mat x, PetscRandom rctx)
80: {
81: PetscRandom randObj = NULL;
83: PetscFunctionBegin;
87: MatCheckPreallocated(x, 1);
89: if (!rctx) {
90: MPI_Comm comm;
91: PetscCall(PetscObjectGetComm((PetscObject)x, &comm));
92: PetscCall(PetscRandomCreate(comm, &randObj));
93: PetscCall(PetscRandomSetType(randObj, x->defaultrandtype));
94: PetscCall(PetscRandomSetFromOptions(randObj));
95: rctx = randObj;
96: }
97: PetscCall(PetscLogEventBegin(MAT_SetRandom, x, rctx, 0, 0));
98: PetscUseTypeMethod(x, setrandom, rctx);
99: PetscCall(PetscLogEventEnd(MAT_SetRandom, x, rctx, 0, 0));
101: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
102: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
103: PetscCall(PetscRandomDestroy(&randObj));
104: PetscFunctionReturn(PETSC_SUCCESS);
105: }
107: /*@
108: MatCopyHashToXAIJ - copy hash table entries into an XAIJ matrix type
110: Logically Collective
112: Input Parameter:
113: . A - A matrix in unassembled, hash table form
115: Output Parameter:
116: . B - The XAIJ matrix. This can either be `A` or some matrix of equivalent size, e.g. obtained from `A` via `MatDuplicate()`
118: Example:
119: .vb
120: PetscCall(MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, &B));
121: PetscCall(MatCopyHashToXAIJ(A, B));
122: .ve
124: Level: advanced
126: Notes:
127: If `B` is `A`, then the hash table data structure will be destroyed. `B` is assembled
129: .seealso: [](ch_matrices), `Mat`, `MAT_USE_HASH_TABLE`
130: @*/
131: PetscErrorCode MatCopyHashToXAIJ(Mat A, Mat B)
132: {
133: PetscFunctionBegin;
135: PetscUseTypeMethod(A, copyhashtoxaij, B);
136: PetscFunctionReturn(PETSC_SUCCESS);
137: }
139: /*@
140: MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in
142: Logically Collective
144: Input Parameter:
145: . mat - the factored matrix
147: Output Parameters:
148: + pivot - the pivot value computed
149: - row - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
150: the share the matrix
152: Level: advanced
154: Notes:
155: This routine does not work for factorizations done with external packages.
157: This routine should only be called if `MatGetFactorError()` returns a value of `MAT_FACTOR_NUMERIC_ZEROPIVOT`
159: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
161: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
162: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
163: `MAT_FACTOR_NUMERIC_ZEROPIVOT`
164: @*/
165: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
166: {
167: PetscFunctionBegin;
169: PetscAssertPointer(pivot, 2);
170: PetscAssertPointer(row, 3);
171: *pivot = mat->factorerror_zeropivot_value;
172: *row = mat->factorerror_zeropivot_row;
173: PetscFunctionReturn(PETSC_SUCCESS);
174: }
176: /*@
177: MatFactorGetError - gets the error code from a factorization
179: Logically Collective
181: Input Parameter:
182: . mat - the factored matrix
184: Output Parameter:
185: . err - the error code
187: Level: advanced
189: Note:
190: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
192: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
193: `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
194: @*/
195: PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
196: {
197: PetscFunctionBegin;
199: PetscAssertPointer(err, 2);
200: *err = mat->factorerrortype;
201: PetscFunctionReturn(PETSC_SUCCESS);
202: }
204: /*@
205: MatFactorClearError - clears the error code in a factorization
207: Logically Collective
209: Input Parameter:
210: . mat - the factored matrix
212: Level: developer
214: Note:
215: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
217: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
218: `MatGetErrorCode()`, `MatFactorError`
219: @*/
220: PetscErrorCode MatFactorClearError(Mat mat)
221: {
222: PetscFunctionBegin;
224: mat->factorerrortype = MAT_FACTOR_NOERROR;
225: mat->factorerror_zeropivot_value = 0.0;
226: mat->factorerror_zeropivot_row = 0;
227: PetscFunctionReturn(PETSC_SUCCESS);
228: }
230: PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
231: {
232: Vec r, l;
233: const PetscScalar *al;
234: PetscInt i, nz, gnz, N, n, st;
236: PetscFunctionBegin;
237: PetscCall(MatCreateVecs(mat, &r, &l));
238: if (!cols) { /* nonzero rows */
239: PetscCall(MatGetOwnershipRange(mat, &st, NULL));
240: PetscCall(MatGetSize(mat, &N, NULL));
241: PetscCall(MatGetLocalSize(mat, &n, NULL));
242: PetscCall(VecSet(l, 0.0));
243: PetscCall(VecSetRandom(r, NULL));
244: PetscCall(MatMult(mat, r, l));
245: PetscCall(VecGetArrayRead(l, &al));
246: } else { /* nonzero columns */
247: PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
248: PetscCall(MatGetSize(mat, NULL, &N));
249: PetscCall(MatGetLocalSize(mat, NULL, &n));
250: PetscCall(VecSet(r, 0.0));
251: PetscCall(VecSetRandom(l, NULL));
252: PetscCall(MatMultTranspose(mat, l, r));
253: PetscCall(VecGetArrayRead(r, &al));
254: }
255: if (tol <= 0.0) {
256: for (i = 0, nz = 0; i < n; i++)
257: if (al[i] != 0.0) nz++;
258: } else {
259: for (i = 0, nz = 0; i < n; i++)
260: if (PetscAbsScalar(al[i]) > tol) nz++;
261: }
262: PetscCallMPI(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
263: if (gnz != N) {
264: PetscInt *nzr;
265: PetscCall(PetscMalloc1(nz, &nzr));
266: if (nz) {
267: if (tol < 0) {
268: for (i = 0, nz = 0; i < n; i++)
269: if (al[i] != 0.0) nzr[nz++] = i + st;
270: } else {
271: for (i = 0, nz = 0; i < n; i++)
272: if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
273: }
274: }
275: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
276: } else *nonzero = NULL;
277: if (!cols) { /* nonzero rows */
278: PetscCall(VecRestoreArrayRead(l, &al));
279: } else {
280: PetscCall(VecRestoreArrayRead(r, &al));
281: }
282: PetscCall(VecDestroy(&l));
283: PetscCall(VecDestroy(&r));
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: /*@
288: MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix
290: Input Parameter:
291: . mat - the matrix
293: Output Parameter:
294: . keptrows - the rows that are not completely zero
296: Level: intermediate
298: Note:
299: `keptrows` is set to `NULL` if all rows are nonzero.
301: Developer Note:
302: If `keptrows` is not `NULL`, it must be sorted.
304: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
305: @*/
306: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
307: {
308: PetscFunctionBegin;
311: PetscAssertPointer(keptrows, 2);
312: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
313: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
314: if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
315: else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
316: if (keptrows && *keptrows) PetscCall(ISSetInfo(*keptrows, IS_SORTED, IS_GLOBAL, PETSC_FALSE, PETSC_TRUE));
317: PetscFunctionReturn(PETSC_SUCCESS);
318: }
320: /*@
321: MatFindZeroRows - Locate all rows that are completely zero in the matrix
323: Input Parameter:
324: . mat - the matrix
326: Output Parameter:
327: . zerorows - the rows that are completely zero
329: Level: intermediate
331: Note:
332: `zerorows` is set to `NULL` if no rows are zero.
334: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
335: @*/
336: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
337: {
338: IS keptrows;
339: PetscInt m, n;
341: PetscFunctionBegin;
344: PetscAssertPointer(zerorows, 2);
345: PetscCall(MatFindNonzeroRows(mat, &keptrows));
346: /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
347: In keeping with this convention, we set zerorows to NULL if there are no zero
348: rows. */
349: if (keptrows == NULL) {
350: *zerorows = NULL;
351: } else {
352: PetscCall(MatGetOwnershipRange(mat, &m, &n));
353: PetscCall(ISComplement(keptrows, m, n, zerorows));
354: PetscCall(ISDestroy(&keptrows));
355: }
356: PetscFunctionReturn(PETSC_SUCCESS);
357: }
359: /*@
360: MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling
362: Not Collective
364: Input Parameter:
365: . A - the matrix
367: Output Parameter:
368: . a - the diagonal part (which is a SEQUENTIAL matrix)
370: Level: advanced
372: Notes:
373: See `MatCreateAIJ()` for more information on the "diagonal part" of the matrix.
375: Use caution, as the reference count on the returned matrix is not incremented and it is used as part of `A`'s normal operation.
377: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
378: @*/
379: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
380: {
381: PetscFunctionBegin;
384: PetscAssertPointer(a, 2);
385: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
386: if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
387: else {
388: PetscMPIInt size;
390: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
391: PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
392: *a = A;
393: }
394: PetscFunctionReturn(PETSC_SUCCESS);
395: }
397: /*@
398: MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.
400: Collective
402: Input Parameter:
403: . mat - the matrix
405: Output Parameter:
406: . trace - the sum of the diagonal entries
408: Level: advanced
410: .seealso: [](ch_matrices), `Mat`
411: @*/
412: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
413: {
414: Vec diag;
416: PetscFunctionBegin;
418: PetscAssertPointer(trace, 2);
419: PetscCall(MatCreateVecs(mat, &diag, NULL));
420: PetscCall(MatGetDiagonal(mat, diag));
421: PetscCall(VecSum(diag, trace));
422: PetscCall(VecDestroy(&diag));
423: PetscFunctionReturn(PETSC_SUCCESS);
424: }
426: /*@
427: MatRealPart - Zeros out the imaginary part of the matrix
429: Logically Collective
431: Input Parameter:
432: . mat - the matrix
434: Level: advanced
436: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
437: @*/
438: PetscErrorCode MatRealPart(Mat mat)
439: {
440: PetscFunctionBegin;
443: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
444: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
445: MatCheckPreallocated(mat, 1);
446: PetscUseTypeMethod(mat, realpart);
447: PetscFunctionReturn(PETSC_SUCCESS);
448: }
450: /*@C
451: MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix
453: Collective
455: Input Parameter:
456: . mat - the matrix
458: Output Parameters:
459: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
460: - ghosts - the global indices of the ghost points
462: Level: advanced
464: Note:
465: `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`
467: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
468: @*/
469: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
470: {
471: PetscFunctionBegin;
474: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
475: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
476: if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
477: else {
478: if (nghosts) *nghosts = 0;
479: if (ghosts) *ghosts = NULL;
480: }
481: PetscFunctionReturn(PETSC_SUCCESS);
482: }
484: /*@
485: MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part
487: Logically Collective
489: Input Parameter:
490: . mat - the matrix
492: Level: advanced
494: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
495: @*/
496: PetscErrorCode MatImaginaryPart(Mat mat)
497: {
498: PetscFunctionBegin;
501: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
502: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
503: MatCheckPreallocated(mat, 1);
504: PetscUseTypeMethod(mat, imaginarypart);
505: PetscFunctionReturn(PETSC_SUCCESS);
506: }
508: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
509: /*@C
510: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
511: for each row that you get to ensure that your application does
512: not bleed memory.
514: Not Collective
516: Input Parameters:
517: + mat - the matrix
518: - row - the row to get
520: Output Parameters:
521: + ncols - if not `NULL`, the number of nonzeros in `row`
522: . cols - if not `NULL`, the column numbers
523: - vals - if not `NULL`, the numerical values
525: Level: advanced
527: Notes:
528: This routine is provided for people who need to have direct access
529: to the structure of a matrix. We hope that we provide enough
530: high-level matrix routines that few users will need it.
532: `MatGetRow()` always returns 0-based column indices, regardless of
533: whether the internal representation is 0-based (default) or 1-based.
535: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
536: not wish to extract these quantities.
538: The user can only examine the values extracted with `MatGetRow()`;
539: the values CANNOT be altered. To change the matrix entries, one
540: must use `MatSetValues()`.
542: You can only have one call to `MatGetRow()` outstanding for a particular
543: matrix at a time, per processor. `MatGetRow()` can only obtain rows
544: associated with the given processor, it cannot get rows from the
545: other processors; for that we suggest using `MatCreateSubMatrices()`, then
546: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
547: is in the global number of rows.
549: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
551: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
553: Fortran Note:
554: .vb
555: PetscInt, pointer :: cols(:)
556: PetscScalar, pointer :: vals(:)
557: .ve
559: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
560: @*/
561: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
562: {
563: PetscInt incols;
565: PetscFunctionBegin;
568: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
569: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
570: MatCheckPreallocated(mat, 1);
571: PetscCheck(row >= mat->rmap->rstart && row < mat->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Only for local rows, %" PetscInt_FMT " not in [%" PetscInt_FMT ",%" PetscInt_FMT ")", row, mat->rmap->rstart, mat->rmap->rend);
572: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
573: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
574: if (ncols) *ncols = incols;
575: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
576: PetscFunctionReturn(PETSC_SUCCESS);
577: }
579: /*@
580: MatConjugate - replaces the matrix values with their complex conjugates
582: Logically Collective
584: Input Parameter:
585: . mat - the matrix
587: Level: advanced
589: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
590: @*/
591: PetscErrorCode MatConjugate(Mat mat)
592: {
593: PetscFunctionBegin;
595: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
596: if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
597: PetscUseTypeMethod(mat, conjugate);
598: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
599: }
600: PetscFunctionReturn(PETSC_SUCCESS);
601: }
603: /*@C
604: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
606: Not Collective
608: Input Parameters:
609: + mat - the matrix
610: . row - the row to get
611: . ncols - the number of nonzeros
612: . cols - the columns of the nonzeros
613: - vals - if nonzero the column values
615: Level: advanced
617: Notes:
618: This routine should be called after you have finished examining the entries.
620: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
621: us of the array after it has been restored. If you pass `NULL`, it will
622: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
624: Fortran Note:
625: .vb
626: PetscInt, pointer :: cols(:)
627: PetscScalar, pointer :: vals(:)
628: .ve
630: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
631: @*/
632: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
633: {
634: PetscFunctionBegin;
636: if (ncols) PetscAssertPointer(ncols, 3);
637: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
638: PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
639: if (ncols) *ncols = 0;
640: if (cols) *cols = NULL;
641: if (vals) *vals = NULL;
642: PetscFunctionReturn(PETSC_SUCCESS);
643: }
645: /*@
646: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
647: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
649: Not Collective
651: Input Parameter:
652: . mat - the matrix
654: Level: advanced
656: Note:
657: 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.
659: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
660: @*/
661: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
662: {
663: PetscFunctionBegin;
666: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
667: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
668: MatCheckPreallocated(mat, 1);
669: PetscTryTypeMethod(mat, getrowuppertriangular);
670: PetscFunctionReturn(PETSC_SUCCESS);
671: }
673: /*@
674: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
676: Not Collective
678: Input Parameter:
679: . mat - the matrix
681: Level: advanced
683: Note:
684: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
686: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
687: @*/
688: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
689: {
690: PetscFunctionBegin;
693: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
694: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
695: MatCheckPreallocated(mat, 1);
696: PetscTryTypeMethod(mat, restorerowuppertriangular);
697: PetscFunctionReturn(PETSC_SUCCESS);
698: }
700: /*@
701: MatSetOptionsPrefix - Sets the prefix used for searching for all
702: `Mat` options in the database.
704: Logically Collective
706: Input Parameters:
707: + A - the matrix
708: - prefix - the prefix to prepend to all option names
710: Level: advanced
712: Notes:
713: A hyphen (-) must NOT be given at the beginning of the prefix name.
714: The first character of all runtime options is AUTOMATICALLY the hyphen.
716: This is NOT used for options for the factorization of the matrix. Normally the
717: prefix is automatically passed in from the PC calling the factorization. To set
718: it directly use `MatSetOptionsPrefixFactor()`
720: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
721: @*/
722: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
723: {
724: PetscFunctionBegin;
726: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
727: PetscTryMethod(A, "MatSetOptionsPrefix_C", (Mat, const char[]), (A, prefix));
728: PetscFunctionReturn(PETSC_SUCCESS);
729: }
731: /*@
732: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
733: for matrices created with `MatGetFactor()`
735: Logically Collective
737: Input Parameters:
738: + A - the matrix
739: - prefix - the prefix to prepend to all option names for the factored matrix
741: Level: developer
743: Notes:
744: A hyphen (-) must NOT be given at the beginning of the prefix name.
745: The first character of all runtime options is AUTOMATICALLY the hyphen.
747: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
748: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
750: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
751: @*/
752: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
753: {
754: PetscFunctionBegin;
756: if (prefix) {
757: PetscAssertPointer(prefix, 2);
758: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
759: if (prefix != A->factorprefix) {
760: PetscCall(PetscFree(A->factorprefix));
761: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
762: }
763: } else PetscCall(PetscFree(A->factorprefix));
764: PetscFunctionReturn(PETSC_SUCCESS);
765: }
767: /*@
768: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
769: for matrices created with `MatGetFactor()`
771: Logically Collective
773: Input Parameters:
774: + A - the matrix
775: - prefix - the prefix to prepend to all option names for the factored matrix
777: Level: developer
779: Notes:
780: A hyphen (-) must NOT be given at the beginning of the prefix name.
781: The first character of all runtime options is AUTOMATICALLY the hyphen.
783: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
784: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
786: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
787: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
788: `MatSetOptionsPrefix()`
789: @*/
790: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
791: {
792: size_t len1, len2, new_len;
794: PetscFunctionBegin;
796: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
797: if (!A->factorprefix) {
798: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
799: PetscFunctionReturn(PETSC_SUCCESS);
800: }
801: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
803: PetscCall(PetscStrlen(A->factorprefix, &len1));
804: PetscCall(PetscStrlen(prefix, &len2));
805: new_len = len1 + len2 + 1;
806: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
807: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
808: PetscFunctionReturn(PETSC_SUCCESS);
809: }
811: /*@
812: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
813: matrix options in the database.
815: Logically Collective
817: Input Parameters:
818: + A - the matrix
819: - prefix - the prefix to prepend to all option names
821: Level: advanced
823: Note:
824: A hyphen (-) must NOT be given at the beginning of the prefix name.
825: The first character of all runtime options is AUTOMATICALLY the hyphen.
827: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
828: @*/
829: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
830: {
831: PetscFunctionBegin;
833: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
834: PetscTryMethod(A, "MatAppendOptionsPrefix_C", (Mat, const char[]), (A, prefix));
835: PetscFunctionReturn(PETSC_SUCCESS);
836: }
838: /*@
839: MatGetOptionsPrefix - Gets the prefix used for searching for all
840: matrix options in the database.
842: Not Collective
844: Input Parameter:
845: . A - the matrix
847: Output Parameter:
848: . prefix - pointer to the prefix string used
850: Level: advanced
852: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
853: @*/
854: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
855: {
856: PetscFunctionBegin;
858: PetscAssertPointer(prefix, 2);
859: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
860: PetscFunctionReturn(PETSC_SUCCESS);
861: }
863: /*@
864: MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`
866: Not Collective
868: Input Parameter:
869: . A - the matrix
871: Output Parameter:
872: . state - the object state
874: Level: advanced
876: Note:
877: Object state is an integer which gets increased every time
878: the object is changed. By saving and later querying the object state
879: one can determine whether information about the object is still current.
881: See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.
883: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
884: @*/
885: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
886: {
887: PetscFunctionBegin;
889: PetscAssertPointer(state, 2);
890: PetscCall(PetscObjectStateGet((PetscObject)A, state));
891: PetscFunctionReturn(PETSC_SUCCESS);
892: }
894: /*@
895: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
897: Collective
899: Input Parameter:
900: . A - the matrix
902: Level: beginner
904: Notes:
905: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
906: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
907: makes all of the preallocation space available
909: Current values in the matrix are lost in this call
911: Currently only supported for `MATAIJ` matrices.
913: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
914: @*/
915: PetscErrorCode MatResetPreallocation(Mat A)
916: {
917: PetscFunctionBegin;
920: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
921: PetscFunctionReturn(PETSC_SUCCESS);
922: }
924: /*@
925: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
927: Collective
929: Input Parameter:
930: . A - the matrix
932: Level: intermediate
934: Notes:
935: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
937: Currently only supported for `MATAIJ` matrices.
939: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
940: @*/
941: PetscErrorCode MatResetHash(Mat A)
942: {
943: PetscFunctionBegin;
946: PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset to hash state after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
947: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
948: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
949: /* These flags are used to determine whether certain setups occur */
950: A->was_assembled = PETSC_FALSE;
951: A->assembled = PETSC_FALSE;
952: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
953: PetscCall(PetscObjectStateIncrease((PetscObject)A));
954: PetscFunctionReturn(PETSC_SUCCESS);
955: }
957: /*@
958: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
960: Collective
962: Input Parameter:
963: . A - the matrix
965: Level: advanced
967: Notes:
968: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
969: setting values in the matrix.
971: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
973: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
974: @*/
975: PetscErrorCode MatSetUp(Mat A)
976: {
977: PetscFunctionBegin;
979: if (!((PetscObject)A)->type_name) {
980: PetscMPIInt size;
982: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
983: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
984: }
985: if (!A->preallocated) PetscTryTypeMethod(A, setup);
986: PetscCall(PetscLayoutSetUp(A->rmap));
987: PetscCall(PetscLayoutSetUp(A->cmap));
988: A->preallocated = PETSC_TRUE;
989: PetscFunctionReturn(PETSC_SUCCESS);
990: }
992: #if defined(PETSC_HAVE_SAWS)
993: #include <petscviewersaws.h>
994: #endif
996: /*
997: If threadsafety is on extraneous matrices may be printed
999: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1000: */
1001: #if !defined(PETSC_HAVE_THREADSAFETY)
1002: static PetscInt insidematview = 0;
1003: #endif
1005: /*@
1006: MatViewFromOptions - View properties of the matrix based on options set in the options database
1008: Collective
1010: Input Parameters:
1011: + A - the matrix
1012: . obj - optional additional object that provides the options prefix to use
1013: - name - command line option
1015: Options Database Key:
1016: . -mat_view [viewertype]:... - the viewer and its options
1018: Level: intermediate
1020: Note:
1021: .vb
1022: If no value is provided ascii:stdout is used
1023: ascii[:[filename][:[format][:append]]] defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
1024: for example ascii::ascii_info prints just the information about the object not all details
1025: unless :append is given filename opens in write mode, overwriting what was already there
1026: binary[:[filename][:[format][:append]]] defaults to the file binaryoutput
1027: draw[:drawtype[:filename]] for example, draw:tikz, draw:tikz:figure.tex or draw:x
1028: socket[:port] defaults to the standard output port
1029: saws[:communicatorname] publishes object to the Scientific Application Webserver (SAWs)
1030: .ve
1032: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1033: @*/
1034: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1035: {
1036: PetscFunctionBegin;
1038: #if !defined(PETSC_HAVE_THREADSAFETY)
1039: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1040: #endif
1041: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1042: PetscFunctionReturn(PETSC_SUCCESS);
1043: }
1045: /*@
1046: MatView - display information about a matrix in a variety ways
1048: Collective on viewer
1050: Input Parameters:
1051: + mat - the matrix
1052: - viewer - visualization context
1054: Options Database Keys:
1055: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1056: . -mat_view ::ascii_info_detail - Prints more detailed info
1057: . -mat_view - Prints matrix in ASCII format
1058: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1059: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1060: . -display <name> - Sets display name (default is host)
1061: . -draw_pause <sec> - Sets number of seconds to pause after display
1062: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1063: . -viewer_socket_machine <machine> - -
1064: . -viewer_socket_port <port> - -
1065: . -mat_view binary - save matrix to file in binary format
1066: - -viewer_binary_filename <name> - -
1068: Level: beginner
1070: Notes:
1071: The available visualization contexts include
1072: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1073: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1074: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1075: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1077: The user can open alternative visualization contexts with
1078: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1079: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1080: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1081: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1083: The user can call `PetscViewerPushFormat()` to specify the output
1084: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1085: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1086: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1087: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1088: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1089: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1090: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1091: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1092: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1094: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1095: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1097: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1099: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1100: viewer is used.
1102: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1103: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1105: One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1106: and then use the following mouse functions.
1107: .vb
1108: left mouse: zoom in
1109: middle mouse: zoom out
1110: right mouse: continue with the simulation
1111: .ve
1113: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1114: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1115: @*/
1116: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1117: {
1118: PetscInt rows, cols, rbs, cbs;
1119: PetscBool isascii, isstring, issaws;
1120: PetscViewerFormat format;
1121: PetscMPIInt size;
1123: PetscFunctionBegin;
1126: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1129: PetscCall(PetscViewerGetFormat(viewer, &format));
1130: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1131: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1133: #if !defined(PETSC_HAVE_THREADSAFETY)
1134: insidematview++;
1135: #endif
1136: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1137: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1138: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1139: PetscCheck((isascii && (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL)) || !mat->factortype, PetscObjectComm((PetscObject)viewer), PETSC_ERR_ARG_WRONGSTATE, "No viewers for factored matrix except ASCII, info, or info_detail");
1141: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1142: if (isascii) {
1143: if (!mat->preallocated) {
1144: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1145: #if !defined(PETSC_HAVE_THREADSAFETY)
1146: insidematview--;
1147: #endif
1148: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1149: PetscFunctionReturn(PETSC_SUCCESS);
1150: }
1151: if (!mat->assembled) {
1152: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1153: #if !defined(PETSC_HAVE_THREADSAFETY)
1154: insidematview--;
1155: #endif
1156: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1157: PetscFunctionReturn(PETSC_SUCCESS);
1158: }
1159: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1160: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1161: MatNullSpace nullsp, transnullsp;
1163: PetscCall(PetscViewerASCIIPushTab(viewer));
1164: PetscCall(MatGetSize(mat, &rows, &cols));
1165: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1166: if (rbs != 1 || cbs != 1) {
1167: if (rbs != cbs) PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", rbs=%" PetscInt_FMT ", cbs=%" PetscInt_FMT "%s\n", rows, cols, rbs, cbs, mat->bsizes ? " variable blocks set" : ""));
1168: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1169: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1170: if (mat->factortype) {
1171: MatSolverType solver;
1172: PetscCall(MatFactorGetSolverType(mat, &solver));
1173: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1174: }
1175: if (mat->ops->getinfo) {
1176: PetscBool is_constant_or_diagonal;
1178: // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1179: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1180: if (!is_constant_or_diagonal) {
1181: MatInfo info;
1183: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1184: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1185: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1186: }
1187: }
1188: PetscCall(MatGetNullSpace(mat, &nullsp));
1189: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1190: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1191: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1192: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1193: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1194: PetscCall(PetscViewerASCIIPushTab(viewer));
1195: PetscCall(MatProductView(mat, viewer));
1196: PetscCall(PetscViewerASCIIPopTab(viewer));
1197: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1198: IS tmp;
1200: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1201: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1202: PetscCall(PetscViewerASCIIPushTab(viewer));
1203: PetscCall(ISView(tmp, viewer));
1204: PetscCall(PetscViewerASCIIPopTab(viewer));
1205: PetscCall(ISDestroy(&tmp));
1206: }
1207: }
1208: } else if (issaws) {
1209: #if defined(PETSC_HAVE_SAWS)
1210: PetscMPIInt rank;
1212: PetscCall(PetscObjectName((PetscObject)mat));
1213: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1214: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1215: #endif
1216: } else if (isstring) {
1217: const char *type;
1218: PetscCall(MatGetType(mat, &type));
1219: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1220: PetscTryTypeMethod(mat, view, viewer);
1221: }
1222: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1223: PetscCall(PetscViewerASCIIPushTab(viewer));
1224: PetscUseTypeMethod(mat, viewnative, viewer);
1225: PetscCall(PetscViewerASCIIPopTab(viewer));
1226: } else if (mat->ops->view) {
1227: PetscCall(PetscViewerASCIIPushTab(viewer));
1228: PetscUseTypeMethod(mat, view, viewer);
1229: PetscCall(PetscViewerASCIIPopTab(viewer));
1230: }
1231: if (isascii) {
1232: PetscCall(PetscViewerGetFormat(viewer, &format));
1233: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1234: }
1235: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1236: #if !defined(PETSC_HAVE_THREADSAFETY)
1237: insidematview--;
1238: #endif
1239: PetscFunctionReturn(PETSC_SUCCESS);
1240: }
1242: #if defined(PETSC_USE_DEBUG)
1243: #include <../src/sys/totalview/tv_data_display.h>
1244: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1245: {
1246: TV_add_row("Local rows", "int", &mat->rmap->n);
1247: TV_add_row("Local columns", "int", &mat->cmap->n);
1248: TV_add_row("Global rows", "int", &mat->rmap->N);
1249: TV_add_row("Global columns", "int", &mat->cmap->N);
1250: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1251: return TV_format_OK;
1252: }
1253: #endif
1255: /*@
1256: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1257: with `MatView()`. The matrix format is determined from the options database.
1258: Generates a parallel MPI matrix if the communicator has more than one
1259: processor. The default matrix type is `MATAIJ`.
1261: Collective
1263: Input Parameters:
1264: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1265: or some related function before a call to `MatLoad()`
1266: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1268: Options Database Key:
1269: . -matload_block_size <bs> - set block size
1271: Level: beginner
1273: Notes:
1274: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1275: `Mat` before calling this routine if you wish to set it from the options database.
1277: `MatLoad()` automatically loads into the options database any options
1278: given in the file filename.info where filename is the name of the file
1279: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1280: file will be ignored if you use the -viewer_binary_skip_info option.
1282: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1283: sets the default matrix type AIJ and sets the local and global sizes.
1284: If type and/or size is already set, then the same are used.
1286: In parallel, each processor can load a subset of rows (or the
1287: entire matrix). This routine is especially useful when a large
1288: matrix is stored on disk and only part of it is desired on each
1289: processor. For example, a parallel solver may access only some of
1290: the rows from each processor. The algorithm used here reads
1291: relatively small blocks of data rather than reading the entire
1292: matrix and then subsetting it.
1294: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1295: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1296: or the sequence like
1297: .vb
1298: `PetscViewer` v;
1299: `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1300: `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1301: `PetscViewerSetFromOptions`(v);
1302: `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1303: `PetscViewerFileSetName`(v,"datafile");
1304: .ve
1305: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1306: .vb
1307: -viewer_type {binary, hdf5}
1308: .ve
1310: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1311: and src/mat/tutorials/ex10.c with the second approach.
1313: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1314: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1315: Multiple objects, both matrices and vectors, can be stored within the same file.
1316: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1318: Most users should not need to know the details of the binary storage
1319: format, since `MatLoad()` and `MatView()` completely hide these details.
1320: But for anyone who is interested, the standard binary matrix storage
1321: format is
1323: .vb
1324: PetscInt MAT_FILE_CLASSID
1325: PetscInt number of rows
1326: PetscInt number of columns
1327: PetscInt total number of nonzeros
1328: PetscInt *number nonzeros in each row
1329: PetscInt *column indices of all nonzeros (starting index is zero)
1330: PetscScalar *values of all nonzeros
1331: .ve
1332: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1333: stored or loaded (each MPI process part of the matrix must have less than `PETSC_INT_MAX` nonzeros). Since the total nonzero count in this
1334: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1336: PETSc automatically does the byte swapping for
1337: machines that store the bytes reversed. Thus if you write your own binary
1338: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1339: and `PetscBinaryWrite()` to see how this may be done.
1341: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1342: Each processor's chunk is loaded independently by its owning MPI process.
1343: Multiple objects, both matrices and vectors, can be stored within the same file.
1344: They are looked up by their PetscObject name.
1346: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1347: by default the same structure and naming of the AIJ arrays and column count
1348: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1349: .vb
1350: save example.mat A b -v7.3
1351: .ve
1352: can be directly read by this routine (see Reference 1 for details).
1354: Depending on your MATLAB version, this format might be a default,
1355: otherwise you can set it as default in Preferences.
1357: Unless -nocompression flag is used to save the file in MATLAB,
1358: PETSc must be configured with ZLIB package.
1360: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1362: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1364: Corresponding `MatView()` is not yet implemented.
1366: The loaded matrix is actually a transpose of the original one in MATLAB,
1367: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1368: With this format, matrix is automatically transposed by PETSc,
1369: unless the matrix is marked as SPD or symmetric
1370: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1372: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1374: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1375: @*/
1376: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1377: {
1378: PetscBool flg;
1380: PetscFunctionBegin;
1384: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1386: flg = PETSC_FALSE;
1387: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1388: if (flg) {
1389: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1390: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1391: }
1392: flg = PETSC_FALSE;
1393: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1394: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1396: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1397: PetscUseTypeMethod(mat, load, viewer);
1398: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1399: PetscFunctionReturn(PETSC_SUCCESS);
1400: }
1402: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1403: {
1404: Mat_Redundant *redund = *redundant;
1406: PetscFunctionBegin;
1407: if (redund) {
1408: if (redund->matseq) { /* via MatCreateSubMatrices() */
1409: PetscCall(ISDestroy(&redund->isrow));
1410: PetscCall(ISDestroy(&redund->iscol));
1411: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1412: } else {
1413: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1414: PetscCall(PetscFree(redund->sbuf_j));
1415: PetscCall(PetscFree(redund->sbuf_a));
1416: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1417: PetscCall(PetscFree(redund->rbuf_j[i]));
1418: PetscCall(PetscFree(redund->rbuf_a[i]));
1419: }
1420: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1421: }
1423: if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1424: PetscCall(PetscFree(redund));
1425: }
1426: PetscFunctionReturn(PETSC_SUCCESS);
1427: }
1429: /*@
1430: MatDestroy - Frees space taken by a matrix.
1432: Collective
1434: Input Parameter:
1435: . A - the matrix
1437: Level: beginner
1439: Developer Note:
1440: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1441: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1442: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1443: if changes are needed here.
1445: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1446: @*/
1447: PetscErrorCode MatDestroy(Mat *A)
1448: {
1449: PetscFunctionBegin;
1450: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1452: if (--((PetscObject)*A)->refct > 0) {
1453: *A = NULL;
1454: PetscFunctionReturn(PETSC_SUCCESS);
1455: }
1457: /* if memory was published with SAWs then destroy it */
1458: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1459: PetscTryTypeMethod(*A, destroy);
1461: PetscCall(PetscFree((*A)->factorprefix));
1462: PetscCall(PetscFree((*A)->defaultvectype));
1463: PetscCall(PetscFree((*A)->defaultrandtype));
1464: PetscCall(PetscFree((*A)->bsizes));
1465: PetscCall(PetscFree((*A)->solvertype));
1466: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1467: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1468: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1469: PetscCall(MatProductClear(*A));
1470: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1471: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1472: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1473: PetscCall(MatDestroy(&(*A)->schur));
1474: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1475: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1476: PetscCall(PetscHeaderDestroy(A));
1477: PetscFunctionReturn(PETSC_SUCCESS);
1478: }
1480: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1481: /*@
1482: MatSetValues - Inserts or adds a block of values into a matrix.
1483: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1484: MUST be called after all calls to `MatSetValues()` have been completed.
1486: Not Collective
1488: Input Parameters:
1489: + mat - the matrix
1490: . m - the number of rows
1491: . idxm - the global indices of the rows
1492: . n - the number of columns
1493: . idxn - the global indices of the columns
1494: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1495: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1496: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1498: Level: beginner
1500: Notes:
1501: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1502: options cannot be mixed without intervening calls to the assembly
1503: routines.
1505: `MatSetValues()` uses 0-based row and column numbers in Fortran
1506: as well as in C.
1508: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1509: simply ignored. This allows easily inserting element stiffness matrices
1510: with homogeneous Dirichlet boundary conditions that you don't want represented
1511: in the matrix.
1513: Efficiency Alert:
1514: The routine `MatSetValuesBlocked()` may offer much better efficiency
1515: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1517: Fortran Notes:
1518: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1519: .vb
1520: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1521: .ve
1523: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1525: Developer Note:
1526: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1527: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1529: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1530: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1531: @*/
1532: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1533: {
1534: PetscFunctionBeginHot;
1537: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1538: PetscAssertPointer(idxm, 3);
1539: PetscAssertPointer(idxn, 5);
1540: MatCheckPreallocated(mat, 1);
1542: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1543: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1545: if (PetscDefined(USE_DEBUG)) {
1546: PetscInt i, j;
1548: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1549: if (v) {
1550: for (i = 0; i < m; i++) {
1551: for (j = 0; j < n; j++) {
1552: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1553: #if defined(PETSC_USE_COMPLEX)
1554: 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]);
1555: #else
1556: 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]);
1557: #endif
1558: }
1559: }
1560: }
1561: 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);
1562: 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);
1563: }
1565: if (mat->assembled) {
1566: mat->was_assembled = PETSC_TRUE;
1567: mat->assembled = PETSC_FALSE;
1568: }
1569: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1570: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1571: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1572: PetscFunctionReturn(PETSC_SUCCESS);
1573: }
1575: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1576: /*@
1577: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1578: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1579: MUST be called after all calls to `MatSetValues()` have been completed.
1581: Not Collective
1583: Input Parameters:
1584: + mat - the matrix
1585: . ism - the rows to provide
1586: . isn - the columns to provide
1587: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1588: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1589: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1591: Level: beginner
1593: Notes:
1594: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1596: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1597: options cannot be mixed without intervening calls to the assembly
1598: routines.
1600: `MatSetValues()` uses 0-based row and column numbers in Fortran
1601: as well as in C.
1603: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1604: simply ignored. This allows easily inserting element stiffness matrices
1605: with homogeneous Dirichlet boundary conditions that you don't want represented
1606: in the matrix.
1608: Fortran Note:
1609: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1611: Efficiency Alert:
1612: The routine `MatSetValuesBlocked()` may offer much better efficiency
1613: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1615: This is currently not optimized for any particular `ISType`
1617: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1618: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1619: @*/
1620: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1621: {
1622: PetscInt m, n;
1623: const PetscInt *rows, *cols;
1625: PetscFunctionBeginHot;
1627: PetscCall(ISGetIndices(ism, &rows));
1628: PetscCall(ISGetIndices(isn, &cols));
1629: PetscCall(ISGetLocalSize(ism, &m));
1630: PetscCall(ISGetLocalSize(isn, &n));
1631: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1632: PetscCall(ISRestoreIndices(ism, &rows));
1633: PetscCall(ISRestoreIndices(isn, &cols));
1634: PetscFunctionReturn(PETSC_SUCCESS);
1635: }
1637: /*@
1638: MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1639: values into a matrix
1641: Not Collective
1643: Input Parameters:
1644: + mat - the matrix
1645: . row - the (block) row to set
1646: - v - a one-dimensional array that contains the values. For `MATBAIJ` they are implicitly stored as a two-dimensional array, by default in row-major order.
1647: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1649: Level: intermediate
1651: Notes:
1652: The values, `v`, are column-oriented (for the block version) and sorted
1654: All the nonzero values in `row` must be provided
1656: The matrix must have previously had its column indices set, likely by having been assembled.
1658: `row` must belong to this MPI process
1660: Fortran Note:
1661: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1663: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1664: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1665: @*/
1666: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1667: {
1668: PetscInt globalrow;
1670: PetscFunctionBegin;
1673: PetscAssertPointer(v, 3);
1674: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1675: PetscCall(MatSetValuesRow(mat, globalrow, v));
1676: PetscFunctionReturn(PETSC_SUCCESS);
1677: }
1679: /*@
1680: MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1681: values into a matrix
1683: Not Collective
1685: Input Parameters:
1686: + mat - the matrix
1687: . row - the (block) row to set
1688: - 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
1690: Level: advanced
1692: Notes:
1693: The values, `v`, are column-oriented for the block version.
1695: All the nonzeros in `row` must be provided
1697: THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.
1699: `row` must belong to this process
1701: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1702: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1703: @*/
1704: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1705: {
1706: PetscFunctionBeginHot;
1709: MatCheckPreallocated(mat, 1);
1710: PetscAssertPointer(v, 3);
1711: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1712: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1713: mat->insertmode = INSERT_VALUES;
1715: if (mat->assembled) {
1716: mat->was_assembled = PETSC_TRUE;
1717: mat->assembled = PETSC_FALSE;
1718: }
1719: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1720: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1721: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1722: PetscFunctionReturn(PETSC_SUCCESS);
1723: }
1725: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1726: /*@
1727: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1728: Using structured grid indexing
1730: Not Collective
1732: Input Parameters:
1733: + mat - the matrix
1734: . m - number of rows being entered
1735: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1736: . n - number of columns being entered
1737: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1738: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1739: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1740: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1742: Level: beginner
1744: Notes:
1745: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1747: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1748: options cannot be mixed without intervening calls to the assembly
1749: routines.
1751: The grid coordinates are across the entire grid, not just the local portion
1753: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1754: as well as in C.
1756: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1758: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1759: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1761: The columns and rows in the stencil passed in MUST be contained within the
1762: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1763: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1764: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1765: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1767: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1768: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1769: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1770: `DM_BOUNDARY_PERIODIC` boundary type.
1772: 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
1773: a single value per point) you can skip filling those indices.
1775: Inspired by the structured grid interface to the HYPRE package
1776: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1778: Fortran Note:
1779: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1781: Efficiency Alert:
1782: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1783: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1785: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1786: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1787: @*/
1788: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1789: {
1790: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1791: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1792: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1794: PetscFunctionBegin;
1795: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1798: PetscAssertPointer(idxm, 3);
1799: PetscAssertPointer(idxn, 5);
1801: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1802: jdxm = buf;
1803: jdxn = buf + m;
1804: } else {
1805: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1806: jdxm = bufm;
1807: jdxn = bufn;
1808: }
1809: for (i = 0; i < m; i++) {
1810: for (j = 0; j < 3 - sdim; j++) dxm++;
1811: tmp = *dxm++ - starts[0];
1812: for (j = 0; j < dim - 1; j++) {
1813: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1814: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1815: }
1816: if (mat->stencil.noc) dxm++;
1817: jdxm[i] = tmp;
1818: }
1819: for (i = 0; i < n; i++) {
1820: for (j = 0; j < 3 - sdim; j++) dxn++;
1821: tmp = *dxn++ - starts[0];
1822: for (j = 0; j < dim - 1; j++) {
1823: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1824: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1825: }
1826: if (mat->stencil.noc) dxn++;
1827: jdxn[i] = tmp;
1828: }
1829: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1830: PetscCall(PetscFree2(bufm, bufn));
1831: PetscFunctionReturn(PETSC_SUCCESS);
1832: }
1834: /*@
1835: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1836: Using structured grid indexing
1838: Not Collective
1840: Input Parameters:
1841: + mat - the matrix
1842: . m - number of rows being entered
1843: . idxm - grid coordinates for matrix rows being entered
1844: . n - number of columns being entered
1845: . idxn - grid coordinates for matrix columns being entered
1846: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1847: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1848: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1850: Level: beginner
1852: Notes:
1853: By default the values, `v`, are row-oriented and unsorted.
1854: See `MatSetOption()` for other options.
1856: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1857: options cannot be mixed without intervening calls to the assembly
1858: routines.
1860: The grid coordinates are across the entire grid, not just the local portion
1862: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1863: as well as in C.
1865: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1867: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1868: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1870: The columns and rows in the stencil passed in MUST be contained within the
1871: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1872: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1873: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1874: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1876: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1877: simply ignored. This allows easily inserting element stiffness matrices
1878: with homogeneous Dirichlet boundary conditions that you don't want represented
1879: in the matrix.
1881: Inspired by the structured grid interface to the HYPRE package
1882: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1884: Fortran Notes:
1885: `idxm` and `idxn` should be declared as
1886: .vb
1887: MatStencil idxm(4,m),idxn(4,n)
1888: .ve
1889: and the values inserted using
1890: .vb
1891: idxm(MatStencil_i,1) = i
1892: idxm(MatStencil_j,1) = j
1893: idxm(MatStencil_k,1) = k
1894: etc
1895: .ve
1897: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1899: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1900: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1901: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1902: @*/
1903: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1904: {
1905: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1906: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1907: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1909: PetscFunctionBegin;
1910: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1913: PetscAssertPointer(idxm, 3);
1914: PetscAssertPointer(idxn, 5);
1915: PetscAssertPointer(v, 6);
1917: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1918: jdxm = buf;
1919: jdxn = buf + m;
1920: } else {
1921: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1922: jdxm = bufm;
1923: jdxn = bufn;
1924: }
1925: for (i = 0; i < m; i++) {
1926: for (j = 0; j < 3 - sdim; j++) dxm++;
1927: tmp = *dxm++ - starts[0];
1928: for (j = 0; j < sdim - 1; j++) {
1929: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1930: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1931: }
1932: dxm++;
1933: jdxm[i] = tmp;
1934: }
1935: for (i = 0; i < n; i++) {
1936: for (j = 0; j < 3 - sdim; j++) dxn++;
1937: tmp = *dxn++ - starts[0];
1938: for (j = 0; j < sdim - 1; j++) {
1939: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1940: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1941: }
1942: dxn++;
1943: jdxn[i] = tmp;
1944: }
1945: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1946: PetscCall(PetscFree2(bufm, bufn));
1947: PetscFunctionReturn(PETSC_SUCCESS);
1948: }
1950: /*@
1951: MatSetStencil - Sets the grid information for setting values into a matrix via
1952: `MatSetValuesStencil()`
1954: Not Collective
1956: Input Parameters:
1957: + mat - the matrix
1958: . dim - dimension of the grid 1, 2, or 3
1959: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1960: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1961: - dof - number of degrees of freedom per node
1963: Level: beginner
1965: Notes:
1966: Inspired by the structured grid interface to the HYPRE package
1967: (www.llnl.gov/CASC/hyper)
1969: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1970: user.
1972: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1973: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1974: @*/
1975: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1976: {
1977: PetscFunctionBegin;
1979: PetscAssertPointer(dims, 3);
1980: PetscAssertPointer(starts, 4);
1982: mat->stencil.dim = dim + (dof > 1);
1983: for (PetscInt i = 0; i < dim; i++) {
1984: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
1985: mat->stencil.starts[i] = starts[dim - i - 1];
1986: }
1987: mat->stencil.dims[dim] = dof;
1988: mat->stencil.starts[dim] = 0;
1989: mat->stencil.noc = (PetscBool)(dof == 1);
1990: PetscFunctionReturn(PETSC_SUCCESS);
1991: }
1993: /*@
1994: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
1996: Not Collective
1998: Input Parameters:
1999: + mat - the matrix
2000: . m - the number of block rows
2001: . idxm - the global block indices
2002: . n - the number of block columns
2003: . idxn - the global block indices
2004: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2005: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2006: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
2008: Level: intermediate
2010: Notes:
2011: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2012: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2014: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2015: NOT the total number of rows/columns; for example, if the block size is 2 and
2016: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2017: The values in `idxm` would be 1 2; that is the first index for each block divided by
2018: the block size.
2020: You must call `MatSetBlockSize()` when constructing this matrix (before
2021: preallocating it).
2023: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2025: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2026: options cannot be mixed without intervening calls to the assembly
2027: routines.
2029: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2030: as well as in C.
2032: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2033: simply ignored. This allows easily inserting element stiffness matrices
2034: with homogeneous Dirichlet boundary conditions that you don't want represented
2035: in the matrix.
2037: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2038: internal searching must be done to determine where to place the
2039: data in the matrix storage space. By instead inserting blocks of
2040: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2041: reduced.
2043: Example:
2044: .vb
2045: Suppose m=n=2 and block size(bs) = 2 The array is
2047: 1 2 | 3 4
2048: 5 6 | 7 8
2049: - - - | - - -
2050: 9 10 | 11 12
2051: 13 14 | 15 16
2053: v[] should be passed in like
2054: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2056: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2057: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2058: .ve
2060: Fortran Notes:
2061: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2062: .vb
2063: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2064: .ve
2066: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2068: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2069: @*/
2070: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2071: {
2072: PetscFunctionBeginHot;
2075: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2076: PetscAssertPointer(idxm, 3);
2077: PetscAssertPointer(idxn, 5);
2078: MatCheckPreallocated(mat, 1);
2079: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2080: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2081: if (PetscDefined(USE_DEBUG)) {
2082: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2083: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2084: }
2085: if (PetscDefined(USE_DEBUG)) {
2086: PetscInt rbs, cbs, M, N, i;
2087: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2088: PetscCall(MatGetSize(mat, &M, &N));
2089: for (i = 0; i < m; i++) PetscCheck(idxm[i] * rbs < M, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row block %" PetscInt_FMT " contains an index %" PetscInt_FMT "*%" PetscInt_FMT " greater than row length %" PetscInt_FMT, i, idxm[i], rbs, M);
2090: for (i = 0; i < n; i++)
2091: PetscCheck(idxn[i] * cbs < N, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column block %" PetscInt_FMT " contains an index %" PetscInt_FMT "*%" PetscInt_FMT " greater than column length %" PetscInt_FMT, i, idxn[i], cbs, N);
2092: }
2093: if (mat->assembled) {
2094: mat->was_assembled = PETSC_TRUE;
2095: mat->assembled = PETSC_FALSE;
2096: }
2097: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2098: if (mat->ops->setvaluesblocked) {
2099: PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2100: } else {
2101: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2102: PetscInt i, j, bs, cbs;
2104: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2105: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2106: iidxm = buf;
2107: iidxn = buf + m * bs;
2108: } else {
2109: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2110: iidxm = bufr;
2111: iidxn = bufc;
2112: }
2113: for (i = 0; i < m; i++) {
2114: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2115: }
2116: if (m != n || bs != cbs || idxm != idxn) {
2117: for (i = 0; i < n; i++) {
2118: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2119: }
2120: } else iidxn = iidxm;
2121: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2122: PetscCall(PetscFree2(bufr, bufc));
2123: }
2124: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2125: PetscFunctionReturn(PETSC_SUCCESS);
2126: }
2128: /*@
2129: MatGetValues - Gets a block of local values from a matrix.
2131: Not Collective; can only return values that are owned by the give process
2133: Input Parameters:
2134: + mat - the matrix
2135: . v - a logically two-dimensional array for storing the values
2136: . m - the number of rows
2137: . idxm - the global indices of the rows
2138: . n - the number of columns
2139: - idxn - the global indices of the columns
2141: Level: advanced
2143: Notes:
2144: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2145: The values, `v`, are then returned in a row-oriented format,
2146: analogous to that used by default in `MatSetValues()`.
2148: `MatGetValues()` uses 0-based row and column numbers in
2149: Fortran as well as in C.
2151: `MatGetValues()` requires that the matrix has been assembled
2152: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2153: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2154: without intermediate matrix assembly.
2156: Negative row or column indices will be ignored and those locations in `v` will be
2157: left unchanged.
2159: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2160: That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2161: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2163: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2164: @*/
2165: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2166: {
2167: PetscFunctionBegin;
2170: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2171: PetscAssertPointer(idxm, 3);
2172: PetscAssertPointer(idxn, 5);
2173: PetscAssertPointer(v, 6);
2174: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2175: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2176: MatCheckPreallocated(mat, 1);
2178: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2179: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2180: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2181: PetscFunctionReturn(PETSC_SUCCESS);
2182: }
2184: /*@
2185: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2186: defined previously by `MatSetLocalToGlobalMapping()`
2188: Not Collective
2190: Input Parameters:
2191: + mat - the matrix
2192: . nrow - number of rows
2193: . irow - the row local indices
2194: . ncol - number of columns
2195: - icol - the column local indices
2197: Output Parameter:
2198: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2199: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2201: Level: advanced
2203: Notes:
2204: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2206: This routine can only return values that are owned by the requesting MPI process. That is, for standard matrix formats, rows that, in the global numbering,
2207: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2208: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2209: with `MatSetLocalToGlobalMapping()`.
2211: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2212: `MatSetValuesLocal()`, `MatGetValues()`
2213: @*/
2214: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2215: {
2216: PetscFunctionBeginHot;
2219: MatCheckPreallocated(mat, 1);
2220: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2221: PetscAssertPointer(irow, 3);
2222: PetscAssertPointer(icol, 5);
2223: if (PetscDefined(USE_DEBUG)) {
2224: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2225: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2226: }
2227: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2228: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2229: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2230: else {
2231: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2232: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2233: irowm = buf;
2234: icolm = buf + nrow;
2235: } else {
2236: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2237: irowm = bufr;
2238: icolm = bufc;
2239: }
2240: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2241: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2242: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2243: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2244: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2245: PetscCall(PetscFree2(bufr, bufc));
2246: }
2247: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2248: PetscFunctionReturn(PETSC_SUCCESS);
2249: }
2251: /*@
2252: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2253: the same size. Currently, this can only be called once and creates the given matrix.
2255: Not Collective
2257: Input Parameters:
2258: + mat - the matrix
2259: . nb - the number of blocks
2260: . bs - the number of rows (and columns) in each block
2261: . rows - a concatenation of the rows for each block
2262: - v - a concatenation of logically two-dimensional arrays of values
2264: Level: advanced
2266: Notes:
2267: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2269: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2271: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2272: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2273: @*/
2274: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2275: {
2276: PetscFunctionBegin;
2279: PetscAssertPointer(rows, 4);
2280: PetscAssertPointer(v, 5);
2281: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2283: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2284: if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2285: else {
2286: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2287: }
2288: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2289: PetscFunctionReturn(PETSC_SUCCESS);
2290: }
2292: /*@
2293: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2294: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2295: using a local (per-processor) numbering.
2297: Not Collective
2299: Input Parameters:
2300: + x - the matrix
2301: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2302: - cmapping - column mapping
2304: Level: intermediate
2306: Note:
2307: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2309: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2310: @*/
2311: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2312: {
2313: PetscFunctionBegin;
2318: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2319: else {
2320: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2321: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2322: }
2323: PetscFunctionReturn(PETSC_SUCCESS);
2324: }
2326: /*@
2327: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2329: Not Collective
2331: Input Parameter:
2332: . A - the matrix
2334: Output Parameters:
2335: + rmapping - row mapping
2336: - cmapping - column mapping
2338: Level: advanced
2340: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2341: @*/
2342: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2343: {
2344: PetscFunctionBegin;
2347: if (rmapping) {
2348: PetscAssertPointer(rmapping, 2);
2349: *rmapping = A->rmap->mapping;
2350: }
2351: if (cmapping) {
2352: PetscAssertPointer(cmapping, 3);
2353: *cmapping = A->cmap->mapping;
2354: }
2355: PetscFunctionReturn(PETSC_SUCCESS);
2356: }
2358: /*@
2359: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2361: Logically Collective
2363: Input Parameters:
2364: + A - the matrix
2365: . rmap - row layout
2366: - cmap - column layout
2368: Level: advanced
2370: Note:
2371: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2373: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2374: @*/
2375: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2376: {
2377: PetscFunctionBegin;
2379: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2380: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2381: PetscFunctionReturn(PETSC_SUCCESS);
2382: }
2384: /*@
2385: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2387: Not Collective
2389: Input Parameter:
2390: . A - the matrix
2392: Output Parameters:
2393: + rmap - row layout
2394: - cmap - column layout
2396: Level: advanced
2398: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2399: @*/
2400: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2401: {
2402: PetscFunctionBegin;
2405: if (rmap) {
2406: PetscAssertPointer(rmap, 2);
2407: *rmap = A->rmap;
2408: }
2409: if (cmap) {
2410: PetscAssertPointer(cmap, 3);
2411: *cmap = A->cmap;
2412: }
2413: PetscFunctionReturn(PETSC_SUCCESS);
2414: }
2416: /*@
2417: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2418: using a local numbering of the rows and columns.
2420: Not Collective
2422: Input Parameters:
2423: + mat - the matrix
2424: . nrow - number of rows
2425: . irow - the row local indices
2426: . ncol - number of columns
2427: . icol - the column local indices
2428: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2429: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2430: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2432: Level: intermediate
2434: Notes:
2435: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2437: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2438: options cannot be mixed without intervening calls to the assembly
2439: routines.
2441: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2442: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2444: Fortran Notes:
2445: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2446: .vb
2447: call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2448: .ve
2450: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2452: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2453: `MatGetValuesLocal()`
2454: @*/
2455: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2456: {
2457: PetscFunctionBeginHot;
2460: MatCheckPreallocated(mat, 1);
2461: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2462: PetscAssertPointer(irow, 3);
2463: PetscAssertPointer(icol, 5);
2464: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2465: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2466: if (PetscDefined(USE_DEBUG)) {
2467: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2468: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2469: }
2471: if (mat->assembled) {
2472: mat->was_assembled = PETSC_TRUE;
2473: mat->assembled = PETSC_FALSE;
2474: }
2475: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2476: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2477: else {
2478: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2479: const PetscInt *irowm, *icolm;
2481: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2482: bufr = buf;
2483: bufc = buf + nrow;
2484: irowm = bufr;
2485: icolm = bufc;
2486: } else {
2487: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2488: irowm = bufr;
2489: icolm = bufc;
2490: }
2491: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2492: else irowm = irow;
2493: if (mat->cmap->mapping) {
2494: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2495: else icolm = irowm;
2496: } else icolm = icol;
2497: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2498: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2499: }
2500: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2501: PetscFunctionReturn(PETSC_SUCCESS);
2502: }
2504: /*@
2505: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2506: using a local ordering of the nodes a block at a time.
2508: Not Collective
2510: Input Parameters:
2511: + mat - the matrix
2512: . nrow - number of rows
2513: . irow - the row local indices
2514: . ncol - number of columns
2515: . icol - the column local indices
2516: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2517: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2518: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2520: Level: intermediate
2522: Notes:
2523: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2524: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2526: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2527: options cannot be mixed without intervening calls to the assembly
2528: routines.
2530: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2531: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2533: Fortran Notes:
2534: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2535: .vb
2536: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2537: .ve
2539: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2541: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2542: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2543: @*/
2544: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2545: {
2546: PetscFunctionBeginHot;
2549: MatCheckPreallocated(mat, 1);
2550: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2551: PetscAssertPointer(irow, 3);
2552: PetscAssertPointer(icol, 5);
2553: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2554: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2555: if (PetscDefined(USE_DEBUG)) {
2556: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2557: 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);
2558: }
2560: if (mat->assembled) {
2561: mat->was_assembled = PETSC_TRUE;
2562: mat->assembled = PETSC_FALSE;
2563: }
2564: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2565: PetscInt irbs, rbs;
2566: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2567: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2568: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2569: }
2570: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2571: PetscInt icbs, cbs;
2572: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2573: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2574: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2575: }
2576: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2577: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2578: else {
2579: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2580: const PetscInt *irowm, *icolm;
2582: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2583: bufr = buf;
2584: bufc = buf + nrow;
2585: irowm = bufr;
2586: icolm = bufc;
2587: } else {
2588: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2589: irowm = bufr;
2590: icolm = bufc;
2591: }
2592: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2593: else irowm = irow;
2594: if (mat->cmap->mapping) {
2595: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2596: else icolm = irowm;
2597: } else icolm = icol;
2598: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2599: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2600: }
2601: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2602: PetscFunctionReturn(PETSC_SUCCESS);
2603: }
2605: /*@
2606: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2608: Collective
2610: Input Parameters:
2611: + mat - the matrix
2612: - x - the vector to be multiplied
2614: Output Parameter:
2615: . y - the result
2617: Level: developer
2619: Note:
2620: The vectors `x` and `y` cannot be the same. I.e., one cannot
2621: call `MatMultDiagonalBlock`(A,y,y).
2623: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2624: @*/
2625: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2626: {
2627: PetscFunctionBegin;
2633: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2634: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2635: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2636: MatCheckPreallocated(mat, 1);
2638: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2639: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2640: PetscFunctionReturn(PETSC_SUCCESS);
2641: }
2643: /*@
2644: MatMult - Computes the matrix-vector product, $y = Ax$.
2646: Neighbor-wise Collective
2648: Input Parameters:
2649: + mat - the matrix
2650: - x - the vector to be multiplied
2652: Output Parameter:
2653: . y - the result
2655: Level: beginner
2657: Note:
2658: The vectors `x` and `y` cannot be the same. I.e., one cannot
2659: call `MatMult`(A,y,y).
2661: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2662: @*/
2663: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2664: {
2665: PetscFunctionBegin;
2669: VecCheckAssembled(x);
2671: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2672: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2673: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2674: 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);
2675: 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);
2676: 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);
2677: 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);
2678: PetscCall(VecSetErrorIfLocked(y, 3));
2679: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2680: MatCheckPreallocated(mat, 1);
2682: PetscCall(VecLockReadPush(x));
2683: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2684: PetscUseTypeMethod(mat, mult, x, y);
2685: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2686: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2687: PetscCall(VecLockReadPop(x));
2688: PetscFunctionReturn(PETSC_SUCCESS);
2689: }
2691: /*@
2692: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2694: Neighbor-wise Collective
2696: Input Parameters:
2697: + mat - the matrix
2698: - x - the vector to be multiplied
2700: Output Parameter:
2701: . y - the result
2703: Level: beginner
2705: Notes:
2706: The vectors `x` and `y` cannot be the same. I.e., one cannot
2707: call `MatMultTranspose`(A,y,y).
2709: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2710: use `MatMultHermitianTranspose()`
2712: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2713: @*/
2714: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2715: {
2716: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2718: PetscFunctionBegin;
2722: VecCheckAssembled(x);
2725: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2726: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2727: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2728: 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);
2729: 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);
2730: 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);
2731: 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);
2732: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2733: MatCheckPreallocated(mat, 1);
2735: if (!mat->ops->multtranspose) {
2736: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2737: 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);
2738: } else op = mat->ops->multtranspose;
2739: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2740: PetscCall(VecLockReadPush(x));
2741: PetscCall((*op)(mat, x, y));
2742: PetscCall(VecLockReadPop(x));
2743: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2744: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2745: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2746: PetscFunctionReturn(PETSC_SUCCESS);
2747: }
2749: /*@
2750: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2752: Neighbor-wise Collective
2754: Input Parameters:
2755: + mat - the matrix
2756: - x - the vector to be multiplied
2758: Output Parameter:
2759: . y - the result
2761: Level: beginner
2763: Notes:
2764: The vectors `x` and `y` cannot be the same. I.e., one cannot
2765: call `MatMultHermitianTranspose`(A,y,y).
2767: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2769: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2771: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2772: @*/
2773: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2774: {
2775: PetscFunctionBegin;
2781: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2782: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2783: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2784: 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);
2785: 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);
2786: 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);
2787: 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);
2788: MatCheckPreallocated(mat, 1);
2790: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2791: #if defined(PETSC_USE_COMPLEX)
2792: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2793: PetscCall(VecLockReadPush(x));
2794: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2795: else PetscUseTypeMethod(mat, mult, x, y);
2796: PetscCall(VecLockReadPop(x));
2797: } else {
2798: Vec w;
2799: PetscCall(VecDuplicate(x, &w));
2800: PetscCall(VecCopy(x, w));
2801: PetscCall(VecConjugate(w));
2802: PetscCall(MatMultTranspose(mat, w, y));
2803: PetscCall(VecDestroy(&w));
2804: PetscCall(VecConjugate(y));
2805: }
2806: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2807: #else
2808: PetscCall(MatMultTranspose(mat, x, y));
2809: #endif
2810: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2811: PetscFunctionReturn(PETSC_SUCCESS);
2812: }
2814: /*@
2815: MatMultAdd - Computes $v3 = v2 + A * v1$.
2817: Neighbor-wise Collective
2819: Input Parameters:
2820: + mat - the matrix
2821: . v1 - the vector to be multiplied by `mat`
2822: - v2 - the vector to be added to the result
2824: Output Parameter:
2825: . v3 - the result
2827: Level: beginner
2829: Note:
2830: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2831: call `MatMultAdd`(A,v1,v2,v1).
2833: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2834: @*/
2835: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2836: {
2837: PetscFunctionBegin;
2844: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2845: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2846: 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);
2847: /* 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);
2848: 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); */
2849: 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);
2850: 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);
2851: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2852: MatCheckPreallocated(mat, 1);
2854: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2855: PetscCall(VecLockReadPush(v1));
2856: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2857: PetscCall(VecLockReadPop(v1));
2858: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2859: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2860: PetscFunctionReturn(PETSC_SUCCESS);
2861: }
2863: /*@
2864: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2866: Neighbor-wise Collective
2868: Input Parameters:
2869: + mat - the matrix
2870: . v1 - the vector to be multiplied by the transpose of the matrix
2871: - v2 - the vector to be added to the result
2873: Output Parameter:
2874: . v3 - the result
2876: Level: beginner
2878: Note:
2879: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2880: call `MatMultTransposeAdd`(A,v1,v2,v1).
2882: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2883: @*/
2884: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2885: {
2886: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2888: PetscFunctionBegin;
2895: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2896: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2897: 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);
2898: 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);
2899: 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);
2900: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2901: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2902: MatCheckPreallocated(mat, 1);
2904: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2905: PetscCall(VecLockReadPush(v1));
2906: PetscCall((*op)(mat, v1, v2, v3));
2907: PetscCall(VecLockReadPop(v1));
2908: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2909: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2910: PetscFunctionReturn(PETSC_SUCCESS);
2911: }
2913: /*@
2914: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2916: Neighbor-wise Collective
2918: Input Parameters:
2919: + mat - the matrix
2920: . v1 - the vector to be multiplied by the Hermitian transpose
2921: - v2 - the vector to be added to the result
2923: Output Parameter:
2924: . v3 - the result
2926: Level: beginner
2928: Note:
2929: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2930: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2932: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2933: @*/
2934: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2935: {
2936: PetscFunctionBegin;
2943: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2944: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2945: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2946: 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);
2947: 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);
2948: 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);
2949: MatCheckPreallocated(mat, 1);
2951: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2952: PetscCall(VecLockReadPush(v1));
2953: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2954: else {
2955: Vec w, z;
2956: PetscCall(VecDuplicate(v1, &w));
2957: PetscCall(VecCopy(v1, w));
2958: PetscCall(VecConjugate(w));
2959: PetscCall(VecDuplicate(v3, &z));
2960: PetscCall(MatMultTranspose(mat, w, z));
2961: PetscCall(VecDestroy(&w));
2962: PetscCall(VecConjugate(z));
2963: if (v2 != v3) {
2964: PetscCall(VecWAXPY(v3, 1.0, v2, z));
2965: } else {
2966: PetscCall(VecAXPY(v3, 1.0, z));
2967: }
2968: PetscCall(VecDestroy(&z));
2969: }
2970: PetscCall(VecLockReadPop(v1));
2971: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2972: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2973: PetscFunctionReturn(PETSC_SUCCESS);
2974: }
2976: /*@
2977: MatGetFactorType - gets the type of factorization a matrix is
2979: Not Collective
2981: Input Parameter:
2982: . mat - the matrix
2984: Output Parameter:
2985: . 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`
2987: Level: intermediate
2989: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2990: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2991: @*/
2992: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
2993: {
2994: PetscFunctionBegin;
2997: PetscAssertPointer(t, 2);
2998: *t = mat->factortype;
2999: PetscFunctionReturn(PETSC_SUCCESS);
3000: }
3002: /*@
3003: MatSetFactorType - sets the type of factorization a matrix is
3005: Logically Collective
3007: Input Parameters:
3008: + mat - the matrix
3009: - 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`
3011: Level: intermediate
3013: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3014: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3015: @*/
3016: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3017: {
3018: PetscFunctionBegin;
3021: mat->factortype = t;
3022: PetscFunctionReturn(PETSC_SUCCESS);
3023: }
3025: /*@
3026: MatGetInfo - Returns information about matrix storage (number of
3027: nonzeros, memory, etc.).
3029: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3031: Input Parameters:
3032: + mat - the matrix
3033: - 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)
3035: Output Parameter:
3036: . info - matrix information context
3038: Options Database Key:
3039: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3041: Level: intermediate
3043: Notes:
3044: The `MatInfo` context contains a variety of matrix data, including
3045: number of nonzeros allocated and used, number of mallocs during
3046: matrix assembly, etc. Additional information for factored matrices
3047: is provided (such as the fill ratio, number of mallocs during
3048: factorization, etc.).
3050: Example:
3051: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3052: data within the `MatInfo` context. For example,
3053: .vb
3054: MatInfo info;
3055: Mat A;
3056: double mal, nz_a, nz_u;
3058: MatGetInfo(A, MAT_LOCAL, &info);
3059: mal = info.mallocs;
3060: nz_a = info.nz_allocated;
3061: .ve
3063: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3064: @*/
3065: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3066: {
3067: PetscFunctionBegin;
3070: PetscAssertPointer(info, 3);
3071: MatCheckPreallocated(mat, 1);
3072: PetscUseTypeMethod(mat, getinfo, flag, info);
3073: PetscFunctionReturn(PETSC_SUCCESS);
3074: }
3076: /*
3077: This is used by external packages where it is not easy to get the info from the actual
3078: matrix factorization.
3079: */
3080: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3081: {
3082: PetscFunctionBegin;
3083: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3084: PetscFunctionReturn(PETSC_SUCCESS);
3085: }
3087: /*@
3088: MatLUFactor - Performs in-place LU factorization of matrix.
3090: Collective
3092: Input Parameters:
3093: + mat - the matrix
3094: . row - row permutation
3095: . col - column permutation
3096: - info - options for factorization, includes
3097: .vb
3098: fill - expected fill as ratio of original fill.
3099: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3100: Run with the option -info to determine an optimal value to use
3101: .ve
3103: Level: developer
3105: Notes:
3106: Most users should employ the `KSP` interface for linear solvers
3107: instead of working directly with matrix algebra routines such as this.
3108: See, e.g., `KSPCreate()`.
3110: This changes the state of the matrix to a factored matrix; it cannot be used
3111: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3113: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3114: when not using `KSP`.
3116: Fortran Note:
3117: A valid (non-null) `info` argument must be provided
3119: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3120: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3121: @*/
3122: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3123: {
3124: MatFactorInfo tinfo;
3126: PetscFunctionBegin;
3130: if (info) PetscAssertPointer(info, 4);
3132: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3133: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3134: MatCheckPreallocated(mat, 1);
3135: if (!info) {
3136: PetscCall(MatFactorInfoInitialize(&tinfo));
3137: info = &tinfo;
3138: }
3140: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3141: PetscUseTypeMethod(mat, lufactor, row, col, info);
3142: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3143: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3144: PetscFunctionReturn(PETSC_SUCCESS);
3145: }
3147: /*@
3148: MatILUFactor - Performs in-place ILU factorization of matrix.
3150: Collective
3152: Input Parameters:
3153: + mat - the matrix
3154: . row - row permutation
3155: . col - column permutation
3156: - info - structure containing
3157: .vb
3158: levels - number of levels of fill.
3159: expected fill - as ratio of original fill.
3160: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3161: missing diagonal entries)
3162: .ve
3164: Level: developer
3166: Notes:
3167: Most users should employ the `KSP` interface for linear solvers
3168: instead of working directly with matrix algebra routines such as this.
3169: See, e.g., `KSPCreate()`.
3171: Probably really in-place only when level of fill is zero, otherwise allocates
3172: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3173: when not using `KSP`.
3175: Fortran Note:
3176: A valid (non-null) `info` argument must be provided
3178: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3179: @*/
3180: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3181: {
3182: PetscFunctionBegin;
3186: PetscAssertPointer(info, 4);
3188: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3189: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3190: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3191: MatCheckPreallocated(mat, 1);
3193: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3194: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3195: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3196: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3197: PetscFunctionReturn(PETSC_SUCCESS);
3198: }
3200: /*@
3201: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3202: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3204: Collective
3206: Input Parameters:
3207: + fact - the factor matrix obtained with `MatGetFactor()`
3208: . mat - the matrix
3209: . row - the row permutation
3210: . col - the column permutation
3211: - info - options for factorization, includes
3212: .vb
3213: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3214: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3215: .ve
3217: Level: developer
3219: Notes:
3220: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3222: Most users should employ the simplified `KSP` interface for linear solvers
3223: instead of working directly with matrix algebra routines such as this.
3224: See, e.g., `KSPCreate()`.
3226: Fortran Note:
3227: A valid (non-null) `info` argument must be provided
3229: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3230: @*/
3231: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3232: {
3233: MatFactorInfo tinfo;
3235: PetscFunctionBegin;
3240: if (info) PetscAssertPointer(info, 5);
3243: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3244: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3245: MatCheckPreallocated(mat, 2);
3246: if (!info) {
3247: PetscCall(MatFactorInfoInitialize(&tinfo));
3248: info = &tinfo;
3249: }
3251: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3252: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3253: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3254: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3255: PetscFunctionReturn(PETSC_SUCCESS);
3256: }
3258: /*@
3259: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3260: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3262: Collective
3264: Input Parameters:
3265: + fact - the factor matrix obtained with `MatGetFactor()`
3266: . mat - the matrix
3267: - info - options for factorization
3269: Level: developer
3271: Notes:
3272: See `MatLUFactor()` for in-place factorization. See
3273: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3275: Most users should employ the `KSP` interface for linear solvers
3276: instead of working directly with matrix algebra routines such as this.
3277: See, e.g., `KSPCreate()`.
3279: Fortran Note:
3280: A valid (non-null) `info` argument must be provided
3282: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3283: @*/
3284: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3285: {
3286: MatFactorInfo tinfo;
3288: PetscFunctionBegin;
3293: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3294: 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,
3295: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3297: MatCheckPreallocated(mat, 2);
3298: if (!info) {
3299: PetscCall(MatFactorInfoInitialize(&tinfo));
3300: info = &tinfo;
3301: }
3303: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3304: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3305: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3306: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3307: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3308: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3309: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3310: PetscFunctionReturn(PETSC_SUCCESS);
3311: }
3313: /*@
3314: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3315: symmetric matrix.
3317: Collective
3319: Input Parameters:
3320: + mat - the matrix
3321: . perm - row and column permutations
3322: - info - expected fill as ratio of original fill
3324: Level: developer
3326: Notes:
3327: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3328: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3330: Most users should employ the `KSP` interface for linear solvers
3331: instead of working directly with matrix algebra routines such as this.
3332: See, e.g., `KSPCreate()`.
3334: Fortran Note:
3335: A valid (non-null) `info` argument must be provided
3337: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3338: `MatGetOrdering()`
3339: @*/
3340: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3341: {
3342: MatFactorInfo tinfo;
3344: PetscFunctionBegin;
3347: if (info) PetscAssertPointer(info, 3);
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, 1);
3353: if (!info) {
3354: PetscCall(MatFactorInfoInitialize(&tinfo));
3355: info = &tinfo;
3356: }
3358: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3359: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3360: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3361: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3362: PetscFunctionReturn(PETSC_SUCCESS);
3363: }
3365: /*@
3366: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3367: of a symmetric matrix.
3369: Collective
3371: Input Parameters:
3372: + fact - the factor matrix obtained with `MatGetFactor()`
3373: . mat - the matrix
3374: . perm - row and column permutations
3375: - info - options for factorization, includes
3376: .vb
3377: fill - expected fill as ratio of original fill.
3378: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3379: Run with the option -info to determine an optimal value to use
3380: .ve
3382: Level: developer
3384: Notes:
3385: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3386: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3388: Most users should employ the `KSP` interface for linear solvers
3389: instead of working directly with matrix algebra routines such as this.
3390: See, e.g., `KSPCreate()`.
3392: Fortran Note:
3393: A valid (non-null) `info` argument must be provided
3395: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3396: `MatGetOrdering()`
3397: @*/
3398: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3399: {
3400: MatFactorInfo tinfo;
3402: PetscFunctionBegin;
3406: if (info) PetscAssertPointer(info, 4);
3409: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3410: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3411: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3412: MatCheckPreallocated(mat, 2);
3413: if (!info) {
3414: PetscCall(MatFactorInfoInitialize(&tinfo));
3415: info = &tinfo;
3416: }
3418: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3419: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3420: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3421: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3422: PetscFunctionReturn(PETSC_SUCCESS);
3423: }
3425: /*@
3426: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3427: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3428: `MatCholeskyFactorSymbolic()`.
3430: Collective
3432: Input Parameters:
3433: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3434: . mat - the initial matrix that is to be factored
3435: - info - options for factorization
3437: Level: developer
3439: Note:
3440: Most users should employ the `KSP` interface for linear solvers
3441: instead of working directly with matrix algebra routines such as this.
3442: See, e.g., `KSPCreate()`.
3444: Fortran Note:
3445: A valid (non-null) `info` argument must be provided
3447: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3448: @*/
3449: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3450: {
3451: MatFactorInfo tinfo;
3453: PetscFunctionBegin;
3458: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3459: 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,
3460: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3461: MatCheckPreallocated(mat, 2);
3462: if (!info) {
3463: PetscCall(MatFactorInfoInitialize(&tinfo));
3464: info = &tinfo;
3465: }
3467: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3468: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3469: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3470: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3471: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3472: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3473: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3474: PetscFunctionReturn(PETSC_SUCCESS);
3475: }
3477: /*@
3478: MatQRFactor - Performs in-place QR factorization of matrix.
3480: Collective
3482: Input Parameters:
3483: + mat - the matrix
3484: . col - column permutation
3485: - info - options for factorization, includes
3486: .vb
3487: fill - expected fill as ratio of original fill.
3488: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3489: Run with the option -info to determine an optimal value to use
3490: .ve
3492: Level: developer
3494: Notes:
3495: Most users should employ the `KSP` interface for linear solvers
3496: instead of working directly with matrix algebra routines such as this.
3497: See, e.g., `KSPCreate()`.
3499: This changes the state of the matrix to a factored matrix; it cannot be used
3500: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3502: Fortran Note:
3503: A valid (non-null) `info` argument must be provided
3505: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3506: `MatSetUnfactored()`
3507: @*/
3508: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3509: {
3510: PetscFunctionBegin;
3513: if (info) PetscAssertPointer(info, 3);
3515: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3516: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3517: MatCheckPreallocated(mat, 1);
3518: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3519: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3520: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3521: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3522: PetscFunctionReturn(PETSC_SUCCESS);
3523: }
3525: /*@
3526: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3527: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3529: Collective
3531: Input Parameters:
3532: + fact - the factor matrix obtained with `MatGetFactor()`
3533: . mat - the matrix
3534: . col - column permutation
3535: - info - options for factorization, includes
3536: .vb
3537: fill - expected fill as ratio of original fill.
3538: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3539: Run with the option -info to determine an optimal value to use
3540: .ve
3542: Level: developer
3544: Note:
3545: Most users should employ the `KSP` interface for linear solvers
3546: instead of working directly with matrix algebra routines such as this.
3547: See, e.g., `KSPCreate()`.
3549: Fortran Note:
3550: A valid (non-null) `info` argument must be provided
3552: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3553: @*/
3554: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3555: {
3556: MatFactorInfo tinfo;
3558: PetscFunctionBegin;
3562: if (info) PetscAssertPointer(info, 4);
3565: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3566: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3567: MatCheckPreallocated(mat, 2);
3568: if (!info) {
3569: PetscCall(MatFactorInfoInitialize(&tinfo));
3570: info = &tinfo;
3571: }
3573: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3574: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3575: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3576: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3577: PetscFunctionReturn(PETSC_SUCCESS);
3578: }
3580: /*@
3581: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3582: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3584: Collective
3586: Input Parameters:
3587: + fact - the factor matrix obtained with `MatGetFactor()`
3588: . mat - the matrix
3589: - info - options for factorization
3591: Level: developer
3593: Notes:
3594: See `MatQRFactor()` for in-place factorization.
3596: Most users should employ the `KSP` interface for linear solvers
3597: instead of working directly with matrix algebra routines such as this.
3598: See, e.g., `KSPCreate()`.
3600: Fortran Note:
3601: A valid (non-null) `info` argument must be provided
3603: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3604: @*/
3605: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3606: {
3607: MatFactorInfo tinfo;
3609: PetscFunctionBegin;
3614: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3615: 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,
3616: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3618: MatCheckPreallocated(mat, 2);
3619: if (!info) {
3620: PetscCall(MatFactorInfoInitialize(&tinfo));
3621: info = &tinfo;
3622: }
3624: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3625: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3626: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3627: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3628: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3629: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3630: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3631: PetscFunctionReturn(PETSC_SUCCESS);
3632: }
3634: /*@
3635: MatSolve - Solves $A x = b$, given a factored matrix.
3637: Neighbor-wise Collective
3639: Input Parameters:
3640: + mat - the factored matrix
3641: - b - the right-hand-side vector
3643: Output Parameter:
3644: . x - the result vector
3646: Level: developer
3648: Notes:
3649: The vectors `b` and `x` cannot be the same. I.e., one cannot
3650: call `MatSolve`(A,x,x).
3652: Most users should employ the `KSP` interface for linear solvers
3653: instead of working directly with matrix algebra routines such as this.
3654: See, e.g., `KSPCreate()`.
3656: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3657: @*/
3658: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3659: {
3660: PetscFunctionBegin;
3665: PetscCheckSameComm(mat, 1, b, 2);
3666: PetscCheckSameComm(mat, 1, x, 3);
3667: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3668: 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);
3669: 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);
3670: 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);
3671: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3672: MatCheckPreallocated(mat, 1);
3674: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3675: PetscCall(VecFlag(x, mat->factorerrortype));
3676: if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3677: else PetscUseTypeMethod(mat, solve, b, x);
3678: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3679: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3680: PetscFunctionReturn(PETSC_SUCCESS);
3681: }
3683: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3684: {
3685: Vec b, x;
3686: PetscInt N, i;
3687: PetscErrorCode (*f)(Mat, Vec, Vec);
3688: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3690: PetscFunctionBegin;
3691: if (A->factorerrortype) {
3692: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3693: PetscCall(MatSetInf(X));
3694: PetscFunctionReturn(PETSC_SUCCESS);
3695: }
3696: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3697: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3698: PetscCall(MatBoundToCPU(A, &Abound));
3699: if (!Abound) {
3700: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3701: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3702: }
3703: #if PetscDefined(HAVE_CUDA)
3704: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3705: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3706: #elif PetscDefined(HAVE_HIP)
3707: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3708: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3709: #endif
3710: PetscCall(MatGetSize(B, NULL, &N));
3711: for (i = 0; i < N; i++) {
3712: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3713: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3714: PetscCall((*f)(A, b, x));
3715: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3716: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3717: }
3718: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3719: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3720: PetscFunctionReturn(PETSC_SUCCESS);
3721: }
3723: /*@
3724: MatMatSolve - Solves $A X = B$, given a factored matrix.
3726: Neighbor-wise Collective
3728: Input Parameters:
3729: + A - the factored matrix
3730: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3732: Output Parameter:
3733: . X - the result matrix (dense matrix)
3735: Level: developer
3737: Note:
3738: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3739: otherwise, `B` and `X` cannot be the same.
3741: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3742: @*/
3743: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3744: {
3745: PetscFunctionBegin;
3750: PetscCheckSameComm(A, 1, B, 2);
3751: PetscCheckSameComm(A, 1, X, 3);
3752: PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
3753: PetscCheck(A->rmap->N == B->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N);
3754: PetscCheck(X->cmap->N == B->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as rhs matrix");
3755: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3756: MatCheckPreallocated(A, 1);
3758: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3759: if (!A->ops->matsolve) {
3760: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3761: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3762: } else PetscUseTypeMethod(A, matsolve, B, X);
3763: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3764: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3765: PetscFunctionReturn(PETSC_SUCCESS);
3766: }
3768: /*@
3769: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3771: Neighbor-wise Collective
3773: Input Parameters:
3774: + A - the factored matrix
3775: - B - the right-hand-side matrix (`MATDENSE` matrix)
3777: Output Parameter:
3778: . X - the result matrix (dense matrix)
3780: Level: developer
3782: Note:
3783: The matrices `B` and `X` cannot be the same. I.e., one cannot
3784: call `MatMatSolveTranspose`(A,X,X).
3786: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3787: @*/
3788: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3789: {
3790: PetscFunctionBegin;
3795: PetscCheckSameComm(A, 1, B, 2);
3796: PetscCheckSameComm(A, 1, X, 3);
3797: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3798: 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);
3799: 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);
3800: 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);
3801: 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");
3802: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3803: MatCheckPreallocated(A, 1);
3805: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3806: if (!A->ops->matsolvetranspose) {
3807: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3808: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3809: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3810: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3811: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3812: PetscFunctionReturn(PETSC_SUCCESS);
3813: }
3815: /*@
3816: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3818: Neighbor-wise Collective
3820: Input Parameters:
3821: + A - the factored matrix
3822: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3824: Output Parameter:
3825: . X - the result matrix (dense matrix)
3827: Level: developer
3829: Note:
3830: For MUMPS, it only supports centralized sparse compressed column format on the host processor for right-hand side matrix. User must create `Bt` in sparse compressed row
3831: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3833: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3834: @*/
3835: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3836: {
3837: PetscFunctionBegin;
3842: PetscCheckSameComm(A, 1, Bt, 2);
3843: PetscCheckSameComm(A, 1, X, 3);
3845: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3846: 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);
3847: 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);
3848: 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");
3849: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3850: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3851: MatCheckPreallocated(A, 1);
3853: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3854: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3855: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3856: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3857: PetscFunctionReturn(PETSC_SUCCESS);
3858: }
3860: /*@
3861: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3862: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3864: Neighbor-wise Collective
3866: Input Parameters:
3867: + mat - the factored matrix
3868: - b - the right-hand-side vector
3870: Output Parameter:
3871: . x - the result vector
3873: Level: developer
3875: Notes:
3876: `MatSolve()` should be used for most applications, as it performs
3877: a forward solve followed by a backward solve.
3879: The vectors `b` and `x` cannot be the same, i.e., one cannot
3880: call `MatForwardSolve`(A,x,x).
3882: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3883: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3884: `MatForwardSolve()` solves $U^T*D y = b$, and
3885: `MatBackwardSolve()` solves $U x = y$.
3886: Thus they do not provide a symmetric preconditioner.
3888: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3889: @*/
3890: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3891: {
3892: PetscFunctionBegin;
3897: PetscCheckSameComm(mat, 1, b, 2);
3898: PetscCheckSameComm(mat, 1, x, 3);
3899: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3900: 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);
3901: 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);
3902: 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);
3903: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3904: MatCheckPreallocated(mat, 1);
3906: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3907: PetscUseTypeMethod(mat, forwardsolve, b, x);
3908: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3909: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3910: PetscFunctionReturn(PETSC_SUCCESS);
3911: }
3913: /*@
3914: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3915: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3917: Neighbor-wise Collective
3919: Input Parameters:
3920: + mat - the factored matrix
3921: - b - the right-hand-side vector
3923: Output Parameter:
3924: . x - the result vector
3926: Level: developer
3928: Notes:
3929: `MatSolve()` should be used for most applications, as it performs
3930: a forward solve followed by a backward solve.
3932: The vectors `b` and `x` cannot be the same. I.e., one cannot
3933: call `MatBackwardSolve`(A,x,x).
3935: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3936: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3937: `MatForwardSolve()` solves $U^T*D y = b$, and
3938: `MatBackwardSolve()` solves $U x = y$.
3939: Thus they do not provide a symmetric preconditioner.
3941: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3942: @*/
3943: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3944: {
3945: PetscFunctionBegin;
3950: PetscCheckSameComm(mat, 1, b, 2);
3951: PetscCheckSameComm(mat, 1, x, 3);
3952: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3953: 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);
3954: 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);
3955: 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);
3956: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3957: MatCheckPreallocated(mat, 1);
3959: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3960: PetscUseTypeMethod(mat, backwardsolve, b, x);
3961: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3962: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3963: PetscFunctionReturn(PETSC_SUCCESS);
3964: }
3966: /*@
3967: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
3969: Neighbor-wise Collective
3971: Input Parameters:
3972: + mat - the factored matrix
3973: . b - the right-hand-side vector
3974: - y - the vector to be added to
3976: Output Parameter:
3977: . x - the result vector
3979: Level: developer
3981: Note:
3982: The vectors `b` and `x` cannot be the same. I.e., one cannot
3983: call `MatSolveAdd`(A,x,y,x).
3985: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3986: @*/
3987: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
3988: {
3989: PetscScalar one = 1.0;
3990: Vec tmp;
3992: PetscFunctionBegin;
3998: PetscCheckSameComm(mat, 1, b, 2);
3999: PetscCheckSameComm(mat, 1, y, 3);
4000: PetscCheckSameComm(mat, 1, x, 4);
4001: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4002: 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);
4003: 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);
4004: 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);
4005: 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);
4006: 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);
4007: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4008: MatCheckPreallocated(mat, 1);
4010: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4011: PetscCall(VecFlag(x, mat->factorerrortype));
4012: if (mat->factorerrortype) {
4013: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4014: } else if (mat->ops->solveadd) {
4015: PetscUseTypeMethod(mat, solveadd, b, y, x);
4016: } else {
4017: /* do the solve then the add manually */
4018: if (x != y) {
4019: PetscCall(MatSolve(mat, b, x));
4020: PetscCall(VecAXPY(x, one, y));
4021: } else {
4022: PetscCall(VecDuplicate(x, &tmp));
4023: PetscCall(VecCopy(x, tmp));
4024: PetscCall(MatSolve(mat, b, x));
4025: PetscCall(VecAXPY(x, one, tmp));
4026: PetscCall(VecDestroy(&tmp));
4027: }
4028: }
4029: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4030: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4031: PetscFunctionReturn(PETSC_SUCCESS);
4032: }
4034: /*@
4035: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4037: Neighbor-wise Collective
4039: Input Parameters:
4040: + mat - the factored matrix
4041: - b - the right-hand-side vector
4043: Output Parameter:
4044: . x - the result vector
4046: Level: developer
4048: Notes:
4049: The vectors `b` and `x` cannot be the same. I.e., one cannot
4050: call `MatSolveTranspose`(A,x,x).
4052: Most users should employ the `KSP` interface for linear solvers
4053: instead of working directly with matrix algebra routines such as this.
4054: See, e.g., `KSPCreate()`.
4056: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4057: @*/
4058: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4059: {
4060: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4062: PetscFunctionBegin;
4067: PetscCheckSameComm(mat, 1, b, 2);
4068: PetscCheckSameComm(mat, 1, x, 3);
4069: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4070: 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);
4071: 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);
4072: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4073: MatCheckPreallocated(mat, 1);
4074: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4075: PetscCall(VecFlag(x, mat->factorerrortype));
4076: if (mat->factorerrortype) {
4077: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4078: } else {
4079: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4080: PetscCall((*f)(mat, b, x));
4081: }
4082: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4083: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4084: PetscFunctionReturn(PETSC_SUCCESS);
4085: }
4087: /*@
4088: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4089: factored matrix.
4091: Neighbor-wise Collective
4093: Input Parameters:
4094: + mat - the factored matrix
4095: . b - the right-hand-side vector
4096: - y - the vector to be added to
4098: Output Parameter:
4099: . x - the result vector
4101: Level: developer
4103: Note:
4104: The vectors `b` and `x` cannot be the same. I.e., one cannot
4105: call `MatSolveTransposeAdd`(A,x,y,x).
4107: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4108: @*/
4109: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4110: {
4111: PetscScalar one = 1.0;
4112: Vec tmp;
4113: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4115: PetscFunctionBegin;
4121: PetscCheckSameComm(mat, 1, b, 2);
4122: PetscCheckSameComm(mat, 1, y, 3);
4123: PetscCheckSameComm(mat, 1, x, 4);
4124: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4125: 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);
4126: 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);
4127: 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);
4128: 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);
4129: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4130: MatCheckPreallocated(mat, 1);
4132: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4133: PetscCall(VecFlag(x, mat->factorerrortype));
4134: if (mat->factorerrortype) {
4135: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4136: } else if (f) {
4137: PetscCall((*f)(mat, b, y, x));
4138: } else {
4139: /* do the solve then the add manually */
4140: if (x != y) {
4141: PetscCall(MatSolveTranspose(mat, b, x));
4142: PetscCall(VecAXPY(x, one, y));
4143: } else {
4144: PetscCall(VecDuplicate(x, &tmp));
4145: PetscCall(VecCopy(x, tmp));
4146: PetscCall(MatSolveTranspose(mat, b, x));
4147: PetscCall(VecAXPY(x, one, tmp));
4148: PetscCall(VecDestroy(&tmp));
4149: }
4150: }
4151: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4152: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4153: PetscFunctionReturn(PETSC_SUCCESS);
4154: }
4156: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4157: /*@
4158: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4160: Neighbor-wise Collective
4162: Input Parameters:
4163: + mat - the matrix
4164: . b - the right-hand side
4165: . omega - the relaxation factor
4166: . flag - flag indicating the type of SOR (see below)
4167: . shift - diagonal shift
4168: . its - the number of iterations
4169: - lits - the number of local iterations
4171: Output Parameter:
4172: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4174: SOR Flags:
4175: + `SOR_FORWARD_SWEEP` - forward SOR
4176: . `SOR_BACKWARD_SWEEP` - backward SOR
4177: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4178: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4179: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4180: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4181: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4182: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies upper/lower triangular part of matrix to vector (with `omega`)
4183: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4185: Level: developer
4187: Notes:
4188: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4189: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4190: on each processor.
4192: Application programmers will not generally use `MatSOR()` directly,
4193: but instead will employ `PCSOR` or `PCEISENSTAT`
4195: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with inodes, this does a block SOR smoothing, otherwise it does a pointwise smoothing.
4196: For `MATAIJ` matrices with inodes, the block sizes are determined by the inode sizes, not the block size set with `MatSetBlockSize()`
4198: Vectors `x` and `b` CANNOT be the same
4200: The flags are implemented as bitwise inclusive or operations.
4201: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4202: to specify a zero initial guess for SSOR.
4204: Developer Note:
4205: We should add block SOR support for `MATAIJ` matrices with block size set to greater than one and no inodes
4207: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4208: @*/
4209: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4210: {
4211: PetscFunctionBegin;
4216: PetscCheckSameComm(mat, 1, b, 2);
4217: PetscCheckSameComm(mat, 1, x, 8);
4218: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4219: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4220: 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);
4221: 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);
4222: 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);
4223: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4224: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4225: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4227: MatCheckPreallocated(mat, 1);
4228: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4229: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4230: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4231: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4232: PetscFunctionReturn(PETSC_SUCCESS);
4233: }
4235: /*
4236: Default matrix copy routine.
4237: */
4238: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4239: {
4240: PetscInt i, rstart = 0, rend = 0, nz;
4241: const PetscInt *cwork;
4242: const PetscScalar *vwork;
4244: PetscFunctionBegin;
4245: if (B->assembled) PetscCall(MatZeroEntries(B));
4246: if (str == SAME_NONZERO_PATTERN) {
4247: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4248: for (i = rstart; i < rend; i++) {
4249: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4250: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4251: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4252: }
4253: } else {
4254: PetscCall(MatAYPX(B, 0.0, A, str));
4255: }
4256: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4257: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4258: PetscFunctionReturn(PETSC_SUCCESS);
4259: }
4261: /*@
4262: MatCopy - Copies a matrix to another matrix.
4264: Collective
4266: Input Parameters:
4267: + A - the matrix
4268: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4270: Output Parameter:
4271: . B - where the copy is put
4273: Level: intermediate
4275: Notes:
4276: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4278: `MatCopy()` copies the matrix entries of a matrix to another existing
4279: matrix (after first zeroing the second matrix). A related routine is
4280: `MatConvert()`, which first creates a new matrix and then copies the data.
4282: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4283: @*/
4284: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4285: {
4286: PetscInt i;
4288: PetscFunctionBegin;
4293: PetscCheckSameComm(A, 1, B, 2);
4294: MatCheckPreallocated(B, 2);
4295: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4296: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4297: 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,
4298: A->cmap->N, B->cmap->N);
4299: MatCheckPreallocated(A, 1);
4300: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4302: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4303: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4304: else PetscCall(MatCopy_Basic(A, B, str));
4306: B->stencil.dim = A->stencil.dim;
4307: B->stencil.noc = A->stencil.noc;
4308: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4309: B->stencil.dims[i] = A->stencil.dims[i];
4310: B->stencil.starts[i] = A->stencil.starts[i];
4311: }
4313: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4314: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4315: PetscFunctionReturn(PETSC_SUCCESS);
4316: }
4318: /*@
4319: MatConvert - Converts a matrix to another matrix, either of the same
4320: or different type.
4322: Collective
4324: Input Parameters:
4325: + mat - the matrix
4326: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4327: same type as the original matrix.
4328: - reuse - denotes if the destination matrix is to be created or reused.
4329: 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
4330: `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).
4332: Output Parameter:
4333: . M - pointer to place new matrix
4335: Level: intermediate
4337: Notes:
4338: `MatConvert()` first creates a new matrix and then copies the data from
4339: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4340: entries of one matrix to another already existing matrix context.
4342: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4343: the MPI communicator of the generated matrix is always the same as the communicator
4344: of the input matrix.
4346: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4347: @*/
4348: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4349: {
4350: PetscBool sametype, issame, flg;
4351: PetscBool3 issymmetric, ishermitian;
4352: char convname[256], mtype[256];
4353: Mat B;
4355: PetscFunctionBegin;
4358: PetscAssertPointer(M, 4);
4359: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4360: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4361: MatCheckPreallocated(mat, 1);
4363: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4364: if (flg) newtype = mtype;
4366: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4367: PetscCall(PetscStrcmp(newtype, "same", &issame));
4368: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4369: if (reuse == MAT_REUSE_MATRIX) {
4371: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4372: }
4374: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4375: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4376: PetscFunctionReturn(PETSC_SUCCESS);
4377: }
4379: /* Cache Mat options because some converters use MatHeaderReplace */
4380: issymmetric = mat->symmetric;
4381: ishermitian = mat->hermitian;
4383: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4384: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4385: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4386: } else {
4387: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4388: const char *prefix[3] = {"seq", "mpi", ""};
4389: PetscInt i;
4390: /*
4391: Order of precedence:
4392: 0) See if newtype is a superclass of the current matrix.
4393: 1) See if a specialized converter is known to the current matrix.
4394: 2) See if a specialized converter is known to the desired matrix class.
4395: 3) See if a good general converter is registered for the desired class
4396: (as of 6/27/03 only MATMPIADJ falls into this category).
4397: 4) See if a good general converter is known for the current matrix.
4398: 5) Use a really basic converter.
4399: */
4401: /* 0) See if newtype is a superclass of the current matrix.
4402: i.e mat is mpiaij and newtype is aij */
4403: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4404: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4405: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4406: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4407: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4408: if (flg) {
4409: if (reuse == MAT_INPLACE_MATRIX) {
4410: PetscCall(PetscInfo(mat, "Early return\n"));
4411: PetscFunctionReturn(PETSC_SUCCESS);
4412: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4413: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4414: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4415: PetscFunctionReturn(PETSC_SUCCESS);
4416: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4417: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4418: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4419: PetscFunctionReturn(PETSC_SUCCESS);
4420: }
4421: }
4422: }
4423: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4424: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4425: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4426: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4427: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4428: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4429: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4430: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4431: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4432: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4433: if (conv) goto foundconv;
4434: }
4436: /* 2) See if a specialized converter is known to the desired matrix class. */
4437: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4438: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4439: PetscCall(MatSetType(B, newtype));
4440: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4441: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4442: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4443: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4444: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4445: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4446: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4447: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4448: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4449: if (conv) {
4450: PetscCall(MatDestroy(&B));
4451: goto foundconv;
4452: }
4453: }
4455: /* 3) See if a good general converter is registered for the desired class */
4456: conv = B->ops->convertfrom;
4457: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4458: PetscCall(MatDestroy(&B));
4459: if (conv) goto foundconv;
4461: /* 4) See if a good general converter is known for the current matrix */
4462: if (mat->ops->convert) conv = mat->ops->convert;
4463: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4464: if (conv) goto foundconv;
4466: /* 5) Use a really basic converter. */
4467: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4468: conv = MatConvert_Basic;
4470: foundconv:
4471: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4472: PetscCall((*conv)(mat, newtype, reuse, M));
4473: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4474: /* the block sizes must be same if the mappings are copied over */
4475: (*M)->rmap->bs = mat->rmap->bs;
4476: (*M)->cmap->bs = mat->cmap->bs;
4477: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4478: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4479: (*M)->rmap->mapping = mat->rmap->mapping;
4480: (*M)->cmap->mapping = mat->cmap->mapping;
4481: }
4482: (*M)->stencil.dim = mat->stencil.dim;
4483: (*M)->stencil.noc = mat->stencil.noc;
4484: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4485: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4486: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4487: }
4488: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4489: }
4490: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4492: /* Copy Mat options */
4493: if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4494: else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4495: if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4496: else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4497: PetscFunctionReturn(PETSC_SUCCESS);
4498: }
4500: /*@
4501: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4503: Not Collective
4505: Input Parameter:
4506: . mat - the matrix, must be a factored matrix
4508: Output Parameter:
4509: . type - the string name of the package (do not free this string)
4511: Level: intermediate
4513: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4514: @*/
4515: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4516: {
4517: PetscErrorCode (*conv)(Mat, MatSolverType *);
4519: PetscFunctionBegin;
4522: PetscAssertPointer(type, 2);
4523: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4524: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4525: if (conv) PetscCall((*conv)(mat, type));
4526: else *type = MATSOLVERPETSC;
4527: PetscFunctionReturn(PETSC_SUCCESS);
4528: }
4530: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4531: struct _MatSolverTypeForSpecifcType {
4532: MatType mtype;
4533: /* no entry for MAT_FACTOR_NONE */
4534: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4535: MatSolverTypeForSpecifcType next;
4536: };
4538: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4539: struct _MatSolverTypeHolder {
4540: char *name;
4541: MatSolverTypeForSpecifcType handlers;
4542: MatSolverTypeHolder next;
4543: };
4545: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4547: /*@C
4548: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4550: Logically Collective, No Fortran Support
4552: Input Parameters:
4553: + package - name of the package, for example `petsc` or `superlu`
4554: . mtype - the matrix type that works with this package
4555: . ftype - the type of factorization supported by the package
4556: - createfactor - routine that will create the factored matrix ready to be used
4558: Level: developer
4560: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4561: `MatGetFactor()`
4562: @*/
4563: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4564: {
4565: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4566: PetscBool flg;
4567: MatSolverTypeForSpecifcType inext, iprev = NULL;
4569: PetscFunctionBegin;
4570: PetscCall(MatInitializePackage());
4571: if (!next) {
4572: PetscCall(PetscNew(&MatSolverTypeHolders));
4573: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4574: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4575: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4576: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4577: PetscFunctionReturn(PETSC_SUCCESS);
4578: }
4579: while (next) {
4580: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4581: if (flg) {
4582: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4583: inext = next->handlers;
4584: while (inext) {
4585: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4586: if (flg) {
4587: inext->createfactor[(int)ftype - 1] = createfactor;
4588: PetscFunctionReturn(PETSC_SUCCESS);
4589: }
4590: iprev = inext;
4591: inext = inext->next;
4592: }
4593: PetscCall(PetscNew(&iprev->next));
4594: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4595: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4596: PetscFunctionReturn(PETSC_SUCCESS);
4597: }
4598: prev = next;
4599: next = next->next;
4600: }
4601: PetscCall(PetscNew(&prev->next));
4602: PetscCall(PetscStrallocpy(package, &prev->next->name));
4603: PetscCall(PetscNew(&prev->next->handlers));
4604: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4605: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4606: PetscFunctionReturn(PETSC_SUCCESS);
4607: }
4609: /*@C
4610: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4612: Input Parameters:
4613: + type - name of the package, for example `petsc` or `superlu`, if this is 'NULL', then the first result that satisfies the other criteria is returned
4614: . ftype - the type of factorization supported by the type
4615: - mtype - the matrix type that works with this type
4617: Output Parameters:
4618: + foundtype - `PETSC_TRUE` if the type was registered
4619: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4620: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4622: Calling sequence of `createfactor`:
4623: + A - the matrix providing the factor matrix
4624: . ftype - the `MatFactorType` of the factor requested
4625: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4627: Level: developer
4629: Note:
4630: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4631: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4632: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4634: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4635: `MatInitializePackage()`
4636: @*/
4637: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4638: {
4639: MatSolverTypeHolder next = MatSolverTypeHolders;
4640: PetscBool flg;
4641: MatSolverTypeForSpecifcType inext;
4643: PetscFunctionBegin;
4644: if (foundtype) *foundtype = PETSC_FALSE;
4645: if (foundmtype) *foundmtype = PETSC_FALSE;
4646: if (createfactor) *createfactor = NULL;
4648: if (type) {
4649: while (next) {
4650: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4651: if (flg) {
4652: if (foundtype) *foundtype = PETSC_TRUE;
4653: inext = next->handlers;
4654: while (inext) {
4655: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4656: if (flg) {
4657: if (foundmtype) *foundmtype = PETSC_TRUE;
4658: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4659: PetscFunctionReturn(PETSC_SUCCESS);
4660: }
4661: inext = inext->next;
4662: }
4663: }
4664: next = next->next;
4665: }
4666: } else {
4667: while (next) {
4668: inext = next->handlers;
4669: while (inext) {
4670: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4671: if (flg && inext->createfactor[(int)ftype - 1]) {
4672: if (foundtype) *foundtype = PETSC_TRUE;
4673: if (foundmtype) *foundmtype = PETSC_TRUE;
4674: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4675: PetscFunctionReturn(PETSC_SUCCESS);
4676: }
4677: inext = inext->next;
4678: }
4679: next = next->next;
4680: }
4681: /* try with base classes inext->mtype */
4682: next = MatSolverTypeHolders;
4683: while (next) {
4684: inext = next->handlers;
4685: while (inext) {
4686: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4687: if (flg && inext->createfactor[(int)ftype - 1]) {
4688: if (foundtype) *foundtype = PETSC_TRUE;
4689: if (foundmtype) *foundmtype = PETSC_TRUE;
4690: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4691: PetscFunctionReturn(PETSC_SUCCESS);
4692: }
4693: inext = inext->next;
4694: }
4695: next = next->next;
4696: }
4697: }
4698: PetscFunctionReturn(PETSC_SUCCESS);
4699: }
4701: PetscErrorCode MatSolverTypeDestroy(void)
4702: {
4703: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4704: MatSolverTypeForSpecifcType inext, iprev;
4706: PetscFunctionBegin;
4707: while (next) {
4708: PetscCall(PetscFree(next->name));
4709: inext = next->handlers;
4710: while (inext) {
4711: PetscCall(PetscFree(inext->mtype));
4712: iprev = inext;
4713: inext = inext->next;
4714: PetscCall(PetscFree(iprev));
4715: }
4716: prev = next;
4717: next = next->next;
4718: PetscCall(PetscFree(prev));
4719: }
4720: MatSolverTypeHolders = NULL;
4721: PetscFunctionReturn(PETSC_SUCCESS);
4722: }
4724: /*@
4725: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4727: Logically Collective
4729: Input Parameter:
4730: . mat - the matrix
4732: Output Parameter:
4733: . flg - `PETSC_TRUE` if uses the ordering
4735: Level: developer
4737: Note:
4738: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4739: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4741: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4742: @*/
4743: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4744: {
4745: PetscFunctionBegin;
4746: *flg = mat->canuseordering;
4747: PetscFunctionReturn(PETSC_SUCCESS);
4748: }
4750: /*@
4751: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4753: Logically Collective
4755: Input Parameters:
4756: + mat - the matrix obtained with `MatGetFactor()`
4757: - ftype - the factorization type to be used
4759: Output Parameter:
4760: . otype - the preferred ordering type
4762: Level: developer
4764: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4765: @*/
4766: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4767: {
4768: PetscFunctionBegin;
4769: *otype = mat->preferredordering[ftype];
4770: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4771: PetscFunctionReturn(PETSC_SUCCESS);
4772: }
4774: /*@
4775: MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()
4777: Collective
4779: Input Parameters:
4780: + mat - the matrix
4781: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's solver if it is available), if this is 'NULL', then the first result that satisfies
4782: the other criteria is returned
4783: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4785: Output Parameter:
4786: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4788: Options Database Keys:
4789: + -pc_factor_mat_solver_type <type> - choose the type at run time. When using `KSP` solvers
4790: . -pc_factor_mat_factor_on_host <bool> - do mat factorization on host (with device matrices). Default is doing it on device
4791: - -pc_factor_mat_solve_on_host <bool> - do mat solve on host (with device matrices). Default is doing it on device
4793: Level: intermediate
4795: Notes:
4796: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4797: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4799: Users usually access the factorization solvers via `KSP`
4801: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4802: such as pastix, superlu, mumps etc. PETSc must have been ./configure to use the external solver, using the option --download-package or --with-package-dir
4804: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4805: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4806: For example if one configuration had --download-mumps while a different one had --download-superlu_dist.
4808: Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4809: where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4810: call `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4812: Developer Note:
4813: This should actually be called `MatCreateFactor()` since it creates a new factor object
4815: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4816: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4817: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4818: @*/
4819: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4820: {
4821: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4822: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4824: PetscFunctionBegin;
4828: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4829: MatCheckPreallocated(mat, 1);
4831: PetscCall(MatIsShell(mat, &shell));
4832: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4833: if (hasop) {
4834: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4835: PetscFunctionReturn(PETSC_SUCCESS);
4836: }
4838: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4839: if (!foundtype) {
4840: if (type) {
4841: 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],
4842: ((PetscObject)mat)->type_name, type);
4843: } else {
4844: 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);
4845: }
4846: }
4847: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4848: 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);
4850: PetscCall((*conv)(mat, ftype, f));
4851: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4852: PetscFunctionReturn(PETSC_SUCCESS);
4853: }
4855: /*@
4856: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4858: Not Collective
4860: Input Parameters:
4861: + mat - the matrix
4862: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4863: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4865: Output Parameter:
4866: . flg - PETSC_TRUE if the factorization is available
4868: Level: intermediate
4870: Notes:
4871: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4872: such as pastix, superlu, mumps etc.
4874: PETSc must have been ./configure to use the external solver, using the option --download-package
4876: Developer Note:
4877: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
4879: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4880: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4881: @*/
4882: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4883: {
4884: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
4886: PetscFunctionBegin;
4888: PetscAssertPointer(flg, 4);
4890: *flg = PETSC_FALSE;
4891: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
4893: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4894: MatCheckPreallocated(mat, 1);
4896: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4897: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4898: PetscFunctionReturn(PETSC_SUCCESS);
4899: }
4901: /*@
4902: MatDuplicate - Duplicates a matrix including the non-zero structure.
4904: Collective
4906: Input Parameters:
4907: + mat - the matrix
4908: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4909: See the manual page for `MatDuplicateOption()` for an explanation of these options.
4911: Output Parameter:
4912: . M - pointer to place new matrix
4914: Level: intermediate
4916: Notes:
4917: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
4919: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
4921: 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.
4923: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4924: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4925: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
4927: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4928: @*/
4929: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4930: {
4931: Mat B;
4932: VecType vtype;
4933: PetscInt i;
4934: PetscObject dm, container_h, container_d;
4935: PetscErrorCodeFn *viewf;
4937: PetscFunctionBegin;
4940: PetscAssertPointer(M, 3);
4941: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4942: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4943: MatCheckPreallocated(mat, 1);
4945: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4946: PetscUseTypeMethod(mat, duplicate, op, M);
4947: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4948: B = *M;
4950: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4951: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4952: PetscCall(MatGetVecType(mat, &vtype));
4953: PetscCall(MatSetVecType(B, vtype));
4955: B->stencil.dim = mat->stencil.dim;
4956: B->stencil.noc = mat->stencil.noc;
4957: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4958: B->stencil.dims[i] = mat->stencil.dims[i];
4959: B->stencil.starts[i] = mat->stencil.starts[i];
4960: }
4962: B->nooffproczerorows = mat->nooffproczerorows;
4963: B->nooffprocentries = mat->nooffprocentries;
4965: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4966: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4967: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
4968: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
4969: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
4970: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
4971: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
4972: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4973: PetscFunctionReturn(PETSC_SUCCESS);
4974: }
4976: /*@
4977: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
4979: Logically Collective
4981: Input Parameter:
4982: . mat - the matrix
4984: Output Parameter:
4985: . v - the diagonal of the matrix
4987: Level: intermediate
4989: Note:
4990: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
4991: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
4992: is larger than `ndiag`, the values of the remaining entries are unspecified.
4994: Currently only correct in parallel for square matrices.
4996: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
4997: @*/
4998: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
4999: {
5000: PetscFunctionBegin;
5004: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5005: MatCheckPreallocated(mat, 1);
5006: if (PetscDefined(USE_DEBUG)) {
5007: PetscInt nv, row, col, ndiag;
5009: PetscCall(VecGetLocalSize(v, &nv));
5010: PetscCall(MatGetLocalSize(mat, &row, &col));
5011: ndiag = PetscMin(row, col);
5012: PetscCheck(nv >= ndiag, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming Mat and Vec. Vec local size %" PetscInt_FMT " < Mat local diagonal length %" PetscInt_FMT, nv, ndiag);
5013: }
5015: PetscUseTypeMethod(mat, getdiagonal, v);
5016: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5017: PetscFunctionReturn(PETSC_SUCCESS);
5018: }
5020: /*@
5021: MatGetRowMin - Gets the minimum value (of the real part) of each
5022: row of the matrix
5024: Logically Collective
5026: Input Parameter:
5027: . mat - the matrix
5029: Output Parameters:
5030: + v - the vector for storing the maximums
5031: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5033: Level: intermediate
5035: Note:
5036: The result of this call are the same as if one converted the matrix to dense format
5037: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5039: This code is only implemented for a couple of matrix formats.
5041: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5042: `MatGetRowMax()`
5043: @*/
5044: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5045: {
5046: PetscFunctionBegin;
5050: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5052: if (!mat->cmap->N) {
5053: PetscCall(VecSet(v, PETSC_MAX_REAL));
5054: if (idx) {
5055: PetscInt i, m = mat->rmap->n;
5056: for (i = 0; i < m; i++) idx[i] = -1;
5057: }
5058: } else {
5059: MatCheckPreallocated(mat, 1);
5060: }
5061: PetscUseTypeMethod(mat, getrowmin, v, idx);
5062: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5063: PetscFunctionReturn(PETSC_SUCCESS);
5064: }
5066: /*@
5067: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5068: row of the matrix
5070: Logically Collective
5072: Input Parameter:
5073: . mat - the matrix
5075: Output Parameters:
5076: + v - the vector for storing the minimums
5077: - idx - the indices of the column found for each row (or `NULL` if not needed)
5079: Level: intermediate
5081: Notes:
5082: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5083: row is 0 (the first column).
5085: This code is only implemented for a couple of matrix formats.
5087: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5088: @*/
5089: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5090: {
5091: PetscFunctionBegin;
5095: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5096: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5098: if (!mat->cmap->N) {
5099: PetscCall(VecSet(v, 0.0));
5100: if (idx) {
5101: PetscInt i, m = mat->rmap->n;
5102: for (i = 0; i < m; i++) idx[i] = -1;
5103: }
5104: } else {
5105: MatCheckPreallocated(mat, 1);
5106: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5107: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5108: }
5109: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5110: PetscFunctionReturn(PETSC_SUCCESS);
5111: }
5113: /*@
5114: MatGetRowMax - Gets the maximum value (of the real part) of each
5115: row of the matrix
5117: Logically Collective
5119: Input Parameter:
5120: . mat - the matrix
5122: Output Parameters:
5123: + v - the vector for storing the maximums
5124: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5126: Level: intermediate
5128: Notes:
5129: The result of this call are the same as if one converted the matrix to dense format
5130: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5132: This code is only implemented for a couple of matrix formats.
5134: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5135: @*/
5136: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5137: {
5138: PetscFunctionBegin;
5142: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5144: if (!mat->cmap->N) {
5145: PetscCall(VecSet(v, PETSC_MIN_REAL));
5146: if (idx) {
5147: PetscInt i, m = mat->rmap->n;
5148: for (i = 0; i < m; i++) idx[i] = -1;
5149: }
5150: } else {
5151: MatCheckPreallocated(mat, 1);
5152: PetscUseTypeMethod(mat, getrowmax, v, idx);
5153: }
5154: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5155: PetscFunctionReturn(PETSC_SUCCESS);
5156: }
5158: /*@
5159: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5160: row of the matrix
5162: Logically Collective
5164: Input Parameter:
5165: . mat - the matrix
5167: Output Parameters:
5168: + v - the vector for storing the maximums
5169: - idx - the indices of the column found for each row (or `NULL` if not needed)
5171: Level: intermediate
5173: Notes:
5174: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5175: row is 0 (the first column).
5177: This code is only implemented for a couple of matrix formats.
5179: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5180: @*/
5181: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5182: {
5183: PetscFunctionBegin;
5187: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5189: if (!mat->cmap->N) {
5190: PetscCall(VecSet(v, 0.0));
5191: if (idx) {
5192: PetscInt i, m = mat->rmap->n;
5193: for (i = 0; i < m; i++) idx[i] = -1;
5194: }
5195: } else {
5196: MatCheckPreallocated(mat, 1);
5197: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5198: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5199: }
5200: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5201: PetscFunctionReturn(PETSC_SUCCESS);
5202: }
5204: /*@
5205: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5207: Logically Collective
5209: Input Parameter:
5210: . mat - the matrix
5212: Output Parameter:
5213: . v - the vector for storing the sum
5215: Level: intermediate
5217: This code is only implemented for a couple of matrix formats.
5219: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5220: @*/
5221: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5222: {
5223: PetscFunctionBegin;
5227: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5229: if (!mat->cmap->N) {
5230: PetscCall(VecSet(v, 0.0));
5231: } else {
5232: MatCheckPreallocated(mat, 1);
5233: PetscUseTypeMethod(mat, getrowsumabs, v);
5234: }
5235: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5236: PetscFunctionReturn(PETSC_SUCCESS);
5237: }
5239: /*@
5240: MatGetRowSum - Gets the sum of each row of the matrix
5242: Logically or Neighborhood Collective
5244: Input Parameter:
5245: . mat - the matrix
5247: Output Parameter:
5248: . v - the vector for storing the sum of rows
5250: Level: intermediate
5252: Note:
5253: This code is slow since it is not currently specialized for different formats
5255: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5256: @*/
5257: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5258: {
5259: Vec ones;
5261: PetscFunctionBegin;
5265: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5266: MatCheckPreallocated(mat, 1);
5267: PetscCall(MatCreateVecs(mat, &ones, NULL));
5268: PetscCall(VecSet(ones, 1.));
5269: PetscCall(MatMult(mat, ones, v));
5270: PetscCall(VecDestroy(&ones));
5271: PetscFunctionReturn(PETSC_SUCCESS);
5272: }
5274: /*@
5275: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5276: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5278: Collective
5280: Input Parameter:
5281: . mat - the matrix to provide the transpose
5283: Output Parameter:
5284: . B - the matrix to contain the transpose; it MUST have the nonzero structure of the transpose of A or the code will crash or generate incorrect results
5286: Level: advanced
5288: Note:
5289: 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
5290: routine allows bypassing that call.
5292: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5293: @*/
5294: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5295: {
5296: MatParentState *rb = NULL;
5298: PetscFunctionBegin;
5299: PetscCall(PetscNew(&rb));
5300: rb->id = ((PetscObject)mat)->id;
5301: rb->state = 0;
5302: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5303: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5304: PetscFunctionReturn(PETSC_SUCCESS);
5305: }
5307: /*@
5308: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5310: Collective
5312: Input Parameters:
5313: + mat - the matrix to transpose
5314: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5316: Output Parameter:
5317: . B - the transpose of the matrix
5319: Level: intermediate
5321: Notes:
5322: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5324: `MAT_REUSE_MATRIX` uses the `B` matrix obtained from a previous call to this function with `MAT_INITIAL_MATRIX` to store the transpose. If you already have a matrix to contain the
5325: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5327: 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.
5329: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5330: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5332: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5334: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5336: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5337: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5338: @*/
5339: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5340: {
5341: PetscContainer rB = NULL;
5342: MatParentState *rb = NULL;
5344: PetscFunctionBegin;
5347: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5348: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5349: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5350: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5351: MatCheckPreallocated(mat, 1);
5352: if (reuse == MAT_REUSE_MATRIX) {
5353: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5354: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5355: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5356: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5357: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5358: }
5360: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5361: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5362: PetscUseTypeMethod(mat, transpose, reuse, B);
5363: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5364: }
5365: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5367: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5368: if (reuse != MAT_INPLACE_MATRIX) {
5369: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5370: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5371: rb->state = ((PetscObject)mat)->state;
5372: rb->nonzerostate = mat->nonzerostate;
5373: }
5374: PetscFunctionReturn(PETSC_SUCCESS);
5375: }
5377: /*@
5378: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5380: Collective
5382: Input Parameter:
5383: . A - the matrix to transpose
5385: Output Parameter:
5386: . 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
5387: numerical portion.
5389: Level: intermediate
5391: Note:
5392: This is not supported for many matrix types, use `MatTranspose()` in those cases
5394: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5395: @*/
5396: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5397: {
5398: PetscFunctionBegin;
5401: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5402: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5403: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5404: PetscUseTypeMethod(A, transposesymbolic, B);
5405: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5407: PetscCall(MatTransposeSetPrecursor(A, *B));
5408: PetscFunctionReturn(PETSC_SUCCESS);
5409: }
5411: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5412: {
5413: PetscContainer rB;
5414: MatParentState *rb;
5416: PetscFunctionBegin;
5419: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5420: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5421: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5422: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5423: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5424: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5425: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5426: PetscFunctionReturn(PETSC_SUCCESS);
5427: }
5429: /*@
5430: MatIsTranspose - Test whether a matrix is another one's transpose,
5431: or its own, in which case it tests symmetry.
5433: Collective
5435: Input Parameters:
5436: + A - the matrix to test
5437: . B - the matrix to test against, this can equal the first parameter
5438: - tol - tolerance, differences between entries smaller than this are counted as zero
5440: Output Parameter:
5441: . flg - the result
5443: Level: intermediate
5445: Notes:
5446: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5447: test involves parallel copies of the block off-diagonal parts of the matrix.
5449: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5450: @*/
5451: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5452: {
5453: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5455: PetscFunctionBegin;
5458: PetscAssertPointer(flg, 4);
5459: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5460: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5461: *flg = PETSC_FALSE;
5462: if (f && g) {
5463: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5464: PetscCall((*f)(A, B, tol, flg));
5465: } else {
5466: MatType mattype;
5468: PetscCall(MatGetType(f ? B : A, &mattype));
5469: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5470: }
5471: PetscFunctionReturn(PETSC_SUCCESS);
5472: }
5474: /*@
5475: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5477: Collective
5479: Input Parameters:
5480: + mat - the matrix to transpose and complex conjugate
5481: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5483: Output Parameter:
5484: . B - the Hermitian transpose
5486: Level: intermediate
5488: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5489: @*/
5490: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5491: {
5492: PetscFunctionBegin;
5493: PetscCall(MatTranspose(mat, reuse, B));
5494: #if defined(PETSC_USE_COMPLEX)
5495: PetscCall(MatConjugate(*B));
5496: #endif
5497: PetscFunctionReturn(PETSC_SUCCESS);
5498: }
5500: /*@
5501: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5503: Collective
5505: Input Parameters:
5506: + A - the matrix to test
5507: . B - the matrix to test against, this can equal the first parameter
5508: - tol - tolerance, differences between entries smaller than this are counted as zero
5510: Output Parameter:
5511: . flg - the result
5513: Level: intermediate
5515: Notes:
5516: Only available for `MATAIJ` matrices.
5518: The sequential algorithm
5519: has a running time of the order of the number of nonzeros; the parallel
5520: test involves parallel copies of the block off-diagonal parts of the matrix.
5522: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5523: @*/
5524: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5525: {
5526: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5528: PetscFunctionBegin;
5531: PetscAssertPointer(flg, 4);
5532: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5533: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5534: if (f && g) {
5535: PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5536: PetscCall((*f)(A, B, tol, flg));
5537: }
5538: PetscFunctionReturn(PETSC_SUCCESS);
5539: }
5541: /*@
5542: MatPermute - Creates a new matrix with rows and columns permuted from the
5543: original.
5545: Collective
5547: Input Parameters:
5548: + mat - the matrix to permute
5549: . row - row permutation, each processor supplies only the permutation for its rows
5550: - col - column permutation, each processor supplies only the permutation for its columns
5552: Output Parameter:
5553: . B - the permuted matrix
5555: Level: advanced
5557: Note:
5558: The index sets map from row/col of permuted matrix to row/col of original matrix.
5559: The index sets should be on the same communicator as mat and have the same local sizes.
5561: Developer Note:
5562: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5563: exploit the fact that row and col are permutations, consider implementing the
5564: more general `MatCreateSubMatrix()` instead.
5566: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5567: @*/
5568: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5569: {
5570: PetscFunctionBegin;
5575: PetscAssertPointer(B, 4);
5576: PetscCheckSameComm(mat, 1, row, 2);
5577: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5578: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5579: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5580: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5581: MatCheckPreallocated(mat, 1);
5583: if (mat->ops->permute) {
5584: PetscUseTypeMethod(mat, permute, row, col, B);
5585: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5586: } else {
5587: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5588: }
5589: PetscFunctionReturn(PETSC_SUCCESS);
5590: }
5592: /*@
5593: MatEqual - Compares two matrices.
5595: Collective
5597: Input Parameters:
5598: + A - the first matrix
5599: - B - the second matrix
5601: Output Parameter:
5602: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5604: Level: intermediate
5606: Note:
5607: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5608: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5610: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5611: @*/
5612: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5613: {
5614: PetscFunctionBegin;
5619: PetscAssertPointer(flg, 3);
5620: PetscCheckSameComm(A, 1, B, 2);
5621: MatCheckPreallocated(A, 1);
5622: MatCheckPreallocated(B, 2);
5623: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5624: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5625: 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,
5626: B->cmap->N);
5627: if (A->ops->equal && A->ops->equal == B->ops->equal) {
5628: PetscUseTypeMethod(A, equal, B, flg);
5629: } else {
5630: PetscCall(MatMultEqual(A, B, 10, flg));
5631: }
5632: PetscFunctionReturn(PETSC_SUCCESS);
5633: }
5635: /*@
5636: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5637: matrices that are stored as vectors. Either of the two scaling
5638: matrices can be `NULL`.
5640: Collective
5642: Input Parameters:
5643: + mat - the matrix to be scaled
5644: . l - the left scaling vector (or `NULL`)
5645: - r - the right scaling vector (or `NULL`)
5647: Level: intermediate
5649: Note:
5650: `MatDiagonalScale()` computes $A = LAR$, where
5651: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5652: The L scales the rows of the matrix, the R scales the columns of the matrix.
5654: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5655: @*/
5656: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5657: {
5658: PetscFunctionBegin;
5661: if (l) {
5663: PetscCheckSameComm(mat, 1, l, 2);
5664: }
5665: if (r) {
5667: PetscCheckSameComm(mat, 1, r, 3);
5668: }
5669: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5670: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5671: MatCheckPreallocated(mat, 1);
5672: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5674: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5675: PetscUseTypeMethod(mat, diagonalscale, l, r);
5676: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5677: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5678: if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5679: PetscFunctionReturn(PETSC_SUCCESS);
5680: }
5682: /*@
5683: MatScale - Scales all elements of a matrix by a given number.
5685: Logically Collective
5687: Input Parameters:
5688: + mat - the matrix to be scaled
5689: - a - the scaling value
5691: Level: intermediate
5693: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5694: @*/
5695: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5696: {
5697: PetscFunctionBegin;
5700: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5701: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5703: MatCheckPreallocated(mat, 1);
5705: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5706: if (a != (PetscScalar)1.0) {
5707: PetscUseTypeMethod(mat, scale, a);
5708: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5709: }
5710: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5711: PetscFunctionReturn(PETSC_SUCCESS);
5712: }
5714: /*@
5715: MatNorm - Calculates various norms of a matrix.
5717: Collective
5719: Input Parameters:
5720: + mat - the matrix
5721: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5723: Output Parameter:
5724: . nrm - the resulting norm
5726: Level: intermediate
5728: .seealso: [](ch_matrices), `Mat`
5729: @*/
5730: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5731: {
5732: PetscFunctionBegin;
5735: PetscAssertPointer(nrm, 3);
5737: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5738: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5739: MatCheckPreallocated(mat, 1);
5741: PetscUseTypeMethod(mat, norm, type, nrm);
5742: PetscFunctionReturn(PETSC_SUCCESS);
5743: }
5745: /*
5746: This variable is used to prevent counting of MatAssemblyBegin() that
5747: are called from within a MatAssemblyEnd().
5748: */
5749: static PetscInt MatAssemblyEnd_InUse = 0;
5750: /*@
5751: MatAssemblyBegin - Begins assembling the matrix. This routine should
5752: be called after completing all calls to `MatSetValues()`.
5754: Collective
5756: Input Parameters:
5757: + mat - the matrix
5758: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5760: Level: beginner
5762: Notes:
5763: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5764: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5766: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5767: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5768: using the matrix.
5770: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5771: 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
5772: a global collective operation requiring all processes that share the matrix.
5774: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5775: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5776: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5778: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5779: @*/
5780: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5781: {
5782: PetscFunctionBegin;
5785: MatCheckPreallocated(mat, 1);
5786: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5787: if (mat->assembled) {
5788: mat->was_assembled = PETSC_TRUE;
5789: mat->assembled = PETSC_FALSE;
5790: }
5792: if (!MatAssemblyEnd_InUse) {
5793: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5794: PetscTryTypeMethod(mat, assemblybegin, type);
5795: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5796: } else PetscTryTypeMethod(mat, assemblybegin, type);
5797: PetscFunctionReturn(PETSC_SUCCESS);
5798: }
5800: /*@
5801: MatAssembled - Indicates if a matrix has been assembled and is ready for
5802: use; for example, in matrix-vector product.
5804: Not Collective
5806: Input Parameter:
5807: . mat - the matrix
5809: Output Parameter:
5810: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5812: Level: advanced
5814: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5815: @*/
5816: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5817: {
5818: PetscFunctionBegin;
5820: PetscAssertPointer(assembled, 2);
5821: *assembled = mat->assembled;
5822: PetscFunctionReturn(PETSC_SUCCESS);
5823: }
5825: /*@
5826: MatAssemblyEnd - Completes assembling the matrix. This routine should
5827: be called after `MatAssemblyBegin()`.
5829: Collective
5831: Input Parameters:
5832: + mat - the matrix
5833: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5835: Options Database Keys:
5836: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5837: . -mat_view ::ascii_info_detail - Prints more detailed info
5838: . -mat_view - Prints matrix in ASCII format
5839: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
5840: . -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5841: . -display <name> - Sets display name (default is host)
5842: . -draw_pause <sec> - Sets number of seconds to pause after display
5843: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5844: . -viewer_socket_machine <machine> - Machine to use for socket
5845: . -viewer_socket_port <port> - Port number to use for socket
5846: - -mat_view binary:filename[:append] - Save matrix to file in binary format
5848: Level: beginner
5850: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5851: @*/
5852: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5853: {
5854: static PetscInt inassm = 0;
5855: PetscBool flg = PETSC_FALSE;
5857: PetscFunctionBegin;
5861: inassm++;
5862: MatAssemblyEnd_InUse++;
5863: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5864: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5865: PetscTryTypeMethod(mat, assemblyend, type);
5866: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5867: } else PetscTryTypeMethod(mat, assemblyend, type);
5869: /* Flush assembly is not a true assembly */
5870: if (type != MAT_FLUSH_ASSEMBLY) {
5871: if (mat->num_ass) {
5872: if (!mat->symmetry_eternal) {
5873: mat->symmetric = PETSC_BOOL3_UNKNOWN;
5874: mat->hermitian = PETSC_BOOL3_UNKNOWN;
5875: }
5876: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5877: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5878: }
5879: mat->num_ass++;
5880: mat->assembled = PETSC_TRUE;
5881: mat->ass_nonzerostate = mat->nonzerostate;
5882: }
5884: mat->insertmode = NOT_SET_VALUES;
5885: MatAssemblyEnd_InUse--;
5886: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5887: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5888: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
5890: if (mat->checksymmetryonassembly) {
5891: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5892: if (flg) {
5893: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5894: } else {
5895: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5896: }
5897: }
5898: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5899: }
5900: inassm--;
5901: PetscFunctionReturn(PETSC_SUCCESS);
5902: }
5904: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5905: /*@
5906: MatSetOption - Sets a parameter option for a matrix. Some options
5907: may be specific to certain storage formats. Some options
5908: determine how values will be inserted (or added). Sorted,
5909: row-oriented input will generally assemble the fastest. The default
5910: is row-oriented.
5912: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
5914: Input Parameters:
5915: + mat - the matrix
5916: . op - the option, one of those listed below (and possibly others),
5917: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
5919: Options Describing Matrix Structure:
5920: + `MAT_SPD` - symmetric positive definite
5921: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
5922: . `MAT_HERMITIAN` - transpose is the complex conjugation
5923: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
5924: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5925: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5926: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
5928: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5929: do not need to be computed (usually at a high cost)
5931: Options For Use with `MatSetValues()`:
5932: Insert a logically dense subblock, which can be
5933: . `MAT_ROW_ORIENTED` - row-oriented (default)
5935: These options reflect the data you pass in with `MatSetValues()`; it has
5936: nothing to do with how the data is stored internally in the matrix
5937: data structure.
5939: When (re)assembling a matrix, we can restrict the input for
5940: efficiency/debugging purposes. These options include
5941: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
5942: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
5943: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
5944: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
5945: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
5946: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
5947: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5948: performance for very large process counts.
5949: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
5950: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5951: functions, instead sending only neighbor messages.
5953: Level: intermediate
5955: Notes:
5956: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
5958: Some options are relevant only for particular matrix types and
5959: are thus ignored by others. Other options are not supported by
5960: certain matrix types and will generate an error message if set.
5962: If using Fortran to compute a matrix, one may need to
5963: use the column-oriented option (or convert to the row-oriented
5964: format).
5966: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5967: that would generate a new entry in the nonzero structure is instead
5968: ignored. Thus, if memory has not already been allocated for this particular
5969: data, then the insertion is ignored. For dense matrices, in which
5970: the entire array is allocated, no entries are ever ignored.
5971: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
5973: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
5974: that would generate a new entry in the nonzero structure instead produces
5975: 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
5977: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
5978: that would generate a new entry that has not been preallocated will
5979: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
5980: only.) This is a useful flag when debugging matrix memory preallocation.
5981: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
5983: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
5984: other processors should be dropped, rather than stashed.
5985: This is useful if you know that the "owning" processor is also
5986: always generating the correct matrix entries, so that PETSc need
5987: not transfer duplicate entries generated on another processor.
5989: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
5990: searches during matrix assembly. When this flag is set, the hash table
5991: is created during the first matrix assembly. This hash table is
5992: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
5993: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
5994: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
5995: supported by `MATMPIBAIJ` format only.
5997: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
5998: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6000: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6001: a zero location in the matrix
6003: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6005: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6006: zero row routines and thus improves performance for very large process counts.
6008: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6009: part of the matrix (since they should match the upper triangular part).
6011: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6012: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6013: with finite difference schemes with non-periodic boundary conditions.
6015: Developer Note:
6016: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6017: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6018: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6019: not changed.
6021: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6022: @*/
6023: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6024: {
6025: PetscFunctionBegin;
6027: if (op > 0) {
6030: }
6032: 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);
6034: switch (op) {
6035: case MAT_FORCE_DIAGONAL_ENTRIES:
6036: mat->force_diagonals = flg;
6037: PetscFunctionReturn(PETSC_SUCCESS);
6038: case MAT_NO_OFF_PROC_ENTRIES:
6039: mat->nooffprocentries = flg;
6040: PetscFunctionReturn(PETSC_SUCCESS);
6041: case MAT_SUBSET_OFF_PROC_ENTRIES:
6042: mat->assembly_subset = flg;
6043: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6044: #if !defined(PETSC_HAVE_MPIUNI)
6045: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6046: #endif
6047: mat->stash.first_assembly_done = PETSC_FALSE;
6048: }
6049: PetscFunctionReturn(PETSC_SUCCESS);
6050: case MAT_NO_OFF_PROC_ZERO_ROWS:
6051: mat->nooffproczerorows = flg;
6052: PetscFunctionReturn(PETSC_SUCCESS);
6053: case MAT_SPD:
6054: if (flg) {
6055: mat->spd = PETSC_BOOL3_TRUE;
6056: mat->symmetric = PETSC_BOOL3_TRUE;
6057: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6058: } else {
6059: mat->spd = PETSC_BOOL3_FALSE;
6060: }
6061: break;
6062: case MAT_SYMMETRIC:
6063: mat->symmetric = PetscBoolToBool3(flg);
6064: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6065: #if !defined(PETSC_USE_COMPLEX)
6066: mat->hermitian = PetscBoolToBool3(flg);
6067: #endif
6068: break;
6069: case MAT_HERMITIAN:
6070: mat->hermitian = PetscBoolToBool3(flg);
6071: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6072: #if !defined(PETSC_USE_COMPLEX)
6073: mat->symmetric = PetscBoolToBool3(flg);
6074: #endif
6075: break;
6076: case MAT_STRUCTURALLY_SYMMETRIC:
6077: mat->structurally_symmetric = PetscBoolToBool3(flg);
6078: break;
6079: case MAT_SYMMETRY_ETERNAL:
6080: 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");
6081: mat->symmetry_eternal = flg;
6082: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6083: break;
6084: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6085: PetscCheck(mat->structurally_symmetric != PETSC_BOOL3_UNKNOWN, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_STRUCTURAL_SYMMETRY_ETERNAL without first setting MAT_STRUCTURALLY_SYMMETRIC to true or false");
6086: mat->structural_symmetry_eternal = flg;
6087: break;
6088: case MAT_SPD_ETERNAL:
6089: 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");
6090: mat->spd_eternal = flg;
6091: if (flg) {
6092: mat->structural_symmetry_eternal = PETSC_TRUE;
6093: mat->symmetry_eternal = PETSC_TRUE;
6094: }
6095: break;
6096: case MAT_STRUCTURE_ONLY:
6097: mat->structure_only = flg;
6098: break;
6099: case MAT_SORTED_FULL:
6100: mat->sortedfull = flg;
6101: break;
6102: default:
6103: break;
6104: }
6105: PetscTryTypeMethod(mat, setoption, op, flg);
6106: PetscFunctionReturn(PETSC_SUCCESS);
6107: }
6109: /*@
6110: MatGetOption - Gets a parameter option that has been set for a matrix.
6112: Logically Collective
6114: Input Parameters:
6115: + mat - the matrix
6116: - op - the option, this only responds to certain options, check the code for which ones
6118: Output Parameter:
6119: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6121: Level: intermediate
6123: Notes:
6124: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6126: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6127: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6129: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6130: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6131: @*/
6132: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6133: {
6134: PetscFunctionBegin;
6138: 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);
6139: 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()");
6141: switch (op) {
6142: case MAT_NO_OFF_PROC_ENTRIES:
6143: *flg = mat->nooffprocentries;
6144: break;
6145: case MAT_NO_OFF_PROC_ZERO_ROWS:
6146: *flg = mat->nooffproczerorows;
6147: break;
6148: case MAT_SYMMETRIC:
6149: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6150: break;
6151: case MAT_HERMITIAN:
6152: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6153: break;
6154: case MAT_STRUCTURALLY_SYMMETRIC:
6155: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6156: break;
6157: case MAT_SPD:
6158: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6159: break;
6160: case MAT_SYMMETRY_ETERNAL:
6161: *flg = mat->symmetry_eternal;
6162: break;
6163: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6164: *flg = mat->symmetry_eternal;
6165: break;
6166: default:
6167: break;
6168: }
6169: PetscFunctionReturn(PETSC_SUCCESS);
6170: }
6172: /*@
6173: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6174: this routine retains the old nonzero structure.
6176: Logically Collective
6178: Input Parameter:
6179: . mat - the matrix
6181: Level: intermediate
6183: Note:
6184: 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.
6185: See the Performance chapter of the users manual for information on preallocating matrices.
6187: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6188: @*/
6189: PetscErrorCode MatZeroEntries(Mat mat)
6190: {
6191: PetscFunctionBegin;
6194: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6195: 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");
6196: MatCheckPreallocated(mat, 1);
6198: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6199: PetscUseTypeMethod(mat, zeroentries);
6200: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6201: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6202: PetscFunctionReturn(PETSC_SUCCESS);
6203: }
6205: /*@
6206: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6207: of a set of rows and columns of a matrix.
6209: Collective
6211: Input Parameters:
6212: + mat - the matrix
6213: . numRows - the number of rows/columns to zero
6214: . rows - the global row indices
6215: . diag - value put in the diagonal of the eliminated rows
6216: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6217: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6219: Level: intermediate
6221: Notes:
6222: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6224: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6225: 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
6227: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6228: Krylov method to take advantage of the known solution on the zeroed rows.
6230: For the parallel case, all processes that share the matrix (i.e.,
6231: those in the communicator used for matrix creation) MUST call this
6232: routine, regardless of whether any rows being zeroed are owned by
6233: them.
6235: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6236: removes them from the nonzero pattern. The nonzero pattern of the matrix can still change if a nonzero needs to be inserted on a diagonal entry that was previously
6237: missing.
6239: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6240: list only rows local to itself).
6242: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6244: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6245: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6246: @*/
6247: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6248: {
6249: PetscFunctionBegin;
6252: if (numRows) PetscAssertPointer(rows, 3);
6253: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6254: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6255: MatCheckPreallocated(mat, 1);
6257: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6258: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6259: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6260: PetscFunctionReturn(PETSC_SUCCESS);
6261: }
6263: /*@
6264: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6265: of a set of rows and columns of a matrix.
6267: Collective
6269: Input Parameters:
6270: + mat - the matrix
6271: . is - the rows to zero
6272: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6273: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6274: - b - optional vector of right-hand side, that will be adjusted by provided solution
6276: Level: intermediate
6278: Note:
6279: See `MatZeroRowsColumns()` for details on how this routine operates.
6281: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6282: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6283: @*/
6284: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6285: {
6286: PetscInt numRows;
6287: const PetscInt *rows;
6289: PetscFunctionBegin;
6294: PetscCall(ISGetLocalSize(is, &numRows));
6295: PetscCall(ISGetIndices(is, &rows));
6296: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6297: PetscCall(ISRestoreIndices(is, &rows));
6298: PetscFunctionReturn(PETSC_SUCCESS);
6299: }
6301: /*@
6302: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6303: of a set of rows of a matrix.
6305: Collective
6307: Input Parameters:
6308: + mat - the matrix
6309: . numRows - the number of rows to zero
6310: . rows - the global row indices
6311: . diag - value put in the diagonal of the zeroed rows
6312: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6313: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6315: Level: intermediate
6317: Notes:
6318: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6320: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6322: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6323: Krylov method to take advantage of the known solution on the zeroed rows.
6325: 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)
6326: from the matrix.
6328: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6329: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6330: formats this does not alter the nonzero structure.
6332: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6333: of the matrix is not changed the values are
6334: merely zeroed.
6336: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6337: formats can optionally remove the main diagonal entry from the
6338: nonzero structure as well, by passing 0.0 as the final argument).
6340: For the parallel case, all processes that share the matrix (i.e.,
6341: those in the communicator used for matrix creation) MUST call this
6342: routine, regardless of whether any rows being zeroed are owned by
6343: them.
6345: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6346: list only rows local to itself).
6348: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6349: owns that are to be zeroed. This saves a global synchronization in the implementation.
6351: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6352: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6353: @*/
6354: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6355: {
6356: PetscFunctionBegin;
6359: if (numRows) PetscAssertPointer(rows, 3);
6360: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6361: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6362: MatCheckPreallocated(mat, 1);
6364: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6365: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6366: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6367: PetscFunctionReturn(PETSC_SUCCESS);
6368: }
6370: /*@
6371: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6372: of a set of rows of a matrix indicated by an `IS`
6374: Collective
6376: Input Parameters:
6377: + mat - the matrix
6378: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6379: . diag - value put in all diagonals of eliminated rows
6380: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6381: - b - optional vector of right-hand side, that will be adjusted by provided solution
6383: Level: intermediate
6385: Note:
6386: See `MatZeroRows()` for details on how this routine operates.
6388: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6389: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6390: @*/
6391: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6392: {
6393: PetscInt numRows = 0;
6394: const PetscInt *rows = NULL;
6396: PetscFunctionBegin;
6399: if (is) {
6401: PetscCall(ISGetLocalSize(is, &numRows));
6402: PetscCall(ISGetIndices(is, &rows));
6403: }
6404: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6405: if (is) PetscCall(ISRestoreIndices(is, &rows));
6406: PetscFunctionReturn(PETSC_SUCCESS);
6407: }
6409: /*@
6410: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6411: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6413: Collective
6415: Input Parameters:
6416: + mat - the matrix
6417: . numRows - the number of rows to remove
6418: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6419: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6420: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6421: - b - optional vector of right-hand side, that will be adjusted by provided solution
6423: Level: intermediate
6425: Notes:
6426: See `MatZeroRows()` for details on how this routine operates.
6428: The grid coordinates are across the entire grid, not just the local portion
6430: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6431: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6432: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6433: `DM_BOUNDARY_PERIODIC` boundary type.
6435: 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
6436: a single value per point) you can skip filling those indices.
6438: Fortran Note:
6439: `idxm` and `idxn` should be declared as
6440: .vb
6441: MatStencil idxm(4, m)
6442: .ve
6443: and the values inserted using
6444: .vb
6445: idxm(MatStencil_i, 1) = i
6446: idxm(MatStencil_j, 1) = j
6447: idxm(MatStencil_k, 1) = k
6448: idxm(MatStencil_c, 1) = c
6449: etc
6450: .ve
6452: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6453: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6454: @*/
6455: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6456: {
6457: PetscInt dim = mat->stencil.dim;
6458: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6459: PetscInt *dims = mat->stencil.dims + 1;
6460: PetscInt *starts = mat->stencil.starts;
6461: PetscInt *dxm = (PetscInt *)rows;
6462: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6464: PetscFunctionBegin;
6467: if (numRows) PetscAssertPointer(rows, 3);
6469: PetscCall(PetscMalloc1(numRows, &jdxm));
6470: for (i = 0; i < numRows; ++i) {
6471: /* Skip unused dimensions (they are ordered k, j, i, c) */
6472: for (j = 0; j < 3 - sdim; ++j) dxm++;
6473: /* Local index in X dir */
6474: tmp = *dxm++ - starts[0];
6475: /* Loop over remaining dimensions */
6476: for (j = 0; j < dim - 1; ++j) {
6477: /* If nonlocal, set index to be negative */
6478: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6479: /* Update local index */
6480: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6481: }
6482: /* Skip component slot if necessary */
6483: if (mat->stencil.noc) dxm++;
6484: /* Local row number */
6485: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6486: }
6487: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6488: PetscCall(PetscFree(jdxm));
6489: PetscFunctionReturn(PETSC_SUCCESS);
6490: }
6492: /*@
6493: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6494: of a set of rows and columns of a matrix.
6496: Collective
6498: Input Parameters:
6499: + mat - the matrix
6500: . numRows - the number of rows/columns to remove
6501: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6502: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6503: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6504: - b - optional vector of right-hand side, that will be adjusted by provided solution
6506: Level: intermediate
6508: Notes:
6509: See `MatZeroRowsColumns()` for details on how this routine operates.
6511: The grid coordinates are across the entire grid, not just the local portion
6513: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6514: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6515: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6516: `DM_BOUNDARY_PERIODIC` boundary type.
6518: 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
6519: a single value per point) you can skip filling those indices.
6521: Fortran Note:
6522: `idxm` and `idxn` should be declared as
6523: .vb
6524: MatStencil idxm(4, m)
6525: .ve
6526: and the values inserted using
6527: .vb
6528: idxm(MatStencil_i, 1) = i
6529: idxm(MatStencil_j, 1) = j
6530: idxm(MatStencil_k, 1) = k
6531: idxm(MatStencil_c, 1) = c
6532: etc
6533: .ve
6535: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6536: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6537: @*/
6538: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6539: {
6540: PetscInt dim = mat->stencil.dim;
6541: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6542: PetscInt *dims = mat->stencil.dims + 1;
6543: PetscInt *starts = mat->stencil.starts;
6544: PetscInt *dxm = (PetscInt *)rows;
6545: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6547: PetscFunctionBegin;
6550: if (numRows) PetscAssertPointer(rows, 3);
6552: PetscCall(PetscMalloc1(numRows, &jdxm));
6553: for (i = 0; i < numRows; ++i) {
6554: /* Skip unused dimensions (they are ordered k, j, i, c) */
6555: for (j = 0; j < 3 - sdim; ++j) dxm++;
6556: /* Local index in X dir */
6557: tmp = *dxm++ - starts[0];
6558: /* Loop over remaining dimensions */
6559: for (j = 0; j < dim - 1; ++j) {
6560: /* If nonlocal, set index to be negative */
6561: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6562: /* Update local index */
6563: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6564: }
6565: /* Skip component slot if necessary */
6566: if (mat->stencil.noc) dxm++;
6567: /* Local row number */
6568: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6569: }
6570: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6571: PetscCall(PetscFree(jdxm));
6572: PetscFunctionReturn(PETSC_SUCCESS);
6573: }
6575: /*@
6576: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6577: of a set of rows of a matrix; using local numbering of rows.
6579: Collective
6581: Input Parameters:
6582: + mat - the matrix
6583: . numRows - the number of rows to remove
6584: . rows - the local row indices
6585: . diag - value put in all diagonals of eliminated rows
6586: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6587: - b - optional vector of right-hand side, that will be adjusted by provided solution
6589: Level: intermediate
6591: Notes:
6592: Before calling `MatZeroRowsLocal()`, the user must first set the
6593: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6595: See `MatZeroRows()` for details on how this routine operates.
6597: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6598: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6599: @*/
6600: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6601: {
6602: PetscFunctionBegin;
6605: if (numRows) PetscAssertPointer(rows, 3);
6606: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6607: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6608: MatCheckPreallocated(mat, 1);
6610: if (mat->ops->zerorowslocal) {
6611: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6612: } else {
6613: IS is, newis;
6614: PetscInt *newRows, nl = 0;
6616: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6617: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6618: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6619: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6620: for (PetscInt i = 0; i < numRows; i++)
6621: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6622: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
6623: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6624: PetscCall(ISDestroy(&newis));
6625: PetscCall(ISDestroy(&is));
6626: }
6627: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6628: PetscFunctionReturn(PETSC_SUCCESS);
6629: }
6631: /*@
6632: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6633: of a set of rows of a matrix; using local numbering of rows.
6635: Collective
6637: Input Parameters:
6638: + mat - the matrix
6639: . is - index set of rows to remove
6640: . diag - value put in all diagonals of eliminated rows
6641: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6642: - b - optional vector of right-hand side, that will be adjusted by provided solution
6644: Level: intermediate
6646: Notes:
6647: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6648: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6650: See `MatZeroRows()` for details on how this routine operates.
6652: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6653: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6654: @*/
6655: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6656: {
6657: PetscInt numRows;
6658: const PetscInt *rows;
6660: PetscFunctionBegin;
6664: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6665: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6666: MatCheckPreallocated(mat, 1);
6668: PetscCall(ISGetLocalSize(is, &numRows));
6669: PetscCall(ISGetIndices(is, &rows));
6670: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6671: PetscCall(ISRestoreIndices(is, &rows));
6672: PetscFunctionReturn(PETSC_SUCCESS);
6673: }
6675: /*@
6676: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6677: of a set of rows and columns of a matrix; using local numbering of rows.
6679: Collective
6681: Input Parameters:
6682: + mat - the matrix
6683: . numRows - the number of rows to remove
6684: . rows - the global row indices
6685: . diag - value put in all diagonals of eliminated rows
6686: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6687: - b - optional vector of right-hand side, that will be adjusted by provided solution
6689: Level: intermediate
6691: Notes:
6692: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6693: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6695: See `MatZeroRowsColumns()` for details on how this routine operates.
6697: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6698: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6699: @*/
6700: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6701: {
6702: PetscFunctionBegin;
6705: if (numRows) PetscAssertPointer(rows, 3);
6706: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6707: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6708: MatCheckPreallocated(mat, 1);
6710: if (mat->ops->zerorowscolumnslocal) {
6711: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
6712: } else {
6713: IS is, newis;
6714: PetscInt *newRows, nl = 0;
6716: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6717: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6718: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6719: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6720: for (PetscInt i = 0; i < numRows; i++)
6721: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6722: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
6723: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6724: PetscCall(ISDestroy(&newis));
6725: PetscCall(ISDestroy(&is));
6726: }
6727: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6728: PetscFunctionReturn(PETSC_SUCCESS);
6729: }
6731: /*@
6732: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6733: of a set of rows and columns of a matrix; using local numbering of rows.
6735: Collective
6737: Input Parameters:
6738: + mat - the matrix
6739: . is - index set of rows to remove
6740: . diag - value put in all diagonals of eliminated rows
6741: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6742: - b - optional vector of right-hand side, that will be adjusted by provided solution
6744: Level: intermediate
6746: Notes:
6747: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6748: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6750: See `MatZeroRowsColumns()` for details on how this routine operates.
6752: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6753: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6754: @*/
6755: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6756: {
6757: PetscInt numRows;
6758: const PetscInt *rows;
6760: PetscFunctionBegin;
6764: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6765: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6766: MatCheckPreallocated(mat, 1);
6768: PetscCall(ISGetLocalSize(is, &numRows));
6769: PetscCall(ISGetIndices(is, &rows));
6770: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6771: PetscCall(ISRestoreIndices(is, &rows));
6772: PetscFunctionReturn(PETSC_SUCCESS);
6773: }
6775: /*@
6776: MatGetSize - Returns the numbers of rows and columns in a matrix.
6778: Not Collective
6780: Input Parameter:
6781: . mat - the matrix
6783: Output Parameters:
6784: + m - the number of global rows
6785: - n - the number of global columns
6787: Level: beginner
6789: Note:
6790: Both output parameters can be `NULL` on input.
6792: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6793: @*/
6794: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6795: {
6796: PetscFunctionBegin;
6798: if (m) *m = mat->rmap->N;
6799: if (n) *n = mat->cmap->N;
6800: PetscFunctionReturn(PETSC_SUCCESS);
6801: }
6803: /*@
6804: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6805: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6807: Not Collective
6809: Input Parameter:
6810: . mat - the matrix
6812: Output Parameters:
6813: + m - the number of local rows, use `NULL` to not obtain this value
6814: - n - the number of local columns, use `NULL` to not obtain this value
6816: Level: beginner
6818: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6819: @*/
6820: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6821: {
6822: PetscFunctionBegin;
6824: if (m) PetscAssertPointer(m, 2);
6825: if (n) PetscAssertPointer(n, 3);
6826: if (m) *m = mat->rmap->n;
6827: if (n) *n = mat->cmap->n;
6828: PetscFunctionReturn(PETSC_SUCCESS);
6829: }
6831: /*@
6832: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6833: vector one multiplies this matrix by that are owned by this processor.
6835: Not Collective, unless matrix has not been allocated, then collective
6837: Input Parameter:
6838: . mat - the matrix
6840: Output Parameters:
6841: + m - the global index of the first local column, use `NULL` to not obtain this value
6842: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
6844: Level: developer
6846: Notes:
6847: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6849: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6850: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6852: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6853: the local values in the matrix.
6855: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6856: Layouts](sec_matlayout) for details on matrix layouts.
6858: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6859: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6860: @*/
6861: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6862: {
6863: PetscFunctionBegin;
6866: if (m) PetscAssertPointer(m, 2);
6867: if (n) PetscAssertPointer(n, 3);
6868: MatCheckPreallocated(mat, 1);
6869: if (m) *m = mat->cmap->rstart;
6870: if (n) *n = mat->cmap->rend;
6871: PetscFunctionReturn(PETSC_SUCCESS);
6872: }
6874: /*@
6875: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6876: this MPI process.
6878: Not Collective
6880: Input Parameter:
6881: . mat - the matrix
6883: Output Parameters:
6884: + m - the global index of the first local row, use `NULL` to not obtain this value
6885: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
6887: Level: beginner
6889: Notes:
6890: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6892: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6893: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6895: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6896: the local values in the matrix.
6898: The high argument is one more than the last element stored locally.
6900: For all matrices it returns the range of matrix rows associated with rows of a vector that
6901: would contain the result of a matrix vector product with this matrix. See [Matrix
6902: Layouts](sec_matlayout) for details on matrix layouts.
6904: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6905: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6906: @*/
6907: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6908: {
6909: PetscFunctionBegin;
6912: if (m) PetscAssertPointer(m, 2);
6913: if (n) PetscAssertPointer(n, 3);
6914: MatCheckPreallocated(mat, 1);
6915: if (m) *m = mat->rmap->rstart;
6916: if (n) *n = mat->rmap->rend;
6917: PetscFunctionReturn(PETSC_SUCCESS);
6918: }
6920: /*@C
6921: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6922: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
6924: Not Collective, unless matrix has not been allocated
6926: Input Parameter:
6927: . mat - the matrix
6929: Output Parameter:
6930: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6931: where `size` is the number of MPI processes used by `mat`
6933: Level: beginner
6935: Notes:
6936: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6938: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6939: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6941: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6942: the local values in the matrix.
6944: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
6945: would contain the result of a matrix vector product with this matrix. See [Matrix
6946: Layouts](sec_matlayout) for details on matrix layouts.
6948: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6949: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6950: `DMDAGetGhostCorners()`, `DM`
6951: @*/
6952: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6953: {
6954: PetscFunctionBegin;
6957: MatCheckPreallocated(mat, 1);
6958: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6959: PetscFunctionReturn(PETSC_SUCCESS);
6960: }
6962: /*@C
6963: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6964: vector one multiplies this vector by that are owned by each processor.
6966: Not Collective, unless matrix has not been allocated
6968: Input Parameter:
6969: . mat - the matrix
6971: Output Parameter:
6972: . ranges - start of each processors portion plus one more than the total length at the end
6974: Level: beginner
6976: Notes:
6977: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6979: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6980: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6982: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6983: the local values in the matrix.
6985: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
6986: Layouts](sec_matlayout) for details on matrix layouts.
6988: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
6989: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
6990: `DMDAGetGhostCorners()`, `DM`
6991: @*/
6992: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
6993: {
6994: PetscFunctionBegin;
6997: MatCheckPreallocated(mat, 1);
6998: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
6999: PetscFunctionReturn(PETSC_SUCCESS);
7000: }
7002: /*@
7003: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7005: Not Collective
7007: Input Parameter:
7008: . A - matrix
7010: Output Parameters:
7011: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7012: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7014: Level: intermediate
7016: Note:
7017: You should call `ISDestroy()` on the returned `IS`
7019: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7020: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7021: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7022: details on matrix layouts.
7024: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7025: @*/
7026: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7027: {
7028: PetscErrorCode (*f)(Mat, IS *, IS *);
7030: PetscFunctionBegin;
7033: MatCheckPreallocated(A, 1);
7034: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7035: if (f) {
7036: PetscCall((*f)(A, rows, cols));
7037: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7038: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7039: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7040: }
7041: PetscFunctionReturn(PETSC_SUCCESS);
7042: }
7044: /*@
7045: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7046: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7047: to complete the factorization.
7049: Collective
7051: Input Parameters:
7052: + fact - the factorized matrix obtained with `MatGetFactor()`
7053: . mat - the matrix
7054: . row - row permutation
7055: . col - column permutation
7056: - info - structure containing
7057: .vb
7058: levels - number of levels of fill.
7059: expected fill - as ratio of original fill.
7060: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7061: missing diagonal entries)
7062: .ve
7064: Level: developer
7066: Notes:
7067: See [Matrix Factorization](sec_matfactor) for additional information.
7069: Most users should employ the `KSP` interface for linear solvers
7070: instead of working directly with matrix algebra routines such as this.
7071: See, e.g., `KSPCreate()`.
7073: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7075: Fortran Note:
7076: A valid (non-null) `info` argument must be provided
7078: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7079: `MatGetOrdering()`, `MatFactorInfo`
7080: @*/
7081: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7082: {
7083: PetscFunctionBegin;
7088: PetscAssertPointer(info, 5);
7089: PetscAssertPointer(fact, 1);
7090: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7091: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7092: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7093: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7094: MatCheckPreallocated(mat, 2);
7096: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7097: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7098: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7099: PetscFunctionReturn(PETSC_SUCCESS);
7100: }
7102: /*@
7103: MatICCFactorSymbolic - Performs symbolic incomplete
7104: Cholesky factorization for a symmetric matrix. Use
7105: `MatCholeskyFactorNumeric()` to complete the factorization.
7107: Collective
7109: Input Parameters:
7110: + fact - the factorized matrix obtained with `MatGetFactor()`
7111: . mat - the matrix to be factored
7112: . perm - row and column permutation
7113: - info - structure containing
7114: .vb
7115: levels - number of levels of fill.
7116: expected fill - as ratio of original fill.
7117: .ve
7119: Level: developer
7121: Notes:
7122: Most users should employ the `KSP` interface for linear solvers
7123: instead of working directly with matrix algebra routines such as this.
7124: See, e.g., `KSPCreate()`.
7126: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7128: Fortran Note:
7129: A valid (non-null) `info` argument must be provided
7131: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7132: @*/
7133: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7134: {
7135: PetscFunctionBegin;
7139: PetscAssertPointer(info, 4);
7140: PetscAssertPointer(fact, 1);
7141: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7142: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7143: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7144: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7145: MatCheckPreallocated(mat, 2);
7147: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7148: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7149: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7150: PetscFunctionReturn(PETSC_SUCCESS);
7151: }
7153: /*@C
7154: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7155: points to an array of valid matrices, they may be reused to store the new
7156: submatrices.
7158: Collective
7160: Input Parameters:
7161: + mat - the matrix
7162: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7163: . irow - index set of rows to extract
7164: . icol - index set of columns to extract
7165: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7167: Output Parameter:
7168: . submat - the array of submatrices
7170: Level: advanced
7172: Notes:
7173: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7174: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7175: to extract a parallel submatrix.
7177: Some matrix types place restrictions on the row and column
7178: indices, such as that they be sorted or that they be equal to each other.
7180: The index sets may not have duplicate entries.
7182: When extracting submatrices from a parallel matrix, each processor can
7183: form a different submatrix by setting the rows and columns of its
7184: individual index sets according to the local submatrix desired.
7186: When finished using the submatrices, the user should destroy
7187: them with `MatDestroySubMatrices()`.
7189: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7190: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7192: This routine creates the matrices in submat; you should NOT create them before
7193: calling it. It also allocates the array of matrix pointers submat.
7195: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7196: request one row/column in a block, they must request all rows/columns that are in
7197: that block. For example, if the block size is 2 you cannot request just row 0 and
7198: column 0.
7200: Fortran Note:
7201: .vb
7202: Mat, pointer :: submat(:)
7203: .ve
7205: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7206: @*/
7207: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7208: {
7209: PetscInt i;
7210: PetscBool eq;
7212: PetscFunctionBegin;
7215: if (n) {
7216: PetscAssertPointer(irow, 3);
7218: PetscAssertPointer(icol, 4);
7220: }
7221: PetscAssertPointer(submat, 6);
7222: if (n && scall == MAT_REUSE_MATRIX) {
7223: PetscAssertPointer(*submat, 6);
7225: }
7226: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7227: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7228: MatCheckPreallocated(mat, 1);
7229: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7230: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7231: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7232: for (i = 0; i < n; i++) {
7233: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7234: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7235: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7236: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7237: if (mat->boundtocpu && mat->bindingpropagates) {
7238: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7239: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7240: }
7241: #endif
7242: }
7243: PetscFunctionReturn(PETSC_SUCCESS);
7244: }
7246: /*@C
7247: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7249: Collective
7251: Input Parameters:
7252: + mat - the matrix
7253: . n - the number of submatrixes to be extracted
7254: . irow - index set of rows to extract
7255: . icol - index set of columns to extract
7256: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7258: Output Parameter:
7259: . submat - the array of submatrices
7261: Level: advanced
7263: Note:
7264: This is used by `PCGASM`
7266: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7267: @*/
7268: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7269: {
7270: PetscInt i;
7271: PetscBool eq;
7273: PetscFunctionBegin;
7276: if (n) {
7277: PetscAssertPointer(irow, 3);
7279: PetscAssertPointer(icol, 4);
7281: }
7282: PetscAssertPointer(submat, 6);
7283: if (n && scall == MAT_REUSE_MATRIX) {
7284: PetscAssertPointer(*submat, 6);
7286: }
7287: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7288: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7289: MatCheckPreallocated(mat, 1);
7291: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7292: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7293: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7294: for (i = 0; i < n; i++) {
7295: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7296: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7297: }
7298: PetscFunctionReturn(PETSC_SUCCESS);
7299: }
7301: /*@C
7302: MatDestroyMatrices - Destroys an array of matrices
7304: Collective
7306: Input Parameters:
7307: + n - the number of local matrices
7308: - mat - the matrices (this is a pointer to the array of matrices)
7310: Level: advanced
7312: Notes:
7313: Frees not only the matrices, but also the array that contains the matrices
7315: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7317: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7318: @*/
7319: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7320: {
7321: PetscInt i;
7323: PetscFunctionBegin;
7324: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7325: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7326: PetscAssertPointer(mat, 2);
7328: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7330: /* memory is allocated even if n = 0 */
7331: PetscCall(PetscFree(*mat));
7332: PetscFunctionReturn(PETSC_SUCCESS);
7333: }
7335: /*@C
7336: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7338: Collective
7340: Input Parameters:
7341: + n - the number of local matrices
7342: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7344: Level: advanced
7346: Note:
7347: Frees not only the matrices, but also the array that contains the matrices
7349: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7350: @*/
7351: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7352: {
7353: Mat mat0;
7355: PetscFunctionBegin;
7356: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7357: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7358: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7359: PetscAssertPointer(mat, 2);
7361: mat0 = (*mat)[0];
7362: if (mat0 && mat0->ops->destroysubmatrices) {
7363: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7364: } else {
7365: PetscCall(MatDestroyMatrices(n, mat));
7366: }
7367: PetscFunctionReturn(PETSC_SUCCESS);
7368: }
7370: /*@
7371: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7373: Collective
7375: Input Parameter:
7376: . mat - the matrix
7378: Output Parameter:
7379: . matstruct - the sequential matrix with the nonzero structure of `mat`
7381: Level: developer
7383: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7384: @*/
7385: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7386: {
7387: PetscFunctionBegin;
7389: PetscAssertPointer(matstruct, 2);
7392: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7393: MatCheckPreallocated(mat, 1);
7395: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7396: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7397: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7398: PetscFunctionReturn(PETSC_SUCCESS);
7399: }
7401: /*@C
7402: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7404: Collective
7406: Input Parameter:
7407: . mat - the matrix
7409: Level: advanced
7411: Note:
7412: This is not needed, one can just call `MatDestroy()`
7414: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7415: @*/
7416: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7417: {
7418: PetscFunctionBegin;
7419: PetscAssertPointer(mat, 1);
7420: PetscCall(MatDestroy(mat));
7421: PetscFunctionReturn(PETSC_SUCCESS);
7422: }
7424: /*@
7425: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7426: replaces the index sets by larger ones that represent submatrices with
7427: additional overlap.
7429: Collective
7431: Input Parameters:
7432: + mat - the matrix
7433: . n - the number of index sets
7434: . is - the array of index sets (these index sets will changed during the call)
7435: - ov - the additional overlap requested
7437: Options Database Key:
7438: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7440: Level: developer
7442: Note:
7443: The computed overlap preserves the matrix block sizes when the blocks are square.
7444: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7445: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7447: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7448: @*/
7449: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7450: {
7451: PetscInt i, bs, cbs;
7453: PetscFunctionBegin;
7457: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7458: if (n) {
7459: PetscAssertPointer(is, 3);
7461: }
7462: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7463: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7464: MatCheckPreallocated(mat, 1);
7466: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7467: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7468: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7469: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7470: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7471: if (bs == cbs) {
7472: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7473: }
7474: PetscFunctionReturn(PETSC_SUCCESS);
7475: }
7477: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7479: /*@
7480: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7481: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7482: additional overlap.
7484: Collective
7486: Input Parameters:
7487: + mat - the matrix
7488: . n - the number of index sets
7489: . is - the array of index sets (these index sets will changed during the call)
7490: - ov - the additional overlap requested
7492: ` Options Database Key:
7493: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7495: Level: developer
7497: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7498: @*/
7499: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7500: {
7501: PetscInt i;
7503: PetscFunctionBegin;
7506: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7507: if (n) {
7508: PetscAssertPointer(is, 3);
7510: }
7511: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7512: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7513: MatCheckPreallocated(mat, 1);
7514: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7515: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7516: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7517: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7518: PetscFunctionReturn(PETSC_SUCCESS);
7519: }
7521: /*@
7522: MatGetBlockSize - Returns the matrix block size.
7524: Not Collective
7526: Input Parameter:
7527: . mat - the matrix
7529: Output Parameter:
7530: . bs - block size
7532: Level: intermediate
7534: Notes:
7535: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7537: If the block size has not been set yet this routine returns 1.
7539: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7540: @*/
7541: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7542: {
7543: PetscFunctionBegin;
7545: PetscAssertPointer(bs, 2);
7546: *bs = mat->rmap->bs;
7547: PetscFunctionReturn(PETSC_SUCCESS);
7548: }
7550: /*@
7551: MatGetBlockSizes - Returns the matrix block row and column sizes.
7553: Not Collective
7555: Input Parameter:
7556: . mat - the matrix
7558: Output Parameters:
7559: + rbs - row block size
7560: - cbs - column block size
7562: Level: intermediate
7564: Notes:
7565: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7566: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7568: If a block size has not been set yet this routine returns 1.
7570: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7571: @*/
7572: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7573: {
7574: PetscFunctionBegin;
7576: if (rbs) PetscAssertPointer(rbs, 2);
7577: if (cbs) PetscAssertPointer(cbs, 3);
7578: if (rbs) *rbs = mat->rmap->bs;
7579: if (cbs) *cbs = mat->cmap->bs;
7580: PetscFunctionReturn(PETSC_SUCCESS);
7581: }
7583: /*@
7584: MatSetBlockSize - Sets the matrix block size.
7586: Logically Collective
7588: Input Parameters:
7589: + mat - the matrix
7590: - bs - block size
7592: Level: intermediate
7594: Notes:
7595: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7596: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7598: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7599: is compatible with the matrix local sizes.
7601: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7602: @*/
7603: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7604: {
7605: PetscFunctionBegin;
7608: PetscCall(MatSetBlockSizes(mat, bs, bs));
7609: PetscFunctionReturn(PETSC_SUCCESS);
7610: }
7612: typedef struct {
7613: PetscInt n;
7614: IS *is;
7615: Mat *mat;
7616: PetscObjectState nonzerostate;
7617: Mat C;
7618: } EnvelopeData;
7620: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7621: {
7622: EnvelopeData *edata = (EnvelopeData *)*ptr;
7624: PetscFunctionBegin;
7625: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7626: PetscCall(PetscFree(edata->is));
7627: PetscCall(PetscFree(edata));
7628: PetscFunctionReturn(PETSC_SUCCESS);
7629: }
7631: /*@
7632: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7633: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7635: Collective
7637: Input Parameter:
7638: . mat - the matrix
7640: Level: intermediate
7642: Notes:
7643: There can be zeros within the blocks
7645: The blocks can overlap between processes, including laying on more than two processes
7647: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7648: @*/
7649: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7650: {
7651: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7652: PetscInt *diag, *odiag, sc;
7653: VecScatter scatter;
7654: PetscScalar *seqv;
7655: const PetscScalar *parv;
7656: const PetscInt *ia, *ja;
7657: PetscBool set, flag, done;
7658: Mat AA = mat, A;
7659: MPI_Comm comm;
7660: PetscMPIInt rank, size, tag;
7661: MPI_Status status;
7662: PetscContainer container;
7663: EnvelopeData *edata;
7664: Vec seq, par;
7665: IS isglobal;
7667: PetscFunctionBegin;
7669: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7670: if (!set || !flag) {
7671: /* TODO: only needs nonzero structure of transpose */
7672: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7673: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7674: }
7675: PetscCall(MatAIJGetLocalMat(AA, &A));
7676: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7677: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7679: PetscCall(MatGetLocalSize(mat, &n, NULL));
7680: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7681: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7682: PetscCallMPI(MPI_Comm_size(comm, &size));
7683: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7685: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7687: if (rank > 0) {
7688: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7689: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7690: }
7691: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7692: for (i = 0; i < n; i++) {
7693: env = PetscMax(env, ja[ia[i + 1] - 1]);
7694: II = rstart + i;
7695: if (env == II) {
7696: starts[lblocks] = tbs;
7697: sizes[lblocks++] = 1 + II - tbs;
7698: tbs = 1 + II;
7699: }
7700: }
7701: if (rank < size - 1) {
7702: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7703: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7704: }
7706: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7707: if (!set || !flag) PetscCall(MatDestroy(&AA));
7708: PetscCall(MatDestroy(&A));
7710: PetscCall(PetscNew(&edata));
7711: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7712: edata->n = lblocks;
7713: /* create IS needed for extracting blocks from the original matrix */
7714: PetscCall(PetscMalloc1(lblocks, &edata->is));
7715: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7717: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7718: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7719: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7720: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7721: PetscCall(MatSetType(edata->C, MATAIJ));
7723: /* Communicate the start and end of each row, from each block to the correct rank */
7724: /* TODO: Use PetscSF instead of VecScatter */
7725: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7726: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7727: PetscCall(VecGetArrayWrite(seq, &seqv));
7728: for (PetscInt i = 0; i < lblocks; i++) {
7729: for (PetscInt j = 0; j < sizes[i]; j++) {
7730: seqv[cnt] = starts[i];
7731: seqv[cnt + 1] = starts[i] + sizes[i];
7732: cnt += 2;
7733: }
7734: }
7735: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7736: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7737: sc -= cnt;
7738: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7739: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7740: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7741: PetscCall(ISDestroy(&isglobal));
7742: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7743: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7744: PetscCall(VecScatterDestroy(&scatter));
7745: PetscCall(VecDestroy(&seq));
7746: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7747: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7748: PetscCall(VecGetArrayRead(par, &parv));
7749: cnt = 0;
7750: PetscCall(MatGetSize(mat, NULL, &n));
7751: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7752: PetscInt start, end, d = 0, od = 0;
7754: start = (PetscInt)PetscRealPart(parv[cnt]);
7755: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7756: cnt += 2;
7758: if (start < cstart) {
7759: od += cstart - start + n - cend;
7760: d += cend - cstart;
7761: } else if (start < cend) {
7762: od += n - cend;
7763: d += cend - start;
7764: } else od += n - start;
7765: if (end <= cstart) {
7766: od -= cstart - end + n - cend;
7767: d -= cend - cstart;
7768: } else if (end < cend) {
7769: od -= n - cend;
7770: d -= cend - end;
7771: } else od -= n - end;
7773: odiag[i] = od;
7774: diag[i] = d;
7775: }
7776: PetscCall(VecRestoreArrayRead(par, &parv));
7777: PetscCall(VecDestroy(&par));
7778: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7779: PetscCall(PetscFree2(diag, odiag));
7780: PetscCall(PetscFree2(sizes, starts));
7782: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7783: PetscCall(PetscContainerSetPointer(container, edata));
7784: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7785: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7786: PetscCall(PetscObjectDereference((PetscObject)container));
7787: PetscFunctionReturn(PETSC_SUCCESS);
7788: }
7790: /*@
7791: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7793: Collective
7795: Input Parameters:
7796: + A - the matrix
7797: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7799: Output Parameter:
7800: . C - matrix with inverted block diagonal of `A`
7802: Level: advanced
7804: Note:
7805: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7807: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7808: @*/
7809: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7810: {
7811: PetscContainer container;
7812: EnvelopeData *edata;
7813: PetscObjectState nonzerostate;
7815: PetscFunctionBegin;
7816: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7817: if (!container) {
7818: PetscCall(MatComputeVariableBlockEnvelope(A));
7819: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7820: }
7821: PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7822: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7823: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7824: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7826: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7827: *C = edata->C;
7829: for (PetscInt i = 0; i < edata->n; i++) {
7830: Mat D;
7831: PetscScalar *dvalues;
7833: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7834: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7835: PetscCall(MatSeqDenseInvert(D));
7836: PetscCall(MatDenseGetArray(D, &dvalues));
7837: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7838: PetscCall(MatDestroy(&D));
7839: }
7840: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7841: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7842: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7843: PetscFunctionReturn(PETSC_SUCCESS);
7844: }
7846: /*@
7847: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
7849: Not Collective
7851: Input Parameters:
7852: + mat - the matrix
7853: . nblocks - the number of blocks on this process, each block can only exist on a single process
7854: - bsizes - the block sizes
7856: Level: intermediate
7858: Notes:
7859: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
7861: Each variable point-block set of degrees of freedom must live on a single MPI process. That is a point block cannot straddle two MPI processes.
7863: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7864: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7865: @*/
7866: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7867: {
7868: PetscInt ncnt = 0, nlocal;
7870: PetscFunctionBegin;
7872: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7873: PetscCheck(nblocks >= 0 && nblocks <= nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks %" PetscInt_FMT " is not in [0, %" PetscInt_FMT "]", nblocks, nlocal);
7874: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7875: 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);
7876: PetscCall(PetscFree(mat->bsizes));
7877: mat->nblocks = nblocks;
7878: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7879: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7880: PetscFunctionReturn(PETSC_SUCCESS);
7881: }
7883: /*@C
7884: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
7886: Not Collective; No Fortran Support
7888: Input Parameter:
7889: . mat - the matrix
7891: Output Parameters:
7892: + nblocks - the number of blocks on this process
7893: - bsizes - the block sizes
7895: Level: intermediate
7897: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7898: @*/
7899: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7900: {
7901: PetscFunctionBegin;
7903: if (nblocks) *nblocks = mat->nblocks;
7904: if (bsizes) *bsizes = mat->bsizes;
7905: PetscFunctionReturn(PETSC_SUCCESS);
7906: }
7908: /*@
7909: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
7911: Not Collective
7913: Input Parameter:
7914: + subA - the submatrix
7915: . A - the original matrix
7916: - isrow - The `IS` of selected rows for the submatrix, must be sorted
7918: Level: developer
7920: Notes:
7921: If the index set is not sorted or contains off-process entries, this function will do nothing.
7923: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7924: @*/
7925: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
7926: {
7927: const PetscInt *rows;
7928: PetscInt n, rStart, rEnd, Nb = 0;
7929: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
7931: PetscFunctionBegin;
7932: // The code for block size extraction does not support an unsorted IS
7933: if (flg) PetscCall(ISSorted(isrow, &flg));
7934: // We don't support originally off-diagonal blocks
7935: if (flg) {
7936: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
7937: PetscCall(ISGetLocalSize(isrow, &n));
7938: PetscCall(ISGetIndices(isrow, &rows));
7939: for (PetscInt i = 0; i < n && flg; ++i) {
7940: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
7941: }
7942: PetscCall(ISRestoreIndices(isrow, &rows));
7943: }
7944: // quiet return if we can't extract block size
7945: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
7946: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
7948: // extract block sizes
7949: PetscCall(ISGetIndices(isrow, &rows));
7950: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
7951: PetscBool occupied = PETSC_FALSE;
7953: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
7954: const PetscInt row = gr + br;
7956: if (i == n) break;
7957: if (rows[i] == row) {
7958: occupied = PETSC_TRUE;
7959: ++i;
7960: }
7961: while (i < n && rows[i] < row) ++i;
7962: }
7963: gr += A->bsizes[b];
7964: if (occupied) ++Nb;
7965: }
7966: subA->nblocks = Nb;
7967: PetscCall(PetscFree(subA->bsizes));
7968: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
7969: PetscInt sb = 0;
7970: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
7971: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
7972: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
7973: const PetscInt row = gr + br;
7975: if (i == n) break;
7976: if (rows[i] == row) {
7977: ++subA->bsizes[sb];
7978: ++i;
7979: }
7980: while (i < n && rows[i] < row) ++i;
7981: }
7982: gr += A->bsizes[b];
7983: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
7984: }
7985: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
7986: PetscInt nlocal, ncnt = 0;
7987: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
7988: PetscCheck(subA->nblocks >= 0 && subA->nblocks <= nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks %" PetscInt_FMT " is not in [0, %" PetscInt_FMT "]", subA->nblocks, nlocal);
7989: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
7990: 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);
7991: PetscCall(ISRestoreIndices(isrow, &rows));
7992: PetscFunctionReturn(PETSC_SUCCESS);
7993: }
7995: /*@
7996: MatSetBlockSizes - Sets the matrix block row and column sizes.
7998: Logically Collective
8000: Input Parameters:
8001: + mat - the matrix
8002: . rbs - row block size
8003: - cbs - column block size
8005: Level: intermediate
8007: Notes:
8008: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8009: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8010: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8012: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8013: are compatible with the matrix local sizes.
8015: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8017: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8018: @*/
8019: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8020: {
8021: PetscFunctionBegin;
8025: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8026: if (mat->rmap->refcnt) {
8027: ISLocalToGlobalMapping l2g = NULL;
8028: PetscLayout nmap = NULL;
8030: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8031: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8032: PetscCall(PetscLayoutDestroy(&mat->rmap));
8033: mat->rmap = nmap;
8034: mat->rmap->mapping = l2g;
8035: }
8036: if (mat->cmap->refcnt) {
8037: ISLocalToGlobalMapping l2g = NULL;
8038: PetscLayout nmap = NULL;
8040: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8041: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8042: PetscCall(PetscLayoutDestroy(&mat->cmap));
8043: mat->cmap = nmap;
8044: mat->cmap->mapping = l2g;
8045: }
8046: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8047: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8048: PetscFunctionReturn(PETSC_SUCCESS);
8049: }
8051: /*@
8052: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8054: Logically Collective
8056: Input Parameters:
8057: + mat - the matrix
8058: . fromRow - matrix from which to copy row block size
8059: - fromCol - matrix from which to copy column block size (can be same as fromRow)
8061: Level: developer
8063: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8064: @*/
8065: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8066: {
8067: PetscFunctionBegin;
8071: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8072: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8073: PetscFunctionReturn(PETSC_SUCCESS);
8074: }
8076: /*@
8077: MatResidual - Default routine to calculate the residual r = b - Ax
8079: Collective
8081: Input Parameters:
8082: + mat - the matrix
8083: . b - the right-hand-side
8084: - x - the approximate solution
8086: Output Parameter:
8087: . r - location to store the residual
8089: Level: developer
8091: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8092: @*/
8093: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8094: {
8095: PetscFunctionBegin;
8101: MatCheckPreallocated(mat, 1);
8102: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8103: if (!mat->ops->residual) {
8104: PetscCall(MatMult(mat, x, r));
8105: PetscCall(VecAYPX(r, -1.0, b));
8106: } else {
8107: PetscUseTypeMethod(mat, residual, b, x, r);
8108: }
8109: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8110: PetscFunctionReturn(PETSC_SUCCESS);
8111: }
8113: /*@C
8114: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8116: Collective
8118: Input Parameters:
8119: + mat - the matrix
8120: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8121: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8122: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8123: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8124: always used.
8126: Output Parameters:
8127: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8128: . 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
8129: . ja - the column indices, use `NULL` if not needed
8130: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8131: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8133: Level: developer
8135: Notes:
8136: You CANNOT change any of the ia[] or ja[] values.
8138: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8140: Fortran Notes:
8141: Use
8142: .vb
8143: PetscInt, pointer :: ia(:),ja(:)
8144: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8145: ! Access the ith and jth entries via ia(i) and ja(j)
8146: .ve
8148: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8149: @*/
8150: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8151: {
8152: PetscFunctionBegin;
8155: if (n) PetscAssertPointer(n, 5);
8156: if (ia) PetscAssertPointer(ia, 6);
8157: if (ja) PetscAssertPointer(ja, 7);
8158: if (done) PetscAssertPointer(done, 8);
8159: MatCheckPreallocated(mat, 1);
8160: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8161: else {
8162: if (done) *done = PETSC_TRUE;
8163: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8164: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8165: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8166: }
8167: PetscFunctionReturn(PETSC_SUCCESS);
8168: }
8170: /*@C
8171: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8173: Collective
8175: Input Parameters:
8176: + mat - the matrix
8177: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8178: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8179: symmetrized
8180: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8181: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8182: always used.
8184: Output Parameters:
8185: + n - number of columns in the (possibly compressed) matrix
8186: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8187: . ja - the row indices
8188: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8190: Level: developer
8192: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8193: @*/
8194: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8195: {
8196: PetscFunctionBegin;
8199: PetscAssertPointer(n, 5);
8200: if (ia) PetscAssertPointer(ia, 6);
8201: if (ja) PetscAssertPointer(ja, 7);
8202: PetscAssertPointer(done, 8);
8203: MatCheckPreallocated(mat, 1);
8204: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8205: else {
8206: *done = PETSC_TRUE;
8207: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8208: }
8209: PetscFunctionReturn(PETSC_SUCCESS);
8210: }
8212: /*@C
8213: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8215: Collective
8217: Input Parameters:
8218: + mat - the matrix
8219: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8220: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8221: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8222: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8223: always used.
8224: . n - size of (possibly compressed) matrix
8225: . ia - the row pointers
8226: - ja - the column indices
8228: Output Parameter:
8229: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8231: Level: developer
8233: Note:
8234: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8235: us of the array after it has been restored. If you pass `NULL`, it will
8236: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8238: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8239: @*/
8240: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8241: {
8242: PetscFunctionBegin;
8245: if (ia) PetscAssertPointer(ia, 6);
8246: if (ja) PetscAssertPointer(ja, 7);
8247: if (done) PetscAssertPointer(done, 8);
8248: MatCheckPreallocated(mat, 1);
8250: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8251: else {
8252: if (done) *done = PETSC_TRUE;
8253: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8254: if (n) *n = 0;
8255: if (ia) *ia = NULL;
8256: if (ja) *ja = NULL;
8257: }
8258: PetscFunctionReturn(PETSC_SUCCESS);
8259: }
8261: /*@C
8262: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8264: Collective
8266: Input Parameters:
8267: + mat - the matrix
8268: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8269: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8270: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8271: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8272: always used.
8274: Output Parameters:
8275: + n - size of (possibly compressed) matrix
8276: . ia - the column pointers
8277: . ja - the row indices
8278: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8280: Level: developer
8282: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8283: @*/
8284: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8285: {
8286: PetscFunctionBegin;
8289: if (ia) PetscAssertPointer(ia, 6);
8290: if (ja) PetscAssertPointer(ja, 7);
8291: PetscAssertPointer(done, 8);
8292: MatCheckPreallocated(mat, 1);
8294: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8295: else {
8296: *done = PETSC_TRUE;
8297: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8298: if (n) *n = 0;
8299: if (ia) *ia = NULL;
8300: if (ja) *ja = NULL;
8301: }
8302: PetscFunctionReturn(PETSC_SUCCESS);
8303: }
8305: /*@
8306: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8307: `MatGetColumnIJ()`.
8309: Collective
8311: Input Parameters:
8312: + mat - the matrix
8313: . ncolors - maximum color value
8314: . n - number of entries in colorarray
8315: - colorarray - array indicating color for each column
8317: Output Parameter:
8318: . iscoloring - coloring generated using colorarray information
8320: Level: developer
8322: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8323: @*/
8324: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8325: {
8326: PetscFunctionBegin;
8329: PetscAssertPointer(colorarray, 4);
8330: PetscAssertPointer(iscoloring, 5);
8331: MatCheckPreallocated(mat, 1);
8333: if (!mat->ops->coloringpatch) {
8334: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8335: } else {
8336: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8337: }
8338: PetscFunctionReturn(PETSC_SUCCESS);
8339: }
8341: /*@
8342: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8344: Logically Collective
8346: Input Parameter:
8347: . mat - the factored matrix to be reset
8349: Level: developer
8351: Notes:
8352: This routine should be used only with factored matrices formed by in-place
8353: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8354: format). This option can save memory, for example, when solving nonlinear
8355: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8356: ILU(0) preconditioner.
8358: One can specify in-place ILU(0) factorization by calling
8359: .vb
8360: PCType(pc,PCILU);
8361: PCFactorSeUseInPlace(pc);
8362: .ve
8363: or by using the options -pc_type ilu -pc_factor_in_place
8365: In-place factorization ILU(0) can also be used as a local
8366: solver for the blocks within the block Jacobi or additive Schwarz
8367: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8368: for details on setting local solver options.
8370: Most users should employ the `KSP` interface for linear solvers
8371: instead of working directly with matrix algebra routines such as this.
8372: See, e.g., `KSPCreate()`.
8374: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8375: @*/
8376: PetscErrorCode MatSetUnfactored(Mat mat)
8377: {
8378: PetscFunctionBegin;
8381: MatCheckPreallocated(mat, 1);
8382: mat->factortype = MAT_FACTOR_NONE;
8383: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8384: PetscUseTypeMethod(mat, setunfactored);
8385: PetscFunctionReturn(PETSC_SUCCESS);
8386: }
8388: /*@
8389: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8390: as the original matrix.
8392: Collective
8394: Input Parameters:
8395: + mat - the original matrix
8396: . isrow - parallel `IS` containing the rows this processor should obtain
8397: . 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.
8398: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8400: Output Parameter:
8401: . newmat - the new submatrix, of the same type as the original matrix
8403: Level: advanced
8405: Notes:
8406: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8408: Some matrix types place restrictions on the row and column indices, such
8409: 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;
8410: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8412: The index sets may not have duplicate entries.
8414: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8415: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8416: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8417: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8418: you are finished using it.
8420: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8421: the input matrix.
8423: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8425: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8426: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8428: Example usage:
8429: Consider the following 8x8 matrix with 34 non-zero values, that is
8430: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8431: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8432: as follows
8433: .vb
8434: 1 2 0 | 0 3 0 | 0 4
8435: Proc0 0 5 6 | 7 0 0 | 8 0
8436: 9 0 10 | 11 0 0 | 12 0
8437: -------------------------------------
8438: 13 0 14 | 15 16 17 | 0 0
8439: Proc1 0 18 0 | 19 20 21 | 0 0
8440: 0 0 0 | 22 23 0 | 24 0
8441: -------------------------------------
8442: Proc2 25 26 27 | 0 0 28 | 29 0
8443: 30 0 0 | 31 32 33 | 0 34
8444: .ve
8446: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8448: .vb
8449: 2 0 | 0 3 0 | 0
8450: Proc0 5 6 | 7 0 0 | 8
8451: -------------------------------
8452: Proc1 18 0 | 19 20 21 | 0
8453: -------------------------------
8454: Proc2 26 27 | 0 0 28 | 29
8455: 0 0 | 31 32 33 | 0
8456: .ve
8458: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8459: @*/
8460: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8461: {
8462: PetscMPIInt size;
8463: Mat *local;
8464: IS iscoltmp;
8465: PetscBool flg;
8467: PetscFunctionBegin;
8471: PetscAssertPointer(newmat, 5);
8474: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8475: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8477: MatCheckPreallocated(mat, 1);
8478: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8480: if (!iscol || isrow == iscol) {
8481: PetscBool stride;
8482: PetscMPIInt grabentirematrix = 0, grab;
8483: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8484: if (stride) {
8485: PetscInt first, step, n, rstart, rend;
8486: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8487: if (step == 1) {
8488: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8489: if (rstart == first) {
8490: PetscCall(ISGetLocalSize(isrow, &n));
8491: if (n == rend - rstart) grabentirematrix = 1;
8492: }
8493: }
8494: }
8495: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8496: if (grab) {
8497: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8498: if (cll == MAT_INITIAL_MATRIX) {
8499: *newmat = mat;
8500: PetscCall(PetscObjectReference((PetscObject)mat));
8501: }
8502: PetscFunctionReturn(PETSC_SUCCESS);
8503: }
8504: }
8506: if (!iscol) {
8507: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8508: } else {
8509: iscoltmp = iscol;
8510: }
8512: /* if original matrix is on just one processor then use submatrix generated */
8513: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8514: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8515: goto setproperties;
8516: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8517: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8518: *newmat = *local;
8519: PetscCall(PetscFree(local));
8520: goto setproperties;
8521: } else if (!mat->ops->createsubmatrix) {
8522: /* Create a new matrix type that implements the operation using the full matrix */
8523: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8524: switch (cll) {
8525: case MAT_INITIAL_MATRIX:
8526: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8527: break;
8528: case MAT_REUSE_MATRIX:
8529: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8530: break;
8531: default:
8532: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8533: }
8534: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8535: goto setproperties;
8536: }
8538: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8539: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8540: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8542: setproperties:
8543: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8544: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8545: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8546: }
8547: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8548: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8549: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
8550: PetscFunctionReturn(PETSC_SUCCESS);
8551: }
8553: /*@
8554: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8556: Not Collective
8558: Input Parameters:
8559: + A - the matrix we wish to propagate options from
8560: - B - the matrix we wish to propagate options to
8562: Level: beginner
8564: Note:
8565: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8567: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8568: @*/
8569: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8570: {
8571: PetscFunctionBegin;
8574: B->symmetry_eternal = A->symmetry_eternal;
8575: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8576: B->symmetric = A->symmetric;
8577: B->structurally_symmetric = A->structurally_symmetric;
8578: B->spd = A->spd;
8579: B->hermitian = A->hermitian;
8580: PetscFunctionReturn(PETSC_SUCCESS);
8581: }
8583: /*@
8584: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8585: used during the assembly process to store values that belong to
8586: other processors.
8588: Not Collective
8590: Input Parameters:
8591: + mat - the matrix
8592: . size - the initial size of the stash.
8593: - bsize - the initial size of the block-stash(if used).
8595: Options Database Keys:
8596: + -matstash_initial_size <size> or <size0,size1,...sizep-1> - set initial size
8597: - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> - set initial block size
8599: Level: intermediate
8601: Notes:
8602: The block-stash is used for values set with `MatSetValuesBlocked()` while
8603: the stash is used for values set with `MatSetValues()`
8605: Run with the option -info and look for output of the form
8606: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8607: to determine the appropriate value, MM, to use for size and
8608: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8609: to determine the value, BMM to use for bsize
8611: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8612: @*/
8613: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8614: {
8615: PetscFunctionBegin;
8618: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8619: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8620: PetscFunctionReturn(PETSC_SUCCESS);
8621: }
8623: /*@
8624: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8625: the matrix
8627: Neighbor-wise Collective
8629: Input Parameters:
8630: + A - the matrix
8631: . x - the vector to be multiplied by the interpolation operator
8632: - y - the vector to be added to the result
8634: Output Parameter:
8635: . w - the resulting vector
8637: Level: intermediate
8639: Notes:
8640: `w` may be the same vector as `y`.
8642: This allows one to use either the restriction or interpolation (its transpose)
8643: matrix to do the interpolation
8645: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8646: @*/
8647: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8648: {
8649: PetscInt M, N, Ny;
8651: PetscFunctionBegin;
8656: PetscCall(MatGetSize(A, &M, &N));
8657: PetscCall(VecGetSize(y, &Ny));
8658: if (M == Ny) {
8659: PetscCall(MatMultAdd(A, x, y, w));
8660: } else {
8661: PetscCall(MatMultTransposeAdd(A, x, y, w));
8662: }
8663: PetscFunctionReturn(PETSC_SUCCESS);
8664: }
8666: /*@
8667: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8668: the matrix
8670: Neighbor-wise Collective
8672: Input Parameters:
8673: + A - the matrix
8674: - x - the vector to be interpolated
8676: Output Parameter:
8677: . y - the resulting vector
8679: Level: intermediate
8681: Note:
8682: This allows one to use either the restriction or interpolation (its transpose)
8683: matrix to do the interpolation
8685: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8686: @*/
8687: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8688: {
8689: PetscInt M, N, Ny;
8691: PetscFunctionBegin;
8695: PetscCall(MatGetSize(A, &M, &N));
8696: PetscCall(VecGetSize(y, &Ny));
8697: if (M == Ny) {
8698: PetscCall(MatMult(A, x, y));
8699: } else {
8700: PetscCall(MatMultTranspose(A, x, y));
8701: }
8702: PetscFunctionReturn(PETSC_SUCCESS);
8703: }
8705: /*@
8706: MatRestrict - $y = A*x$ or $A^T*x$
8708: Neighbor-wise Collective
8710: Input Parameters:
8711: + A - the matrix
8712: - x - the vector to be restricted
8714: Output Parameter:
8715: . y - the resulting vector
8717: Level: intermediate
8719: Note:
8720: This allows one to use either the restriction or interpolation (its transpose)
8721: matrix to do the restriction
8723: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8724: @*/
8725: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8726: {
8727: PetscInt M, N, Nx;
8729: PetscFunctionBegin;
8733: PetscCall(MatGetSize(A, &M, &N));
8734: PetscCall(VecGetSize(x, &Nx));
8735: if (M == Nx) {
8736: PetscCall(MatMultTranspose(A, x, y));
8737: } else {
8738: PetscCall(MatMult(A, x, y));
8739: }
8740: PetscFunctionReturn(PETSC_SUCCESS);
8741: }
8743: /*@
8744: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8746: Neighbor-wise Collective
8748: Input Parameters:
8749: + A - the matrix
8750: . x - the input dense matrix to be multiplied
8751: - w - the input dense matrix to be added to the result
8753: Output Parameter:
8754: . y - the output dense matrix
8756: Level: intermediate
8758: Note:
8759: This allows one to use either the restriction or interpolation (its transpose)
8760: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8761: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8763: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8764: @*/
8765: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8766: {
8767: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8768: PetscBool trans = PETSC_TRUE;
8769: MatReuse reuse = MAT_INITIAL_MATRIX;
8771: PetscFunctionBegin;
8777: PetscCall(MatGetSize(A, &M, &N));
8778: PetscCall(MatGetSize(x, &Mx, &Nx));
8779: if (N == Mx) trans = PETSC_FALSE;
8780: 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);
8781: Mo = trans ? N : M;
8782: if (*y) {
8783: PetscCall(MatGetSize(*y, &My, &Ny));
8784: if (Mo == My && Nx == Ny) {
8785: reuse = MAT_REUSE_MATRIX;
8786: } else {
8787: 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);
8788: PetscCall(MatDestroy(y));
8789: }
8790: }
8792: if (w && *y == w) { /* this is to minimize changes in PCMG */
8793: PetscBool flg;
8795: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8796: if (w) {
8797: PetscInt My, Ny, Mw, Nw;
8799: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8800: PetscCall(MatGetSize(*y, &My, &Ny));
8801: PetscCall(MatGetSize(w, &Mw, &Nw));
8802: if (!flg || My != Mw || Ny != Nw) w = NULL;
8803: }
8804: if (!w) {
8805: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8806: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8807: PetscCall(PetscObjectDereference((PetscObject)w));
8808: } else {
8809: PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8810: }
8811: }
8812: if (!trans) {
8813: PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8814: } else {
8815: PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8816: }
8817: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8818: PetscFunctionReturn(PETSC_SUCCESS);
8819: }
8821: /*@
8822: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8824: Neighbor-wise Collective
8826: Input Parameters:
8827: + A - the matrix
8828: - x - the input dense matrix
8830: Output Parameter:
8831: . y - the output dense matrix
8833: Level: intermediate
8835: Note:
8836: This allows one to use either the restriction or interpolation (its transpose)
8837: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8838: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8840: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8841: @*/
8842: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8843: {
8844: PetscFunctionBegin;
8845: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8846: PetscFunctionReturn(PETSC_SUCCESS);
8847: }
8849: /*@
8850: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8852: Neighbor-wise Collective
8854: Input Parameters:
8855: + A - the matrix
8856: - x - the input dense matrix
8858: Output Parameter:
8859: . y - the output dense matrix
8861: Level: intermediate
8863: Note:
8864: This allows one to use either the restriction or interpolation (its transpose)
8865: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8866: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8868: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8869: @*/
8870: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8871: {
8872: PetscFunctionBegin;
8873: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8874: PetscFunctionReturn(PETSC_SUCCESS);
8875: }
8877: /*@
8878: MatGetNullSpace - retrieves the null space of a matrix.
8880: Logically Collective
8882: Input Parameters:
8883: + mat - the matrix
8884: - nullsp - the null space object
8886: Level: developer
8888: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8889: @*/
8890: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8891: {
8892: PetscFunctionBegin;
8894: PetscAssertPointer(nullsp, 2);
8895: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8896: PetscFunctionReturn(PETSC_SUCCESS);
8897: }
8899: /*@C
8900: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
8902: Logically Collective
8904: Input Parameters:
8905: + n - the number of matrices
8906: - mat - the array of matrices
8908: Output Parameters:
8909: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
8911: Level: developer
8913: Note:
8914: Call `MatRestoreNullspaces()` to provide these to another array of matrices
8916: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8917: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8918: @*/
8919: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8920: {
8921: PetscFunctionBegin;
8922: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8923: PetscAssertPointer(mat, 2);
8924: PetscAssertPointer(nullsp, 3);
8926: PetscCall(PetscCalloc1(3 * n, nullsp));
8927: for (PetscInt i = 0; i < n; i++) {
8929: (*nullsp)[i] = mat[i]->nullsp;
8930: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8931: (*nullsp)[n + i] = mat[i]->nearnullsp;
8932: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8933: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8934: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8935: }
8936: PetscFunctionReturn(PETSC_SUCCESS);
8937: }
8939: /*@C
8940: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
8942: Logically Collective
8944: Input Parameters:
8945: + n - the number of matrices
8946: . mat - the array of matrices
8947: - nullsp - an array of null spaces
8949: Level: developer
8951: Note:
8952: Call `MatGetNullSpaces()` to create `nullsp`
8954: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8955: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8956: @*/
8957: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8958: {
8959: PetscFunctionBegin;
8960: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8961: PetscAssertPointer(mat, 2);
8962: PetscAssertPointer(nullsp, 3);
8963: PetscAssertPointer(*nullsp, 3);
8965: for (PetscInt i = 0; i < n; i++) {
8967: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8968: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
8969: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
8970: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
8971: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
8972: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
8973: }
8974: PetscCall(PetscFree(*nullsp));
8975: PetscFunctionReturn(PETSC_SUCCESS);
8976: }
8978: /*@
8979: MatSetNullSpace - attaches a null space to a matrix.
8981: Logically Collective
8983: Input Parameters:
8984: + mat - the matrix
8985: - nullsp - the null space object
8987: Level: advanced
8989: Notes:
8990: This null space is used by the `KSP` linear solvers to solve singular systems.
8992: 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`
8994: 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
8995: to zero but the linear system will still be solved in a least squares sense.
8997: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8998: the domain of a matrix $A$ (from $R^n$ to $R^m$ ($m$ rows, $n$ columns) $R^n$ = the direct sum of the null space of $A$, $n(A)$, plus the range of $A^T$, $R(A^T)$.
8999: 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
9000: $n(A^T))$ 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
9001: 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 $n(A^T)$.
9002: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9004: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9005: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9006: routine also automatically calls `MatSetTransposeNullSpace()`.
9008: The user should call `MatNullSpaceDestroy()`.
9010: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9011: `KSPSetPCSide()`
9012: @*/
9013: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9014: {
9015: PetscFunctionBegin;
9018: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9019: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9020: mat->nullsp = nullsp;
9021: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9022: PetscFunctionReturn(PETSC_SUCCESS);
9023: }
9025: /*@
9026: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
9028: Logically Collective
9030: Input Parameters:
9031: + mat - the matrix
9032: - nullsp - the null space object
9034: Level: developer
9036: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9037: @*/
9038: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9039: {
9040: PetscFunctionBegin;
9043: PetscAssertPointer(nullsp, 2);
9044: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9045: PetscFunctionReturn(PETSC_SUCCESS);
9046: }
9048: /*@
9049: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9051: Logically Collective
9053: Input Parameters:
9054: + mat - the matrix
9055: - nullsp - the null space object
9057: Level: advanced
9059: Notes:
9060: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9062: See `MatSetNullSpace()`
9064: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9065: @*/
9066: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9067: {
9068: PetscFunctionBegin;
9071: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9072: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9073: mat->transnullsp = nullsp;
9074: PetscFunctionReturn(PETSC_SUCCESS);
9075: }
9077: /*@
9078: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9079: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9081: Logically Collective
9083: Input Parameters:
9084: + mat - the matrix
9085: - nullsp - the null space object
9087: Level: advanced
9089: Notes:
9090: Overwrites any previous near null space that may have been attached
9092: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9094: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9095: @*/
9096: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9097: {
9098: PetscFunctionBegin;
9102: MatCheckPreallocated(mat, 1);
9103: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9104: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9105: mat->nearnullsp = nullsp;
9106: PetscFunctionReturn(PETSC_SUCCESS);
9107: }
9109: /*@
9110: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9112: Not Collective
9114: Input Parameter:
9115: . mat - the matrix
9117: Output Parameter:
9118: . nullsp - the null space object, `NULL` if not set
9120: Level: advanced
9122: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9123: @*/
9124: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9125: {
9126: PetscFunctionBegin;
9129: PetscAssertPointer(nullsp, 2);
9130: MatCheckPreallocated(mat, 1);
9131: *nullsp = mat->nearnullsp;
9132: PetscFunctionReturn(PETSC_SUCCESS);
9133: }
9135: /*@
9136: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9138: Collective
9140: Input Parameters:
9141: + mat - the matrix
9142: . row - row/column permutation
9143: - info - information on desired factorization process
9145: Level: developer
9147: Notes:
9148: Probably really in-place only when level of fill is zero, otherwise allocates
9149: new space to store factored matrix and deletes previous memory.
9151: Most users should employ the `KSP` interface for linear solvers
9152: instead of working directly with matrix algebra routines such as this.
9153: See, e.g., `KSPCreate()`.
9155: Fortran Note:
9156: A valid (non-null) `info` argument must be provided
9158: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9159: @*/
9160: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9161: {
9162: PetscFunctionBegin;
9166: PetscAssertPointer(info, 3);
9167: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9168: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9169: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9170: MatCheckPreallocated(mat, 1);
9171: PetscUseTypeMethod(mat, iccfactor, row, info);
9172: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9173: PetscFunctionReturn(PETSC_SUCCESS);
9174: }
9176: /*@
9177: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9178: ghosted ones.
9180: Not Collective
9182: Input Parameters:
9183: + mat - the matrix
9184: - diag - the diagonal values, including ghost ones
9186: Level: developer
9188: Notes:
9189: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9191: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9193: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9194: @*/
9195: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9196: {
9197: PetscMPIInt size;
9199: PetscFunctionBegin;
9204: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9205: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9206: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9207: if (size == 1) {
9208: PetscInt n, m;
9209: PetscCall(VecGetSize(diag, &n));
9210: PetscCall(MatGetSize(mat, NULL, &m));
9211: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9212: PetscCall(MatDiagonalScale(mat, NULL, diag));
9213: } else {
9214: PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9215: }
9216: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9217: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9218: PetscFunctionReturn(PETSC_SUCCESS);
9219: }
9221: /*@
9222: MatGetInertia - Gets the inertia from a factored matrix
9224: Collective
9226: Input Parameter:
9227: . mat - the matrix
9229: Output Parameters:
9230: + nneg - number of negative eigenvalues
9231: . nzero - number of zero eigenvalues
9232: - npos - number of positive eigenvalues
9234: Level: advanced
9236: Note:
9237: Matrix must have been factored by `MatCholeskyFactor()`
9239: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9240: @*/
9241: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9242: {
9243: PetscFunctionBegin;
9246: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9247: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9248: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9249: PetscFunctionReturn(PETSC_SUCCESS);
9250: }
9252: /*@C
9253: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9255: Neighbor-wise Collective
9257: Input Parameters:
9258: + mat - the factored matrix obtained with `MatGetFactor()`
9259: - b - the right-hand-side vectors
9261: Output Parameter:
9262: . x - the result vectors
9264: Level: developer
9266: Note:
9267: The vectors `b` and `x` cannot be the same. I.e., one cannot
9268: call `MatSolves`(A,x,x).
9270: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9271: @*/
9272: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9273: {
9274: PetscFunctionBegin;
9277: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9278: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9279: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9281: MatCheckPreallocated(mat, 1);
9282: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9283: PetscUseTypeMethod(mat, solves, b, x);
9284: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9285: PetscFunctionReturn(PETSC_SUCCESS);
9286: }
9288: /*@
9289: MatIsSymmetric - Test whether a matrix is symmetric
9291: Collective
9293: Input Parameters:
9294: + A - the matrix to test
9295: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9297: Output Parameter:
9298: . flg - the result
9300: Level: intermediate
9302: Notes:
9303: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9305: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9307: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9308: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9310: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9311: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9312: @*/
9313: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9314: {
9315: PetscFunctionBegin;
9317: PetscAssertPointer(flg, 3);
9318: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9319: else {
9320: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9321: else PetscCall(MatIsTranspose(A, A, tol, flg));
9322: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9323: }
9324: PetscFunctionReturn(PETSC_SUCCESS);
9325: }
9327: /*@
9328: MatIsHermitian - Test whether a matrix is Hermitian
9330: Collective
9332: Input Parameters:
9333: + A - the matrix to test
9334: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9336: Output Parameter:
9337: . flg - the result
9339: Level: intermediate
9341: Notes:
9342: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9344: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9346: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9347: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9349: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9350: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9351: @*/
9352: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9353: {
9354: PetscFunctionBegin;
9356: PetscAssertPointer(flg, 3);
9357: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9358: else {
9359: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9360: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9361: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9362: }
9363: PetscFunctionReturn(PETSC_SUCCESS);
9364: }
9366: /*@
9367: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9369: Not Collective
9371: Input Parameter:
9372: . A - the matrix to check
9374: Output Parameters:
9375: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9376: - flg - the result (only valid if set is `PETSC_TRUE`)
9378: Level: advanced
9380: Notes:
9381: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9382: if you want it explicitly checked
9384: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9385: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9387: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9388: @*/
9389: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9390: {
9391: PetscFunctionBegin;
9393: PetscAssertPointer(set, 2);
9394: PetscAssertPointer(flg, 3);
9395: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9396: *set = PETSC_TRUE;
9397: *flg = PetscBool3ToBool(A->symmetric);
9398: } else {
9399: *set = PETSC_FALSE;
9400: }
9401: PetscFunctionReturn(PETSC_SUCCESS);
9402: }
9404: /*@
9405: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9407: Not Collective
9409: Input Parameter:
9410: . A - the matrix to check
9412: Output Parameters:
9413: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9414: - flg - the result (only valid if set is `PETSC_TRUE`)
9416: Level: advanced
9418: Notes:
9419: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9421: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9422: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9424: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9425: @*/
9426: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9427: {
9428: PetscFunctionBegin;
9430: PetscAssertPointer(set, 2);
9431: PetscAssertPointer(flg, 3);
9432: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9433: *set = PETSC_TRUE;
9434: *flg = PetscBool3ToBool(A->spd);
9435: } else {
9436: *set = PETSC_FALSE;
9437: }
9438: PetscFunctionReturn(PETSC_SUCCESS);
9439: }
9441: /*@
9442: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9444: Not Collective
9446: Input Parameter:
9447: . A - the matrix to check
9449: Output Parameters:
9450: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9451: - flg - the result (only valid if set is `PETSC_TRUE`)
9453: Level: advanced
9455: Notes:
9456: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9457: if you want it explicitly checked
9459: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9460: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9462: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9463: @*/
9464: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9465: {
9466: PetscFunctionBegin;
9468: PetscAssertPointer(set, 2);
9469: PetscAssertPointer(flg, 3);
9470: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9471: *set = PETSC_TRUE;
9472: *flg = PetscBool3ToBool(A->hermitian);
9473: } else {
9474: *set = PETSC_FALSE;
9475: }
9476: PetscFunctionReturn(PETSC_SUCCESS);
9477: }
9479: /*@
9480: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9482: Collective
9484: Input Parameter:
9485: . A - the matrix to test
9487: Output Parameter:
9488: . flg - the result
9490: Level: intermediate
9492: Notes:
9493: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9495: 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
9496: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9498: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9499: @*/
9500: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9501: {
9502: PetscFunctionBegin;
9504: PetscAssertPointer(flg, 2);
9505: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9506: *flg = PetscBool3ToBool(A->structurally_symmetric);
9507: } else {
9508: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9509: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9510: }
9511: PetscFunctionReturn(PETSC_SUCCESS);
9512: }
9514: /*@
9515: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9517: Not Collective
9519: Input Parameter:
9520: . A - the matrix to check
9522: Output Parameters:
9523: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9524: - flg - the result (only valid if set is PETSC_TRUE)
9526: Level: advanced
9528: Notes:
9529: 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
9530: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9532: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9534: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9535: @*/
9536: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9537: {
9538: PetscFunctionBegin;
9540: PetscAssertPointer(set, 2);
9541: PetscAssertPointer(flg, 3);
9542: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9543: *set = PETSC_TRUE;
9544: *flg = PetscBool3ToBool(A->structurally_symmetric);
9545: } else {
9546: *set = PETSC_FALSE;
9547: }
9548: PetscFunctionReturn(PETSC_SUCCESS);
9549: }
9551: /*@
9552: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9553: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9555: Not Collective
9557: Input Parameter:
9558: . mat - the matrix
9560: Output Parameters:
9561: + nstash - the size of the stash
9562: . reallocs - the number of additional mallocs incurred.
9563: . bnstash - the size of the block stash
9564: - breallocs - the number of additional mallocs incurred.in the block stash
9566: Level: advanced
9568: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9569: @*/
9570: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9571: {
9572: PetscFunctionBegin;
9573: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9574: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9575: PetscFunctionReturn(PETSC_SUCCESS);
9576: }
9578: /*@
9579: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9580: parallel layout, `PetscLayout` for rows and columns
9582: Collective
9584: Input Parameter:
9585: . mat - the matrix
9587: Output Parameters:
9588: + right - (optional) vector that the matrix can be multiplied against
9589: - left - (optional) vector that the matrix vector product can be stored in
9591: Level: advanced
9593: Notes:
9594: 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()`.
9596: These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed
9598: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9599: @*/
9600: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9601: {
9602: PetscFunctionBegin;
9605: if (mat->ops->getvecs) {
9606: PetscUseTypeMethod(mat, getvecs, right, left);
9607: } else {
9608: if (right) {
9609: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9610: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9611: PetscCall(VecSetType(*right, mat->defaultvectype));
9612: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9613: if (mat->boundtocpu && mat->bindingpropagates) {
9614: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9615: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9616: }
9617: #endif
9618: }
9619: if (left) {
9620: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9621: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9622: PetscCall(VecSetType(*left, mat->defaultvectype));
9623: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9624: if (mat->boundtocpu && mat->bindingpropagates) {
9625: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9626: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9627: }
9628: #endif
9629: }
9630: }
9631: PetscFunctionReturn(PETSC_SUCCESS);
9632: }
9634: /*@
9635: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9636: with default values.
9638: Not Collective
9640: Input Parameter:
9641: . info - the `MatFactorInfo` data structure
9643: Level: developer
9645: Notes:
9646: The solvers are generally used through the `KSP` and `PC` objects, for example
9647: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9649: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9651: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9652: @*/
9653: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9654: {
9655: PetscFunctionBegin;
9656: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9657: PetscFunctionReturn(PETSC_SUCCESS);
9658: }
9660: /*@
9661: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9663: Collective
9665: Input Parameters:
9666: + mat - the factored matrix
9667: - is - the index set defining the Schur indices (0-based)
9669: Level: advanced
9671: Notes:
9672: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9674: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9676: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9678: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9679: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9680: @*/
9681: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9682: {
9683: PetscErrorCode (*f)(Mat, IS);
9685: PetscFunctionBegin;
9690: PetscCheckSameComm(mat, 1, is, 2);
9691: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9692: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9693: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9694: PetscCall(MatDestroy(&mat->schur));
9695: PetscCall((*f)(mat, is));
9696: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9697: PetscFunctionReturn(PETSC_SUCCESS);
9698: }
9700: /*@
9701: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9703: Logically Collective
9705: Input Parameters:
9706: + F - the factored matrix obtained by calling `MatGetFactor()`
9707: . S - location where to return the Schur complement, can be `NULL`
9708: - status - the status of the Schur complement matrix, can be `NULL`
9710: Level: advanced
9712: Notes:
9713: You must call `MatFactorSetSchurIS()` before calling this routine.
9715: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9717: The routine provides a copy of the Schur matrix stored within the solver data structures.
9718: The caller must destroy the object when it is no longer needed.
9719: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9721: 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)
9723: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9725: Developer Note:
9726: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9727: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9729: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9730: @*/
9731: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9732: {
9733: PetscFunctionBegin;
9735: if (S) PetscAssertPointer(S, 2);
9736: if (status) PetscAssertPointer(status, 3);
9737: if (S) {
9738: PetscErrorCode (*f)(Mat, Mat *);
9740: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9741: if (f) {
9742: PetscCall((*f)(F, S));
9743: } else {
9744: PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9745: }
9746: }
9747: if (status) *status = F->schur_status;
9748: PetscFunctionReturn(PETSC_SUCCESS);
9749: }
9751: /*@
9752: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9754: Logically Collective
9756: Input Parameters:
9757: + F - the factored matrix obtained by calling `MatGetFactor()`
9758: . S - location where to return the Schur complement, can be `NULL`
9759: - status - the status of the Schur complement matrix, can be `NULL`
9761: Level: advanced
9763: Notes:
9764: You must call `MatFactorSetSchurIS()` before calling this routine.
9766: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9768: The routine returns a the Schur Complement stored within the data structures of the solver.
9770: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9772: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9774: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9776: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9778: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9779: @*/
9780: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9781: {
9782: PetscFunctionBegin;
9784: if (S) {
9785: PetscAssertPointer(S, 2);
9786: *S = F->schur;
9787: }
9788: if (status) {
9789: PetscAssertPointer(status, 3);
9790: *status = F->schur_status;
9791: }
9792: PetscFunctionReturn(PETSC_SUCCESS);
9793: }
9795: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9796: {
9797: Mat S = F->schur;
9799: PetscFunctionBegin;
9800: switch (F->schur_status) {
9801: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9802: case MAT_FACTOR_SCHUR_INVERTED:
9803: if (S) {
9804: S->ops->solve = NULL;
9805: S->ops->matsolve = NULL;
9806: S->ops->solvetranspose = NULL;
9807: S->ops->matsolvetranspose = NULL;
9808: S->ops->solveadd = NULL;
9809: S->ops->solvetransposeadd = NULL;
9810: S->factortype = MAT_FACTOR_NONE;
9811: PetscCall(PetscFree(S->solvertype));
9812: }
9813: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9814: break;
9815: default:
9816: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9817: }
9818: PetscFunctionReturn(PETSC_SUCCESS);
9819: }
9821: /*@
9822: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9824: Logically Collective
9826: Input Parameters:
9827: + F - the factored matrix obtained by calling `MatGetFactor()`
9828: . S - location where the Schur complement is stored
9829: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9831: Level: advanced
9833: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9834: @*/
9835: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9836: {
9837: PetscFunctionBegin;
9839: if (S) {
9841: *S = NULL;
9842: }
9843: F->schur_status = status;
9844: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9845: PetscFunctionReturn(PETSC_SUCCESS);
9846: }
9848: /*@
9849: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9851: Logically Collective
9853: Input Parameters:
9854: + F - the factored matrix obtained by calling `MatGetFactor()`
9855: . rhs - location where the right-hand side of the Schur complement system is stored
9856: - sol - location where the solution of the Schur complement system has to be returned
9858: Level: advanced
9860: Notes:
9861: The sizes of the vectors should match the size of the Schur complement
9863: Must be called after `MatFactorSetSchurIS()`
9865: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9866: @*/
9867: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9868: {
9869: PetscFunctionBegin;
9876: PetscCheckSameComm(F, 1, rhs, 2);
9877: PetscCheckSameComm(F, 1, sol, 3);
9878: PetscCall(MatFactorFactorizeSchurComplement(F));
9879: switch (F->schur_status) {
9880: case MAT_FACTOR_SCHUR_FACTORED:
9881: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9882: break;
9883: case MAT_FACTOR_SCHUR_INVERTED:
9884: PetscCall(MatMultTranspose(F->schur, rhs, sol));
9885: break;
9886: default:
9887: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9888: }
9889: PetscFunctionReturn(PETSC_SUCCESS);
9890: }
9892: /*@
9893: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
9895: Logically Collective
9897: Input Parameters:
9898: + F - the factored matrix obtained by calling `MatGetFactor()`
9899: . rhs - location where the right-hand side of the Schur complement system is stored
9900: - sol - location where the solution of the Schur complement system has to be returned
9902: Level: advanced
9904: Notes:
9905: The sizes of the vectors should match the size of the Schur complement
9907: Must be called after `MatFactorSetSchurIS()`
9909: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9910: @*/
9911: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9912: {
9913: PetscFunctionBegin;
9920: PetscCheckSameComm(F, 1, rhs, 2);
9921: PetscCheckSameComm(F, 1, sol, 3);
9922: PetscCall(MatFactorFactorizeSchurComplement(F));
9923: switch (F->schur_status) {
9924: case MAT_FACTOR_SCHUR_FACTORED:
9925: PetscCall(MatSolve(F->schur, rhs, sol));
9926: break;
9927: case MAT_FACTOR_SCHUR_INVERTED:
9928: PetscCall(MatMult(F->schur, rhs, sol));
9929: break;
9930: default:
9931: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9932: }
9933: PetscFunctionReturn(PETSC_SUCCESS);
9934: }
9936: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9937: #if PetscDefined(HAVE_CUDA)
9938: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9939: #endif
9941: /* Schur status updated in the interface */
9942: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9943: {
9944: Mat S = F->schur;
9946: PetscFunctionBegin;
9947: if (S) {
9948: PetscMPIInt size;
9949: PetscBool isdense, isdensecuda;
9951: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9952: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9953: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9954: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9955: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9956: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9957: if (isdense) {
9958: PetscCall(MatSeqDenseInvertFactors_Private(S));
9959: } else if (isdensecuda) {
9960: #if defined(PETSC_HAVE_CUDA)
9961: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9962: #endif
9963: }
9964: // HIP??????????????
9965: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9966: }
9967: PetscFunctionReturn(PETSC_SUCCESS);
9968: }
9970: /*@
9971: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
9973: Logically Collective
9975: Input Parameter:
9976: . F - the factored matrix obtained by calling `MatGetFactor()`
9978: Level: advanced
9980: Notes:
9981: Must be called after `MatFactorSetSchurIS()`.
9983: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
9985: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9986: @*/
9987: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9988: {
9989: PetscFunctionBegin;
9992: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9993: PetscCall(MatFactorFactorizeSchurComplement(F));
9994: PetscCall(MatFactorInvertSchurComplement_Private(F));
9995: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9996: PetscFunctionReturn(PETSC_SUCCESS);
9997: }
9999: /*@
10000: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10002: Logically Collective
10004: Input Parameter:
10005: . F - the factored matrix obtained by calling `MatGetFactor()`
10007: Level: advanced
10009: Note:
10010: Must be called after `MatFactorSetSchurIS()`
10012: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10013: @*/
10014: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10015: {
10016: MatFactorInfo info;
10018: PetscFunctionBegin;
10021: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10022: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10023: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10024: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10025: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10026: } else {
10027: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10028: }
10029: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10030: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10031: PetscFunctionReturn(PETSC_SUCCESS);
10032: }
10034: /*@
10035: MatPtAP - Creates the matrix product $C = P^T * A * P$
10037: Neighbor-wise Collective
10039: Input Parameters:
10040: + A - the matrix
10041: . P - the projection matrix
10042: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10043: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10044: if the result is a dense matrix this is irrelevant
10046: Output Parameter:
10047: . C - the product matrix
10049: Level: intermediate
10051: Notes:
10052: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10054: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_PtAP`
10055: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10057: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10059: Developer Note:
10060: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10062: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10063: @*/
10064: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10065: {
10066: PetscFunctionBegin;
10067: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10068: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10070: if (scall == MAT_INITIAL_MATRIX) {
10071: PetscCall(MatProductCreate(A, P, NULL, C));
10072: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10073: PetscCall(MatProductSetAlgorithm(*C, "default"));
10074: PetscCall(MatProductSetFill(*C, fill));
10076: (*C)->product->api_user = PETSC_TRUE;
10077: PetscCall(MatProductSetFromOptions(*C));
10078: 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);
10079: PetscCall(MatProductSymbolic(*C));
10080: } else { /* scall == MAT_REUSE_MATRIX */
10081: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10082: }
10084: PetscCall(MatProductNumeric(*C));
10085: (*C)->symmetric = A->symmetric;
10086: (*C)->spd = A->spd;
10087: PetscFunctionReturn(PETSC_SUCCESS);
10088: }
10090: /*@
10091: MatRARt - Creates the matrix product $C = R * A * R^T$
10093: Neighbor-wise Collective
10095: Input Parameters:
10096: + A - the matrix
10097: . R - the projection matrix
10098: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10099: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10100: if the result is a dense matrix this is irrelevant
10102: Output Parameter:
10103: . C - the product matrix
10105: Level: intermediate
10107: Notes:
10108: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10110: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_RARt`
10111: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10113: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10114: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10115: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10116: We recommend using `MatPtAP()` when possible.
10118: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10120: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10121: @*/
10122: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10123: {
10124: PetscFunctionBegin;
10125: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10126: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10128: if (scall == MAT_INITIAL_MATRIX) {
10129: PetscCall(MatProductCreate(A, R, NULL, C));
10130: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10131: PetscCall(MatProductSetAlgorithm(*C, "default"));
10132: PetscCall(MatProductSetFill(*C, fill));
10134: (*C)->product->api_user = PETSC_TRUE;
10135: PetscCall(MatProductSetFromOptions(*C));
10136: 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);
10137: PetscCall(MatProductSymbolic(*C));
10138: } else { /* scall == MAT_REUSE_MATRIX */
10139: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10140: }
10142: PetscCall(MatProductNumeric(*C));
10143: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10144: PetscFunctionReturn(PETSC_SUCCESS);
10145: }
10147: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10148: {
10149: PetscBool flg = PETSC_TRUE;
10151: PetscFunctionBegin;
10152: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10153: if (scall == MAT_INITIAL_MATRIX) {
10154: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10155: PetscCall(MatProductCreate(A, B, NULL, C));
10156: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10157: PetscCall(MatProductSetFill(*C, fill));
10158: } else { /* scall == MAT_REUSE_MATRIX */
10159: Mat_Product *product = (*C)->product;
10161: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10162: if (flg && product && product->type != ptype) {
10163: PetscCall(MatProductClear(*C));
10164: product = NULL;
10165: }
10166: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10167: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10168: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10169: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10170: product = (*C)->product;
10171: product->fill = fill;
10172: product->clear = PETSC_TRUE;
10173: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10174: flg = PETSC_FALSE;
10175: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10176: }
10177: }
10178: if (flg) {
10179: (*C)->product->api_user = PETSC_TRUE;
10180: PetscCall(MatProductSetType(*C, ptype));
10181: PetscCall(MatProductSetFromOptions(*C));
10182: PetscCall(MatProductSymbolic(*C));
10183: }
10184: PetscCall(MatProductNumeric(*C));
10185: PetscFunctionReturn(PETSC_SUCCESS);
10186: }
10188: /*@
10189: MatMatMult - Performs matrix-matrix multiplication $ C=A*B $.
10191: Neighbor-wise Collective
10193: Input Parameters:
10194: + A - the left matrix
10195: . B - the right matrix
10196: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10197: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10198: if the result is a dense matrix this is irrelevant
10200: Output Parameter:
10201: . C - the product matrix
10203: Notes:
10204: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10206: `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
10207: call to this function with `MAT_INITIAL_MATRIX`.
10209: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10211: 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`,
10212: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10214: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10216: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AB`
10217: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10219: Example of Usage:
10220: .vb
10221: MatProductCreate(A,B,NULL,&C);
10222: MatProductSetType(C,MATPRODUCT_AB);
10223: MatProductSymbolic(C);
10224: MatProductNumeric(C); // compute C=A * B
10225: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10226: MatProductNumeric(C);
10227: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10228: MatProductNumeric(C);
10229: .ve
10231: Level: intermediate
10233: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10234: @*/
10235: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10236: {
10237: PetscFunctionBegin;
10238: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10239: PetscFunctionReturn(PETSC_SUCCESS);
10240: }
10242: /*@
10243: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10245: Neighbor-wise Collective
10247: Input Parameters:
10248: + A - the left matrix
10249: . B - the right matrix
10250: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10251: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10253: Output Parameter:
10254: . C - the product matrix
10256: Options Database Key:
10257: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10258: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10259: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10261: Level: intermediate
10263: Notes:
10264: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10266: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10268: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10269: actually needed.
10271: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10272: and for pairs of `MATMPIDENSE` matrices.
10274: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABt`
10275: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10277: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10279: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10280: @*/
10281: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10282: {
10283: PetscFunctionBegin;
10284: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10285: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10286: PetscFunctionReturn(PETSC_SUCCESS);
10287: }
10289: /*@
10290: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10292: Neighbor-wise Collective
10294: Input Parameters:
10295: + A - the left matrix
10296: . B - the right matrix
10297: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10298: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10300: Output Parameter:
10301: . C - the product matrix
10303: Level: intermediate
10305: Notes:
10306: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10308: `MAT_REUSE_MATRIX` can only be used if `A` and `B` have the same nonzero pattern as in the previous call.
10310: This is a convenience routine that wraps the use of `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AtB`
10311: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10313: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10314: actually needed.
10316: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10317: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10319: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10321: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10322: @*/
10323: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10324: {
10325: PetscFunctionBegin;
10326: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10327: PetscFunctionReturn(PETSC_SUCCESS);
10328: }
10330: /*@
10331: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10333: Neighbor-wise Collective
10335: Input Parameters:
10336: + A - the left matrix
10337: . B - the middle matrix
10338: . C - the right matrix
10339: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10340: - fill - expected fill as ratio of nnz(D)/(nnz(A) + nnz(B)+nnz(C)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10341: if the result is a dense matrix this is irrelevant
10343: Output Parameter:
10344: . D - the product matrix
10346: Level: intermediate
10348: Notes:
10349: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10351: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10353: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABC`
10354: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10356: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10357: actually needed.
10359: If you have many matrices with the same non-zero structure to multiply, you
10360: should use `MAT_REUSE_MATRIX` in all calls but the first
10362: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10364: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10365: @*/
10366: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10367: {
10368: PetscFunctionBegin;
10369: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10370: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10372: if (scall == MAT_INITIAL_MATRIX) {
10373: PetscCall(MatProductCreate(A, B, C, D));
10374: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10375: PetscCall(MatProductSetAlgorithm(*D, "default"));
10376: PetscCall(MatProductSetFill(*D, fill));
10378: (*D)->product->api_user = PETSC_TRUE;
10379: PetscCall(MatProductSetFromOptions(*D));
10380: 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,
10381: ((PetscObject)C)->type_name);
10382: PetscCall(MatProductSymbolic(*D));
10383: } else { /* user may change input matrices when REUSE */
10384: PetscCall(MatProductReplaceMats(A, B, C, *D));
10385: }
10386: PetscCall(MatProductNumeric(*D));
10387: PetscFunctionReturn(PETSC_SUCCESS);
10388: }
10390: /*@
10391: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10393: Collective
10395: Input Parameters:
10396: + mat - the matrix
10397: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10398: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10399: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10401: Output Parameter:
10402: . matredundant - redundant matrix
10404: Level: advanced
10406: Notes:
10407: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10408: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10410: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10411: calling it.
10413: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10415: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10416: @*/
10417: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10418: {
10419: MPI_Comm comm;
10420: PetscMPIInt size;
10421: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10422: Mat_Redundant *redund = NULL;
10423: PetscSubcomm psubcomm = NULL;
10424: MPI_Comm subcomm_in = subcomm;
10425: Mat *matseq;
10426: IS isrow, iscol;
10427: PetscBool newsubcomm = PETSC_FALSE;
10429: PetscFunctionBegin;
10431: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10432: PetscAssertPointer(*matredundant, 5);
10434: }
10436: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10437: if (size == 1 || nsubcomm == 1) {
10438: if (reuse == MAT_INITIAL_MATRIX) {
10439: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10440: } else {
10441: 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");
10442: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10443: }
10444: PetscFunctionReturn(PETSC_SUCCESS);
10445: }
10447: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10448: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10449: MatCheckPreallocated(mat, 1);
10451: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10452: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10453: /* create psubcomm, then get subcomm */
10454: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10455: PetscCallMPI(MPI_Comm_size(comm, &size));
10456: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10458: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10459: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10460: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10461: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10462: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10463: newsubcomm = PETSC_TRUE;
10464: PetscCall(PetscSubcommDestroy(&psubcomm));
10465: }
10467: /* get isrow, iscol and a local sequential matrix matseq[0] */
10468: if (reuse == MAT_INITIAL_MATRIX) {
10469: mloc_sub = PETSC_DECIDE;
10470: nloc_sub = PETSC_DECIDE;
10471: if (bs < 1) {
10472: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10473: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10474: } else {
10475: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10476: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10477: }
10478: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10479: rstart = rend - mloc_sub;
10480: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10481: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10482: PetscCall(ISSetIdentity(iscol));
10483: } else { /* reuse == MAT_REUSE_MATRIX */
10484: 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");
10485: /* retrieve subcomm */
10486: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10487: redund = (*matredundant)->redundant;
10488: isrow = redund->isrow;
10489: iscol = redund->iscol;
10490: matseq = redund->matseq;
10491: }
10492: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10494: /* get matredundant over subcomm */
10495: if (reuse == MAT_INITIAL_MATRIX) {
10496: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10498: /* create a supporting struct and attach it to C for reuse */
10499: PetscCall(PetscNew(&redund));
10500: (*matredundant)->redundant = redund;
10501: redund->isrow = isrow;
10502: redund->iscol = iscol;
10503: redund->matseq = matseq;
10504: if (newsubcomm) {
10505: redund->subcomm = subcomm;
10506: } else {
10507: redund->subcomm = MPI_COMM_NULL;
10508: }
10509: } else {
10510: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10511: }
10512: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10513: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10514: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10515: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10516: }
10517: #endif
10518: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10519: PetscFunctionReturn(PETSC_SUCCESS);
10520: }
10522: /*@C
10523: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10524: a given `Mat`. Each submatrix can span multiple procs.
10526: Collective
10528: Input Parameters:
10529: + mat - the matrix
10530: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10531: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10533: Output Parameter:
10534: . subMat - parallel sub-matrices each spanning a given `subcomm`
10536: Level: advanced
10538: Notes:
10539: The submatrix partition across processors is dictated by `subComm` a
10540: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10541: is not restricted to be grouped with consecutive original MPI processes.
10543: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10544: map directly to the layout of the original matrix [wrt the local
10545: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10546: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10547: the `subMat`. However the offDiagMat looses some columns - and this is
10548: reconstructed with `MatSetValues()`
10550: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10552: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10553: @*/
10554: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10555: {
10556: PetscMPIInt commsize, subCommSize;
10558: PetscFunctionBegin;
10559: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10560: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10561: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10563: 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");
10564: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10565: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10566: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10567: PetscFunctionReturn(PETSC_SUCCESS);
10568: }
10570: /*@
10571: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10573: Not Collective
10575: Input Parameters:
10576: + mat - matrix to extract local submatrix from
10577: . isrow - local row indices for submatrix
10578: - iscol - local column indices for submatrix
10580: Output Parameter:
10581: . submat - the submatrix
10583: Level: intermediate
10585: Notes:
10586: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10588: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10589: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10591: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10592: `MatSetValuesBlockedLocal()` will also be implemented.
10594: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10595: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10597: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10598: @*/
10599: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10600: {
10601: PetscFunctionBegin;
10605: PetscCheckSameComm(isrow, 2, iscol, 3);
10606: PetscAssertPointer(submat, 4);
10607: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10609: if (mat->ops->getlocalsubmatrix) {
10610: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10611: } else {
10612: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10613: }
10614: (*submat)->assembled = mat->assembled;
10615: PetscFunctionReturn(PETSC_SUCCESS);
10616: }
10618: /*@
10619: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10621: Not Collective
10623: Input Parameters:
10624: + mat - matrix to extract local submatrix from
10625: . isrow - local row indices for submatrix
10626: . iscol - local column indices for submatrix
10627: - submat - the submatrix
10629: Level: intermediate
10631: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10632: @*/
10633: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10634: {
10635: PetscFunctionBegin;
10639: PetscCheckSameComm(isrow, 2, iscol, 3);
10640: PetscAssertPointer(submat, 4);
10643: if (mat->ops->restorelocalsubmatrix) {
10644: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10645: } else {
10646: PetscCall(MatDestroy(submat));
10647: }
10648: *submat = NULL;
10649: PetscFunctionReturn(PETSC_SUCCESS);
10650: }
10652: /*@
10653: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10655: Collective
10657: Input Parameter:
10658: . mat - the matrix
10660: Output Parameter:
10661: . is - if any rows have zero diagonals this contains the list of them
10663: Level: developer
10665: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10666: @*/
10667: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10668: {
10669: PetscFunctionBegin;
10672: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10673: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10675: if (!mat->ops->findzerodiagonals) {
10676: Vec diag;
10677: const PetscScalar *a;
10678: PetscInt *rows;
10679: PetscInt rStart, rEnd, r, nrow = 0;
10681: PetscCall(MatCreateVecs(mat, &diag, NULL));
10682: PetscCall(MatGetDiagonal(mat, diag));
10683: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10684: PetscCall(VecGetArrayRead(diag, &a));
10685: for (r = 0; r < rEnd - rStart; ++r)
10686: if (a[r] == 0.0) ++nrow;
10687: PetscCall(PetscMalloc1(nrow, &rows));
10688: nrow = 0;
10689: for (r = 0; r < rEnd - rStart; ++r)
10690: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10691: PetscCall(VecRestoreArrayRead(diag, &a));
10692: PetscCall(VecDestroy(&diag));
10693: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10694: } else {
10695: PetscUseTypeMethod(mat, findzerodiagonals, is);
10696: }
10697: PetscFunctionReturn(PETSC_SUCCESS);
10698: }
10700: /*@
10701: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10703: Collective
10705: Input Parameter:
10706: . mat - the matrix
10708: Output Parameter:
10709: . is - contains the list of rows with off block diagonal entries
10711: Level: developer
10713: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10714: @*/
10715: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10716: {
10717: PetscFunctionBegin;
10720: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10721: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10723: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10724: PetscFunctionReturn(PETSC_SUCCESS);
10725: }
10727: /*@C
10728: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10730: Collective; No Fortran Support
10732: Input Parameter:
10733: . mat - the matrix
10735: Output Parameter:
10736: . values - the block inverses in column major order (FORTRAN-like)
10738: Level: advanced
10740: Notes:
10741: The size of the blocks is determined by the block size of the matrix.
10743: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10745: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10747: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10748: @*/
10749: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10750: {
10751: PetscFunctionBegin;
10753: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10754: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10755: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10756: PetscFunctionReturn(PETSC_SUCCESS);
10757: }
10759: /*@
10760: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10762: Collective; No Fortran Support
10764: Input Parameters:
10765: + mat - the matrix
10766: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10767: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10769: Output Parameter:
10770: . values - the block inverses in column major order (FORTRAN-like)
10772: Level: advanced
10774: Notes:
10775: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10777: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10779: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10780: @*/
10781: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10782: {
10783: PetscFunctionBegin;
10785: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10786: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10787: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10788: PetscFunctionReturn(PETSC_SUCCESS);
10789: }
10791: /*@
10792: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10794: Collective
10796: Input Parameters:
10797: + A - the matrix
10798: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10800: Level: advanced
10802: Note:
10803: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10805: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10806: @*/
10807: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10808: {
10809: const PetscScalar *vals;
10810: PetscInt *dnnz;
10811: PetscInt m, rstart, rend, bs, i, j;
10813: PetscFunctionBegin;
10814: PetscCall(MatInvertBlockDiagonal(A, &vals));
10815: PetscCall(MatGetBlockSize(A, &bs));
10816: PetscCall(MatGetLocalSize(A, &m, NULL));
10817: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10818: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10819: PetscCall(PetscMalloc1(m / bs, &dnnz));
10820: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10821: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10822: PetscCall(PetscFree(dnnz));
10823: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10824: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10825: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10826: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10827: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10828: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10829: PetscFunctionReturn(PETSC_SUCCESS);
10830: }
10832: /*@
10833: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10834: via `MatTransposeColoringCreate()`.
10836: Collective
10838: Input Parameter:
10839: . c - coloring context
10841: Level: intermediate
10843: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10844: @*/
10845: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10846: {
10847: MatTransposeColoring matcolor = *c;
10849: PetscFunctionBegin;
10850: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10851: if (--((PetscObject)matcolor)->refct > 0) {
10852: matcolor = NULL;
10853: PetscFunctionReturn(PETSC_SUCCESS);
10854: }
10856: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10857: PetscCall(PetscFree(matcolor->rows));
10858: PetscCall(PetscFree(matcolor->den2sp));
10859: PetscCall(PetscFree(matcolor->colorforcol));
10860: PetscCall(PetscFree(matcolor->columns));
10861: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10862: PetscCall(PetscHeaderDestroy(c));
10863: PetscFunctionReturn(PETSC_SUCCESS);
10864: }
10866: /*@
10867: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10868: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10869: `MatTransposeColoring` to sparse `B`.
10871: Collective
10873: Input Parameters:
10874: + coloring - coloring context created with `MatTransposeColoringCreate()`
10875: - B - sparse matrix
10877: Output Parameter:
10878: . Btdense - dense matrix $B^T$
10880: Level: developer
10882: Note:
10883: These are used internally for some implementations of `MatRARt()`
10885: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10886: @*/
10887: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10888: {
10889: PetscFunctionBegin;
10894: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10895: PetscFunctionReturn(PETSC_SUCCESS);
10896: }
10898: /*@
10899: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10900: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10901: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10902: $C_{sp}$ from $C_{den}$.
10904: Collective
10906: Input Parameters:
10907: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10908: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
10910: Output Parameter:
10911: . Csp - sparse matrix
10913: Level: developer
10915: Note:
10916: These are used internally for some implementations of `MatRARt()`
10918: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10919: @*/
10920: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10921: {
10922: PetscFunctionBegin;
10927: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10928: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10929: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10930: PetscFunctionReturn(PETSC_SUCCESS);
10931: }
10933: /*@
10934: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
10936: Collective
10938: Input Parameters:
10939: + mat - the matrix product C
10940: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
10942: Output Parameter:
10943: . color - the new coloring context
10945: Level: intermediate
10947: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10948: `MatTransColoringApplyDenToSp()`
10949: @*/
10950: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10951: {
10952: MatTransposeColoring c;
10953: MPI_Comm comm;
10955: PetscFunctionBegin;
10956: PetscAssertPointer(color, 3);
10958: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10959: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10960: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10961: c->ctype = iscoloring->ctype;
10962: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10963: *color = c;
10964: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10965: PetscFunctionReturn(PETSC_SUCCESS);
10966: }
10968: /*@
10969: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10970: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
10972: Not Collective
10974: Input Parameter:
10975: . mat - the matrix
10977: Output Parameter:
10978: . state - the current state
10980: Level: intermediate
10982: Notes:
10983: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10984: different matrices
10986: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
10988: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
10990: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10991: @*/
10992: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10993: {
10994: PetscFunctionBegin;
10996: *state = mat->nonzerostate;
10997: PetscFunctionReturn(PETSC_SUCCESS);
10998: }
11000: /*@
11001: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11002: matrices from each processor
11004: Collective
11006: Input Parameters:
11007: + comm - the communicators the parallel matrix will live on
11008: . seqmat - the input sequential matrices
11009: . n - number of local columns (or `PETSC_DECIDE`)
11010: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11012: Output Parameter:
11013: . mpimat - the parallel matrix generated
11015: Level: developer
11017: Note:
11018: The number of columns of the matrix in EACH processor MUST be the same.
11020: .seealso: [](ch_matrices), `Mat`
11021: @*/
11022: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11023: {
11024: PetscMPIInt size;
11026: PetscFunctionBegin;
11027: PetscCallMPI(MPI_Comm_size(comm, &size));
11028: if (size == 1) {
11029: if (reuse == MAT_INITIAL_MATRIX) {
11030: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11031: } else {
11032: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11033: }
11034: PetscFunctionReturn(PETSC_SUCCESS);
11035: }
11037: 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");
11039: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11040: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11041: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11042: PetscFunctionReturn(PETSC_SUCCESS);
11043: }
11045: /*@
11046: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11048: Collective
11050: Input Parameters:
11051: + A - the matrix to create subdomains from
11052: - N - requested number of subdomains
11054: Output Parameters:
11055: + n - number of subdomains resulting on this MPI process
11056: - iss - `IS` list with indices of subdomains on this MPI process
11058: Level: advanced
11060: Note:
11061: The number of subdomains must be smaller than the communicator size
11063: .seealso: [](ch_matrices), `Mat`, `IS`
11064: @*/
11065: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11066: {
11067: MPI_Comm comm, subcomm;
11068: PetscMPIInt size, rank, color;
11069: PetscInt rstart, rend, k;
11071: PetscFunctionBegin;
11072: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11073: PetscCallMPI(MPI_Comm_size(comm, &size));
11074: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11075: PetscCheck(N >= 1 && N < size, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "number of subdomains must be > 0 and < %d, got N = %" PetscInt_FMT, size, N);
11076: *n = 1;
11077: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11078: color = rank / k;
11079: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11080: PetscCall(PetscMalloc1(1, iss));
11081: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11082: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11083: PetscCallMPI(MPI_Comm_free(&subcomm));
11084: PetscFunctionReturn(PETSC_SUCCESS);
11085: }
11087: /*@
11088: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11090: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11091: If they are not the same, uses `MatMatMatMult()`.
11093: Once the coarse grid problem is constructed, correct for interpolation operators
11094: that are not of full rank, which can legitimately happen in the case of non-nested
11095: geometric multigrid.
11097: Input Parameters:
11098: + restrct - restriction operator
11099: . dA - fine grid matrix
11100: . interpolate - interpolation operator
11101: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11102: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11104: Output Parameter:
11105: . A - the Galerkin coarse matrix
11107: Options Database Key:
11108: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used
11110: Level: developer
11112: Note:
11113: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11115: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11116: @*/
11117: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11118: {
11119: IS zerorows;
11120: Vec diag;
11122: PetscFunctionBegin;
11123: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11124: /* Construct the coarse grid matrix */
11125: if (interpolate == restrct) {
11126: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11127: } else {
11128: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11129: }
11131: /* If the interpolation matrix is not of full rank, A will have zero rows.
11132: This can legitimately happen in the case of non-nested geometric multigrid.
11133: In that event, we set the rows of the matrix to the rows of the identity,
11134: ignoring the equations (as the RHS will also be zero). */
11136: PetscCall(MatFindZeroRows(*A, &zerorows));
11138: if (zerorows != NULL) { /* if there are any zero rows */
11139: PetscCall(MatCreateVecs(*A, &diag, NULL));
11140: PetscCall(MatGetDiagonal(*A, diag));
11141: PetscCall(VecISSet(diag, zerorows, 1.0));
11142: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11143: PetscCall(VecDestroy(&diag));
11144: PetscCall(ISDestroy(&zerorows));
11145: }
11146: PetscFunctionReturn(PETSC_SUCCESS);
11147: }
11149: /*@C
11150: MatSetOperation - Allows user to set a matrix operation for any matrix type
11152: Logically Collective
11154: Input Parameters:
11155: + mat - the matrix
11156: . op - the name of the operation
11157: - f - the function that provides the operation
11159: Level: developer
11161: Example Usage:
11162: .vb
11163: extern PetscErrorCode usermult(Mat, Vec, Vec);
11165: PetscCall(MatCreateXXX(comm, ..., &A));
11166: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11167: .ve
11169: Notes:
11170: See the file `include/petscmat.h` for a complete list of matrix
11171: operations, which all have the form MATOP_<OPERATION>, where
11172: <OPERATION> is the name (in all capital letters) of the
11173: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11175: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11176: sequence as the usual matrix interface routines, since they
11177: are intended to be accessed via the usual matrix interface
11178: routines, e.g.,
11179: .vb
11180: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11181: .ve
11183: In particular each function MUST return `PETSC_SUCCESS` on success and
11184: nonzero on failure.
11186: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11188: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11189: @*/
11190: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11191: {
11192: PetscFunctionBegin;
11194: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11195: (((PetscErrorCodeFn **)mat->ops)[op]) = f;
11196: PetscFunctionReturn(PETSC_SUCCESS);
11197: }
11199: /*@C
11200: MatGetOperation - Gets a matrix operation for any matrix type.
11202: Not Collective
11204: Input Parameters:
11205: + mat - the matrix
11206: - op - the name of the operation
11208: Output Parameter:
11209: . f - the function that provides the operation
11211: Level: developer
11213: Example Usage:
11214: .vb
11215: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11217: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11218: .ve
11220: Notes:
11221: See the file `include/petscmat.h` for a complete list of matrix
11222: operations, which all have the form MATOP_<OPERATION>, where
11223: <OPERATION> is the name (in all capital letters) of the
11224: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11226: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11228: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11229: @*/
11230: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11231: {
11232: PetscFunctionBegin;
11234: *f = (((PetscErrorCodeFn **)mat->ops)[op]);
11235: PetscFunctionReturn(PETSC_SUCCESS);
11236: }
11238: /*@
11239: MatHasOperation - Determines whether the given matrix supports the particular operation.
11241: Not Collective
11243: Input Parameters:
11244: + mat - the matrix
11245: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11247: Output Parameter:
11248: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11250: Level: advanced
11252: Note:
11253: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11255: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11256: @*/
11257: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11258: {
11259: PetscFunctionBegin;
11261: PetscAssertPointer(has, 3);
11262: if (mat->ops->hasoperation) {
11263: PetscUseTypeMethod(mat, hasoperation, op, has);
11264: } else {
11265: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11266: else {
11267: *has = PETSC_FALSE;
11268: if (op == MATOP_CREATE_SUBMATRIX) {
11269: PetscMPIInt size;
11271: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11272: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11273: }
11274: }
11275: }
11276: PetscFunctionReturn(PETSC_SUCCESS);
11277: }
11279: /*@
11280: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11282: Collective
11284: Input Parameter:
11285: . mat - the matrix
11287: Output Parameter:
11288: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11290: Level: beginner
11292: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11293: @*/
11294: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11295: {
11296: PetscFunctionBegin;
11299: PetscAssertPointer(cong, 2);
11300: if (!mat->rmap || !mat->cmap) {
11301: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11302: PetscFunctionReturn(PETSC_SUCCESS);
11303: }
11304: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11305: PetscCall(PetscLayoutSetUp(mat->rmap));
11306: PetscCall(PetscLayoutSetUp(mat->cmap));
11307: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11308: if (*cong) mat->congruentlayouts = 1;
11309: else mat->congruentlayouts = 0;
11310: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11311: PetscFunctionReturn(PETSC_SUCCESS);
11312: }
11314: PetscErrorCode MatSetInf(Mat A)
11315: {
11316: PetscFunctionBegin;
11317: PetscUseTypeMethod(A, setinf);
11318: PetscFunctionReturn(PETSC_SUCCESS);
11319: }
11321: /*@
11322: 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
11323: and possibly removes small values from the graph structure.
11325: Collective
11327: Input Parameters:
11328: + A - the matrix
11329: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11330: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11331: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11332: . num_idx - size of 'index' array
11333: - index - array of block indices to use for graph strength of connection weight
11335: Output Parameter:
11336: . graph - the resulting graph
11338: Level: advanced
11340: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11341: @*/
11342: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11343: {
11344: PetscFunctionBegin;
11348: PetscAssertPointer(graph, 7);
11349: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11350: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11351: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11352: PetscFunctionReturn(PETSC_SUCCESS);
11353: }
11355: /*@
11356: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11357: meaning the same memory is used for the matrix, and no new memory is allocated.
11359: Collective
11361: Input Parameters:
11362: + A - the matrix
11363: - keep - if for a given row of `A`, the diagonal coefficient is zero, indicates whether it should be left in the structure or eliminated as well
11365: Level: intermediate
11367: Developer Note:
11368: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11369: of the arrays in the data structure are unneeded.
11371: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11372: @*/
11373: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11374: {
11375: PetscFunctionBegin;
11377: PetscUseTypeMethod(A, eliminatezeros, keep);
11378: PetscFunctionReturn(PETSC_SUCCESS);
11379: }
11381: /*@C
11382: MatGetCurrentMemType - Get the memory location of the matrix
11384: Not Collective, but the result will be the same on all MPI processes
11386: Input Parameter:
11387: . A - the matrix whose memory type we are checking
11389: Output Parameter:
11390: . m - the memory type
11392: Level: intermediate
11394: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11395: @*/
11396: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11397: {
11398: PetscFunctionBegin;
11400: PetscAssertPointer(m, 2);
11401: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11402: else *m = PETSC_MEMTYPE_HOST;
11403: PetscFunctionReturn(PETSC_SUCCESS);
11404: }