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, isspd;
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;
4382: isspd = mat->spd;
4384: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4385: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4386: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4387: } else {
4388: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4389: const char *prefix[3] = {"seq", "mpi", ""};
4390: PetscInt i;
4391: /*
4392: Order of precedence:
4393: 0) See if newtype is a superclass of the current matrix.
4394: 1) See if a specialized converter is known to the current matrix.
4395: 2) See if a specialized converter is known to the desired matrix class.
4396: 3) See if a good general converter is registered for the desired class
4397: (as of 6/27/03 only MATMPIADJ falls into this category).
4398: 4) See if a good general converter is known for the current matrix.
4399: 5) Use a really basic converter.
4400: */
4402: /* 0) See if newtype is a superclass of the current matrix.
4403: i.e mat is mpiaij and newtype is aij */
4404: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4405: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4406: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4407: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4408: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4409: if (flg) {
4410: if (reuse == MAT_INPLACE_MATRIX) {
4411: PetscCall(PetscInfo(mat, "Early return\n"));
4412: PetscFunctionReturn(PETSC_SUCCESS);
4413: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4414: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4415: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4416: PetscFunctionReturn(PETSC_SUCCESS);
4417: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4418: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4419: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4420: PetscFunctionReturn(PETSC_SUCCESS);
4421: }
4422: }
4423: }
4424: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4425: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4426: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4427: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4428: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4429: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4430: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4431: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4432: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4433: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4434: if (conv) goto foundconv;
4435: }
4437: /* 2) See if a specialized converter is known to the desired matrix class. */
4438: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4439: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4440: PetscCall(MatSetType(B, newtype));
4441: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4442: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4443: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4444: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4445: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4446: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4447: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4448: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4449: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4450: if (conv) {
4451: PetscCall(MatDestroy(&B));
4452: goto foundconv;
4453: }
4454: }
4456: /* 3) See if a good general converter is registered for the desired class */
4457: conv = B->ops->convertfrom;
4458: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4459: PetscCall(MatDestroy(&B));
4460: if (conv) goto foundconv;
4462: /* 4) See if a good general converter is known for the current matrix */
4463: if (mat->ops->convert) conv = mat->ops->convert;
4464: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4465: if (conv) goto foundconv;
4467: /* 5) Use a really basic converter. */
4468: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4469: conv = MatConvert_Basic;
4471: foundconv:
4472: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4473: PetscCall((*conv)(mat, newtype, reuse, M));
4474: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4475: /* the block sizes must be same if the mappings are copied over */
4476: (*M)->rmap->bs = mat->rmap->bs;
4477: (*M)->cmap->bs = mat->cmap->bs;
4478: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4479: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4480: (*M)->rmap->mapping = mat->rmap->mapping;
4481: (*M)->cmap->mapping = mat->cmap->mapping;
4482: }
4483: (*M)->stencil.dim = mat->stencil.dim;
4484: (*M)->stencil.noc = mat->stencil.noc;
4485: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4486: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4487: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4488: }
4489: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4490: }
4491: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4493: /* Reset Mat options */
4494: if (issymmetric != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PetscBool3ToBool(issymmetric)));
4495: if (ishermitian != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PetscBool3ToBool(ishermitian)));
4496: if (isspd != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SPD, PetscBool3ToBool(isspd)));
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: } else {
5538: MatType mattype;
5540: PetscCall(MatGetType(f ? B : A, &mattype));
5541: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for Hermitian transpose", mattype);
5542: }
5543: PetscFunctionReturn(PETSC_SUCCESS);
5544: }
5546: /*@
5547: MatPermute - Creates a new matrix with rows and columns permuted from the
5548: original.
5550: Collective
5552: Input Parameters:
5553: + mat - the matrix to permute
5554: . row - row permutation, each processor supplies only the permutation for its rows
5555: - col - column permutation, each processor supplies only the permutation for its columns
5557: Output Parameter:
5558: . B - the permuted matrix
5560: Level: advanced
5562: Note:
5563: The index sets map from row/col of permuted matrix to row/col of original matrix.
5564: The index sets should be on the same communicator as mat and have the same local sizes.
5566: Developer Note:
5567: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5568: exploit the fact that row and col are permutations, consider implementing the
5569: more general `MatCreateSubMatrix()` instead.
5571: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5572: @*/
5573: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5574: {
5575: PetscFunctionBegin;
5580: PetscAssertPointer(B, 4);
5581: PetscCheckSameComm(mat, 1, row, 2);
5582: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5583: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5584: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5585: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5586: MatCheckPreallocated(mat, 1);
5588: if (mat->ops->permute) {
5589: PetscUseTypeMethod(mat, permute, row, col, B);
5590: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5591: } else {
5592: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5593: }
5594: PetscFunctionReturn(PETSC_SUCCESS);
5595: }
5597: /*@
5598: MatEqual - Compares two matrices.
5600: Collective
5602: Input Parameters:
5603: + A - the first matrix
5604: - B - the second matrix
5606: Output Parameter:
5607: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5609: Level: intermediate
5611: Note:
5612: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5613: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5615: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5616: @*/
5617: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5618: {
5619: PetscFunctionBegin;
5624: PetscAssertPointer(flg, 3);
5625: PetscCheckSameComm(A, 1, B, 2);
5626: MatCheckPreallocated(A, 1);
5627: MatCheckPreallocated(B, 2);
5628: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5629: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5630: 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,
5631: B->cmap->N);
5632: if (A->ops->equal && A->ops->equal == B->ops->equal) {
5633: PetscUseTypeMethod(A, equal, B, flg);
5634: } else {
5635: PetscCall(MatMultEqual(A, B, 10, flg));
5636: }
5637: PetscFunctionReturn(PETSC_SUCCESS);
5638: }
5640: /*@
5641: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5642: matrices that are stored as vectors. Either of the two scaling
5643: matrices can be `NULL`.
5645: Collective
5647: Input Parameters:
5648: + mat - the matrix to be scaled
5649: . l - the left scaling vector (or `NULL`)
5650: - r - the right scaling vector (or `NULL`)
5652: Level: intermediate
5654: Note:
5655: `MatDiagonalScale()` computes $A = LAR$, where
5656: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5657: The L scales the rows of the matrix, the R scales the columns of the matrix.
5659: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5660: @*/
5661: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5662: {
5663: PetscBool flg = PETSC_FALSE;
5665: PetscFunctionBegin;
5668: if (l) {
5670: PetscCheckSameComm(mat, 1, l, 2);
5671: }
5672: if (r) {
5674: PetscCheckSameComm(mat, 1, r, 3);
5675: }
5676: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5677: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5678: MatCheckPreallocated(mat, 1);
5679: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5681: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5682: PetscUseTypeMethod(mat, diagonalscale, l, r);
5683: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5684: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5685: if (l != r && (PetscBool3ToBool(mat->symmetric) || PetscBool3ToBool(mat->hermitian))) {
5686: if (!PetscDefined(USE_COMPLEX) || PetscBool3ToBool(mat->symmetric)) {
5687: if (l && r) PetscCall(VecEqual(l, r, &flg));
5688: if (!flg) {
5689: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5690: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format, left and right scaling vectors must be the same");
5691: mat->symmetric = mat->spd = PETSC_BOOL3_FALSE;
5692: if (!PetscDefined(USE_COMPLEX)) mat->hermitian = PETSC_BOOL3_FALSE;
5693: else mat->hermitian = PETSC_BOOL3_UNKNOWN;
5694: }
5695: }
5696: if (PetscDefined(USE_COMPLEX) && PetscBool3ToBool(mat->hermitian)) {
5697: flg = PETSC_FALSE;
5698: if (l && r) {
5699: Vec conjugate;
5701: PetscCall(VecDuplicate(l, &conjugate));
5702: PetscCall(VecCopy(l, conjugate));
5703: PetscCall(VecConjugate(conjugate));
5704: PetscCall(VecEqual(conjugate, r, &flg));
5705: PetscCall(VecDestroy(&conjugate));
5706: }
5707: if (!flg) {
5708: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5709: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format and Hermitian matrix, left and right scaling vectors must be conjugate one of the other");
5710: mat->hermitian = PETSC_BOOL3_FALSE;
5711: mat->symmetric = mat->spd = PETSC_BOOL3_UNKNOWN;
5712: }
5713: }
5714: }
5715: PetscFunctionReturn(PETSC_SUCCESS);
5716: }
5718: /*@
5719: MatScale - Scales all elements of a matrix by a given number.
5721: Logically Collective
5723: Input Parameters:
5724: + mat - the matrix to be scaled
5725: - a - the scaling value
5727: Level: intermediate
5729: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5730: @*/
5731: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5732: {
5733: PetscFunctionBegin;
5736: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5737: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5739: MatCheckPreallocated(mat, 1);
5741: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5742: if (a != (PetscScalar)1.0) {
5743: PetscUseTypeMethod(mat, scale, a);
5744: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5745: }
5746: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5747: PetscFunctionReturn(PETSC_SUCCESS);
5748: }
5750: /*@
5751: MatNorm - Calculates various norms of a matrix.
5753: Collective
5755: Input Parameters:
5756: + mat - the matrix
5757: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5759: Output Parameter:
5760: . nrm - the resulting norm
5762: Level: intermediate
5764: .seealso: [](ch_matrices), `Mat`
5765: @*/
5766: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5767: {
5768: PetscFunctionBegin;
5771: PetscAssertPointer(nrm, 3);
5773: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5774: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5775: MatCheckPreallocated(mat, 1);
5777: PetscUseTypeMethod(mat, norm, type, nrm);
5778: PetscFunctionReturn(PETSC_SUCCESS);
5779: }
5781: /*
5782: This variable is used to prevent counting of MatAssemblyBegin() that
5783: are called from within a MatAssemblyEnd().
5784: */
5785: static PetscInt MatAssemblyEnd_InUse = 0;
5786: /*@
5787: MatAssemblyBegin - Begins assembling the matrix. This routine should
5788: be called after completing all calls to `MatSetValues()`.
5790: Collective
5792: Input Parameters:
5793: + mat - the matrix
5794: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5796: Level: beginner
5798: Notes:
5799: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5800: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5802: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5803: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5804: using the matrix.
5806: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5807: 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
5808: a global collective operation requiring all processes that share the matrix.
5810: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5811: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5812: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5814: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5815: @*/
5816: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5817: {
5818: PetscFunctionBegin;
5821: MatCheckPreallocated(mat, 1);
5822: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5823: if (mat->assembled) {
5824: mat->was_assembled = PETSC_TRUE;
5825: mat->assembled = PETSC_FALSE;
5826: }
5828: if (!MatAssemblyEnd_InUse) {
5829: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5830: PetscTryTypeMethod(mat, assemblybegin, type);
5831: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5832: } else PetscTryTypeMethod(mat, assemblybegin, type);
5833: PetscFunctionReturn(PETSC_SUCCESS);
5834: }
5836: /*@
5837: MatAssembled - Indicates if a matrix has been assembled and is ready for
5838: use; for example, in matrix-vector product.
5840: Not Collective
5842: Input Parameter:
5843: . mat - the matrix
5845: Output Parameter:
5846: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5848: Level: advanced
5850: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5851: @*/
5852: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5853: {
5854: PetscFunctionBegin;
5856: PetscAssertPointer(assembled, 2);
5857: *assembled = mat->assembled;
5858: PetscFunctionReturn(PETSC_SUCCESS);
5859: }
5861: /*@
5862: MatAssemblyEnd - Completes assembling the matrix. This routine should
5863: be called after `MatAssemblyBegin()`.
5865: Collective
5867: Input Parameters:
5868: + mat - the matrix
5869: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5871: Options Database Keys:
5872: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5873: . -mat_view ::ascii_info_detail - Prints more detailed info
5874: . -mat_view - Prints matrix in ASCII format
5875: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
5876: . -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5877: . -display <name> - Sets display name (default is host)
5878: . -draw_pause <sec> - Sets number of seconds to pause after display
5879: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5880: . -viewer_socket_machine <machine> - Machine to use for socket
5881: . -viewer_socket_port <port> - Port number to use for socket
5882: - -mat_view binary:filename[:append] - Save matrix to file in binary format
5884: Level: beginner
5886: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5887: @*/
5888: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5889: {
5890: static PetscInt inassm = 0;
5891: PetscBool flg = PETSC_FALSE;
5893: PetscFunctionBegin;
5897: inassm++;
5898: MatAssemblyEnd_InUse++;
5899: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5900: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5901: PetscTryTypeMethod(mat, assemblyend, type);
5902: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5903: } else PetscTryTypeMethod(mat, assemblyend, type);
5905: /* Flush assembly is not a true assembly */
5906: if (type != MAT_FLUSH_ASSEMBLY) {
5907: if (mat->num_ass) {
5908: if (!mat->symmetry_eternal) {
5909: mat->symmetric = PETSC_BOOL3_UNKNOWN;
5910: mat->hermitian = PETSC_BOOL3_UNKNOWN;
5911: }
5912: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5913: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5914: }
5915: mat->num_ass++;
5916: mat->assembled = PETSC_TRUE;
5917: mat->ass_nonzerostate = mat->nonzerostate;
5918: }
5920: mat->insertmode = NOT_SET_VALUES;
5921: MatAssemblyEnd_InUse--;
5922: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5923: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5924: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
5926: if (mat->checksymmetryonassembly) {
5927: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5928: if (flg) {
5929: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5930: } else {
5931: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5932: }
5933: }
5934: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5935: }
5936: inassm--;
5937: PetscFunctionReturn(PETSC_SUCCESS);
5938: }
5940: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5941: /*@
5942: MatSetOption - Sets a parameter option for a matrix. Some options
5943: may be specific to certain storage formats. Some options
5944: determine how values will be inserted (or added). Sorted,
5945: row-oriented input will generally assemble the fastest. The default
5946: is row-oriented.
5948: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
5950: Input Parameters:
5951: + mat - the matrix
5952: . op - the option, one of those listed below (and possibly others),
5953: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
5955: Options Describing Matrix Structure:
5956: + `MAT_SPD` - symmetric positive definite
5957: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
5958: . `MAT_HERMITIAN` - transpose is the complex conjugation
5959: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
5960: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5961: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5962: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
5964: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5965: do not need to be computed (usually at a high cost)
5967: Options For Use with `MatSetValues()`:
5968: Insert a logically dense subblock, which can be
5969: . `MAT_ROW_ORIENTED` - row-oriented (default)
5971: These options reflect the data you pass in with `MatSetValues()`; it has
5972: nothing to do with how the data is stored internally in the matrix
5973: data structure.
5975: When (re)assembling a matrix, we can restrict the input for
5976: efficiency/debugging purposes. These options include
5977: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
5978: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
5979: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
5980: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
5981: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
5982: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
5983: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5984: performance for very large process counts.
5985: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
5986: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5987: functions, instead sending only neighbor messages.
5989: Level: intermediate
5991: Notes:
5992: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
5994: Some options are relevant only for particular matrix types and
5995: are thus ignored by others. Other options are not supported by
5996: certain matrix types and will generate an error message if set.
5998: If using Fortran to compute a matrix, one may need to
5999: use the column-oriented option (or convert to the row-oriented
6000: format).
6002: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6003: that would generate a new entry in the nonzero structure is instead
6004: ignored. Thus, if memory has not already been allocated for this particular
6005: data, then the insertion is ignored. For dense matrices, in which
6006: the entire array is allocated, no entries are ever ignored.
6007: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6009: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6010: that would generate a new entry in the nonzero structure instead produces
6011: 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
6013: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6014: that would generate a new entry that has not been preallocated will
6015: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6016: only.) This is a useful flag when debugging matrix memory preallocation.
6017: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6019: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6020: other processors should be dropped, rather than stashed.
6021: This is useful if you know that the "owning" processor is also
6022: always generating the correct matrix entries, so that PETSc need
6023: not transfer duplicate entries generated on another processor.
6025: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6026: searches during matrix assembly. When this flag is set, the hash table
6027: is created during the first matrix assembly. This hash table is
6028: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6029: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6030: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6031: supported by `MATMPIBAIJ` format only.
6033: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6034: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6036: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6037: a zero location in the matrix
6039: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6041: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6042: zero row routines and thus improves performance for very large process counts.
6044: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6045: part of the matrix (since they should match the upper triangular part).
6047: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6048: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6049: with finite difference schemes with non-periodic boundary conditions.
6051: Developer Note:
6052: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6053: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6054: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6055: not changed.
6057: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6058: @*/
6059: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6060: {
6061: PetscFunctionBegin;
6063: if (op > 0) {
6066: }
6068: 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);
6070: switch (op) {
6071: case MAT_FORCE_DIAGONAL_ENTRIES:
6072: mat->force_diagonals = flg;
6073: PetscFunctionReturn(PETSC_SUCCESS);
6074: case MAT_NO_OFF_PROC_ENTRIES:
6075: mat->nooffprocentries = flg;
6076: PetscFunctionReturn(PETSC_SUCCESS);
6077: case MAT_SUBSET_OFF_PROC_ENTRIES:
6078: mat->assembly_subset = flg;
6079: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6080: #if !defined(PETSC_HAVE_MPIUNI)
6081: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6082: #endif
6083: mat->stash.first_assembly_done = PETSC_FALSE;
6084: }
6085: PetscFunctionReturn(PETSC_SUCCESS);
6086: case MAT_NO_OFF_PROC_ZERO_ROWS:
6087: mat->nooffproczerorows = flg;
6088: PetscFunctionReturn(PETSC_SUCCESS);
6089: case MAT_SPD:
6090: if (flg) {
6091: mat->spd = PETSC_BOOL3_TRUE;
6092: mat->symmetric = PETSC_BOOL3_TRUE;
6093: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6094: #if !defined(PETSC_USE_COMPLEX)
6095: mat->hermitian = PETSC_BOOL3_TRUE;
6096: #endif
6097: } else {
6098: mat->spd = PETSC_BOOL3_FALSE;
6099: }
6100: break;
6101: case MAT_SYMMETRIC:
6102: mat->symmetric = PetscBoolToBool3(flg);
6103: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6104: #if !defined(PETSC_USE_COMPLEX)
6105: mat->hermitian = PetscBoolToBool3(flg);
6106: #endif
6107: break;
6108: case MAT_HERMITIAN:
6109: mat->hermitian = PetscBoolToBool3(flg);
6110: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6111: #if !defined(PETSC_USE_COMPLEX)
6112: mat->symmetric = PetscBoolToBool3(flg);
6113: #endif
6114: break;
6115: case MAT_STRUCTURALLY_SYMMETRIC:
6116: mat->structurally_symmetric = PetscBoolToBool3(flg);
6117: break;
6118: case MAT_SYMMETRY_ETERNAL:
6119: 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");
6120: mat->symmetry_eternal = flg;
6121: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6122: break;
6123: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6124: 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");
6125: mat->structural_symmetry_eternal = flg;
6126: break;
6127: case MAT_SPD_ETERNAL:
6128: 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");
6129: mat->spd_eternal = flg;
6130: if (flg) {
6131: mat->structural_symmetry_eternal = PETSC_TRUE;
6132: mat->symmetry_eternal = PETSC_TRUE;
6133: }
6134: break;
6135: case MAT_STRUCTURE_ONLY:
6136: mat->structure_only = flg;
6137: break;
6138: case MAT_SORTED_FULL:
6139: mat->sortedfull = flg;
6140: break;
6141: default:
6142: break;
6143: }
6144: PetscTryTypeMethod(mat, setoption, op, flg);
6145: PetscFunctionReturn(PETSC_SUCCESS);
6146: }
6148: /*@
6149: MatGetOption - Gets a parameter option that has been set for a matrix.
6151: Logically Collective
6153: Input Parameters:
6154: + mat - the matrix
6155: - op - the option, this only responds to certain options, check the code for which ones
6157: Output Parameter:
6158: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6160: Level: intermediate
6162: Notes:
6163: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6165: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6166: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6168: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6169: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6170: @*/
6171: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6172: {
6173: PetscFunctionBegin;
6177: 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);
6178: 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()");
6180: switch (op) {
6181: case MAT_NO_OFF_PROC_ENTRIES:
6182: *flg = mat->nooffprocentries;
6183: break;
6184: case MAT_NO_OFF_PROC_ZERO_ROWS:
6185: *flg = mat->nooffproczerorows;
6186: break;
6187: case MAT_SYMMETRIC:
6188: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6189: break;
6190: case MAT_HERMITIAN:
6191: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6192: break;
6193: case MAT_STRUCTURALLY_SYMMETRIC:
6194: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6195: break;
6196: case MAT_SPD:
6197: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6198: break;
6199: case MAT_SYMMETRY_ETERNAL:
6200: *flg = mat->symmetry_eternal;
6201: break;
6202: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6203: *flg = mat->symmetry_eternal;
6204: break;
6205: default:
6206: break;
6207: }
6208: PetscFunctionReturn(PETSC_SUCCESS);
6209: }
6211: /*@
6212: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6213: this routine retains the old nonzero structure.
6215: Logically Collective
6217: Input Parameter:
6218: . mat - the matrix
6220: Level: intermediate
6222: Note:
6223: 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.
6224: See the Performance chapter of the users manual for information on preallocating matrices.
6226: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6227: @*/
6228: PetscErrorCode MatZeroEntries(Mat mat)
6229: {
6230: PetscFunctionBegin;
6233: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6234: 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");
6235: MatCheckPreallocated(mat, 1);
6237: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6238: PetscUseTypeMethod(mat, zeroentries);
6239: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6240: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6241: PetscFunctionReturn(PETSC_SUCCESS);
6242: }
6244: /*@
6245: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6246: of a set of rows and columns of a matrix.
6248: Collective
6250: Input Parameters:
6251: + mat - the matrix
6252: . numRows - the number of rows/columns to zero
6253: . rows - the global row indices
6254: . diag - value put in the diagonal of the eliminated rows
6255: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6256: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6258: Level: intermediate
6260: Notes:
6261: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6263: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6264: 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
6266: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6267: Krylov method to take advantage of the known solution on the zeroed rows.
6269: For the parallel case, all processes that share the matrix (i.e.,
6270: those in the communicator used for matrix creation) MUST call this
6271: routine, regardless of whether any rows being zeroed are owned by
6272: them.
6274: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6275: 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
6276: missing.
6278: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6279: list only rows local to itself).
6281: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6283: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6284: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6285: @*/
6286: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6287: {
6288: PetscFunctionBegin;
6291: if (numRows) PetscAssertPointer(rows, 3);
6292: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6293: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6294: MatCheckPreallocated(mat, 1);
6296: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6297: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6298: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6299: PetscFunctionReturn(PETSC_SUCCESS);
6300: }
6302: /*@
6303: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6304: of a set of rows and columns of a matrix.
6306: Collective
6308: Input Parameters:
6309: + mat - the matrix
6310: . is - the rows to zero
6311: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6312: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6313: - b - optional vector of right-hand side, that will be adjusted by provided solution
6315: Level: intermediate
6317: Note:
6318: See `MatZeroRowsColumns()` for details on how this routine operates.
6320: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6321: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6322: @*/
6323: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6324: {
6325: PetscInt numRows;
6326: const PetscInt *rows;
6328: PetscFunctionBegin;
6333: PetscCall(ISGetLocalSize(is, &numRows));
6334: PetscCall(ISGetIndices(is, &rows));
6335: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6336: PetscCall(ISRestoreIndices(is, &rows));
6337: PetscFunctionReturn(PETSC_SUCCESS);
6338: }
6340: /*@
6341: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6342: of a set of rows of a matrix.
6344: Collective
6346: Input Parameters:
6347: + mat - the matrix
6348: . numRows - the number of rows to zero
6349: . rows - the global row indices
6350: . diag - value put in the diagonal of the zeroed rows
6351: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6352: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6354: Level: intermediate
6356: Notes:
6357: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6359: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6361: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6362: Krylov method to take advantage of the known solution on the zeroed rows.
6364: 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)
6365: from the matrix.
6367: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6368: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6369: formats this does not alter the nonzero structure.
6371: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6372: of the matrix is not changed the values are
6373: merely zeroed.
6375: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6376: formats can optionally remove the main diagonal entry from the
6377: nonzero structure as well, by passing 0.0 as the final argument).
6379: For the parallel case, all processes that share the matrix (i.e.,
6380: those in the communicator used for matrix creation) MUST call this
6381: routine, regardless of whether any rows being zeroed are owned by
6382: them.
6384: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6385: list only rows local to itself).
6387: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6388: owns that are to be zeroed. This saves a global synchronization in the implementation.
6390: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6391: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6392: @*/
6393: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6394: {
6395: PetscFunctionBegin;
6398: if (numRows) PetscAssertPointer(rows, 3);
6399: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6400: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6401: MatCheckPreallocated(mat, 1);
6403: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6404: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6405: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6406: PetscFunctionReturn(PETSC_SUCCESS);
6407: }
6409: /*@
6410: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6411: of a set of rows of a matrix indicated by an `IS`
6413: Collective
6415: Input Parameters:
6416: + mat - the matrix
6417: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6418: . diag - value put in all diagonals of eliminated rows
6419: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6420: - b - optional vector of right-hand side, that will be adjusted by provided solution
6422: Level: intermediate
6424: Note:
6425: See `MatZeroRows()` for details on how this routine operates.
6427: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6428: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6429: @*/
6430: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6431: {
6432: PetscInt numRows = 0;
6433: const PetscInt *rows = NULL;
6435: PetscFunctionBegin;
6438: if (is) {
6440: PetscCall(ISGetLocalSize(is, &numRows));
6441: PetscCall(ISGetIndices(is, &rows));
6442: }
6443: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6444: if (is) PetscCall(ISRestoreIndices(is, &rows));
6445: PetscFunctionReturn(PETSC_SUCCESS);
6446: }
6448: /*@
6449: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6450: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6452: Collective
6454: Input Parameters:
6455: + mat - the matrix
6456: . numRows - the number of rows to remove
6457: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6458: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6459: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6460: - b - optional vector of right-hand side, that will be adjusted by provided solution
6462: Level: intermediate
6464: Notes:
6465: See `MatZeroRows()` for details on how this routine operates.
6467: The grid coordinates are across the entire grid, not just the local portion
6469: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6470: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6471: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6472: `DM_BOUNDARY_PERIODIC` boundary type.
6474: 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
6475: a single value per point) you can skip filling those indices.
6477: Fortran Note:
6478: `idxm` and `idxn` should be declared as
6479: .vb
6480: MatStencil idxm(4, m)
6481: .ve
6482: and the values inserted using
6483: .vb
6484: idxm(MatStencil_i, 1) = i
6485: idxm(MatStencil_j, 1) = j
6486: idxm(MatStencil_k, 1) = k
6487: idxm(MatStencil_c, 1) = c
6488: etc
6489: .ve
6491: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6492: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6493: @*/
6494: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6495: {
6496: PetscInt dim = mat->stencil.dim;
6497: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6498: PetscInt *dims = mat->stencil.dims + 1;
6499: PetscInt *starts = mat->stencil.starts;
6500: PetscInt *dxm = (PetscInt *)rows;
6501: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6503: PetscFunctionBegin;
6506: if (numRows) PetscAssertPointer(rows, 3);
6508: PetscCall(PetscMalloc1(numRows, &jdxm));
6509: for (i = 0; i < numRows; ++i) {
6510: /* Skip unused dimensions (they are ordered k, j, i, c) */
6511: for (j = 0; j < 3 - sdim; ++j) dxm++;
6512: /* Local index in X dir */
6513: tmp = *dxm++ - starts[0];
6514: /* Loop over remaining dimensions */
6515: for (j = 0; j < dim - 1; ++j) {
6516: /* If nonlocal, set index to be negative */
6517: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6518: /* Update local index */
6519: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6520: }
6521: /* Skip component slot if necessary */
6522: if (mat->stencil.noc) dxm++;
6523: /* Local row number */
6524: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6525: }
6526: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6527: PetscCall(PetscFree(jdxm));
6528: PetscFunctionReturn(PETSC_SUCCESS);
6529: }
6531: /*@
6532: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6533: of a set of rows and columns of a matrix.
6535: Collective
6537: Input Parameters:
6538: + mat - the matrix
6539: . numRows - the number of rows/columns to remove
6540: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6541: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6542: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6543: - b - optional vector of right-hand side, that will be adjusted by provided solution
6545: Level: intermediate
6547: Notes:
6548: See `MatZeroRowsColumns()` for details on how this routine operates.
6550: The grid coordinates are across the entire grid, not just the local portion
6552: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6553: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6554: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6555: `DM_BOUNDARY_PERIODIC` boundary type.
6557: 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
6558: a single value per point) you can skip filling those indices.
6560: Fortran Note:
6561: `idxm` and `idxn` should be declared as
6562: .vb
6563: MatStencil idxm(4, m)
6564: .ve
6565: and the values inserted using
6566: .vb
6567: idxm(MatStencil_i, 1) = i
6568: idxm(MatStencil_j, 1) = j
6569: idxm(MatStencil_k, 1) = k
6570: idxm(MatStencil_c, 1) = c
6571: etc
6572: .ve
6574: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6575: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6576: @*/
6577: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6578: {
6579: PetscInt dim = mat->stencil.dim;
6580: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6581: PetscInt *dims = mat->stencil.dims + 1;
6582: PetscInt *starts = mat->stencil.starts;
6583: PetscInt *dxm = (PetscInt *)rows;
6584: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6586: PetscFunctionBegin;
6589: if (numRows) PetscAssertPointer(rows, 3);
6591: PetscCall(PetscMalloc1(numRows, &jdxm));
6592: for (i = 0; i < numRows; ++i) {
6593: /* Skip unused dimensions (they are ordered k, j, i, c) */
6594: for (j = 0; j < 3 - sdim; ++j) dxm++;
6595: /* Local index in X dir */
6596: tmp = *dxm++ - starts[0];
6597: /* Loop over remaining dimensions */
6598: for (j = 0; j < dim - 1; ++j) {
6599: /* If nonlocal, set index to be negative */
6600: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6601: /* Update local index */
6602: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6603: }
6604: /* Skip component slot if necessary */
6605: if (mat->stencil.noc) dxm++;
6606: /* Local row number */
6607: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6608: }
6609: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6610: PetscCall(PetscFree(jdxm));
6611: PetscFunctionReturn(PETSC_SUCCESS);
6612: }
6614: /*@
6615: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6616: of a set of rows of a matrix; using local numbering of rows.
6618: Collective
6620: Input Parameters:
6621: + mat - the matrix
6622: . numRows - the number of rows to remove
6623: . rows - the local row indices
6624: . diag - value put in all diagonals of eliminated rows
6625: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6626: - b - optional vector of right-hand side, that will be adjusted by provided solution
6628: Level: intermediate
6630: Notes:
6631: Before calling `MatZeroRowsLocal()`, the user must first set the
6632: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6634: See `MatZeroRows()` for details on how this routine operates.
6636: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6637: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6638: @*/
6639: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6640: {
6641: PetscFunctionBegin;
6644: if (numRows) PetscAssertPointer(rows, 3);
6645: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6646: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6647: MatCheckPreallocated(mat, 1);
6649: if (mat->ops->zerorowslocal) {
6650: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6651: } else {
6652: IS is, newis;
6653: PetscInt *newRows, nl = 0;
6655: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6656: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6657: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6658: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6659: for (PetscInt i = 0; i < numRows; i++)
6660: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6661: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
6662: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6663: PetscCall(ISDestroy(&newis));
6664: PetscCall(ISDestroy(&is));
6665: }
6666: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6667: PetscFunctionReturn(PETSC_SUCCESS);
6668: }
6670: /*@
6671: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6672: of a set of rows of a matrix; using local numbering of rows.
6674: Collective
6676: Input Parameters:
6677: + mat - the matrix
6678: . is - index set of rows to remove
6679: . diag - value put in all diagonals of eliminated rows
6680: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6681: - b - optional vector of right-hand side, that will be adjusted by provided solution
6683: Level: intermediate
6685: Notes:
6686: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6687: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6689: See `MatZeroRows()` for details on how this routine operates.
6691: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6692: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6693: @*/
6694: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6695: {
6696: PetscInt numRows;
6697: const PetscInt *rows;
6699: PetscFunctionBegin;
6703: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6704: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6705: MatCheckPreallocated(mat, 1);
6707: PetscCall(ISGetLocalSize(is, &numRows));
6708: PetscCall(ISGetIndices(is, &rows));
6709: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6710: PetscCall(ISRestoreIndices(is, &rows));
6711: PetscFunctionReturn(PETSC_SUCCESS);
6712: }
6714: /*@
6715: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6716: of a set of rows and columns of a matrix; using local numbering of rows.
6718: Collective
6720: Input Parameters:
6721: + mat - the matrix
6722: . numRows - the number of rows to remove
6723: . rows - the global row indices
6724: . diag - value put in all diagonals of eliminated rows
6725: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6726: - b - optional vector of right-hand side, that will be adjusted by provided solution
6728: Level: intermediate
6730: Notes:
6731: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6732: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6734: See `MatZeroRowsColumns()` for details on how this routine operates.
6736: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6737: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6738: @*/
6739: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6740: {
6741: PetscFunctionBegin;
6744: if (numRows) PetscAssertPointer(rows, 3);
6745: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6746: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6747: MatCheckPreallocated(mat, 1);
6749: if (mat->ops->zerorowscolumnslocal) {
6750: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
6751: } else {
6752: IS is, newis;
6753: PetscInt *newRows, nl = 0;
6755: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6756: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6757: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6758: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6759: for (PetscInt i = 0; i < numRows; i++)
6760: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6761: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
6762: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6763: PetscCall(ISDestroy(&newis));
6764: PetscCall(ISDestroy(&is));
6765: }
6766: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6767: PetscFunctionReturn(PETSC_SUCCESS);
6768: }
6770: /*@
6771: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6772: of a set of rows and columns of a matrix; using local numbering of rows.
6774: Collective
6776: Input Parameters:
6777: + mat - the matrix
6778: . is - index set of rows to remove
6779: . diag - value put in all diagonals of eliminated rows
6780: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6781: - b - optional vector of right-hand side, that will be adjusted by provided solution
6783: Level: intermediate
6785: Notes:
6786: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6787: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6789: See `MatZeroRowsColumns()` for details on how this routine operates.
6791: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6792: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6793: @*/
6794: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6795: {
6796: PetscInt numRows;
6797: const PetscInt *rows;
6799: PetscFunctionBegin;
6803: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6804: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6805: MatCheckPreallocated(mat, 1);
6807: PetscCall(ISGetLocalSize(is, &numRows));
6808: PetscCall(ISGetIndices(is, &rows));
6809: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6810: PetscCall(ISRestoreIndices(is, &rows));
6811: PetscFunctionReturn(PETSC_SUCCESS);
6812: }
6814: /*@
6815: MatGetSize - Returns the numbers of rows and columns in a matrix.
6817: Not Collective
6819: Input Parameter:
6820: . mat - the matrix
6822: Output Parameters:
6823: + m - the number of global rows
6824: - n - the number of global columns
6826: Level: beginner
6828: Note:
6829: Both output parameters can be `NULL` on input.
6831: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6832: @*/
6833: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6834: {
6835: PetscFunctionBegin;
6837: if (m) *m = mat->rmap->N;
6838: if (n) *n = mat->cmap->N;
6839: PetscFunctionReturn(PETSC_SUCCESS);
6840: }
6842: /*@
6843: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6844: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6846: Not Collective
6848: Input Parameter:
6849: . mat - the matrix
6851: Output Parameters:
6852: + m - the number of local rows, use `NULL` to not obtain this value
6853: - n - the number of local columns, use `NULL` to not obtain this value
6855: Level: beginner
6857: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6858: @*/
6859: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6860: {
6861: PetscFunctionBegin;
6863: if (m) PetscAssertPointer(m, 2);
6864: if (n) PetscAssertPointer(n, 3);
6865: if (m) *m = mat->rmap->n;
6866: if (n) *n = mat->cmap->n;
6867: PetscFunctionReturn(PETSC_SUCCESS);
6868: }
6870: /*@
6871: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6872: vector one multiplies this matrix by that are owned by this processor.
6874: Not Collective, unless matrix has not been allocated, then collective
6876: Input Parameter:
6877: . mat - the matrix
6879: Output Parameters:
6880: + m - the global index of the first local column, use `NULL` to not obtain this value
6881: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
6883: Level: developer
6885: Notes:
6886: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6888: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6889: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6891: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6892: the local values in the matrix.
6894: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6895: Layouts](sec_matlayout) for details on matrix layouts.
6897: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6898: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6899: @*/
6900: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6901: {
6902: PetscFunctionBegin;
6905: if (m) PetscAssertPointer(m, 2);
6906: if (n) PetscAssertPointer(n, 3);
6907: MatCheckPreallocated(mat, 1);
6908: if (m) *m = mat->cmap->rstart;
6909: if (n) *n = mat->cmap->rend;
6910: PetscFunctionReturn(PETSC_SUCCESS);
6911: }
6913: /*@
6914: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6915: this MPI process.
6917: Not Collective
6919: Input Parameter:
6920: . mat - the matrix
6922: Output Parameters:
6923: + m - the global index of the first local row, use `NULL` to not obtain this value
6924: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
6926: Level: beginner
6928: Notes:
6929: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6931: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6932: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6934: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6935: the local values in the matrix.
6937: The high argument is one more than the last element stored locally.
6939: For all matrices it returns the range of matrix rows associated with rows of a vector that
6940: would contain the result of a matrix vector product with this matrix. See [Matrix
6941: Layouts](sec_matlayout) for details on matrix layouts.
6943: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6944: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6945: @*/
6946: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6947: {
6948: PetscFunctionBegin;
6951: if (m) PetscAssertPointer(m, 2);
6952: if (n) PetscAssertPointer(n, 3);
6953: MatCheckPreallocated(mat, 1);
6954: if (m) *m = mat->rmap->rstart;
6955: if (n) *n = mat->rmap->rend;
6956: PetscFunctionReturn(PETSC_SUCCESS);
6957: }
6959: /*@C
6960: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6961: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
6963: Not Collective, unless matrix has not been allocated
6965: Input Parameter:
6966: . mat - the matrix
6968: Output Parameter:
6969: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6970: where `size` is the number of MPI processes used by `mat`
6972: Level: beginner
6974: Notes:
6975: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6977: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6978: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6980: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6981: the local values in the matrix.
6983: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
6984: would contain the result of a matrix vector product with this matrix. See [Matrix
6985: Layouts](sec_matlayout) for details on matrix layouts.
6987: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6988: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6989: `DMDAGetGhostCorners()`, `DM`
6990: @*/
6991: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6992: {
6993: PetscFunctionBegin;
6996: MatCheckPreallocated(mat, 1);
6997: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6998: PetscFunctionReturn(PETSC_SUCCESS);
6999: }
7001: /*@C
7002: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
7003: vector one multiplies this vector by that are owned by each processor.
7005: Not Collective, unless matrix has not been allocated
7007: Input Parameter:
7008: . mat - the matrix
7010: Output Parameter:
7011: . ranges - start of each processors portion plus one more than the total length at the end
7013: Level: beginner
7015: Notes:
7016: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7018: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7019: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7021: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7022: the local values in the matrix.
7024: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7025: Layouts](sec_matlayout) for details on matrix layouts.
7027: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7028: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7029: `DMDAGetGhostCorners()`, `DM`
7030: @*/
7031: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7032: {
7033: PetscFunctionBegin;
7036: MatCheckPreallocated(mat, 1);
7037: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7038: PetscFunctionReturn(PETSC_SUCCESS);
7039: }
7041: /*@
7042: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7044: Not Collective
7046: Input Parameter:
7047: . A - matrix
7049: Output Parameters:
7050: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7051: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7053: Level: intermediate
7055: Note:
7056: You should call `ISDestroy()` on the returned `IS`
7058: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7059: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7060: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7061: details on matrix layouts.
7063: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7064: @*/
7065: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7066: {
7067: PetscErrorCode (*f)(Mat, IS *, IS *);
7069: PetscFunctionBegin;
7072: MatCheckPreallocated(A, 1);
7073: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7074: if (f) {
7075: PetscCall((*f)(A, rows, cols));
7076: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7077: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7078: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7079: }
7080: PetscFunctionReturn(PETSC_SUCCESS);
7081: }
7083: /*@
7084: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7085: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7086: to complete the factorization.
7088: Collective
7090: Input Parameters:
7091: + fact - the factorized matrix obtained with `MatGetFactor()`
7092: . mat - the matrix
7093: . row - row permutation
7094: . col - column permutation
7095: - info - structure containing
7096: .vb
7097: levels - number of levels of fill.
7098: expected fill - as ratio of original fill.
7099: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7100: missing diagonal entries)
7101: .ve
7103: Level: developer
7105: Notes:
7106: See [Matrix Factorization](sec_matfactor) for additional information.
7108: Most users should employ the `KSP` interface for linear solvers
7109: instead of working directly with matrix algebra routines such as this.
7110: See, e.g., `KSPCreate()`.
7112: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7114: Fortran Note:
7115: A valid (non-null) `info` argument must be provided
7117: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7118: `MatGetOrdering()`, `MatFactorInfo`
7119: @*/
7120: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7121: {
7122: PetscFunctionBegin;
7127: PetscAssertPointer(info, 5);
7128: PetscAssertPointer(fact, 1);
7129: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7130: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7131: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7132: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7133: MatCheckPreallocated(mat, 2);
7135: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7136: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7137: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7138: PetscFunctionReturn(PETSC_SUCCESS);
7139: }
7141: /*@
7142: MatICCFactorSymbolic - Performs symbolic incomplete
7143: Cholesky factorization for a symmetric matrix. Use
7144: `MatCholeskyFactorNumeric()` to complete the factorization.
7146: Collective
7148: Input Parameters:
7149: + fact - the factorized matrix obtained with `MatGetFactor()`
7150: . mat - the matrix to be factored
7151: . perm - row and column permutation
7152: - info - structure containing
7153: .vb
7154: levels - number of levels of fill.
7155: expected fill - as ratio of original fill.
7156: .ve
7158: Level: developer
7160: Notes:
7161: Most users should employ the `KSP` interface for linear solvers
7162: instead of working directly with matrix algebra routines such as this.
7163: See, e.g., `KSPCreate()`.
7165: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7167: Fortran Note:
7168: A valid (non-null) `info` argument must be provided
7170: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7171: @*/
7172: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7173: {
7174: PetscFunctionBegin;
7178: PetscAssertPointer(info, 4);
7179: PetscAssertPointer(fact, 1);
7180: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7181: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7182: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7183: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7184: MatCheckPreallocated(mat, 2);
7186: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7187: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7188: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7189: PetscFunctionReturn(PETSC_SUCCESS);
7190: }
7192: /*@C
7193: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7194: points to an array of valid matrices, they may be reused to store the new
7195: submatrices.
7197: Collective
7199: Input Parameters:
7200: + mat - the matrix
7201: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7202: . irow - index set of rows to extract
7203: . icol - index set of columns to extract
7204: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7206: Output Parameter:
7207: . submat - the array of submatrices
7209: Level: advanced
7211: Notes:
7212: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7213: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7214: to extract a parallel submatrix.
7216: Some matrix types place restrictions on the row and column
7217: indices, such as that they be sorted or that they be equal to each other.
7219: The index sets may not have duplicate entries.
7221: When extracting submatrices from a parallel matrix, each processor can
7222: form a different submatrix by setting the rows and columns of its
7223: individual index sets according to the local submatrix desired.
7225: When finished using the submatrices, the user should destroy
7226: them with `MatDestroySubMatrices()`.
7228: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7229: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7231: This routine creates the matrices in submat; you should NOT create them before
7232: calling it. It also allocates the array of matrix pointers submat.
7234: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7235: request one row/column in a block, they must request all rows/columns that are in
7236: that block. For example, if the block size is 2 you cannot request just row 0 and
7237: column 0.
7239: Fortran Note:
7240: .vb
7241: Mat, pointer :: submat(:)
7242: .ve
7244: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7245: @*/
7246: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7247: {
7248: PetscInt i;
7249: PetscBool eq;
7251: PetscFunctionBegin;
7254: if (n) {
7255: PetscAssertPointer(irow, 3);
7257: PetscAssertPointer(icol, 4);
7259: }
7260: PetscAssertPointer(submat, 6);
7261: if (n && scall == MAT_REUSE_MATRIX) {
7262: PetscAssertPointer(*submat, 6);
7264: }
7265: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7266: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7267: MatCheckPreallocated(mat, 1);
7268: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7269: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7270: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7271: for (i = 0; i < n; i++) {
7272: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7273: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7274: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7275: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7276: if (mat->boundtocpu && mat->bindingpropagates) {
7277: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7278: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7279: }
7280: #endif
7281: }
7282: PetscFunctionReturn(PETSC_SUCCESS);
7283: }
7285: /*@C
7286: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7288: Collective
7290: Input Parameters:
7291: + mat - the matrix
7292: . n - the number of submatrixes to be extracted
7293: . irow - index set of rows to extract
7294: . icol - index set of columns to extract
7295: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7297: Output Parameter:
7298: . submat - the array of submatrices
7300: Level: advanced
7302: Note:
7303: This is used by `PCGASM`
7305: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7306: @*/
7307: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7308: {
7309: PetscInt i;
7310: PetscBool eq;
7312: PetscFunctionBegin;
7315: if (n) {
7316: PetscAssertPointer(irow, 3);
7318: PetscAssertPointer(icol, 4);
7320: }
7321: PetscAssertPointer(submat, 6);
7322: if (n && scall == MAT_REUSE_MATRIX) {
7323: PetscAssertPointer(*submat, 6);
7325: }
7326: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7327: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7328: MatCheckPreallocated(mat, 1);
7330: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7331: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7332: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7333: for (i = 0; i < n; i++) {
7334: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7335: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7336: }
7337: PetscFunctionReturn(PETSC_SUCCESS);
7338: }
7340: /*@C
7341: MatDestroyMatrices - Destroys an array of matrices
7343: Collective
7345: Input Parameters:
7346: + n - the number of local matrices
7347: - mat - the matrices (this is a pointer to the array of matrices)
7349: Level: advanced
7351: Notes:
7352: Frees not only the matrices, but also the array that contains the matrices
7354: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7356: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7357: @*/
7358: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7359: {
7360: PetscInt i;
7362: PetscFunctionBegin;
7363: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7364: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7365: PetscAssertPointer(mat, 2);
7367: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7369: /* memory is allocated even if n = 0 */
7370: PetscCall(PetscFree(*mat));
7371: PetscFunctionReturn(PETSC_SUCCESS);
7372: }
7374: /*@C
7375: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7377: Collective
7379: Input Parameters:
7380: + n - the number of local matrices
7381: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7383: Level: advanced
7385: Note:
7386: Frees not only the matrices, but also the array that contains the matrices
7388: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7389: @*/
7390: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7391: {
7392: Mat mat0;
7394: PetscFunctionBegin;
7395: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7396: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7397: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7398: PetscAssertPointer(mat, 2);
7400: mat0 = (*mat)[0];
7401: if (mat0 && mat0->ops->destroysubmatrices) {
7402: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7403: } else {
7404: PetscCall(MatDestroyMatrices(n, mat));
7405: }
7406: PetscFunctionReturn(PETSC_SUCCESS);
7407: }
7409: /*@
7410: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7412: Collective
7414: Input Parameter:
7415: . mat - the matrix
7417: Output Parameter:
7418: . matstruct - the sequential matrix with the nonzero structure of `mat`
7420: Level: developer
7422: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7423: @*/
7424: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7425: {
7426: PetscFunctionBegin;
7428: PetscAssertPointer(matstruct, 2);
7431: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7432: MatCheckPreallocated(mat, 1);
7434: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7435: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7436: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7437: PetscFunctionReturn(PETSC_SUCCESS);
7438: }
7440: /*@C
7441: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7443: Collective
7445: Input Parameter:
7446: . mat - the matrix
7448: Level: advanced
7450: Note:
7451: This is not needed, one can just call `MatDestroy()`
7453: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7454: @*/
7455: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7456: {
7457: PetscFunctionBegin;
7458: PetscAssertPointer(mat, 1);
7459: PetscCall(MatDestroy(mat));
7460: PetscFunctionReturn(PETSC_SUCCESS);
7461: }
7463: /*@
7464: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7465: replaces the index sets by larger ones that represent submatrices with
7466: additional overlap.
7468: Collective
7470: Input Parameters:
7471: + mat - the matrix
7472: . n - the number of index sets
7473: . is - the array of index sets (these index sets will changed during the call)
7474: - ov - the additional overlap requested
7476: Options Database Key:
7477: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7479: Level: developer
7481: Note:
7482: The computed overlap preserves the matrix block sizes when the blocks are square.
7483: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7484: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7486: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7487: @*/
7488: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7489: {
7490: PetscInt i, bs, cbs;
7492: PetscFunctionBegin;
7496: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7497: if (n) {
7498: PetscAssertPointer(is, 3);
7500: }
7501: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7502: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7503: MatCheckPreallocated(mat, 1);
7505: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7506: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7507: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7508: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7509: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7510: if (bs == cbs) {
7511: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7512: }
7513: PetscFunctionReturn(PETSC_SUCCESS);
7514: }
7516: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7518: /*@
7519: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7520: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7521: additional overlap.
7523: Collective
7525: Input Parameters:
7526: + mat - the matrix
7527: . n - the number of index sets
7528: . is - the array of index sets (these index sets will changed during the call)
7529: - ov - the additional overlap requested
7531: ` Options Database Key:
7532: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7534: Level: developer
7536: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7537: @*/
7538: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7539: {
7540: PetscInt i;
7542: PetscFunctionBegin;
7545: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7546: if (n) {
7547: PetscAssertPointer(is, 3);
7549: }
7550: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7551: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7552: MatCheckPreallocated(mat, 1);
7553: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7554: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7555: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7556: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7557: PetscFunctionReturn(PETSC_SUCCESS);
7558: }
7560: /*@
7561: MatGetBlockSize - Returns the matrix block size.
7563: Not Collective
7565: Input Parameter:
7566: . mat - the matrix
7568: Output Parameter:
7569: . bs - block size
7571: Level: intermediate
7573: Notes:
7574: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7576: If the block size has not been set yet this routine returns 1.
7578: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7579: @*/
7580: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7581: {
7582: PetscFunctionBegin;
7584: PetscAssertPointer(bs, 2);
7585: *bs = mat->rmap->bs;
7586: PetscFunctionReturn(PETSC_SUCCESS);
7587: }
7589: /*@
7590: MatGetBlockSizes - Returns the matrix block row and column sizes.
7592: Not Collective
7594: Input Parameter:
7595: . mat - the matrix
7597: Output Parameters:
7598: + rbs - row block size
7599: - cbs - column block size
7601: Level: intermediate
7603: Notes:
7604: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7605: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7607: If a block size has not been set yet this routine returns 1.
7609: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7610: @*/
7611: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7612: {
7613: PetscFunctionBegin;
7615: if (rbs) PetscAssertPointer(rbs, 2);
7616: if (cbs) PetscAssertPointer(cbs, 3);
7617: if (rbs) *rbs = mat->rmap->bs;
7618: if (cbs) *cbs = mat->cmap->bs;
7619: PetscFunctionReturn(PETSC_SUCCESS);
7620: }
7622: /*@
7623: MatSetBlockSize - Sets the matrix block size.
7625: Logically Collective
7627: Input Parameters:
7628: + mat - the matrix
7629: - bs - block size
7631: Level: intermediate
7633: Notes:
7634: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7635: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7637: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7638: is compatible with the matrix local sizes.
7640: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7641: @*/
7642: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7643: {
7644: PetscFunctionBegin;
7647: PetscCall(MatSetBlockSizes(mat, bs, bs));
7648: PetscFunctionReturn(PETSC_SUCCESS);
7649: }
7651: typedef struct {
7652: PetscInt n;
7653: IS *is;
7654: Mat *mat;
7655: PetscObjectState nonzerostate;
7656: Mat C;
7657: } EnvelopeData;
7659: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7660: {
7661: EnvelopeData *edata = (EnvelopeData *)*ptr;
7663: PetscFunctionBegin;
7664: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7665: PetscCall(PetscFree(edata->is));
7666: PetscCall(PetscFree(edata));
7667: PetscFunctionReturn(PETSC_SUCCESS);
7668: }
7670: /*@
7671: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7672: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7674: Collective
7676: Input Parameter:
7677: . mat - the matrix
7679: Level: intermediate
7681: Notes:
7682: There can be zeros within the blocks
7684: The blocks can overlap between processes, including laying on more than two processes
7686: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7687: @*/
7688: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7689: {
7690: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7691: PetscInt *diag, *odiag, sc;
7692: VecScatter scatter;
7693: PetscScalar *seqv;
7694: const PetscScalar *parv;
7695: const PetscInt *ia, *ja;
7696: PetscBool set, flag, done;
7697: Mat AA = mat, A;
7698: MPI_Comm comm;
7699: PetscMPIInt rank, size, tag;
7700: MPI_Status status;
7701: PetscContainer container;
7702: EnvelopeData *edata;
7703: Vec seq, par;
7704: IS isglobal;
7706: PetscFunctionBegin;
7708: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7709: if (!set || !flag) {
7710: /* TODO: only needs nonzero structure of transpose */
7711: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7712: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7713: }
7714: PetscCall(MatAIJGetLocalMat(AA, &A));
7715: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7716: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7718: PetscCall(MatGetLocalSize(mat, &n, NULL));
7719: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7720: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7721: PetscCallMPI(MPI_Comm_size(comm, &size));
7722: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7724: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7726: if (rank > 0) {
7727: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7728: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7729: }
7730: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7731: for (i = 0; i < n; i++) {
7732: env = PetscMax(env, ja[ia[i + 1] - 1]);
7733: II = rstart + i;
7734: if (env == II) {
7735: starts[lblocks] = tbs;
7736: sizes[lblocks++] = 1 + II - tbs;
7737: tbs = 1 + II;
7738: }
7739: }
7740: if (rank < size - 1) {
7741: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7742: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7743: }
7745: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7746: if (!set || !flag) PetscCall(MatDestroy(&AA));
7747: PetscCall(MatDestroy(&A));
7749: PetscCall(PetscNew(&edata));
7750: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7751: edata->n = lblocks;
7752: /* create IS needed for extracting blocks from the original matrix */
7753: PetscCall(PetscMalloc1(lblocks, &edata->is));
7754: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7756: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7757: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7758: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7759: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7760: PetscCall(MatSetType(edata->C, MATAIJ));
7762: /* Communicate the start and end of each row, from each block to the correct rank */
7763: /* TODO: Use PetscSF instead of VecScatter */
7764: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7765: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7766: PetscCall(VecGetArrayWrite(seq, &seqv));
7767: for (PetscInt i = 0; i < lblocks; i++) {
7768: for (PetscInt j = 0; j < sizes[i]; j++) {
7769: seqv[cnt] = starts[i];
7770: seqv[cnt + 1] = starts[i] + sizes[i];
7771: cnt += 2;
7772: }
7773: }
7774: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7775: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7776: sc -= cnt;
7777: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7778: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7779: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7780: PetscCall(ISDestroy(&isglobal));
7781: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7782: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7783: PetscCall(VecScatterDestroy(&scatter));
7784: PetscCall(VecDestroy(&seq));
7785: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7786: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7787: PetscCall(VecGetArrayRead(par, &parv));
7788: cnt = 0;
7789: PetscCall(MatGetSize(mat, NULL, &n));
7790: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7791: PetscInt start, end, d = 0, od = 0;
7793: start = (PetscInt)PetscRealPart(parv[cnt]);
7794: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7795: cnt += 2;
7797: if (start < cstart) {
7798: od += cstart - start + n - cend;
7799: d += cend - cstart;
7800: } else if (start < cend) {
7801: od += n - cend;
7802: d += cend - start;
7803: } else od += n - start;
7804: if (end <= cstart) {
7805: od -= cstart - end + n - cend;
7806: d -= cend - cstart;
7807: } else if (end < cend) {
7808: od -= n - cend;
7809: d -= cend - end;
7810: } else od -= n - end;
7812: odiag[i] = od;
7813: diag[i] = d;
7814: }
7815: PetscCall(VecRestoreArrayRead(par, &parv));
7816: PetscCall(VecDestroy(&par));
7817: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7818: PetscCall(PetscFree2(diag, odiag));
7819: PetscCall(PetscFree2(sizes, starts));
7821: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7822: PetscCall(PetscContainerSetPointer(container, edata));
7823: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7824: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7825: PetscCall(PetscObjectDereference((PetscObject)container));
7826: PetscFunctionReturn(PETSC_SUCCESS);
7827: }
7829: /*@
7830: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7832: Collective
7834: Input Parameters:
7835: + A - the matrix
7836: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7838: Output Parameter:
7839: . C - matrix with inverted block diagonal of `A`
7841: Level: advanced
7843: Note:
7844: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7846: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7847: @*/
7848: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7849: {
7850: PetscContainer container;
7851: EnvelopeData *edata;
7852: PetscObjectState nonzerostate;
7854: PetscFunctionBegin;
7855: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7856: if (!container) {
7857: PetscCall(MatComputeVariableBlockEnvelope(A));
7858: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7859: }
7860: PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7861: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7862: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7863: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7865: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7866: *C = edata->C;
7868: for (PetscInt i = 0; i < edata->n; i++) {
7869: Mat D;
7870: PetscScalar *dvalues;
7872: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7873: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7874: PetscCall(MatSeqDenseInvert(D));
7875: PetscCall(MatDenseGetArray(D, &dvalues));
7876: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7877: PetscCall(MatDestroy(&D));
7878: }
7879: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7880: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7881: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7882: PetscFunctionReturn(PETSC_SUCCESS);
7883: }
7885: /*@
7886: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
7888: Not Collective
7890: Input Parameters:
7891: + mat - the matrix
7892: . nblocks - the number of blocks on this process, each block can only exist on a single process
7893: - bsizes - the block sizes
7895: Level: intermediate
7897: Notes:
7898: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
7900: 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.
7902: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7903: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7904: @*/
7905: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7906: {
7907: PetscInt ncnt = 0, nlocal;
7909: PetscFunctionBegin;
7911: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7912: 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);
7913: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7914: 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);
7915: PetscCall(PetscFree(mat->bsizes));
7916: mat->nblocks = nblocks;
7917: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7918: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7919: PetscFunctionReturn(PETSC_SUCCESS);
7920: }
7922: /*@C
7923: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
7925: Not Collective; No Fortran Support
7927: Input Parameter:
7928: . mat - the matrix
7930: Output Parameters:
7931: + nblocks - the number of blocks on this process
7932: - bsizes - the block sizes
7934: Level: intermediate
7936: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7937: @*/
7938: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7939: {
7940: PetscFunctionBegin;
7942: if (nblocks) *nblocks = mat->nblocks;
7943: if (bsizes) *bsizes = mat->bsizes;
7944: PetscFunctionReturn(PETSC_SUCCESS);
7945: }
7947: /*@
7948: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
7950: Not Collective
7952: Input Parameter:
7953: + subA - the submatrix
7954: . A - the original matrix
7955: - isrow - The `IS` of selected rows for the submatrix, must be sorted
7957: Level: developer
7959: Notes:
7960: If the index set is not sorted or contains off-process entries, this function will do nothing.
7962: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7963: @*/
7964: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
7965: {
7966: const PetscInt *rows;
7967: PetscInt n, rStart, rEnd, Nb = 0;
7968: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
7970: PetscFunctionBegin;
7971: // The code for block size extraction does not support an unsorted IS
7972: if (flg) PetscCall(ISSorted(isrow, &flg));
7973: // We don't support originally off-diagonal blocks
7974: if (flg) {
7975: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
7976: PetscCall(ISGetLocalSize(isrow, &n));
7977: PetscCall(ISGetIndices(isrow, &rows));
7978: for (PetscInt i = 0; i < n && flg; ++i) {
7979: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
7980: }
7981: PetscCall(ISRestoreIndices(isrow, &rows));
7982: }
7983: // quiet return if we can't extract block size
7984: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
7985: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
7987: // extract block sizes
7988: PetscCall(ISGetIndices(isrow, &rows));
7989: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
7990: PetscBool occupied = PETSC_FALSE;
7992: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
7993: const PetscInt row = gr + br;
7995: if (i == n) break;
7996: if (rows[i] == row) {
7997: occupied = PETSC_TRUE;
7998: ++i;
7999: }
8000: while (i < n && rows[i] < row) ++i;
8001: }
8002: gr += A->bsizes[b];
8003: if (occupied) ++Nb;
8004: }
8005: subA->nblocks = Nb;
8006: PetscCall(PetscFree(subA->bsizes));
8007: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
8008: PetscInt sb = 0;
8009: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8010: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
8011: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8012: const PetscInt row = gr + br;
8014: if (i == n) break;
8015: if (rows[i] == row) {
8016: ++subA->bsizes[sb];
8017: ++i;
8018: }
8019: while (i < n && rows[i] < row) ++i;
8020: }
8021: gr += A->bsizes[b];
8022: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8023: }
8024: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8025: PetscInt nlocal, ncnt = 0;
8026: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8027: 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);
8028: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8029: 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);
8030: PetscCall(ISRestoreIndices(isrow, &rows));
8031: PetscFunctionReturn(PETSC_SUCCESS);
8032: }
8034: /*@
8035: MatSetBlockSizes - Sets the matrix block row and column sizes.
8037: Logically Collective
8039: Input Parameters:
8040: + mat - the matrix
8041: . rbs - row block size
8042: - cbs - column block size
8044: Level: intermediate
8046: Notes:
8047: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8048: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8049: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8051: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8052: are compatible with the matrix local sizes.
8054: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8056: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8057: @*/
8058: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8059: {
8060: PetscFunctionBegin;
8064: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8065: if (mat->rmap->refcnt) {
8066: ISLocalToGlobalMapping l2g = NULL;
8067: PetscLayout nmap = NULL;
8069: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8070: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8071: PetscCall(PetscLayoutDestroy(&mat->rmap));
8072: mat->rmap = nmap;
8073: mat->rmap->mapping = l2g;
8074: }
8075: if (mat->cmap->refcnt) {
8076: ISLocalToGlobalMapping l2g = NULL;
8077: PetscLayout nmap = NULL;
8079: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8080: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8081: PetscCall(PetscLayoutDestroy(&mat->cmap));
8082: mat->cmap = nmap;
8083: mat->cmap->mapping = l2g;
8084: }
8085: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8086: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8087: PetscFunctionReturn(PETSC_SUCCESS);
8088: }
8090: /*@
8091: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8093: Logically Collective
8095: Input Parameters:
8096: + mat - the matrix
8097: . fromRow - matrix from which to copy row block size
8098: - fromCol - matrix from which to copy column block size (can be same as fromRow)
8100: Level: developer
8102: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8103: @*/
8104: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8105: {
8106: PetscFunctionBegin;
8110: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8111: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8112: PetscFunctionReturn(PETSC_SUCCESS);
8113: }
8115: /*@
8116: MatResidual - Default routine to calculate the residual r = b - Ax
8118: Collective
8120: Input Parameters:
8121: + mat - the matrix
8122: . b - the right-hand-side
8123: - x - the approximate solution
8125: Output Parameter:
8126: . r - location to store the residual
8128: Level: developer
8130: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8131: @*/
8132: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8133: {
8134: PetscFunctionBegin;
8140: MatCheckPreallocated(mat, 1);
8141: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8142: if (!mat->ops->residual) {
8143: PetscCall(MatMult(mat, x, r));
8144: PetscCall(VecAYPX(r, -1.0, b));
8145: } else {
8146: PetscUseTypeMethod(mat, residual, b, x, r);
8147: }
8148: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8149: PetscFunctionReturn(PETSC_SUCCESS);
8150: }
8152: /*@C
8153: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8155: Collective
8157: Input Parameters:
8158: + mat - the matrix
8159: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8160: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8161: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8162: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8163: always used.
8165: Output Parameters:
8166: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8167: . 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
8168: . ja - the column indices, use `NULL` if not needed
8169: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8170: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8172: Level: developer
8174: Notes:
8175: You CANNOT change any of the ia[] or ja[] values.
8177: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8179: Fortran Notes:
8180: Use
8181: .vb
8182: PetscInt, pointer :: ia(:),ja(:)
8183: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8184: ! Access the ith and jth entries via ia(i) and ja(j)
8185: .ve
8187: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8188: @*/
8189: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8190: {
8191: PetscFunctionBegin;
8194: if (n) PetscAssertPointer(n, 5);
8195: if (ia) PetscAssertPointer(ia, 6);
8196: if (ja) PetscAssertPointer(ja, 7);
8197: if (done) PetscAssertPointer(done, 8);
8198: MatCheckPreallocated(mat, 1);
8199: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8200: else {
8201: if (done) *done = PETSC_TRUE;
8202: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8203: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8204: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8205: }
8206: PetscFunctionReturn(PETSC_SUCCESS);
8207: }
8209: /*@C
8210: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8212: Collective
8214: Input Parameters:
8215: + mat - the matrix
8216: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8217: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8218: symmetrized
8219: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8220: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8221: always used.
8223: Output Parameters:
8224: + n - number of columns in the (possibly compressed) matrix
8225: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8226: . ja - the row indices
8227: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8229: Level: developer
8231: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8232: @*/
8233: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8234: {
8235: PetscFunctionBegin;
8238: PetscAssertPointer(n, 5);
8239: if (ia) PetscAssertPointer(ia, 6);
8240: if (ja) PetscAssertPointer(ja, 7);
8241: PetscAssertPointer(done, 8);
8242: MatCheckPreallocated(mat, 1);
8243: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8244: else {
8245: *done = PETSC_TRUE;
8246: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8247: }
8248: PetscFunctionReturn(PETSC_SUCCESS);
8249: }
8251: /*@C
8252: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8254: Collective
8256: Input Parameters:
8257: + mat - the matrix
8258: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8259: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8260: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8261: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8262: always used.
8263: . n - size of (possibly compressed) matrix
8264: . ia - the row pointers
8265: - ja - the column indices
8267: Output Parameter:
8268: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8270: Level: developer
8272: Note:
8273: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8274: us of the array after it has been restored. If you pass `NULL`, it will
8275: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8277: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8278: @*/
8279: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8280: {
8281: PetscFunctionBegin;
8284: if (ia) PetscAssertPointer(ia, 6);
8285: if (ja) PetscAssertPointer(ja, 7);
8286: if (done) PetscAssertPointer(done, 8);
8287: MatCheckPreallocated(mat, 1);
8289: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8290: else {
8291: if (done) *done = PETSC_TRUE;
8292: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8293: if (n) *n = 0;
8294: if (ia) *ia = NULL;
8295: if (ja) *ja = NULL;
8296: }
8297: PetscFunctionReturn(PETSC_SUCCESS);
8298: }
8300: /*@C
8301: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8303: Collective
8305: Input Parameters:
8306: + mat - the matrix
8307: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8308: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8309: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8310: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8311: always used.
8313: Output Parameters:
8314: + n - size of (possibly compressed) matrix
8315: . ia - the column pointers
8316: . ja - the row indices
8317: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8319: Level: developer
8321: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8322: @*/
8323: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8324: {
8325: PetscFunctionBegin;
8328: if (ia) PetscAssertPointer(ia, 6);
8329: if (ja) PetscAssertPointer(ja, 7);
8330: PetscAssertPointer(done, 8);
8331: MatCheckPreallocated(mat, 1);
8333: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8334: else {
8335: *done = PETSC_TRUE;
8336: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8337: if (n) *n = 0;
8338: if (ia) *ia = NULL;
8339: if (ja) *ja = NULL;
8340: }
8341: PetscFunctionReturn(PETSC_SUCCESS);
8342: }
8344: /*@
8345: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8346: `MatGetColumnIJ()`.
8348: Collective
8350: Input Parameters:
8351: + mat - the matrix
8352: . ncolors - maximum color value
8353: . n - number of entries in colorarray
8354: - colorarray - array indicating color for each column
8356: Output Parameter:
8357: . iscoloring - coloring generated using colorarray information
8359: Level: developer
8361: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8362: @*/
8363: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8364: {
8365: PetscFunctionBegin;
8368: PetscAssertPointer(colorarray, 4);
8369: PetscAssertPointer(iscoloring, 5);
8370: MatCheckPreallocated(mat, 1);
8372: if (!mat->ops->coloringpatch) {
8373: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8374: } else {
8375: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8376: }
8377: PetscFunctionReturn(PETSC_SUCCESS);
8378: }
8380: /*@
8381: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8383: Logically Collective
8385: Input Parameter:
8386: . mat - the factored matrix to be reset
8388: Level: developer
8390: Notes:
8391: This routine should be used only with factored matrices formed by in-place
8392: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8393: format). This option can save memory, for example, when solving nonlinear
8394: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8395: ILU(0) preconditioner.
8397: One can specify in-place ILU(0) factorization by calling
8398: .vb
8399: PCType(pc,PCILU);
8400: PCFactorSeUseInPlace(pc);
8401: .ve
8402: or by using the options -pc_type ilu -pc_factor_in_place
8404: In-place factorization ILU(0) can also be used as a local
8405: solver for the blocks within the block Jacobi or additive Schwarz
8406: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8407: for details on setting local solver options.
8409: Most users should employ the `KSP` interface for linear solvers
8410: instead of working directly with matrix algebra routines such as this.
8411: See, e.g., `KSPCreate()`.
8413: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8414: @*/
8415: PetscErrorCode MatSetUnfactored(Mat mat)
8416: {
8417: PetscFunctionBegin;
8420: MatCheckPreallocated(mat, 1);
8421: mat->factortype = MAT_FACTOR_NONE;
8422: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8423: PetscUseTypeMethod(mat, setunfactored);
8424: PetscFunctionReturn(PETSC_SUCCESS);
8425: }
8427: /*@
8428: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8429: as the original matrix.
8431: Collective
8433: Input Parameters:
8434: + mat - the original matrix
8435: . isrow - parallel `IS` containing the rows this processor should obtain
8436: . 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.
8437: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8439: Output Parameter:
8440: . newmat - the new submatrix, of the same type as the original matrix
8442: Level: advanced
8444: Notes:
8445: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8447: Some matrix types place restrictions on the row and column indices, such
8448: 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;
8449: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8451: The index sets may not have duplicate entries.
8453: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8454: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8455: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8456: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8457: you are finished using it.
8459: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8460: the input matrix.
8462: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8464: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8465: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8467: Example usage:
8468: Consider the following 8x8 matrix with 34 non-zero values, that is
8469: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8470: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8471: as follows
8472: .vb
8473: 1 2 0 | 0 3 0 | 0 4
8474: Proc0 0 5 6 | 7 0 0 | 8 0
8475: 9 0 10 | 11 0 0 | 12 0
8476: -------------------------------------
8477: 13 0 14 | 15 16 17 | 0 0
8478: Proc1 0 18 0 | 19 20 21 | 0 0
8479: 0 0 0 | 22 23 0 | 24 0
8480: -------------------------------------
8481: Proc2 25 26 27 | 0 0 28 | 29 0
8482: 30 0 0 | 31 32 33 | 0 34
8483: .ve
8485: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8487: .vb
8488: 2 0 | 0 3 0 | 0
8489: Proc0 5 6 | 7 0 0 | 8
8490: -------------------------------
8491: Proc1 18 0 | 19 20 21 | 0
8492: -------------------------------
8493: Proc2 26 27 | 0 0 28 | 29
8494: 0 0 | 31 32 33 | 0
8495: .ve
8497: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8498: @*/
8499: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8500: {
8501: PetscMPIInt size;
8502: Mat *local;
8503: IS iscoltmp;
8504: PetscBool flg;
8506: PetscFunctionBegin;
8510: PetscAssertPointer(newmat, 5);
8513: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8514: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8515: PetscCheck(cll != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_INPLACE_MATRIX");
8517: MatCheckPreallocated(mat, 1);
8518: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8520: if (!iscol || isrow == iscol) {
8521: PetscBool stride;
8522: PetscMPIInt grabentirematrix = 0, grab;
8523: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8524: if (stride) {
8525: PetscInt first, step, n, rstart, rend;
8526: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8527: if (step == 1) {
8528: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8529: if (rstart == first) {
8530: PetscCall(ISGetLocalSize(isrow, &n));
8531: if (n == rend - rstart) grabentirematrix = 1;
8532: }
8533: }
8534: }
8535: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8536: if (grab) {
8537: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8538: if (cll == MAT_INITIAL_MATRIX) {
8539: *newmat = mat;
8540: PetscCall(PetscObjectReference((PetscObject)mat));
8541: }
8542: PetscFunctionReturn(PETSC_SUCCESS);
8543: }
8544: }
8546: if (!iscol) {
8547: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8548: } else {
8549: iscoltmp = iscol;
8550: }
8552: /* if original matrix is on just one processor then use submatrix generated */
8553: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8554: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8555: goto setproperties;
8556: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8557: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8558: *newmat = *local;
8559: PetscCall(PetscFree(local));
8560: goto setproperties;
8561: } else if (!mat->ops->createsubmatrix) {
8562: /* Create a new matrix type that implements the operation using the full matrix */
8563: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8564: switch (cll) {
8565: case MAT_INITIAL_MATRIX:
8566: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8567: break;
8568: case MAT_REUSE_MATRIX:
8569: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8570: break;
8571: default:
8572: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8573: }
8574: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8575: goto setproperties;
8576: }
8578: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8579: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8580: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8582: setproperties:
8583: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8584: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8585: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8586: }
8587: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8588: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8589: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
8590: PetscFunctionReturn(PETSC_SUCCESS);
8591: }
8593: /*@
8594: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8596: Not Collective
8598: Input Parameters:
8599: + A - the matrix we wish to propagate options from
8600: - B - the matrix we wish to propagate options to
8602: Level: beginner
8604: Note:
8605: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8607: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8608: @*/
8609: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8610: {
8611: PetscFunctionBegin;
8614: B->symmetry_eternal = A->symmetry_eternal;
8615: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8616: B->symmetric = A->symmetric;
8617: B->structurally_symmetric = A->structurally_symmetric;
8618: B->spd = A->spd;
8619: B->hermitian = A->hermitian;
8620: PetscFunctionReturn(PETSC_SUCCESS);
8621: }
8623: /*@
8624: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8625: used during the assembly process to store values that belong to
8626: other processors.
8628: Not Collective
8630: Input Parameters:
8631: + mat - the matrix
8632: . size - the initial size of the stash.
8633: - bsize - the initial size of the block-stash(if used).
8635: Options Database Keys:
8636: + -matstash_initial_size <size> or <size0,size1,...sizep-1> - set initial size
8637: - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> - set initial block size
8639: Level: intermediate
8641: Notes:
8642: The block-stash is used for values set with `MatSetValuesBlocked()` while
8643: the stash is used for values set with `MatSetValues()`
8645: Run with the option -info and look for output of the form
8646: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8647: to determine the appropriate value, MM, to use for size and
8648: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8649: to determine the value, BMM to use for bsize
8651: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8652: @*/
8653: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8654: {
8655: PetscFunctionBegin;
8658: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8659: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8660: PetscFunctionReturn(PETSC_SUCCESS);
8661: }
8663: /*@
8664: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8665: the matrix
8667: Neighbor-wise Collective
8669: Input Parameters:
8670: + A - the matrix
8671: . x - the vector to be multiplied by the interpolation operator
8672: - y - the vector to be added to the result
8674: Output Parameter:
8675: . w - the resulting vector
8677: Level: intermediate
8679: Notes:
8680: `w` may be the same vector as `y`.
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 MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8688: {
8689: PetscInt M, N, Ny;
8691: PetscFunctionBegin;
8696: PetscCall(MatGetSize(A, &M, &N));
8697: PetscCall(VecGetSize(y, &Ny));
8698: if (M == Ny) {
8699: PetscCall(MatMultAdd(A, x, y, w));
8700: } else {
8701: PetscCall(MatMultTransposeAdd(A, x, y, w));
8702: }
8703: PetscFunctionReturn(PETSC_SUCCESS);
8704: }
8706: /*@
8707: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8708: the matrix
8710: Neighbor-wise Collective
8712: Input Parameters:
8713: + A - the matrix
8714: - x - the vector to be interpolated
8716: Output Parameter:
8717: . y - the resulting vector
8719: Level: intermediate
8721: Note:
8722: This allows one to use either the restriction or interpolation (its transpose)
8723: matrix to do the interpolation
8725: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8726: @*/
8727: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8728: {
8729: PetscInt M, N, Ny;
8731: PetscFunctionBegin;
8735: PetscCall(MatGetSize(A, &M, &N));
8736: PetscCall(VecGetSize(y, &Ny));
8737: if (M == Ny) {
8738: PetscCall(MatMult(A, x, y));
8739: } else {
8740: PetscCall(MatMultTranspose(A, x, y));
8741: }
8742: PetscFunctionReturn(PETSC_SUCCESS);
8743: }
8745: /*@
8746: MatRestrict - $y = A*x$ or $A^T*x$
8748: Neighbor-wise Collective
8750: Input Parameters:
8751: + A - the matrix
8752: - x - the vector to be restricted
8754: Output Parameter:
8755: . y - the resulting vector
8757: Level: intermediate
8759: Note:
8760: This allows one to use either the restriction or interpolation (its transpose)
8761: matrix to do the restriction
8763: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8764: @*/
8765: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8766: {
8767: PetscInt M, N, Nx;
8769: PetscFunctionBegin;
8773: PetscCall(MatGetSize(A, &M, &N));
8774: PetscCall(VecGetSize(x, &Nx));
8775: if (M == Nx) {
8776: PetscCall(MatMultTranspose(A, x, y));
8777: } else {
8778: PetscCall(MatMult(A, x, y));
8779: }
8780: PetscFunctionReturn(PETSC_SUCCESS);
8781: }
8783: /*@
8784: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8786: Neighbor-wise Collective
8788: Input Parameters:
8789: + A - the matrix
8790: . x - the input dense matrix to be multiplied
8791: - w - the input dense matrix to be added to the result
8793: Output Parameter:
8794: . y - the output dense matrix
8796: Level: intermediate
8798: Note:
8799: This allows one to use either the restriction or interpolation (its transpose)
8800: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8801: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8803: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8804: @*/
8805: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8806: {
8807: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8808: PetscBool trans = PETSC_TRUE;
8809: MatReuse reuse = MAT_INITIAL_MATRIX;
8811: PetscFunctionBegin;
8817: PetscCall(MatGetSize(A, &M, &N));
8818: PetscCall(MatGetSize(x, &Mx, &Nx));
8819: if (N == Mx) trans = PETSC_FALSE;
8820: 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);
8821: Mo = trans ? N : M;
8822: if (*y) {
8823: PetscCall(MatGetSize(*y, &My, &Ny));
8824: if (Mo == My && Nx == Ny) {
8825: reuse = MAT_REUSE_MATRIX;
8826: } else {
8827: 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);
8828: PetscCall(MatDestroy(y));
8829: }
8830: }
8832: if (w && *y == w) { /* this is to minimize changes in PCMG */
8833: PetscBool flg;
8835: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8836: if (w) {
8837: PetscInt My, Ny, Mw, Nw;
8839: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8840: PetscCall(MatGetSize(*y, &My, &Ny));
8841: PetscCall(MatGetSize(w, &Mw, &Nw));
8842: if (!flg || My != Mw || Ny != Nw) w = NULL;
8843: }
8844: if (!w) {
8845: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8846: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8847: PetscCall(PetscObjectDereference((PetscObject)w));
8848: } else {
8849: PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8850: }
8851: }
8852: if (!trans) {
8853: PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8854: } else {
8855: PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8856: }
8857: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8858: PetscFunctionReturn(PETSC_SUCCESS);
8859: }
8861: /*@
8862: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8864: Neighbor-wise Collective
8866: Input Parameters:
8867: + A - the matrix
8868: - x - the input dense matrix
8870: Output Parameter:
8871: . y - the output dense matrix
8873: Level: intermediate
8875: Note:
8876: This allows one to use either the restriction or interpolation (its transpose)
8877: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8878: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8880: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8881: @*/
8882: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8883: {
8884: PetscFunctionBegin;
8885: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8886: PetscFunctionReturn(PETSC_SUCCESS);
8887: }
8889: /*@
8890: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8892: Neighbor-wise Collective
8894: Input Parameters:
8895: + A - the matrix
8896: - x - the input dense matrix
8898: Output Parameter:
8899: . y - the output dense matrix
8901: Level: intermediate
8903: Note:
8904: This allows one to use either the restriction or interpolation (its transpose)
8905: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8906: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8908: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8909: @*/
8910: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8911: {
8912: PetscFunctionBegin;
8913: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8914: PetscFunctionReturn(PETSC_SUCCESS);
8915: }
8917: /*@
8918: MatGetNullSpace - retrieves the null space of a matrix.
8920: Logically Collective
8922: Input Parameters:
8923: + mat - the matrix
8924: - nullsp - the null space object
8926: Level: developer
8928: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8929: @*/
8930: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8931: {
8932: PetscFunctionBegin;
8934: PetscAssertPointer(nullsp, 2);
8935: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8936: PetscFunctionReturn(PETSC_SUCCESS);
8937: }
8939: /*@C
8940: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
8942: Logically Collective
8944: Input Parameters:
8945: + n - the number of matrices
8946: - mat - the array of matrices
8948: Output Parameters:
8949: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
8951: Level: developer
8953: Note:
8954: Call `MatRestoreNullspaces()` to provide these to another array of matrices
8956: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8957: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8958: @*/
8959: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8960: {
8961: PetscFunctionBegin;
8962: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8963: PetscAssertPointer(mat, 2);
8964: PetscAssertPointer(nullsp, 3);
8966: PetscCall(PetscCalloc1(3 * n, nullsp));
8967: for (PetscInt i = 0; i < n; i++) {
8969: (*nullsp)[i] = mat[i]->nullsp;
8970: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8971: (*nullsp)[n + i] = mat[i]->nearnullsp;
8972: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8973: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8974: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8975: }
8976: PetscFunctionReturn(PETSC_SUCCESS);
8977: }
8979: /*@C
8980: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
8982: Logically Collective
8984: Input Parameters:
8985: + n - the number of matrices
8986: . mat - the array of matrices
8987: - nullsp - an array of null spaces
8989: Level: developer
8991: Note:
8992: Call `MatGetNullSpaces()` to create `nullsp`
8994: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8995: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8996: @*/
8997: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8998: {
8999: PetscFunctionBegin;
9000: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9001: PetscAssertPointer(mat, 2);
9002: PetscAssertPointer(nullsp, 3);
9003: PetscAssertPointer(*nullsp, 3);
9005: for (PetscInt i = 0; i < n; i++) {
9007: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
9008: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9009: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9010: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9011: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9012: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9013: }
9014: PetscCall(PetscFree(*nullsp));
9015: PetscFunctionReturn(PETSC_SUCCESS);
9016: }
9018: /*@
9019: MatSetNullSpace - attaches a null space to a matrix.
9021: Logically Collective
9023: Input Parameters:
9024: + mat - the matrix
9025: - nullsp - the null space object
9027: Level: advanced
9029: Notes:
9030: This null space is used by the `KSP` linear solvers to solve singular systems.
9032: 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`
9034: 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
9035: to zero but the linear system will still be solved in a least squares sense.
9037: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9038: 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 $A^T$, $R(A^T)$.
9039: 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
9040: $n(A^T))$ 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
9041: 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 $n(A^T)$.
9042: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9044: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9045: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9046: routine also automatically calls `MatSetTransposeNullSpace()`.
9048: The user should call `MatNullSpaceDestroy()`.
9050: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9051: `KSPSetPCSide()`
9052: @*/
9053: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9054: {
9055: PetscFunctionBegin;
9058: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9059: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9060: mat->nullsp = nullsp;
9061: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9062: PetscFunctionReturn(PETSC_SUCCESS);
9063: }
9065: /*@
9066: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
9068: Logically Collective
9070: Input Parameters:
9071: + mat - the matrix
9072: - nullsp - the null space object
9074: Level: developer
9076: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9077: @*/
9078: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9079: {
9080: PetscFunctionBegin;
9083: PetscAssertPointer(nullsp, 2);
9084: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9085: PetscFunctionReturn(PETSC_SUCCESS);
9086: }
9088: /*@
9089: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9091: Logically Collective
9093: Input Parameters:
9094: + mat - the matrix
9095: - nullsp - the null space object
9097: Level: advanced
9099: Notes:
9100: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9102: See `MatSetNullSpace()`
9104: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9105: @*/
9106: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9107: {
9108: PetscFunctionBegin;
9111: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9112: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9113: mat->transnullsp = nullsp;
9114: PetscFunctionReturn(PETSC_SUCCESS);
9115: }
9117: /*@
9118: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9119: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9121: Logically Collective
9123: Input Parameters:
9124: + mat - the matrix
9125: - nullsp - the null space object
9127: Level: advanced
9129: Notes:
9130: Overwrites any previous near null space that may have been attached
9132: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9134: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9135: @*/
9136: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9137: {
9138: PetscFunctionBegin;
9142: MatCheckPreallocated(mat, 1);
9143: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9144: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9145: mat->nearnullsp = nullsp;
9146: PetscFunctionReturn(PETSC_SUCCESS);
9147: }
9149: /*@
9150: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9152: Not Collective
9154: Input Parameter:
9155: . mat - the matrix
9157: Output Parameter:
9158: . nullsp - the null space object, `NULL` if not set
9160: Level: advanced
9162: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9163: @*/
9164: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9165: {
9166: PetscFunctionBegin;
9169: PetscAssertPointer(nullsp, 2);
9170: MatCheckPreallocated(mat, 1);
9171: *nullsp = mat->nearnullsp;
9172: PetscFunctionReturn(PETSC_SUCCESS);
9173: }
9175: /*@
9176: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9178: Collective
9180: Input Parameters:
9181: + mat - the matrix
9182: . row - row/column permutation
9183: - info - information on desired factorization process
9185: Level: developer
9187: Notes:
9188: Probably really in-place only when level of fill is zero, otherwise allocates
9189: new space to store factored matrix and deletes previous memory.
9191: Most users should employ the `KSP` interface for linear solvers
9192: instead of working directly with matrix algebra routines such as this.
9193: See, e.g., `KSPCreate()`.
9195: Fortran Note:
9196: A valid (non-null) `info` argument must be provided
9198: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9199: @*/
9200: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9201: {
9202: PetscFunctionBegin;
9206: PetscAssertPointer(info, 3);
9207: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9208: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9209: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9210: MatCheckPreallocated(mat, 1);
9211: PetscUseTypeMethod(mat, iccfactor, row, info);
9212: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9213: PetscFunctionReturn(PETSC_SUCCESS);
9214: }
9216: /*@
9217: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9218: ghosted ones.
9220: Not Collective
9222: Input Parameters:
9223: + mat - the matrix
9224: - diag - the diagonal values, including ghost ones
9226: Level: developer
9228: Notes:
9229: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9231: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9233: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9234: @*/
9235: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9236: {
9237: PetscMPIInt size;
9239: PetscFunctionBegin;
9244: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9245: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9246: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9247: if (size == 1) {
9248: PetscInt n, m;
9249: PetscCall(VecGetSize(diag, &n));
9250: PetscCall(MatGetSize(mat, NULL, &m));
9251: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9252: PetscCall(MatDiagonalScale(mat, NULL, diag));
9253: } else {
9254: PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9255: }
9256: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9257: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9258: PetscFunctionReturn(PETSC_SUCCESS);
9259: }
9261: /*@
9262: MatGetInertia - Gets the inertia from a factored matrix
9264: Collective
9266: Input Parameter:
9267: . mat - the matrix
9269: Output Parameters:
9270: + nneg - number of negative eigenvalues
9271: . nzero - number of zero eigenvalues
9272: - npos - number of positive eigenvalues
9274: Level: advanced
9276: Note:
9277: Matrix must have been factored by `MatCholeskyFactor()`
9279: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9280: @*/
9281: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9282: {
9283: PetscFunctionBegin;
9286: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9287: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9288: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9289: PetscFunctionReturn(PETSC_SUCCESS);
9290: }
9292: /*@C
9293: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9295: Neighbor-wise Collective
9297: Input Parameters:
9298: + mat - the factored matrix obtained with `MatGetFactor()`
9299: - b - the right-hand-side vectors
9301: Output Parameter:
9302: . x - the result vectors
9304: Level: developer
9306: Note:
9307: The vectors `b` and `x` cannot be the same. I.e., one cannot
9308: call `MatSolves`(A,x,x).
9310: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9311: @*/
9312: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9313: {
9314: PetscFunctionBegin;
9317: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9318: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9319: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9321: MatCheckPreallocated(mat, 1);
9322: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9323: PetscUseTypeMethod(mat, solves, b, x);
9324: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9325: PetscFunctionReturn(PETSC_SUCCESS);
9326: }
9328: /*@
9329: MatIsSymmetric - Test whether a matrix is symmetric
9331: Collective
9333: Input Parameters:
9334: + A - the matrix to test
9335: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9337: Output Parameter:
9338: . flg - the result
9340: Level: intermediate
9342: Notes:
9343: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9345: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9347: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9348: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9350: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9351: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9352: @*/
9353: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9354: {
9355: PetscFunctionBegin;
9357: PetscAssertPointer(flg, 3);
9358: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9359: else {
9360: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9361: else PetscCall(MatIsTranspose(A, A, tol, flg));
9362: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9363: }
9364: PetscFunctionReturn(PETSC_SUCCESS);
9365: }
9367: /*@
9368: MatIsHermitian - Test whether a matrix is Hermitian
9370: Collective
9372: Input Parameters:
9373: + A - the matrix to test
9374: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9376: Output Parameter:
9377: . flg - the result
9379: Level: intermediate
9381: Notes:
9382: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9384: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9386: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9387: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9389: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9390: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9391: @*/
9392: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9393: {
9394: PetscFunctionBegin;
9396: PetscAssertPointer(flg, 3);
9397: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9398: else {
9399: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9400: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9401: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9402: }
9403: PetscFunctionReturn(PETSC_SUCCESS);
9404: }
9406: /*@
9407: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9409: Not Collective
9411: Input Parameter:
9412: . A - the matrix to check
9414: Output Parameters:
9415: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9416: - flg - the result (only valid if set is `PETSC_TRUE`)
9418: Level: advanced
9420: Notes:
9421: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9422: if you want it explicitly checked
9424: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9425: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9427: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9428: @*/
9429: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9430: {
9431: PetscFunctionBegin;
9433: PetscAssertPointer(set, 2);
9434: PetscAssertPointer(flg, 3);
9435: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9436: *set = PETSC_TRUE;
9437: *flg = PetscBool3ToBool(A->symmetric);
9438: } else {
9439: *set = PETSC_FALSE;
9440: }
9441: PetscFunctionReturn(PETSC_SUCCESS);
9442: }
9444: /*@
9445: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9447: Not Collective
9449: Input Parameter:
9450: . A - the matrix to check
9452: Output Parameters:
9453: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9454: - flg - the result (only valid if set is `PETSC_TRUE`)
9456: Level: advanced
9458: Notes:
9459: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9461: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9462: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9464: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9465: @*/
9466: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9467: {
9468: PetscFunctionBegin;
9470: PetscAssertPointer(set, 2);
9471: PetscAssertPointer(flg, 3);
9472: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9473: *set = PETSC_TRUE;
9474: *flg = PetscBool3ToBool(A->spd);
9475: } else {
9476: *set = PETSC_FALSE;
9477: }
9478: PetscFunctionReturn(PETSC_SUCCESS);
9479: }
9481: /*@
9482: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9484: Not Collective
9486: Input Parameter:
9487: . A - the matrix to check
9489: Output Parameters:
9490: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9491: - flg - the result (only valid if set is `PETSC_TRUE`)
9493: Level: advanced
9495: Notes:
9496: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9497: if you want it explicitly checked
9499: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9500: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9502: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9503: @*/
9504: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9505: {
9506: PetscFunctionBegin;
9508: PetscAssertPointer(set, 2);
9509: PetscAssertPointer(flg, 3);
9510: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9511: *set = PETSC_TRUE;
9512: *flg = PetscBool3ToBool(A->hermitian);
9513: } else {
9514: *set = PETSC_FALSE;
9515: }
9516: PetscFunctionReturn(PETSC_SUCCESS);
9517: }
9519: /*@
9520: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9522: Collective
9524: Input Parameter:
9525: . A - the matrix to test
9527: Output Parameter:
9528: . flg - the result
9530: Level: intermediate
9532: Notes:
9533: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9535: 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
9536: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9538: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9539: @*/
9540: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9541: {
9542: PetscFunctionBegin;
9544: PetscAssertPointer(flg, 2);
9545: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9546: *flg = PetscBool3ToBool(A->structurally_symmetric);
9547: } else {
9548: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9549: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9550: }
9551: PetscFunctionReturn(PETSC_SUCCESS);
9552: }
9554: /*@
9555: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9557: Not Collective
9559: Input Parameter:
9560: . A - the matrix to check
9562: Output Parameters:
9563: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9564: - flg - the result (only valid if set is PETSC_TRUE)
9566: Level: advanced
9568: Notes:
9569: 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
9570: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9572: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9574: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9575: @*/
9576: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9577: {
9578: PetscFunctionBegin;
9580: PetscAssertPointer(set, 2);
9581: PetscAssertPointer(flg, 3);
9582: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9583: *set = PETSC_TRUE;
9584: *flg = PetscBool3ToBool(A->structurally_symmetric);
9585: } else {
9586: *set = PETSC_FALSE;
9587: }
9588: PetscFunctionReturn(PETSC_SUCCESS);
9589: }
9591: /*@
9592: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9593: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9595: Not Collective
9597: Input Parameter:
9598: . mat - the matrix
9600: Output Parameters:
9601: + nstash - the size of the stash
9602: . reallocs - the number of additional mallocs incurred.
9603: . bnstash - the size of the block stash
9604: - breallocs - the number of additional mallocs incurred.in the block stash
9606: Level: advanced
9608: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9609: @*/
9610: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9611: {
9612: PetscFunctionBegin;
9613: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9614: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9615: PetscFunctionReturn(PETSC_SUCCESS);
9616: }
9618: /*@
9619: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9620: parallel layout, `PetscLayout` for rows and columns
9622: Collective
9624: Input Parameter:
9625: . mat - the matrix
9627: Output Parameters:
9628: + right - (optional) vector that the matrix can be multiplied against
9629: - left - (optional) vector that the matrix vector product can be stored in
9631: Level: advanced
9633: Notes:
9634: 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()`.
9636: These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed
9638: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9639: @*/
9640: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9641: {
9642: PetscFunctionBegin;
9645: if (mat->ops->getvecs) {
9646: PetscUseTypeMethod(mat, getvecs, right, left);
9647: } else {
9648: if (right) {
9649: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9650: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9651: PetscCall(VecSetType(*right, mat->defaultvectype));
9652: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9653: if (mat->boundtocpu && mat->bindingpropagates) {
9654: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9655: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9656: }
9657: #endif
9658: }
9659: if (left) {
9660: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9661: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9662: PetscCall(VecSetType(*left, mat->defaultvectype));
9663: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9664: if (mat->boundtocpu && mat->bindingpropagates) {
9665: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9666: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9667: }
9668: #endif
9669: }
9670: }
9671: PetscFunctionReturn(PETSC_SUCCESS);
9672: }
9674: /*@
9675: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9676: with default values.
9678: Not Collective
9680: Input Parameter:
9681: . info - the `MatFactorInfo` data structure
9683: Level: developer
9685: Notes:
9686: The solvers are generally used through the `KSP` and `PC` objects, for example
9687: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9689: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9691: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9692: @*/
9693: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9694: {
9695: PetscFunctionBegin;
9696: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9697: PetscFunctionReturn(PETSC_SUCCESS);
9698: }
9700: /*@
9701: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9703: Collective
9705: Input Parameters:
9706: + mat - the factored matrix
9707: - is - the index set defining the Schur indices (0-based)
9709: Level: advanced
9711: Notes:
9712: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9714: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9716: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9718: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9719: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9720: @*/
9721: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9722: {
9723: PetscErrorCode (*f)(Mat, IS);
9725: PetscFunctionBegin;
9730: PetscCheckSameComm(mat, 1, is, 2);
9731: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9732: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9733: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9734: PetscCall(MatDestroy(&mat->schur));
9735: PetscCall((*f)(mat, is));
9736: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9737: PetscFunctionReturn(PETSC_SUCCESS);
9738: }
9740: /*@
9741: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9743: Logically Collective
9745: Input Parameters:
9746: + F - the factored matrix obtained by calling `MatGetFactor()`
9747: . S - location where to return the Schur complement, can be `NULL`
9748: - status - the status of the Schur complement matrix, can be `NULL`
9750: Level: advanced
9752: Notes:
9753: You must call `MatFactorSetSchurIS()` before calling this routine.
9755: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9757: The routine provides a copy of the Schur matrix stored within the solver data structures.
9758: The caller must destroy the object when it is no longer needed.
9759: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9761: 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)
9763: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9765: Developer Note:
9766: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9767: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9769: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9770: @*/
9771: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9772: {
9773: PetscFunctionBegin;
9775: if (S) PetscAssertPointer(S, 2);
9776: if (status) PetscAssertPointer(status, 3);
9777: if (S) {
9778: PetscErrorCode (*f)(Mat, Mat *);
9780: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9781: if (f) {
9782: PetscCall((*f)(F, S));
9783: } else {
9784: PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9785: }
9786: }
9787: if (status) *status = F->schur_status;
9788: PetscFunctionReturn(PETSC_SUCCESS);
9789: }
9791: /*@
9792: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9794: Logically Collective
9796: Input Parameters:
9797: + F - the factored matrix obtained by calling `MatGetFactor()`
9798: . S - location where to return the Schur complement, can be `NULL`
9799: - status - the status of the Schur complement matrix, can be `NULL`
9801: Level: advanced
9803: Notes:
9804: You must call `MatFactorSetSchurIS()` before calling this routine.
9806: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9808: The routine returns a the Schur Complement stored within the data structures of the solver.
9810: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9812: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9814: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9816: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9818: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9819: @*/
9820: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9821: {
9822: PetscFunctionBegin;
9824: if (S) {
9825: PetscAssertPointer(S, 2);
9826: *S = F->schur;
9827: }
9828: if (status) {
9829: PetscAssertPointer(status, 3);
9830: *status = F->schur_status;
9831: }
9832: PetscFunctionReturn(PETSC_SUCCESS);
9833: }
9835: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9836: {
9837: Mat S = F->schur;
9839: PetscFunctionBegin;
9840: switch (F->schur_status) {
9841: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9842: case MAT_FACTOR_SCHUR_INVERTED:
9843: if (S) {
9844: S->ops->solve = NULL;
9845: S->ops->matsolve = NULL;
9846: S->ops->solvetranspose = NULL;
9847: S->ops->matsolvetranspose = NULL;
9848: S->ops->solveadd = NULL;
9849: S->ops->solvetransposeadd = NULL;
9850: S->factortype = MAT_FACTOR_NONE;
9851: PetscCall(PetscFree(S->solvertype));
9852: }
9853: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9854: break;
9855: default:
9856: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9857: }
9858: PetscFunctionReturn(PETSC_SUCCESS);
9859: }
9861: /*@
9862: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9864: Logically Collective
9866: Input Parameters:
9867: + F - the factored matrix obtained by calling `MatGetFactor()`
9868: . S - location where the Schur complement is stored
9869: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9871: Level: advanced
9873: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9874: @*/
9875: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9876: {
9877: PetscFunctionBegin;
9879: if (S) {
9881: *S = NULL;
9882: }
9883: F->schur_status = status;
9884: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9885: PetscFunctionReturn(PETSC_SUCCESS);
9886: }
9888: /*@
9889: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9891: Logically Collective
9893: Input Parameters:
9894: + F - the factored matrix obtained by calling `MatGetFactor()`
9895: . rhs - location where the right-hand side of the Schur complement system is stored
9896: - sol - location where the solution of the Schur complement system has to be returned
9898: Level: advanced
9900: Notes:
9901: The sizes of the vectors should match the size of the Schur complement
9903: Must be called after `MatFactorSetSchurIS()`
9905: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9906: @*/
9907: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9908: {
9909: PetscFunctionBegin;
9916: PetscCheckSameComm(F, 1, rhs, 2);
9917: PetscCheckSameComm(F, 1, sol, 3);
9918: PetscCall(MatFactorFactorizeSchurComplement(F));
9919: switch (F->schur_status) {
9920: case MAT_FACTOR_SCHUR_FACTORED:
9921: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9922: break;
9923: case MAT_FACTOR_SCHUR_INVERTED:
9924: PetscCall(MatMultTranspose(F->schur, rhs, sol));
9925: break;
9926: default:
9927: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9928: }
9929: PetscFunctionReturn(PETSC_SUCCESS);
9930: }
9932: /*@
9933: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
9935: Logically Collective
9937: Input Parameters:
9938: + F - the factored matrix obtained by calling `MatGetFactor()`
9939: . rhs - location where the right-hand side of the Schur complement system is stored
9940: - sol - location where the solution of the Schur complement system has to be returned
9942: Level: advanced
9944: Notes:
9945: The sizes of the vectors should match the size of the Schur complement
9947: Must be called after `MatFactorSetSchurIS()`
9949: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9950: @*/
9951: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9952: {
9953: PetscFunctionBegin;
9960: PetscCheckSameComm(F, 1, rhs, 2);
9961: PetscCheckSameComm(F, 1, sol, 3);
9962: PetscCall(MatFactorFactorizeSchurComplement(F));
9963: switch (F->schur_status) {
9964: case MAT_FACTOR_SCHUR_FACTORED:
9965: PetscCall(MatSolve(F->schur, rhs, sol));
9966: break;
9967: case MAT_FACTOR_SCHUR_INVERTED:
9968: PetscCall(MatMult(F->schur, rhs, sol));
9969: break;
9970: default:
9971: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9972: }
9973: PetscFunctionReturn(PETSC_SUCCESS);
9974: }
9976: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9977: #if PetscDefined(HAVE_CUDA)
9978: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9979: #endif
9981: /* Schur status updated in the interface */
9982: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9983: {
9984: Mat S = F->schur;
9986: PetscFunctionBegin;
9987: if (S) {
9988: PetscMPIInt size;
9989: PetscBool isdense, isdensecuda;
9991: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9992: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9993: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9994: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9995: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9996: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9997: if (isdense) {
9998: PetscCall(MatSeqDenseInvertFactors_Private(S));
9999: } else if (isdensecuda) {
10000: #if defined(PETSC_HAVE_CUDA)
10001: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
10002: #endif
10003: }
10004: // HIP??????????????
10005: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
10006: }
10007: PetscFunctionReturn(PETSC_SUCCESS);
10008: }
10010: /*@
10011: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
10013: Logically Collective
10015: Input Parameter:
10016: . F - the factored matrix obtained by calling `MatGetFactor()`
10018: Level: advanced
10020: Notes:
10021: Must be called after `MatFactorSetSchurIS()`.
10023: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
10025: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10026: @*/
10027: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10028: {
10029: PetscFunctionBegin;
10032: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10033: PetscCall(MatFactorFactorizeSchurComplement(F));
10034: PetscCall(MatFactorInvertSchurComplement_Private(F));
10035: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10036: PetscFunctionReturn(PETSC_SUCCESS);
10037: }
10039: /*@
10040: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10042: Logically Collective
10044: Input Parameter:
10045: . F - the factored matrix obtained by calling `MatGetFactor()`
10047: Level: advanced
10049: Note:
10050: Must be called after `MatFactorSetSchurIS()`
10052: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10053: @*/
10054: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10055: {
10056: MatFactorInfo info;
10058: PetscFunctionBegin;
10061: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10062: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10063: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10064: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10065: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10066: } else {
10067: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10068: }
10069: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10070: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10071: PetscFunctionReturn(PETSC_SUCCESS);
10072: }
10074: /*@
10075: MatPtAP - Creates the matrix product $C = P^T * A * P$
10077: Neighbor-wise Collective
10079: Input Parameters:
10080: + A - the matrix
10081: . P - the projection matrix
10082: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10083: - 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
10084: if the result is a dense matrix this is irrelevant
10086: Output Parameter:
10087: . C - the product matrix
10089: Level: intermediate
10091: Notes:
10092: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10094: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_PtAP`
10095: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10097: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10099: Developer Note:
10100: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10102: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10103: @*/
10104: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10105: {
10106: PetscFunctionBegin;
10107: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10108: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10110: if (scall == MAT_INITIAL_MATRIX) {
10111: PetscCall(MatProductCreate(A, P, NULL, C));
10112: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10113: PetscCall(MatProductSetAlgorithm(*C, "default"));
10114: PetscCall(MatProductSetFill(*C, fill));
10116: (*C)->product->api_user = PETSC_TRUE;
10117: PetscCall(MatProductSetFromOptions(*C));
10118: 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);
10119: PetscCall(MatProductSymbolic(*C));
10120: } else { /* scall == MAT_REUSE_MATRIX */
10121: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10122: }
10124: PetscCall(MatProductNumeric(*C));
10125: if (A->symmetric == PETSC_BOOL3_TRUE) {
10126: PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10127: (*C)->spd = A->spd;
10128: }
10129: PetscFunctionReturn(PETSC_SUCCESS);
10130: }
10132: /*@
10133: MatRARt - Creates the matrix product $C = R * A * R^T$
10135: Neighbor-wise Collective
10137: Input Parameters:
10138: + A - the matrix
10139: . R - the projection matrix
10140: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10141: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10142: if the result is a dense matrix this is irrelevant
10144: Output Parameter:
10145: . C - the product matrix
10147: Level: intermediate
10149: Notes:
10150: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10152: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_RARt`
10153: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10155: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10156: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10157: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10158: We recommend using `MatPtAP()` when possible.
10160: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10162: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10163: @*/
10164: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10165: {
10166: PetscFunctionBegin;
10167: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10168: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10170: if (scall == MAT_INITIAL_MATRIX) {
10171: PetscCall(MatProductCreate(A, R, NULL, C));
10172: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10173: PetscCall(MatProductSetAlgorithm(*C, "default"));
10174: PetscCall(MatProductSetFill(*C, fill));
10176: (*C)->product->api_user = PETSC_TRUE;
10177: PetscCall(MatProductSetFromOptions(*C));
10178: 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);
10179: PetscCall(MatProductSymbolic(*C));
10180: } else { /* scall == MAT_REUSE_MATRIX */
10181: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10182: }
10184: PetscCall(MatProductNumeric(*C));
10185: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10186: PetscFunctionReturn(PETSC_SUCCESS);
10187: }
10189: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10190: {
10191: PetscBool flg = PETSC_TRUE;
10193: PetscFunctionBegin;
10194: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10195: if (scall == MAT_INITIAL_MATRIX) {
10196: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10197: PetscCall(MatProductCreate(A, B, NULL, C));
10198: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10199: PetscCall(MatProductSetFill(*C, fill));
10200: } else { /* scall == MAT_REUSE_MATRIX */
10201: Mat_Product *product = (*C)->product;
10203: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10204: if (flg && product && product->type != ptype) {
10205: PetscCall(MatProductClear(*C));
10206: product = NULL;
10207: }
10208: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10209: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10210: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10211: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10212: product = (*C)->product;
10213: product->fill = fill;
10214: product->clear = PETSC_TRUE;
10215: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10216: flg = PETSC_FALSE;
10217: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10218: }
10219: }
10220: if (flg) {
10221: (*C)->product->api_user = PETSC_TRUE;
10222: PetscCall(MatProductSetType(*C, ptype));
10223: PetscCall(MatProductSetFromOptions(*C));
10224: PetscCall(MatProductSymbolic(*C));
10225: }
10226: PetscCall(MatProductNumeric(*C));
10227: PetscFunctionReturn(PETSC_SUCCESS);
10228: }
10230: /*@
10231: MatMatMult - Performs matrix-matrix multiplication $ C=A*B $.
10233: Neighbor-wise Collective
10235: Input Parameters:
10236: + A - the left matrix
10237: . B - the right matrix
10238: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10239: - 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
10240: if the result is a dense matrix this is irrelevant
10242: Output Parameter:
10243: . C - the product matrix
10245: Notes:
10246: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10248: `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
10249: call to this function with `MAT_INITIAL_MATRIX`.
10251: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10253: 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`,
10254: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10256: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10258: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AB`
10259: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10261: Example of Usage:
10262: .vb
10263: MatProductCreate(A,B,NULL,&C);
10264: MatProductSetType(C,MATPRODUCT_AB);
10265: MatProductSymbolic(C);
10266: MatProductNumeric(C); // compute C=A * B
10267: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10268: MatProductNumeric(C);
10269: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10270: MatProductNumeric(C);
10271: .ve
10273: Level: intermediate
10275: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10276: @*/
10277: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10278: {
10279: PetscFunctionBegin;
10280: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10281: PetscFunctionReturn(PETSC_SUCCESS);
10282: }
10284: /*@
10285: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10287: Neighbor-wise Collective
10289: Input Parameters:
10290: + A - the left matrix
10291: . B - the right matrix
10292: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10293: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10295: Output Parameter:
10296: . C - the product matrix
10298: Options Database Key:
10299: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10300: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10301: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
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 the matrices A and B have the same nonzero pattern as in the previous call
10310: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10311: actually needed.
10313: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10314: and for pairs of `MATMPIDENSE` matrices.
10316: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABt`
10317: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10319: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10321: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10322: @*/
10323: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10324: {
10325: PetscFunctionBegin;
10326: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10327: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10328: PetscFunctionReturn(PETSC_SUCCESS);
10329: }
10331: /*@
10332: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10334: Neighbor-wise Collective
10336: Input Parameters:
10337: + A - the left matrix
10338: . B - the right matrix
10339: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10340: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10342: Output Parameter:
10343: . C - the product matrix
10345: Level: intermediate
10347: Notes:
10348: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10350: `MAT_REUSE_MATRIX` can only be used if `A` and `B` have the same nonzero pattern as in the previous call.
10352: This is a convenience routine that wraps the use of `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AtB`
10353: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10355: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10356: actually needed.
10358: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10359: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10361: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10363: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10364: @*/
10365: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10366: {
10367: PetscFunctionBegin;
10368: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10369: PetscFunctionReturn(PETSC_SUCCESS);
10370: }
10372: /*@
10373: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10375: Neighbor-wise Collective
10377: Input Parameters:
10378: + A - the left matrix
10379: . B - the middle matrix
10380: . C - the right matrix
10381: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10382: - 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
10383: if the result is a dense matrix this is irrelevant
10385: Output Parameter:
10386: . D - the product matrix
10388: Level: intermediate
10390: Notes:
10391: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10393: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10395: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABC`
10396: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10398: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10399: actually needed.
10401: If you have many matrices with the same non-zero structure to multiply, you
10402: should use `MAT_REUSE_MATRIX` in all calls but the first
10404: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10406: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10407: @*/
10408: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10409: {
10410: PetscFunctionBegin;
10411: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10412: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10414: if (scall == MAT_INITIAL_MATRIX) {
10415: PetscCall(MatProductCreate(A, B, C, D));
10416: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10417: PetscCall(MatProductSetAlgorithm(*D, "default"));
10418: PetscCall(MatProductSetFill(*D, fill));
10420: (*D)->product->api_user = PETSC_TRUE;
10421: PetscCall(MatProductSetFromOptions(*D));
10422: 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,
10423: ((PetscObject)C)->type_name);
10424: PetscCall(MatProductSymbolic(*D));
10425: } else { /* user may change input matrices when REUSE */
10426: PetscCall(MatProductReplaceMats(A, B, C, *D));
10427: }
10428: PetscCall(MatProductNumeric(*D));
10429: PetscFunctionReturn(PETSC_SUCCESS);
10430: }
10432: /*@
10433: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10435: Collective
10437: Input Parameters:
10438: + mat - the matrix
10439: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10440: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10441: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10443: Output Parameter:
10444: . matredundant - redundant matrix
10446: Level: advanced
10448: Notes:
10449: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10450: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10452: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10453: calling it.
10455: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10457: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10458: @*/
10459: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10460: {
10461: MPI_Comm comm;
10462: PetscMPIInt size;
10463: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10464: Mat_Redundant *redund = NULL;
10465: PetscSubcomm psubcomm = NULL;
10466: MPI_Comm subcomm_in = subcomm;
10467: Mat *matseq;
10468: IS isrow, iscol;
10469: PetscBool newsubcomm = PETSC_FALSE;
10471: PetscFunctionBegin;
10473: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10474: PetscAssertPointer(*matredundant, 5);
10476: }
10478: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10479: if (size == 1 || nsubcomm == 1) {
10480: if (reuse == MAT_INITIAL_MATRIX) {
10481: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10482: } else {
10483: 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");
10484: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10485: }
10486: PetscFunctionReturn(PETSC_SUCCESS);
10487: }
10489: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10490: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10491: MatCheckPreallocated(mat, 1);
10493: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10494: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10495: /* create psubcomm, then get subcomm */
10496: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10497: PetscCallMPI(MPI_Comm_size(comm, &size));
10498: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10500: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10501: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10502: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10503: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10504: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10505: newsubcomm = PETSC_TRUE;
10506: PetscCall(PetscSubcommDestroy(&psubcomm));
10507: }
10509: /* get isrow, iscol and a local sequential matrix matseq[0] */
10510: if (reuse == MAT_INITIAL_MATRIX) {
10511: mloc_sub = PETSC_DECIDE;
10512: nloc_sub = PETSC_DECIDE;
10513: if (bs < 1) {
10514: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10515: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10516: } else {
10517: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10518: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10519: }
10520: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10521: rstart = rend - mloc_sub;
10522: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10523: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10524: PetscCall(ISSetIdentity(iscol));
10525: } else { /* reuse == MAT_REUSE_MATRIX */
10526: 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");
10527: /* retrieve subcomm */
10528: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10529: redund = (*matredundant)->redundant;
10530: isrow = redund->isrow;
10531: iscol = redund->iscol;
10532: matseq = redund->matseq;
10533: }
10534: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10536: /* get matredundant over subcomm */
10537: if (reuse == MAT_INITIAL_MATRIX) {
10538: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10540: /* create a supporting struct and attach it to C for reuse */
10541: PetscCall(PetscNew(&redund));
10542: (*matredundant)->redundant = redund;
10543: redund->isrow = isrow;
10544: redund->iscol = iscol;
10545: redund->matseq = matseq;
10546: if (newsubcomm) {
10547: redund->subcomm = subcomm;
10548: } else {
10549: redund->subcomm = MPI_COMM_NULL;
10550: }
10551: } else {
10552: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10553: }
10554: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10555: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10556: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10557: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10558: }
10559: #endif
10560: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10561: PetscFunctionReturn(PETSC_SUCCESS);
10562: }
10564: /*@C
10565: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10566: a given `Mat`. Each submatrix can span multiple procs.
10568: Collective
10570: Input Parameters:
10571: + mat - the matrix
10572: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10573: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10575: Output Parameter:
10576: . subMat - parallel sub-matrices each spanning a given `subcomm`
10578: Level: advanced
10580: Notes:
10581: The submatrix partition across processors is dictated by `subComm` a
10582: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10583: is not restricted to be grouped with consecutive original MPI processes.
10585: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10586: map directly to the layout of the original matrix [wrt the local
10587: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10588: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10589: the `subMat`. However the offDiagMat looses some columns - and this is
10590: reconstructed with `MatSetValues()`
10592: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10594: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10595: @*/
10596: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10597: {
10598: PetscMPIInt commsize, subCommSize;
10600: PetscFunctionBegin;
10601: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10602: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10603: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10605: 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");
10606: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10607: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10608: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10609: PetscFunctionReturn(PETSC_SUCCESS);
10610: }
10612: /*@
10613: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10615: Not Collective
10617: Input Parameters:
10618: + mat - matrix to extract local submatrix from
10619: . isrow - local row indices for submatrix
10620: - iscol - local column indices for submatrix
10622: Output Parameter:
10623: . submat - the submatrix
10625: Level: intermediate
10627: Notes:
10628: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10630: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10631: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10633: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10634: `MatSetValuesBlockedLocal()` will also be implemented.
10636: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10637: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10639: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10640: @*/
10641: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10642: {
10643: PetscFunctionBegin;
10647: PetscCheckSameComm(isrow, 2, iscol, 3);
10648: PetscAssertPointer(submat, 4);
10649: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10651: if (mat->ops->getlocalsubmatrix) {
10652: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10653: } else {
10654: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10655: }
10656: (*submat)->assembled = mat->assembled;
10657: PetscFunctionReturn(PETSC_SUCCESS);
10658: }
10660: /*@
10661: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10663: Not Collective
10665: Input Parameters:
10666: + mat - matrix to extract local submatrix from
10667: . isrow - local row indices for submatrix
10668: . iscol - local column indices for submatrix
10669: - submat - the submatrix
10671: Level: intermediate
10673: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10674: @*/
10675: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10676: {
10677: PetscFunctionBegin;
10681: PetscCheckSameComm(isrow, 2, iscol, 3);
10682: PetscAssertPointer(submat, 4);
10685: if (mat->ops->restorelocalsubmatrix) {
10686: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10687: } else {
10688: PetscCall(MatDestroy(submat));
10689: }
10690: *submat = NULL;
10691: PetscFunctionReturn(PETSC_SUCCESS);
10692: }
10694: /*@
10695: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10697: Collective
10699: Input Parameter:
10700: . mat - the matrix
10702: Output Parameter:
10703: . is - if any rows have zero diagonals this contains the list of them
10705: Level: developer
10707: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10708: @*/
10709: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10710: {
10711: PetscFunctionBegin;
10714: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10715: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10717: if (!mat->ops->findzerodiagonals) {
10718: Vec diag;
10719: const PetscScalar *a;
10720: PetscInt *rows;
10721: PetscInt rStart, rEnd, r, nrow = 0;
10723: PetscCall(MatCreateVecs(mat, &diag, NULL));
10724: PetscCall(MatGetDiagonal(mat, diag));
10725: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10726: PetscCall(VecGetArrayRead(diag, &a));
10727: for (r = 0; r < rEnd - rStart; ++r)
10728: if (a[r] == 0.0) ++nrow;
10729: PetscCall(PetscMalloc1(nrow, &rows));
10730: nrow = 0;
10731: for (r = 0; r < rEnd - rStart; ++r)
10732: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10733: PetscCall(VecRestoreArrayRead(diag, &a));
10734: PetscCall(VecDestroy(&diag));
10735: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10736: } else {
10737: PetscUseTypeMethod(mat, findzerodiagonals, is);
10738: }
10739: PetscFunctionReturn(PETSC_SUCCESS);
10740: }
10742: /*@
10743: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10745: Collective
10747: Input Parameter:
10748: . mat - the matrix
10750: Output Parameter:
10751: . is - contains the list of rows with off block diagonal entries
10753: Level: developer
10755: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10756: @*/
10757: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10758: {
10759: PetscFunctionBegin;
10762: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10763: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10765: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10766: PetscFunctionReturn(PETSC_SUCCESS);
10767: }
10769: /*@C
10770: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10772: Collective; No Fortran Support
10774: Input Parameter:
10775: . mat - the matrix
10777: Output Parameter:
10778: . values - the block inverses in column major order (FORTRAN-like)
10780: Level: advanced
10782: Notes:
10783: The size of the blocks is determined by the block size of the matrix.
10785: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10787: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10789: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10790: @*/
10791: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10792: {
10793: PetscFunctionBegin;
10795: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10796: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10797: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10798: PetscFunctionReturn(PETSC_SUCCESS);
10799: }
10801: /*@
10802: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10804: Collective; No Fortran Support
10806: Input Parameters:
10807: + mat - the matrix
10808: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10809: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10811: Output Parameter:
10812: . values - the block inverses in column major order (FORTRAN-like)
10814: Level: advanced
10816: Notes:
10817: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10819: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10821: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10822: @*/
10823: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10824: {
10825: PetscFunctionBegin;
10827: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10828: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10829: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10830: PetscFunctionReturn(PETSC_SUCCESS);
10831: }
10833: /*@
10834: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10836: Collective
10838: Input Parameters:
10839: + A - the matrix
10840: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10842: Level: advanced
10844: Note:
10845: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10847: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10848: @*/
10849: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10850: {
10851: const PetscScalar *vals;
10852: PetscInt *dnnz;
10853: PetscInt m, rstart, rend, bs, i, j;
10855: PetscFunctionBegin;
10856: PetscCall(MatInvertBlockDiagonal(A, &vals));
10857: PetscCall(MatGetBlockSize(A, &bs));
10858: PetscCall(MatGetLocalSize(A, &m, NULL));
10859: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10860: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10861: PetscCall(PetscMalloc1(m / bs, &dnnz));
10862: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10863: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10864: PetscCall(PetscFree(dnnz));
10865: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10866: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10867: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10868: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10869: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10870: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10871: PetscFunctionReturn(PETSC_SUCCESS);
10872: }
10874: /*@
10875: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10876: via `MatTransposeColoringCreate()`.
10878: Collective
10880: Input Parameter:
10881: . c - coloring context
10883: Level: intermediate
10885: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10886: @*/
10887: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10888: {
10889: MatTransposeColoring matcolor = *c;
10891: PetscFunctionBegin;
10892: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10893: if (--((PetscObject)matcolor)->refct > 0) {
10894: matcolor = NULL;
10895: PetscFunctionReturn(PETSC_SUCCESS);
10896: }
10898: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10899: PetscCall(PetscFree(matcolor->rows));
10900: PetscCall(PetscFree(matcolor->den2sp));
10901: PetscCall(PetscFree(matcolor->colorforcol));
10902: PetscCall(PetscFree(matcolor->columns));
10903: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10904: PetscCall(PetscHeaderDestroy(c));
10905: PetscFunctionReturn(PETSC_SUCCESS);
10906: }
10908: /*@
10909: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10910: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10911: `MatTransposeColoring` to sparse `B`.
10913: Collective
10915: Input Parameters:
10916: + coloring - coloring context created with `MatTransposeColoringCreate()`
10917: - B - sparse matrix
10919: Output Parameter:
10920: . Btdense - dense matrix $B^T$
10922: Level: developer
10924: Note:
10925: These are used internally for some implementations of `MatRARt()`
10927: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10928: @*/
10929: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10930: {
10931: PetscFunctionBegin;
10936: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10937: PetscFunctionReturn(PETSC_SUCCESS);
10938: }
10940: /*@
10941: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10942: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10943: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10944: $C_{sp}$ from $C_{den}$.
10946: Collective
10948: Input Parameters:
10949: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10950: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
10952: Output Parameter:
10953: . Csp - sparse matrix
10955: Level: developer
10957: Note:
10958: These are used internally for some implementations of `MatRARt()`
10960: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10961: @*/
10962: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10963: {
10964: PetscFunctionBegin;
10969: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10970: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10971: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10972: PetscFunctionReturn(PETSC_SUCCESS);
10973: }
10975: /*@
10976: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
10978: Collective
10980: Input Parameters:
10981: + mat - the matrix product C
10982: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
10984: Output Parameter:
10985: . color - the new coloring context
10987: Level: intermediate
10989: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10990: `MatTransColoringApplyDenToSp()`
10991: @*/
10992: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10993: {
10994: MatTransposeColoring c;
10995: MPI_Comm comm;
10997: PetscFunctionBegin;
10998: PetscAssertPointer(color, 3);
11000: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11001: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
11002: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
11003: c->ctype = iscoloring->ctype;
11004: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
11005: *color = c;
11006: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11007: PetscFunctionReturn(PETSC_SUCCESS);
11008: }
11010: /*@
11011: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
11012: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
11014: Not Collective
11016: Input Parameter:
11017: . mat - the matrix
11019: Output Parameter:
11020: . state - the current state
11022: Level: intermediate
11024: Notes:
11025: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11026: different matrices
11028: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
11030: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
11032: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11033: @*/
11034: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11035: {
11036: PetscFunctionBegin;
11038: *state = mat->nonzerostate;
11039: PetscFunctionReturn(PETSC_SUCCESS);
11040: }
11042: /*@
11043: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11044: matrices from each processor
11046: Collective
11048: Input Parameters:
11049: + comm - the communicators the parallel matrix will live on
11050: . seqmat - the input sequential matrices
11051: . n - number of local columns (or `PETSC_DECIDE`)
11052: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11054: Output Parameter:
11055: . mpimat - the parallel matrix generated
11057: Level: developer
11059: Note:
11060: The number of columns of the matrix in EACH processor MUST be the same.
11062: .seealso: [](ch_matrices), `Mat`
11063: @*/
11064: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11065: {
11066: PetscMPIInt size;
11068: PetscFunctionBegin;
11069: PetscCallMPI(MPI_Comm_size(comm, &size));
11070: if (size == 1) {
11071: if (reuse == MAT_INITIAL_MATRIX) {
11072: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11073: } else {
11074: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11075: }
11076: PetscFunctionReturn(PETSC_SUCCESS);
11077: }
11079: 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");
11081: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11082: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11083: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11084: PetscFunctionReturn(PETSC_SUCCESS);
11085: }
11087: /*@
11088: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11090: Collective
11092: Input Parameters:
11093: + A - the matrix to create subdomains from
11094: - N - requested number of subdomains
11096: Output Parameters:
11097: + n - number of subdomains resulting on this MPI process
11098: - iss - `IS` list with indices of subdomains on this MPI process
11100: Level: advanced
11102: Note:
11103: The number of subdomains must be smaller than the communicator size
11105: .seealso: [](ch_matrices), `Mat`, `IS`
11106: @*/
11107: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11108: {
11109: MPI_Comm comm, subcomm;
11110: PetscMPIInt size, rank, color;
11111: PetscInt rstart, rend, k;
11113: PetscFunctionBegin;
11114: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11115: PetscCallMPI(MPI_Comm_size(comm, &size));
11116: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11117: 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);
11118: *n = 1;
11119: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11120: color = rank / k;
11121: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11122: PetscCall(PetscMalloc1(1, iss));
11123: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11124: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11125: PetscCallMPI(MPI_Comm_free(&subcomm));
11126: PetscFunctionReturn(PETSC_SUCCESS);
11127: }
11129: /*@
11130: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11132: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11133: If they are not the same, uses `MatMatMatMult()`.
11135: Once the coarse grid problem is constructed, correct for interpolation operators
11136: that are not of full rank, which can legitimately happen in the case of non-nested
11137: geometric multigrid.
11139: Input Parameters:
11140: + restrct - restriction operator
11141: . dA - fine grid matrix
11142: . interpolate - interpolation operator
11143: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11144: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11146: Output Parameter:
11147: . A - the Galerkin coarse matrix
11149: Options Database Key:
11150: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used
11152: Level: developer
11154: Note:
11155: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11157: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11158: @*/
11159: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11160: {
11161: IS zerorows;
11162: Vec diag;
11164: PetscFunctionBegin;
11165: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11166: /* Construct the coarse grid matrix */
11167: if (interpolate == restrct) {
11168: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11169: } else {
11170: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11171: }
11173: /* If the interpolation matrix is not of full rank, A will have zero rows.
11174: This can legitimately happen in the case of non-nested geometric multigrid.
11175: In that event, we set the rows of the matrix to the rows of the identity,
11176: ignoring the equations (as the RHS will also be zero). */
11178: PetscCall(MatFindZeroRows(*A, &zerorows));
11180: if (zerorows != NULL) { /* if there are any zero rows */
11181: PetscCall(MatCreateVecs(*A, &diag, NULL));
11182: PetscCall(MatGetDiagonal(*A, diag));
11183: PetscCall(VecISSet(diag, zerorows, 1.0));
11184: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11185: PetscCall(VecDestroy(&diag));
11186: PetscCall(ISDestroy(&zerorows));
11187: }
11188: PetscFunctionReturn(PETSC_SUCCESS);
11189: }
11191: /*@C
11192: MatSetOperation - Allows user to set a matrix operation for any matrix type
11194: Logically Collective
11196: Input Parameters:
11197: + mat - the matrix
11198: . op - the name of the operation
11199: - f - the function that provides the operation
11201: Level: developer
11203: Example Usage:
11204: .vb
11205: extern PetscErrorCode usermult(Mat, Vec, Vec);
11207: PetscCall(MatCreateXXX(comm, ..., &A));
11208: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11209: .ve
11211: Notes:
11212: See the file `include/petscmat.h` for a complete list of matrix
11213: operations, which all have the form MATOP_<OPERATION>, where
11214: <OPERATION> is the name (in all capital letters) of the
11215: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11217: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11218: sequence as the usual matrix interface routines, since they
11219: are intended to be accessed via the usual matrix interface
11220: routines, e.g.,
11221: .vb
11222: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11223: .ve
11225: In particular each function MUST return `PETSC_SUCCESS` on success and
11226: nonzero on failure.
11228: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11230: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11231: @*/
11232: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11233: {
11234: PetscFunctionBegin;
11236: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11237: (((PetscErrorCodeFn **)mat->ops)[op]) = f;
11238: PetscFunctionReturn(PETSC_SUCCESS);
11239: }
11241: /*@C
11242: MatGetOperation - Gets a matrix operation for any matrix type.
11244: Not Collective
11246: Input Parameters:
11247: + mat - the matrix
11248: - op - the name of the operation
11250: Output Parameter:
11251: . f - the function that provides the operation
11253: Level: developer
11255: Example Usage:
11256: .vb
11257: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11259: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11260: .ve
11262: Notes:
11263: See the file `include/petscmat.h` for a complete list of matrix
11264: operations, which all have the form MATOP_<OPERATION>, where
11265: <OPERATION> is the name (in all capital letters) of the
11266: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11268: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11270: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11271: @*/
11272: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11273: {
11274: PetscFunctionBegin;
11276: *f = (((PetscErrorCodeFn **)mat->ops)[op]);
11277: PetscFunctionReturn(PETSC_SUCCESS);
11278: }
11280: /*@
11281: MatHasOperation - Determines whether the given matrix supports the particular operation.
11283: Not Collective
11285: Input Parameters:
11286: + mat - the matrix
11287: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11289: Output Parameter:
11290: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11292: Level: advanced
11294: Note:
11295: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11297: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11298: @*/
11299: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11300: {
11301: PetscFunctionBegin;
11303: PetscAssertPointer(has, 3);
11304: if (mat->ops->hasoperation) {
11305: PetscUseTypeMethod(mat, hasoperation, op, has);
11306: } else {
11307: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11308: else {
11309: *has = PETSC_FALSE;
11310: if (op == MATOP_CREATE_SUBMATRIX) {
11311: PetscMPIInt size;
11313: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11314: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11315: }
11316: }
11317: }
11318: PetscFunctionReturn(PETSC_SUCCESS);
11319: }
11321: /*@
11322: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11324: Collective
11326: Input Parameter:
11327: . mat - the matrix
11329: Output Parameter:
11330: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11332: Level: beginner
11334: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11335: @*/
11336: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11337: {
11338: PetscFunctionBegin;
11341: PetscAssertPointer(cong, 2);
11342: if (!mat->rmap || !mat->cmap) {
11343: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11344: PetscFunctionReturn(PETSC_SUCCESS);
11345: }
11346: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11347: PetscCall(PetscLayoutSetUp(mat->rmap));
11348: PetscCall(PetscLayoutSetUp(mat->cmap));
11349: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11350: if (*cong) mat->congruentlayouts = 1;
11351: else mat->congruentlayouts = 0;
11352: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11353: PetscFunctionReturn(PETSC_SUCCESS);
11354: }
11356: PetscErrorCode MatSetInf(Mat A)
11357: {
11358: PetscFunctionBegin;
11359: PetscUseTypeMethod(A, setinf);
11360: PetscFunctionReturn(PETSC_SUCCESS);
11361: }
11363: /*@
11364: 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
11365: and possibly removes small values from the graph structure.
11367: Collective
11369: Input Parameters:
11370: + A - the matrix
11371: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11372: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11373: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11374: . num_idx - size of 'index' array
11375: - index - array of block indices to use for graph strength of connection weight
11377: Output Parameter:
11378: . graph - the resulting graph
11380: Level: advanced
11382: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11383: @*/
11384: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11385: {
11386: PetscFunctionBegin;
11390: PetscAssertPointer(graph, 7);
11391: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11392: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11393: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11394: PetscFunctionReturn(PETSC_SUCCESS);
11395: }
11397: /*@
11398: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11399: meaning the same memory is used for the matrix, and no new memory is allocated.
11401: Collective
11403: Input Parameters:
11404: + A - the matrix
11405: - 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
11407: Level: intermediate
11409: Developer Note:
11410: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11411: of the arrays in the data structure are unneeded.
11413: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11414: @*/
11415: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11416: {
11417: PetscFunctionBegin;
11419: PetscUseTypeMethod(A, eliminatezeros, keep);
11420: PetscFunctionReturn(PETSC_SUCCESS);
11421: }
11423: /*@C
11424: MatGetCurrentMemType - Get the memory location of the matrix
11426: Not Collective, but the result will be the same on all MPI processes
11428: Input Parameter:
11429: . A - the matrix whose memory type we are checking
11431: Output Parameter:
11432: . m - the memory type
11434: Level: intermediate
11436: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11437: @*/
11438: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11439: {
11440: PetscFunctionBegin;
11442: PetscAssertPointer(m, 2);
11443: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11444: else *m = PETSC_MEMTYPE_HOST;
11445: PetscFunctionReturn(PETSC_SUCCESS);
11446: }