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: /*@
509: MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices) in the nonzero structure
511: Not Collective
513: Input Parameter:
514: . mat - the matrix
516: Output Parameters:
517: + missing - is any diagonal entry missing
518: - dd - first diagonal entry that is missing (optional) on this process
520: Level: advanced
522: Note:
523: This does not return diagonal entries that are in the nonzero structure but happen to have a zero numerical value
525: .seealso: [](ch_matrices), `Mat`
526: @*/
527: PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
528: {
529: PetscFunctionBegin;
532: PetscAssertPointer(missing, 2);
533: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
534: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
535: PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
536: PetscFunctionReturn(PETSC_SUCCESS);
537: }
539: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
540: /*@C
541: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
542: for each row that you get to ensure that your application does
543: not bleed memory.
545: Not Collective
547: Input Parameters:
548: + mat - the matrix
549: - row - the row to get
551: Output Parameters:
552: + ncols - if not `NULL`, the number of nonzeros in `row`
553: . cols - if not `NULL`, the column numbers
554: - vals - if not `NULL`, the numerical values
556: Level: advanced
558: Notes:
559: This routine is provided for people who need to have direct access
560: to the structure of a matrix. We hope that we provide enough
561: high-level matrix routines that few users will need it.
563: `MatGetRow()` always returns 0-based column indices, regardless of
564: whether the internal representation is 0-based (default) or 1-based.
566: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
567: not wish to extract these quantities.
569: The user can only examine the values extracted with `MatGetRow()`;
570: the values CANNOT be altered. To change the matrix entries, one
571: must use `MatSetValues()`.
573: You can only have one call to `MatGetRow()` outstanding for a particular
574: matrix at a time, per processor. `MatGetRow()` can only obtain rows
575: associated with the given processor, it cannot get rows from the
576: other processors; for that we suggest using `MatCreateSubMatrices()`, then
577: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
578: is in the global number of rows.
580: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
582: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
584: Fortran Note:
585: .vb
586: PetscInt, pointer :: cols(:)
587: PetscScalar, pointer :: vals(:)
588: .ve
590: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
591: @*/
592: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
593: {
594: PetscInt incols;
596: PetscFunctionBegin;
599: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
600: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
601: MatCheckPreallocated(mat, 1);
602: 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);
603: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
604: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
605: if (ncols) *ncols = incols;
606: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
607: PetscFunctionReturn(PETSC_SUCCESS);
608: }
610: /*@
611: MatConjugate - replaces the matrix values with their complex conjugates
613: Logically Collective
615: Input Parameter:
616: . mat - the matrix
618: Level: advanced
620: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
621: @*/
622: PetscErrorCode MatConjugate(Mat mat)
623: {
624: PetscFunctionBegin;
626: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
627: if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
628: PetscUseTypeMethod(mat, conjugate);
629: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
630: }
631: PetscFunctionReturn(PETSC_SUCCESS);
632: }
634: /*@C
635: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
637: Not Collective
639: Input Parameters:
640: + mat - the matrix
641: . row - the row to get
642: . ncols - the number of nonzeros
643: . cols - the columns of the nonzeros
644: - vals - if nonzero the column values
646: Level: advanced
648: Notes:
649: This routine should be called after you have finished examining the entries.
651: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
652: us of the array after it has been restored. If you pass `NULL`, it will
653: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
655: Fortran Note:
656: .vb
657: PetscInt, pointer :: cols(:)
658: PetscScalar, pointer :: vals(:)
659: .ve
661: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
662: @*/
663: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
664: {
665: PetscFunctionBegin;
667: if (ncols) PetscAssertPointer(ncols, 3);
668: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
669: PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
670: if (ncols) *ncols = 0;
671: if (cols) *cols = NULL;
672: if (vals) *vals = NULL;
673: PetscFunctionReturn(PETSC_SUCCESS);
674: }
676: /*@
677: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
678: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
680: Not Collective
682: Input Parameter:
683: . mat - the matrix
685: Level: advanced
687: Note:
688: 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.
690: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
691: @*/
692: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
693: {
694: PetscFunctionBegin;
697: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
698: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
699: MatCheckPreallocated(mat, 1);
700: PetscTryTypeMethod(mat, getrowuppertriangular);
701: PetscFunctionReturn(PETSC_SUCCESS);
702: }
704: /*@
705: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
707: Not Collective
709: Input Parameter:
710: . mat - the matrix
712: Level: advanced
714: Note:
715: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
717: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
718: @*/
719: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
720: {
721: PetscFunctionBegin;
724: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
725: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
726: MatCheckPreallocated(mat, 1);
727: PetscTryTypeMethod(mat, restorerowuppertriangular);
728: PetscFunctionReturn(PETSC_SUCCESS);
729: }
731: /*@
732: MatSetOptionsPrefix - Sets the prefix used for searching for all
733: `Mat` options in the database.
735: Logically Collective
737: Input Parameters:
738: + A - the matrix
739: - prefix - the prefix to prepend to all option names
741: Level: advanced
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: This is NOT used for options for the factorization of the matrix. Normally the
748: prefix is automatically passed in from the PC calling the factorization. To set
749: it directly use `MatSetOptionsPrefixFactor()`
751: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
752: @*/
753: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
754: {
755: PetscFunctionBegin;
757: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
758: PetscTryMethod(A, "MatSetOptionsPrefix_C", (Mat, const char[]), (A, prefix));
759: PetscFunctionReturn(PETSC_SUCCESS);
760: }
762: /*@
763: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
764: for matrices created with `MatGetFactor()`
766: Logically Collective
768: Input Parameters:
769: + A - the matrix
770: - prefix - the prefix to prepend to all option names for the factored matrix
772: Level: developer
774: Notes:
775: A hyphen (-) must NOT be given at the beginning of the prefix name.
776: The first character of all runtime options is AUTOMATICALLY the hyphen.
778: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
779: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
781: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
782: @*/
783: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
784: {
785: PetscFunctionBegin;
787: if (prefix) {
788: PetscAssertPointer(prefix, 2);
789: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
790: if (prefix != A->factorprefix) {
791: PetscCall(PetscFree(A->factorprefix));
792: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
793: }
794: } else PetscCall(PetscFree(A->factorprefix));
795: PetscFunctionReturn(PETSC_SUCCESS);
796: }
798: /*@
799: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
800: for matrices created with `MatGetFactor()`
802: Logically Collective
804: Input Parameters:
805: + A - the matrix
806: - prefix - the prefix to prepend to all option names for the factored matrix
808: Level: developer
810: Notes:
811: A hyphen (-) must NOT be given at the beginning of the prefix name.
812: The first character of all runtime options is AUTOMATICALLY the hyphen.
814: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
815: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
817: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
818: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
819: `MatSetOptionsPrefix()`
820: @*/
821: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
822: {
823: size_t len1, len2, new_len;
825: PetscFunctionBegin;
827: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
828: if (!A->factorprefix) {
829: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
830: PetscFunctionReturn(PETSC_SUCCESS);
831: }
832: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
834: PetscCall(PetscStrlen(A->factorprefix, &len1));
835: PetscCall(PetscStrlen(prefix, &len2));
836: new_len = len1 + len2 + 1;
837: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
838: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
839: PetscFunctionReturn(PETSC_SUCCESS);
840: }
842: /*@
843: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
844: matrix options in the database.
846: Logically Collective
848: Input Parameters:
849: + A - the matrix
850: - prefix - the prefix to prepend to all option names
852: Level: advanced
854: Note:
855: A hyphen (-) must NOT be given at the beginning of the prefix name.
856: The first character of all runtime options is AUTOMATICALLY the hyphen.
858: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
859: @*/
860: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
861: {
862: PetscFunctionBegin;
864: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
865: PetscTryMethod(A, "MatAppendOptionsPrefix_C", (Mat, const char[]), (A, prefix));
866: PetscFunctionReturn(PETSC_SUCCESS);
867: }
869: /*@
870: MatGetOptionsPrefix - Gets the prefix used for searching for all
871: matrix options in the database.
873: Not Collective
875: Input Parameter:
876: . A - the matrix
878: Output Parameter:
879: . prefix - pointer to the prefix string used
881: Level: advanced
883: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
884: @*/
885: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
886: {
887: PetscFunctionBegin;
889: PetscAssertPointer(prefix, 2);
890: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
891: PetscFunctionReturn(PETSC_SUCCESS);
892: }
894: /*@
895: MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`
897: Not Collective
899: Input Parameter:
900: . A - the matrix
902: Output Parameter:
903: . state - the object state
905: Level: advanced
907: Note:
908: Object state is an integer which gets increased every time
909: the object is changed. By saving and later querying the object state
910: one can determine whether information about the object is still current.
912: See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.
914: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
915: @*/
916: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
917: {
918: PetscFunctionBegin;
920: PetscAssertPointer(state, 2);
921: PetscCall(PetscObjectStateGet((PetscObject)A, state));
922: PetscFunctionReturn(PETSC_SUCCESS);
923: }
925: /*@
926: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
928: Collective
930: Input Parameter:
931: . A - the matrix
933: Level: beginner
935: Notes:
936: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
937: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
938: makes all of the preallocation space available
940: Current values in the matrix are lost in this call
942: Currently only supported for `MATAIJ` matrices.
944: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
945: @*/
946: PetscErrorCode MatResetPreallocation(Mat A)
947: {
948: PetscFunctionBegin;
951: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
952: PetscFunctionReturn(PETSC_SUCCESS);
953: }
955: /*@
956: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
958: Collective
960: Input Parameter:
961: . A - the matrix
963: Level: intermediate
965: Notes:
966: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
968: Currently only supported for `MATAIJ` matrices.
970: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
971: @*/
972: PetscErrorCode MatResetHash(Mat A)
973: {
974: PetscFunctionBegin;
977: 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()");
978: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
979: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
980: /* These flags are used to determine whether certain setups occur */
981: A->was_assembled = PETSC_FALSE;
982: A->assembled = PETSC_FALSE;
983: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
984: PetscCall(PetscObjectStateIncrease((PetscObject)A));
985: PetscFunctionReturn(PETSC_SUCCESS);
986: }
988: /*@
989: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
991: Collective
993: Input Parameter:
994: . A - the matrix
996: Level: advanced
998: Notes:
999: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
1000: setting values in the matrix.
1002: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
1004: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1005: @*/
1006: PetscErrorCode MatSetUp(Mat A)
1007: {
1008: PetscFunctionBegin;
1010: if (!((PetscObject)A)->type_name) {
1011: PetscMPIInt size;
1013: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1014: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1015: }
1016: if (!A->preallocated) PetscTryTypeMethod(A, setup);
1017: PetscCall(PetscLayoutSetUp(A->rmap));
1018: PetscCall(PetscLayoutSetUp(A->cmap));
1019: A->preallocated = PETSC_TRUE;
1020: PetscFunctionReturn(PETSC_SUCCESS);
1021: }
1023: #if defined(PETSC_HAVE_SAWS)
1024: #include <petscviewersaws.h>
1025: #endif
1027: /*
1028: If threadsafety is on extraneous matrices may be printed
1030: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1031: */
1032: #if !defined(PETSC_HAVE_THREADSAFETY)
1033: static PetscInt insidematview = 0;
1034: #endif
1036: /*@
1037: MatViewFromOptions - View properties of the matrix based on options set in the options database
1039: Collective
1041: Input Parameters:
1042: + A - the matrix
1043: . obj - optional additional object that provides the options prefix to use
1044: - name - command line option
1046: Options Database Key:
1047: . -mat_view [viewertype]:... - the viewer and its options
1049: Level: intermediate
1051: Note:
1052: .vb
1053: If no value is provided ascii:stdout is used
1054: ascii[:[filename][:[format][:append]]] defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
1055: for example ascii::ascii_info prints just the information about the object not all details
1056: unless :append is given filename opens in write mode, overwriting what was already there
1057: binary[:[filename][:[format][:append]]] defaults to the file binaryoutput
1058: draw[:drawtype[:filename]] for example, draw:tikz, draw:tikz:figure.tex or draw:x
1059: socket[:port] defaults to the standard output port
1060: saws[:communicatorname] publishes object to the Scientific Application Webserver (SAWs)
1061: .ve
1063: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1064: @*/
1065: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1066: {
1067: PetscFunctionBegin;
1069: #if !defined(PETSC_HAVE_THREADSAFETY)
1070: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1071: #endif
1072: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1073: PetscFunctionReturn(PETSC_SUCCESS);
1074: }
1076: /*@
1077: MatView - display information about a matrix in a variety ways
1079: Collective on viewer
1081: Input Parameters:
1082: + mat - the matrix
1083: - viewer - visualization context
1085: Options Database Keys:
1086: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1087: . -mat_view ::ascii_info_detail - Prints more detailed info
1088: . -mat_view - Prints matrix in ASCII format
1089: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1090: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1091: . -display <name> - Sets display name (default is host)
1092: . -draw_pause <sec> - Sets number of seconds to pause after display
1093: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1094: . -viewer_socket_machine <machine> - -
1095: . -viewer_socket_port <port> - -
1096: . -mat_view binary - save matrix to file in binary format
1097: - -viewer_binary_filename <name> - -
1099: Level: beginner
1101: Notes:
1102: The available visualization contexts include
1103: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1104: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1105: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1106: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1108: The user can open alternative visualization contexts with
1109: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1110: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1111: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1112: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1114: The user can call `PetscViewerPushFormat()` to specify the output
1115: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1116: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1117: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1118: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1119: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1120: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1121: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1122: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1123: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1125: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1126: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1128: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1130: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1131: viewer is used.
1133: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1134: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1136: One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1137: and then use the following mouse functions.
1138: .vb
1139: left mouse: zoom in
1140: middle mouse: zoom out
1141: right mouse: continue with the simulation
1142: .ve
1144: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1145: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1146: @*/
1147: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1148: {
1149: PetscInt rows, cols, rbs, cbs;
1150: PetscBool isascii, isstring, issaws;
1151: PetscViewerFormat format;
1152: PetscMPIInt size;
1154: PetscFunctionBegin;
1157: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1160: PetscCall(PetscViewerGetFormat(viewer, &format));
1161: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1162: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1164: #if !defined(PETSC_HAVE_THREADSAFETY)
1165: insidematview++;
1166: #endif
1167: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1168: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1169: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1170: 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");
1172: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1173: if (isascii) {
1174: if (!mat->preallocated) {
1175: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1176: #if !defined(PETSC_HAVE_THREADSAFETY)
1177: insidematview--;
1178: #endif
1179: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1180: PetscFunctionReturn(PETSC_SUCCESS);
1181: }
1182: if (!mat->assembled) {
1183: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1184: #if !defined(PETSC_HAVE_THREADSAFETY)
1185: insidematview--;
1186: #endif
1187: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1188: PetscFunctionReturn(PETSC_SUCCESS);
1189: }
1190: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1191: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1192: MatNullSpace nullsp, transnullsp;
1194: PetscCall(PetscViewerASCIIPushTab(viewer));
1195: PetscCall(MatGetSize(mat, &rows, &cols));
1196: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1197: if (rbs != 1 || cbs != 1) {
1198: 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" : ""));
1199: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1200: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1201: if (mat->factortype) {
1202: MatSolverType solver;
1203: PetscCall(MatFactorGetSolverType(mat, &solver));
1204: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1205: }
1206: if (mat->ops->getinfo) {
1207: PetscBool is_constant_or_diagonal;
1209: // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1210: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1211: if (!is_constant_or_diagonal) {
1212: MatInfo info;
1214: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1215: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1216: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1217: }
1218: }
1219: PetscCall(MatGetNullSpace(mat, &nullsp));
1220: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1221: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1222: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1223: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1224: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1225: PetscCall(PetscViewerASCIIPushTab(viewer));
1226: PetscCall(MatProductView(mat, viewer));
1227: PetscCall(PetscViewerASCIIPopTab(viewer));
1228: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1229: IS tmp;
1231: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1232: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1233: PetscCall(PetscViewerASCIIPushTab(viewer));
1234: PetscCall(ISView(tmp, viewer));
1235: PetscCall(PetscViewerASCIIPopTab(viewer));
1236: PetscCall(ISDestroy(&tmp));
1237: }
1238: }
1239: } else if (issaws) {
1240: #if defined(PETSC_HAVE_SAWS)
1241: PetscMPIInt rank;
1243: PetscCall(PetscObjectName((PetscObject)mat));
1244: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1245: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1246: #endif
1247: } else if (isstring) {
1248: const char *type;
1249: PetscCall(MatGetType(mat, &type));
1250: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1251: PetscTryTypeMethod(mat, view, viewer);
1252: }
1253: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1254: PetscCall(PetscViewerASCIIPushTab(viewer));
1255: PetscUseTypeMethod(mat, viewnative, viewer);
1256: PetscCall(PetscViewerASCIIPopTab(viewer));
1257: } else if (mat->ops->view) {
1258: PetscCall(PetscViewerASCIIPushTab(viewer));
1259: PetscUseTypeMethod(mat, view, viewer);
1260: PetscCall(PetscViewerASCIIPopTab(viewer));
1261: }
1262: if (isascii) {
1263: PetscCall(PetscViewerGetFormat(viewer, &format));
1264: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1265: }
1266: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1267: #if !defined(PETSC_HAVE_THREADSAFETY)
1268: insidematview--;
1269: #endif
1270: PetscFunctionReturn(PETSC_SUCCESS);
1271: }
1273: #if defined(PETSC_USE_DEBUG)
1274: #include <../src/sys/totalview/tv_data_display.h>
1275: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1276: {
1277: TV_add_row("Local rows", "int", &mat->rmap->n);
1278: TV_add_row("Local columns", "int", &mat->cmap->n);
1279: TV_add_row("Global rows", "int", &mat->rmap->N);
1280: TV_add_row("Global columns", "int", &mat->cmap->N);
1281: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1282: return TV_format_OK;
1283: }
1284: #endif
1286: /*@
1287: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1288: with `MatView()`. The matrix format is determined from the options database.
1289: Generates a parallel MPI matrix if the communicator has more than one
1290: processor. The default matrix type is `MATAIJ`.
1292: Collective
1294: Input Parameters:
1295: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1296: or some related function before a call to `MatLoad()`
1297: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1299: Options Database Key:
1300: . -matload_block_size <bs> - set block size
1302: Level: beginner
1304: Notes:
1305: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1306: `Mat` before calling this routine if you wish to set it from the options database.
1308: `MatLoad()` automatically loads into the options database any options
1309: given in the file filename.info where filename is the name of the file
1310: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1311: file will be ignored if you use the -viewer_binary_skip_info option.
1313: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1314: sets the default matrix type AIJ and sets the local and global sizes.
1315: If type and/or size is already set, then the same are used.
1317: In parallel, each processor can load a subset of rows (or the
1318: entire matrix). This routine is especially useful when a large
1319: matrix is stored on disk and only part of it is desired on each
1320: processor. For example, a parallel solver may access only some of
1321: the rows from each processor. The algorithm used here reads
1322: relatively small blocks of data rather than reading the entire
1323: matrix and then subsetting it.
1325: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1326: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1327: or the sequence like
1328: .vb
1329: `PetscViewer` v;
1330: `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1331: `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1332: `PetscViewerSetFromOptions`(v);
1333: `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1334: `PetscViewerFileSetName`(v,"datafile");
1335: .ve
1336: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1337: .vb
1338: -viewer_type {binary, hdf5}
1339: .ve
1341: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1342: and src/mat/tutorials/ex10.c with the second approach.
1344: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1345: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1346: Multiple objects, both matrices and vectors, can be stored within the same file.
1347: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1349: Most users should not need to know the details of the binary storage
1350: format, since `MatLoad()` and `MatView()` completely hide these details.
1351: But for anyone who is interested, the standard binary matrix storage
1352: format is
1354: .vb
1355: PetscInt MAT_FILE_CLASSID
1356: PetscInt number of rows
1357: PetscInt number of columns
1358: PetscInt total number of nonzeros
1359: PetscInt *number nonzeros in each row
1360: PetscInt *column indices of all nonzeros (starting index is zero)
1361: PetscScalar *values of all nonzeros
1362: .ve
1363: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1364: 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
1365: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1367: PETSc automatically does the byte swapping for
1368: machines that store the bytes reversed. Thus if you write your own binary
1369: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1370: and `PetscBinaryWrite()` to see how this may be done.
1372: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1373: Each processor's chunk is loaded independently by its owning MPI process.
1374: Multiple objects, both matrices and vectors, can be stored within the same file.
1375: They are looked up by their PetscObject name.
1377: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1378: by default the same structure and naming of the AIJ arrays and column count
1379: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1380: .vb
1381: save example.mat A b -v7.3
1382: .ve
1383: can be directly read by this routine (see Reference 1 for details).
1385: Depending on your MATLAB version, this format might be a default,
1386: otherwise you can set it as default in Preferences.
1388: Unless -nocompression flag is used to save the file in MATLAB,
1389: PETSc must be configured with ZLIB package.
1391: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1393: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1395: Corresponding `MatView()` is not yet implemented.
1397: The loaded matrix is actually a transpose of the original one in MATLAB,
1398: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1399: With this format, matrix is automatically transposed by PETSc,
1400: unless the matrix is marked as SPD or symmetric
1401: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1403: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1405: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1406: @*/
1407: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1408: {
1409: PetscBool flg;
1411: PetscFunctionBegin;
1415: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1417: flg = PETSC_FALSE;
1418: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1419: if (flg) {
1420: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1421: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1422: }
1423: flg = PETSC_FALSE;
1424: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1425: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1427: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1428: PetscUseTypeMethod(mat, load, viewer);
1429: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1430: PetscFunctionReturn(PETSC_SUCCESS);
1431: }
1433: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1434: {
1435: Mat_Redundant *redund = *redundant;
1437: PetscFunctionBegin;
1438: if (redund) {
1439: if (redund->matseq) { /* via MatCreateSubMatrices() */
1440: PetscCall(ISDestroy(&redund->isrow));
1441: PetscCall(ISDestroy(&redund->iscol));
1442: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1443: } else {
1444: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1445: PetscCall(PetscFree(redund->sbuf_j));
1446: PetscCall(PetscFree(redund->sbuf_a));
1447: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1448: PetscCall(PetscFree(redund->rbuf_j[i]));
1449: PetscCall(PetscFree(redund->rbuf_a[i]));
1450: }
1451: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1452: }
1454: if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1455: PetscCall(PetscFree(redund));
1456: }
1457: PetscFunctionReturn(PETSC_SUCCESS);
1458: }
1460: /*@
1461: MatDestroy - Frees space taken by a matrix.
1463: Collective
1465: Input Parameter:
1466: . A - the matrix
1468: Level: beginner
1470: Developer Note:
1471: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1472: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1473: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1474: if changes are needed here.
1476: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1477: @*/
1478: PetscErrorCode MatDestroy(Mat *A)
1479: {
1480: PetscFunctionBegin;
1481: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1483: if (--((PetscObject)*A)->refct > 0) {
1484: *A = NULL;
1485: PetscFunctionReturn(PETSC_SUCCESS);
1486: }
1488: /* if memory was published with SAWs then destroy it */
1489: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1490: PetscTryTypeMethod(*A, destroy);
1492: PetscCall(PetscFree((*A)->factorprefix));
1493: PetscCall(PetscFree((*A)->defaultvectype));
1494: PetscCall(PetscFree((*A)->defaultrandtype));
1495: PetscCall(PetscFree((*A)->bsizes));
1496: PetscCall(PetscFree((*A)->solvertype));
1497: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1498: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1499: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1500: PetscCall(MatProductClear(*A));
1501: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1502: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1503: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1504: PetscCall(MatDestroy(&(*A)->schur));
1505: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1506: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1507: PetscCall(PetscHeaderDestroy(A));
1508: PetscFunctionReturn(PETSC_SUCCESS);
1509: }
1511: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1512: /*@
1513: MatSetValues - Inserts or adds a block of values into a matrix.
1514: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1515: MUST be called after all calls to `MatSetValues()` have been completed.
1517: Not Collective
1519: Input Parameters:
1520: + mat - the matrix
1521: . m - the number of rows
1522: . idxm - the global indices of the rows
1523: . n - the number of columns
1524: . idxn - the global indices of the columns
1525: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1526: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1527: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1529: Level: beginner
1531: Notes:
1532: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1533: options cannot be mixed without intervening calls to the assembly
1534: routines.
1536: `MatSetValues()` uses 0-based row and column numbers in Fortran
1537: as well as in C.
1539: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1540: simply ignored. This allows easily inserting element stiffness matrices
1541: with homogeneous Dirichlet boundary conditions that you don't want represented
1542: in the matrix.
1544: Efficiency Alert:
1545: The routine `MatSetValuesBlocked()` may offer much better efficiency
1546: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1548: Fortran Notes:
1549: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1550: .vb
1551: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1552: .ve
1554: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1556: Developer Note:
1557: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1558: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1560: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1561: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1562: @*/
1563: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1564: {
1565: PetscFunctionBeginHot;
1568: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1569: PetscAssertPointer(idxm, 3);
1570: PetscAssertPointer(idxn, 5);
1571: MatCheckPreallocated(mat, 1);
1573: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1574: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1576: if (PetscDefined(USE_DEBUG)) {
1577: PetscInt i, j;
1579: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1580: if (v) {
1581: for (i = 0; i < m; i++) {
1582: for (j = 0; j < n; j++) {
1583: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1584: #if defined(PETSC_USE_COMPLEX)
1585: 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]);
1586: #else
1587: 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]);
1588: #endif
1589: }
1590: }
1591: }
1592: 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);
1593: 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);
1594: }
1596: if (mat->assembled) {
1597: mat->was_assembled = PETSC_TRUE;
1598: mat->assembled = PETSC_FALSE;
1599: }
1600: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1601: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1602: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1603: PetscFunctionReturn(PETSC_SUCCESS);
1604: }
1606: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1607: /*@
1608: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1609: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1610: MUST be called after all calls to `MatSetValues()` have been completed.
1612: Not Collective
1614: Input Parameters:
1615: + mat - the matrix
1616: . ism - the rows to provide
1617: . isn - the columns to provide
1618: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1619: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1620: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1622: Level: beginner
1624: Notes:
1625: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1627: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1628: options cannot be mixed without intervening calls to the assembly
1629: routines.
1631: `MatSetValues()` uses 0-based row and column numbers in Fortran
1632: as well as in C.
1634: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1635: simply ignored. This allows easily inserting element stiffness matrices
1636: with homogeneous Dirichlet boundary conditions that you don't want represented
1637: in the matrix.
1639: Fortran Note:
1640: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1642: Efficiency Alert:
1643: The routine `MatSetValuesBlocked()` may offer much better efficiency
1644: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1646: This is currently not optimized for any particular `ISType`
1648: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1649: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1650: @*/
1651: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1652: {
1653: PetscInt m, n;
1654: const PetscInt *rows, *cols;
1656: PetscFunctionBeginHot;
1658: PetscCall(ISGetIndices(ism, &rows));
1659: PetscCall(ISGetIndices(isn, &cols));
1660: PetscCall(ISGetLocalSize(ism, &m));
1661: PetscCall(ISGetLocalSize(isn, &n));
1662: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1663: PetscCall(ISRestoreIndices(ism, &rows));
1664: PetscCall(ISRestoreIndices(isn, &cols));
1665: PetscFunctionReturn(PETSC_SUCCESS);
1666: }
1668: /*@
1669: MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1670: values into a matrix
1672: Not Collective
1674: Input Parameters:
1675: + mat - the matrix
1676: . row - the (block) row to set
1677: - 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.
1678: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1680: Level: intermediate
1682: Notes:
1683: The values, `v`, are column-oriented (for the block version) and sorted
1685: All the nonzero values in `row` must be provided
1687: The matrix must have previously had its column indices set, likely by having been assembled.
1689: `row` must belong to this MPI process
1691: Fortran Note:
1692: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1694: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1695: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1696: @*/
1697: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1698: {
1699: PetscInt globalrow;
1701: PetscFunctionBegin;
1704: PetscAssertPointer(v, 3);
1705: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1706: PetscCall(MatSetValuesRow(mat, globalrow, v));
1707: PetscFunctionReturn(PETSC_SUCCESS);
1708: }
1710: /*@
1711: MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1712: values into a matrix
1714: Not Collective
1716: Input Parameters:
1717: + mat - the matrix
1718: . row - the (block) row to set
1719: - 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
1721: Level: advanced
1723: Notes:
1724: The values, `v`, are column-oriented for the block version.
1726: All the nonzeros in `row` must be provided
1728: THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.
1730: `row` must belong to this process
1732: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1733: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1734: @*/
1735: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1736: {
1737: PetscFunctionBeginHot;
1740: MatCheckPreallocated(mat, 1);
1741: PetscAssertPointer(v, 3);
1742: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1743: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1744: mat->insertmode = INSERT_VALUES;
1746: if (mat->assembled) {
1747: mat->was_assembled = PETSC_TRUE;
1748: mat->assembled = PETSC_FALSE;
1749: }
1750: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1751: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1752: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1753: PetscFunctionReturn(PETSC_SUCCESS);
1754: }
1756: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1757: /*@
1758: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1759: Using structured grid indexing
1761: Not Collective
1763: Input Parameters:
1764: + mat - the matrix
1765: . m - number of rows being entered
1766: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1767: . n - number of columns being entered
1768: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1769: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1770: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1771: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1773: Level: beginner
1775: Notes:
1776: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1778: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1779: options cannot be mixed without intervening calls to the assembly
1780: routines.
1782: The grid coordinates are across the entire grid, not just the local portion
1784: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1785: as well as in C.
1787: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1789: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1790: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1792: The columns and rows in the stencil passed in MUST be contained within the
1793: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1794: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1795: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1796: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1798: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1799: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1800: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1801: `DM_BOUNDARY_PERIODIC` boundary type.
1803: 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
1804: a single value per point) you can skip filling those indices.
1806: Inspired by the structured grid interface to the HYPRE package
1807: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1809: Fortran Note:
1810: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1812: Efficiency Alert:
1813: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1814: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1816: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1817: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1818: @*/
1819: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1820: {
1821: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1822: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1823: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1825: PetscFunctionBegin;
1826: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1829: PetscAssertPointer(idxm, 3);
1830: PetscAssertPointer(idxn, 5);
1832: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1833: jdxm = buf;
1834: jdxn = buf + m;
1835: } else {
1836: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1837: jdxm = bufm;
1838: jdxn = bufn;
1839: }
1840: for (i = 0; i < m; i++) {
1841: for (j = 0; j < 3 - sdim; j++) dxm++;
1842: tmp = *dxm++ - starts[0];
1843: for (j = 0; j < dim - 1; j++) {
1844: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1845: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1846: }
1847: if (mat->stencil.noc) dxm++;
1848: jdxm[i] = tmp;
1849: }
1850: for (i = 0; i < n; i++) {
1851: for (j = 0; j < 3 - sdim; j++) dxn++;
1852: tmp = *dxn++ - starts[0];
1853: for (j = 0; j < dim - 1; j++) {
1854: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1855: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1856: }
1857: if (mat->stencil.noc) dxn++;
1858: jdxn[i] = tmp;
1859: }
1860: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1861: PetscCall(PetscFree2(bufm, bufn));
1862: PetscFunctionReturn(PETSC_SUCCESS);
1863: }
1865: /*@
1866: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1867: Using structured grid indexing
1869: Not Collective
1871: Input Parameters:
1872: + mat - the matrix
1873: . m - number of rows being entered
1874: . idxm - grid coordinates for matrix rows being entered
1875: . n - number of columns being entered
1876: . idxn - grid coordinates for matrix columns being entered
1877: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1878: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1879: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1881: Level: beginner
1883: Notes:
1884: By default the values, `v`, are row-oriented and unsorted.
1885: See `MatSetOption()` for other options.
1887: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1888: options cannot be mixed without intervening calls to the assembly
1889: routines.
1891: The grid coordinates are across the entire grid, not just the local portion
1893: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1894: as well as in C.
1896: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1898: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1899: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1901: The columns and rows in the stencil passed in MUST be contained within the
1902: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1903: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1904: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1905: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1907: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1908: simply ignored. This allows easily inserting element stiffness matrices
1909: with homogeneous Dirichlet boundary conditions that you don't want represented
1910: in the matrix.
1912: Inspired by the structured grid interface to the HYPRE package
1913: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1915: Fortran Notes:
1916: `idxm` and `idxn` should be declared as
1917: .vb
1918: MatStencil idxm(4,m),idxn(4,n)
1919: .ve
1920: and the values inserted using
1921: .vb
1922: idxm(MatStencil_i,1) = i
1923: idxm(MatStencil_j,1) = j
1924: idxm(MatStencil_k,1) = k
1925: etc
1926: .ve
1928: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1930: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1931: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1932: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1933: @*/
1934: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1935: {
1936: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1937: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1938: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1940: PetscFunctionBegin;
1941: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1944: PetscAssertPointer(idxm, 3);
1945: PetscAssertPointer(idxn, 5);
1946: PetscAssertPointer(v, 6);
1948: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1949: jdxm = buf;
1950: jdxn = buf + m;
1951: } else {
1952: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1953: jdxm = bufm;
1954: jdxn = bufn;
1955: }
1956: for (i = 0; i < m; i++) {
1957: for (j = 0; j < 3 - sdim; j++) dxm++;
1958: tmp = *dxm++ - starts[0];
1959: for (j = 0; j < sdim - 1; j++) {
1960: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1961: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1962: }
1963: dxm++;
1964: jdxm[i] = tmp;
1965: }
1966: for (i = 0; i < n; i++) {
1967: for (j = 0; j < 3 - sdim; j++) dxn++;
1968: tmp = *dxn++ - starts[0];
1969: for (j = 0; j < sdim - 1; j++) {
1970: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1971: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1972: }
1973: dxn++;
1974: jdxn[i] = tmp;
1975: }
1976: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1977: PetscCall(PetscFree2(bufm, bufn));
1978: PetscFunctionReturn(PETSC_SUCCESS);
1979: }
1981: /*@
1982: MatSetStencil - Sets the grid information for setting values into a matrix via
1983: `MatSetValuesStencil()`
1985: Not Collective
1987: Input Parameters:
1988: + mat - the matrix
1989: . dim - dimension of the grid 1, 2, or 3
1990: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1991: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1992: - dof - number of degrees of freedom per node
1994: Level: beginner
1996: Notes:
1997: Inspired by the structured grid interface to the HYPRE package
1998: (www.llnl.gov/CASC/hyper)
2000: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
2001: user.
2003: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
2004: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
2005: @*/
2006: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
2007: {
2008: PetscFunctionBegin;
2010: PetscAssertPointer(dims, 3);
2011: PetscAssertPointer(starts, 4);
2013: mat->stencil.dim = dim + (dof > 1);
2014: for (PetscInt i = 0; i < dim; i++) {
2015: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
2016: mat->stencil.starts[i] = starts[dim - i - 1];
2017: }
2018: mat->stencil.dims[dim] = dof;
2019: mat->stencil.starts[dim] = 0;
2020: mat->stencil.noc = (PetscBool)(dof == 1);
2021: PetscFunctionReturn(PETSC_SUCCESS);
2022: }
2024: /*@
2025: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
2027: Not Collective
2029: Input Parameters:
2030: + mat - the matrix
2031: . m - the number of block rows
2032: . idxm - the global block indices
2033: . n - the number of block columns
2034: . idxn - the global block indices
2035: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2036: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2037: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
2039: Level: intermediate
2041: Notes:
2042: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2043: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2045: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2046: NOT the total number of rows/columns; for example, if the block size is 2 and
2047: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2048: The values in `idxm` would be 1 2; that is the first index for each block divided by
2049: the block size.
2051: You must call `MatSetBlockSize()` when constructing this matrix (before
2052: preallocating it).
2054: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2056: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2057: options cannot be mixed without intervening calls to the assembly
2058: routines.
2060: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2061: as well as in C.
2063: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2064: simply ignored. This allows easily inserting element stiffness matrices
2065: with homogeneous Dirichlet boundary conditions that you don't want represented
2066: in the matrix.
2068: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2069: internal searching must be done to determine where to place the
2070: data in the matrix storage space. By instead inserting blocks of
2071: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2072: reduced.
2074: Example:
2075: .vb
2076: Suppose m=n=2 and block size(bs) = 2 The array is
2078: 1 2 | 3 4
2079: 5 6 | 7 8
2080: - - - | - - -
2081: 9 10 | 11 12
2082: 13 14 | 15 16
2084: v[] should be passed in like
2085: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2087: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2088: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2089: .ve
2091: Fortran Notes:
2092: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2093: .vb
2094: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2095: .ve
2097: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2099: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2100: @*/
2101: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2102: {
2103: PetscFunctionBeginHot;
2106: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2107: PetscAssertPointer(idxm, 3);
2108: PetscAssertPointer(idxn, 5);
2109: MatCheckPreallocated(mat, 1);
2110: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2111: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2112: if (PetscDefined(USE_DEBUG)) {
2113: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2114: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2115: }
2116: if (PetscDefined(USE_DEBUG)) {
2117: PetscInt rbs, cbs, M, N, i;
2118: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2119: PetscCall(MatGetSize(mat, &M, &N));
2120: 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);
2121: for (i = 0; i < n; i++)
2122: 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);
2123: }
2124: if (mat->assembled) {
2125: mat->was_assembled = PETSC_TRUE;
2126: mat->assembled = PETSC_FALSE;
2127: }
2128: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2129: if (mat->ops->setvaluesblocked) {
2130: PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2131: } else {
2132: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2133: PetscInt i, j, bs, cbs;
2135: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2136: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2137: iidxm = buf;
2138: iidxn = buf + m * bs;
2139: } else {
2140: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2141: iidxm = bufr;
2142: iidxn = bufc;
2143: }
2144: for (i = 0; i < m; i++) {
2145: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2146: }
2147: if (m != n || bs != cbs || idxm != idxn) {
2148: for (i = 0; i < n; i++) {
2149: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2150: }
2151: } else iidxn = iidxm;
2152: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2153: PetscCall(PetscFree2(bufr, bufc));
2154: }
2155: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2156: PetscFunctionReturn(PETSC_SUCCESS);
2157: }
2159: /*@
2160: MatGetValues - Gets a block of local values from a matrix.
2162: Not Collective; can only return values that are owned by the give process
2164: Input Parameters:
2165: + mat - the matrix
2166: . v - a logically two-dimensional array for storing the values
2167: . m - the number of rows
2168: . idxm - the global indices of the rows
2169: . n - the number of columns
2170: - idxn - the global indices of the columns
2172: Level: advanced
2174: Notes:
2175: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2176: The values, `v`, are then returned in a row-oriented format,
2177: analogous to that used by default in `MatSetValues()`.
2179: `MatGetValues()` uses 0-based row and column numbers in
2180: Fortran as well as in C.
2182: `MatGetValues()` requires that the matrix has been assembled
2183: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2184: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2185: without intermediate matrix assembly.
2187: Negative row or column indices will be ignored and those locations in `v` will be
2188: left unchanged.
2190: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2191: That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2192: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2194: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2195: @*/
2196: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2197: {
2198: PetscFunctionBegin;
2201: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2202: PetscAssertPointer(idxm, 3);
2203: PetscAssertPointer(idxn, 5);
2204: PetscAssertPointer(v, 6);
2205: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2206: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2207: MatCheckPreallocated(mat, 1);
2209: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2210: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2211: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2212: PetscFunctionReturn(PETSC_SUCCESS);
2213: }
2215: /*@
2216: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2217: defined previously by `MatSetLocalToGlobalMapping()`
2219: Not Collective
2221: Input Parameters:
2222: + mat - the matrix
2223: . nrow - number of rows
2224: . irow - the row local indices
2225: . ncol - number of columns
2226: - icol - the column local indices
2228: Output Parameter:
2229: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2230: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2232: Level: advanced
2234: Notes:
2235: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2237: 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,
2238: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2239: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2240: with `MatSetLocalToGlobalMapping()`.
2242: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2243: `MatSetValuesLocal()`, `MatGetValues()`
2244: @*/
2245: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2246: {
2247: PetscFunctionBeginHot;
2250: MatCheckPreallocated(mat, 1);
2251: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2252: PetscAssertPointer(irow, 3);
2253: PetscAssertPointer(icol, 5);
2254: if (PetscDefined(USE_DEBUG)) {
2255: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2256: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2257: }
2258: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2259: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2260: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2261: else {
2262: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2263: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2264: irowm = buf;
2265: icolm = buf + nrow;
2266: } else {
2267: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2268: irowm = bufr;
2269: icolm = bufc;
2270: }
2271: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2272: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2273: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2274: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2275: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2276: PetscCall(PetscFree2(bufr, bufc));
2277: }
2278: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2279: PetscFunctionReturn(PETSC_SUCCESS);
2280: }
2282: /*@
2283: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2284: the same size. Currently, this can only be called once and creates the given matrix.
2286: Not Collective
2288: Input Parameters:
2289: + mat - the matrix
2290: . nb - the number of blocks
2291: . bs - the number of rows (and columns) in each block
2292: . rows - a concatenation of the rows for each block
2293: - v - a concatenation of logically two-dimensional arrays of values
2295: Level: advanced
2297: Notes:
2298: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2300: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2302: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2303: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2304: @*/
2305: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2306: {
2307: PetscFunctionBegin;
2310: PetscAssertPointer(rows, 4);
2311: PetscAssertPointer(v, 5);
2312: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2314: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2315: if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2316: else {
2317: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2318: }
2319: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2320: PetscFunctionReturn(PETSC_SUCCESS);
2321: }
2323: /*@
2324: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2325: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2326: using a local (per-processor) numbering.
2328: Not Collective
2330: Input Parameters:
2331: + x - the matrix
2332: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2333: - cmapping - column mapping
2335: Level: intermediate
2337: Note:
2338: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2340: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2341: @*/
2342: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2343: {
2344: PetscFunctionBegin;
2349: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2350: else {
2351: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2352: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2353: }
2354: PetscFunctionReturn(PETSC_SUCCESS);
2355: }
2357: /*@
2358: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2360: Not Collective
2362: Input Parameter:
2363: . A - the matrix
2365: Output Parameters:
2366: + rmapping - row mapping
2367: - cmapping - column mapping
2369: Level: advanced
2371: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2372: @*/
2373: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2374: {
2375: PetscFunctionBegin;
2378: if (rmapping) {
2379: PetscAssertPointer(rmapping, 2);
2380: *rmapping = A->rmap->mapping;
2381: }
2382: if (cmapping) {
2383: PetscAssertPointer(cmapping, 3);
2384: *cmapping = A->cmap->mapping;
2385: }
2386: PetscFunctionReturn(PETSC_SUCCESS);
2387: }
2389: /*@
2390: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2392: Logically Collective
2394: Input Parameters:
2395: + A - the matrix
2396: . rmap - row layout
2397: - cmap - column layout
2399: Level: advanced
2401: Note:
2402: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2404: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2405: @*/
2406: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2407: {
2408: PetscFunctionBegin;
2410: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2411: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2412: PetscFunctionReturn(PETSC_SUCCESS);
2413: }
2415: /*@
2416: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2418: Not Collective
2420: Input Parameter:
2421: . A - the matrix
2423: Output Parameters:
2424: + rmap - row layout
2425: - cmap - column layout
2427: Level: advanced
2429: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2430: @*/
2431: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2432: {
2433: PetscFunctionBegin;
2436: if (rmap) {
2437: PetscAssertPointer(rmap, 2);
2438: *rmap = A->rmap;
2439: }
2440: if (cmap) {
2441: PetscAssertPointer(cmap, 3);
2442: *cmap = A->cmap;
2443: }
2444: PetscFunctionReturn(PETSC_SUCCESS);
2445: }
2447: /*@
2448: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2449: using a local numbering of the rows and columns.
2451: Not Collective
2453: Input Parameters:
2454: + mat - the matrix
2455: . nrow - number of rows
2456: . irow - the row local indices
2457: . ncol - number of columns
2458: . icol - the column local indices
2459: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2460: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2461: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2463: Level: intermediate
2465: Notes:
2466: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2468: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2469: options cannot be mixed without intervening calls to the assembly
2470: routines.
2472: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2473: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2475: Fortran Notes:
2476: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2477: .vb
2478: call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2479: .ve
2481: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2483: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2484: `MatGetValuesLocal()`
2485: @*/
2486: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2487: {
2488: PetscFunctionBeginHot;
2491: MatCheckPreallocated(mat, 1);
2492: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2493: PetscAssertPointer(irow, 3);
2494: PetscAssertPointer(icol, 5);
2495: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2496: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2497: if (PetscDefined(USE_DEBUG)) {
2498: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2499: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2500: }
2502: if (mat->assembled) {
2503: mat->was_assembled = PETSC_TRUE;
2504: mat->assembled = PETSC_FALSE;
2505: }
2506: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2507: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2508: else {
2509: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2510: const PetscInt *irowm, *icolm;
2512: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2513: bufr = buf;
2514: bufc = buf + nrow;
2515: irowm = bufr;
2516: icolm = bufc;
2517: } else {
2518: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2519: irowm = bufr;
2520: icolm = bufc;
2521: }
2522: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2523: else irowm = irow;
2524: if (mat->cmap->mapping) {
2525: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2526: else icolm = irowm;
2527: } else icolm = icol;
2528: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2529: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2530: }
2531: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2532: PetscFunctionReturn(PETSC_SUCCESS);
2533: }
2535: /*@
2536: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2537: using a local ordering of the nodes a block at a time.
2539: Not Collective
2541: Input Parameters:
2542: + mat - the matrix
2543: . nrow - number of rows
2544: . irow - the row local indices
2545: . ncol - number of columns
2546: . icol - the column local indices
2547: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2548: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2549: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2551: Level: intermediate
2553: Notes:
2554: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2555: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2557: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2558: options cannot be mixed without intervening calls to the assembly
2559: routines.
2561: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2562: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2564: Fortran Notes:
2565: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2566: .vb
2567: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2568: .ve
2570: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2572: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2573: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2574: @*/
2575: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2576: {
2577: PetscFunctionBeginHot;
2580: MatCheckPreallocated(mat, 1);
2581: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2582: PetscAssertPointer(irow, 3);
2583: PetscAssertPointer(icol, 5);
2584: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2585: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2586: if (PetscDefined(USE_DEBUG)) {
2587: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2588: 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);
2589: }
2591: if (mat->assembled) {
2592: mat->was_assembled = PETSC_TRUE;
2593: mat->assembled = PETSC_FALSE;
2594: }
2595: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2596: PetscInt irbs, rbs;
2597: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2598: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2599: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2600: }
2601: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2602: PetscInt icbs, cbs;
2603: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2604: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2605: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2606: }
2607: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2608: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2609: else {
2610: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2611: const PetscInt *irowm, *icolm;
2613: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2614: bufr = buf;
2615: bufc = buf + nrow;
2616: irowm = bufr;
2617: icolm = bufc;
2618: } else {
2619: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2620: irowm = bufr;
2621: icolm = bufc;
2622: }
2623: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2624: else irowm = irow;
2625: if (mat->cmap->mapping) {
2626: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2627: else icolm = irowm;
2628: } else icolm = icol;
2629: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2630: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2631: }
2632: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2633: PetscFunctionReturn(PETSC_SUCCESS);
2634: }
2636: /*@
2637: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2639: Collective
2641: Input Parameters:
2642: + mat - the matrix
2643: - x - the vector to be multiplied
2645: Output Parameter:
2646: . y - the result
2648: Level: developer
2650: Note:
2651: The vectors `x` and `y` cannot be the same. I.e., one cannot
2652: call `MatMultDiagonalBlock`(A,y,y).
2654: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2655: @*/
2656: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2657: {
2658: PetscFunctionBegin;
2664: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2665: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2666: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2667: MatCheckPreallocated(mat, 1);
2669: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2670: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2671: PetscFunctionReturn(PETSC_SUCCESS);
2672: }
2674: /*@
2675: MatMult - Computes the matrix-vector product, $y = Ax$.
2677: Neighbor-wise Collective
2679: Input Parameters:
2680: + mat - the matrix
2681: - x - the vector to be multiplied
2683: Output Parameter:
2684: . y - the result
2686: Level: beginner
2688: Note:
2689: The vectors `x` and `y` cannot be the same. I.e., one cannot
2690: call `MatMult`(A,y,y).
2692: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2693: @*/
2694: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2695: {
2696: PetscFunctionBegin;
2700: VecCheckAssembled(x);
2702: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2703: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2704: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2705: 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);
2706: 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);
2707: 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);
2708: 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);
2709: PetscCall(VecSetErrorIfLocked(y, 3));
2710: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2711: MatCheckPreallocated(mat, 1);
2713: PetscCall(VecLockReadPush(x));
2714: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2715: PetscUseTypeMethod(mat, mult, x, y);
2716: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2717: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2718: PetscCall(VecLockReadPop(x));
2719: PetscFunctionReturn(PETSC_SUCCESS);
2720: }
2722: /*@
2723: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2725: Neighbor-wise Collective
2727: Input Parameters:
2728: + mat - the matrix
2729: - x - the vector to be multiplied
2731: Output Parameter:
2732: . y - the result
2734: Level: beginner
2736: Notes:
2737: The vectors `x` and `y` cannot be the same. I.e., one cannot
2738: call `MatMultTranspose`(A,y,y).
2740: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2741: use `MatMultHermitianTranspose()`
2743: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2744: @*/
2745: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2746: {
2747: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2749: PetscFunctionBegin;
2753: VecCheckAssembled(x);
2756: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2757: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2758: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2759: 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);
2760: 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);
2761: 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);
2762: 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);
2763: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2764: MatCheckPreallocated(mat, 1);
2766: if (!mat->ops->multtranspose) {
2767: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2768: 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);
2769: } else op = mat->ops->multtranspose;
2770: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2771: PetscCall(VecLockReadPush(x));
2772: PetscCall((*op)(mat, x, y));
2773: PetscCall(VecLockReadPop(x));
2774: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2775: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2776: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2777: PetscFunctionReturn(PETSC_SUCCESS);
2778: }
2780: /*@
2781: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2783: Neighbor-wise Collective
2785: Input Parameters:
2786: + mat - the matrix
2787: - x - the vector to be multiplied
2789: Output Parameter:
2790: . y - the result
2792: Level: beginner
2794: Notes:
2795: The vectors `x` and `y` cannot be the same. I.e., one cannot
2796: call `MatMultHermitianTranspose`(A,y,y).
2798: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2800: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2802: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2803: @*/
2804: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2805: {
2806: PetscFunctionBegin;
2812: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2813: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2814: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2815: 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);
2816: 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);
2817: 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);
2818: 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);
2819: MatCheckPreallocated(mat, 1);
2821: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2822: #if defined(PETSC_USE_COMPLEX)
2823: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2824: PetscCall(VecLockReadPush(x));
2825: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2826: else PetscUseTypeMethod(mat, mult, x, y);
2827: PetscCall(VecLockReadPop(x));
2828: } else {
2829: Vec w;
2830: PetscCall(VecDuplicate(x, &w));
2831: PetscCall(VecCopy(x, w));
2832: PetscCall(VecConjugate(w));
2833: PetscCall(MatMultTranspose(mat, w, y));
2834: PetscCall(VecDestroy(&w));
2835: PetscCall(VecConjugate(y));
2836: }
2837: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2838: #else
2839: PetscCall(MatMultTranspose(mat, x, y));
2840: #endif
2841: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2842: PetscFunctionReturn(PETSC_SUCCESS);
2843: }
2845: /*@
2846: MatMultAdd - Computes $v3 = v2 + A * v1$.
2848: Neighbor-wise Collective
2850: Input Parameters:
2851: + mat - the matrix
2852: . v1 - the vector to be multiplied by `mat`
2853: - v2 - the vector to be added to the result
2855: Output Parameter:
2856: . v3 - the result
2858: Level: beginner
2860: Note:
2861: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2862: call `MatMultAdd`(A,v1,v2,v1).
2864: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2865: @*/
2866: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2867: {
2868: PetscFunctionBegin;
2875: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2876: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2877: 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);
2878: /* 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);
2879: 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); */
2880: 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);
2881: 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);
2882: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2883: MatCheckPreallocated(mat, 1);
2885: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2886: PetscCall(VecLockReadPush(v1));
2887: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2888: PetscCall(VecLockReadPop(v1));
2889: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2890: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2891: PetscFunctionReturn(PETSC_SUCCESS);
2892: }
2894: /*@
2895: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2897: Neighbor-wise Collective
2899: Input Parameters:
2900: + mat - the matrix
2901: . v1 - the vector to be multiplied by the transpose of the matrix
2902: - v2 - the vector to be added to the result
2904: Output Parameter:
2905: . v3 - the result
2907: Level: beginner
2909: Note:
2910: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2911: call `MatMultTransposeAdd`(A,v1,v2,v1).
2913: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2914: @*/
2915: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2916: {
2917: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2919: PetscFunctionBegin;
2926: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2927: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2928: 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);
2929: 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);
2930: 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);
2931: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2932: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2933: MatCheckPreallocated(mat, 1);
2935: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2936: PetscCall(VecLockReadPush(v1));
2937: PetscCall((*op)(mat, v1, v2, v3));
2938: PetscCall(VecLockReadPop(v1));
2939: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2940: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2941: PetscFunctionReturn(PETSC_SUCCESS);
2942: }
2944: /*@
2945: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2947: Neighbor-wise Collective
2949: Input Parameters:
2950: + mat - the matrix
2951: . v1 - the vector to be multiplied by the Hermitian transpose
2952: - v2 - the vector to be added to the result
2954: Output Parameter:
2955: . v3 - the result
2957: Level: beginner
2959: Note:
2960: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2961: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2963: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2964: @*/
2965: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2966: {
2967: PetscFunctionBegin;
2974: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2975: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2976: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2977: 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);
2978: 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);
2979: 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);
2980: MatCheckPreallocated(mat, 1);
2982: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2983: PetscCall(VecLockReadPush(v1));
2984: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2985: else {
2986: Vec w, z;
2987: PetscCall(VecDuplicate(v1, &w));
2988: PetscCall(VecCopy(v1, w));
2989: PetscCall(VecConjugate(w));
2990: PetscCall(VecDuplicate(v3, &z));
2991: PetscCall(MatMultTranspose(mat, w, z));
2992: PetscCall(VecDestroy(&w));
2993: PetscCall(VecConjugate(z));
2994: if (v2 != v3) {
2995: PetscCall(VecWAXPY(v3, 1.0, v2, z));
2996: } else {
2997: PetscCall(VecAXPY(v3, 1.0, z));
2998: }
2999: PetscCall(VecDestroy(&z));
3000: }
3001: PetscCall(VecLockReadPop(v1));
3002: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
3003: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
3004: PetscFunctionReturn(PETSC_SUCCESS);
3005: }
3007: /*@
3008: MatGetFactorType - gets the type of factorization a matrix is
3010: Not Collective
3012: Input Parameter:
3013: . mat - the matrix
3015: Output Parameter:
3016: . 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`
3018: Level: intermediate
3020: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3021: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3022: @*/
3023: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3024: {
3025: PetscFunctionBegin;
3028: PetscAssertPointer(t, 2);
3029: *t = mat->factortype;
3030: PetscFunctionReturn(PETSC_SUCCESS);
3031: }
3033: /*@
3034: MatSetFactorType - sets the type of factorization a matrix is
3036: Logically Collective
3038: Input Parameters:
3039: + mat - the matrix
3040: - 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`
3042: Level: intermediate
3044: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3045: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3046: @*/
3047: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3048: {
3049: PetscFunctionBegin;
3052: mat->factortype = t;
3053: PetscFunctionReturn(PETSC_SUCCESS);
3054: }
3056: /*@
3057: MatGetInfo - Returns information about matrix storage (number of
3058: nonzeros, memory, etc.).
3060: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3062: Input Parameters:
3063: + mat - the matrix
3064: - 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)
3066: Output Parameter:
3067: . info - matrix information context
3069: Options Database Key:
3070: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3072: Level: intermediate
3074: Notes:
3075: The `MatInfo` context contains a variety of matrix data, including
3076: number of nonzeros allocated and used, number of mallocs during
3077: matrix assembly, etc. Additional information for factored matrices
3078: is provided (such as the fill ratio, number of mallocs during
3079: factorization, etc.).
3081: Example:
3082: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3083: data within the `MatInfo` context. For example,
3084: .vb
3085: MatInfo info;
3086: Mat A;
3087: double mal, nz_a, nz_u;
3089: MatGetInfo(A, MAT_LOCAL, &info);
3090: mal = info.mallocs;
3091: nz_a = info.nz_allocated;
3092: .ve
3094: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3095: @*/
3096: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3097: {
3098: PetscFunctionBegin;
3101: PetscAssertPointer(info, 3);
3102: MatCheckPreallocated(mat, 1);
3103: PetscUseTypeMethod(mat, getinfo, flag, info);
3104: PetscFunctionReturn(PETSC_SUCCESS);
3105: }
3107: /*
3108: This is used by external packages where it is not easy to get the info from the actual
3109: matrix factorization.
3110: */
3111: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3112: {
3113: PetscFunctionBegin;
3114: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3115: PetscFunctionReturn(PETSC_SUCCESS);
3116: }
3118: /*@
3119: MatLUFactor - Performs in-place LU factorization of matrix.
3121: Collective
3123: Input Parameters:
3124: + mat - the matrix
3125: . row - row permutation
3126: . col - column permutation
3127: - info - options for factorization, includes
3128: .vb
3129: fill - expected fill as ratio of original fill.
3130: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3131: Run with the option -info to determine an optimal value to use
3132: .ve
3134: Level: developer
3136: Notes:
3137: Most users should employ the `KSP` interface for linear solvers
3138: instead of working directly with matrix algebra routines such as this.
3139: See, e.g., `KSPCreate()`.
3141: This changes the state of the matrix to a factored matrix; it cannot be used
3142: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3144: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3145: when not using `KSP`.
3147: Fortran Note:
3148: A valid (non-null) `info` argument must be provided
3150: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3151: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3152: @*/
3153: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3154: {
3155: MatFactorInfo tinfo;
3157: PetscFunctionBegin;
3161: if (info) PetscAssertPointer(info, 4);
3163: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3164: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3165: MatCheckPreallocated(mat, 1);
3166: if (!info) {
3167: PetscCall(MatFactorInfoInitialize(&tinfo));
3168: info = &tinfo;
3169: }
3171: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3172: PetscUseTypeMethod(mat, lufactor, row, col, info);
3173: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3174: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3175: PetscFunctionReturn(PETSC_SUCCESS);
3176: }
3178: /*@
3179: MatILUFactor - Performs in-place ILU factorization of matrix.
3181: Collective
3183: Input Parameters:
3184: + mat - the matrix
3185: . row - row permutation
3186: . col - column permutation
3187: - info - structure containing
3188: .vb
3189: levels - number of levels of fill.
3190: expected fill - as ratio of original fill.
3191: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3192: missing diagonal entries)
3193: .ve
3195: Level: developer
3197: Notes:
3198: Most users should employ the `KSP` interface for linear solvers
3199: instead of working directly with matrix algebra routines such as this.
3200: See, e.g., `KSPCreate()`.
3202: Probably really in-place only when level of fill is zero, otherwise allocates
3203: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3204: when not using `KSP`.
3206: Fortran Note:
3207: A valid (non-null) `info` argument must be provided
3209: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3210: @*/
3211: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3212: {
3213: PetscFunctionBegin;
3217: PetscAssertPointer(info, 4);
3219: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3220: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3221: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3222: MatCheckPreallocated(mat, 1);
3224: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3225: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3226: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3227: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3228: PetscFunctionReturn(PETSC_SUCCESS);
3229: }
3231: /*@
3232: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3233: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3235: Collective
3237: Input Parameters:
3238: + fact - the factor matrix obtained with `MatGetFactor()`
3239: . mat - the matrix
3240: . row - the row permutation
3241: . col - the column permutation
3242: - info - options for factorization, includes
3243: .vb
3244: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3245: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3246: .ve
3248: Level: developer
3250: Notes:
3251: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3253: Most users should employ the simplified `KSP` interface for linear solvers
3254: instead of working directly with matrix algebra routines such as this.
3255: See, e.g., `KSPCreate()`.
3257: Fortran Note:
3258: A valid (non-null) `info` argument must be provided
3260: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3261: @*/
3262: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3263: {
3264: MatFactorInfo tinfo;
3266: PetscFunctionBegin;
3271: if (info) PetscAssertPointer(info, 5);
3274: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3275: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3276: MatCheckPreallocated(mat, 2);
3277: if (!info) {
3278: PetscCall(MatFactorInfoInitialize(&tinfo));
3279: info = &tinfo;
3280: }
3282: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3283: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3284: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3285: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3286: PetscFunctionReturn(PETSC_SUCCESS);
3287: }
3289: /*@
3290: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3291: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3293: Collective
3295: Input Parameters:
3296: + fact - the factor matrix obtained with `MatGetFactor()`
3297: . mat - the matrix
3298: - info - options for factorization
3300: Level: developer
3302: Notes:
3303: See `MatLUFactor()` for in-place factorization. See
3304: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3306: Most users should employ the `KSP` interface for linear solvers
3307: instead of working directly with matrix algebra routines such as this.
3308: See, e.g., `KSPCreate()`.
3310: Fortran Note:
3311: A valid (non-null) `info` argument must be provided
3313: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3314: @*/
3315: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3316: {
3317: MatFactorInfo tinfo;
3319: PetscFunctionBegin;
3324: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3325: 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,
3326: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3328: MatCheckPreallocated(mat, 2);
3329: if (!info) {
3330: PetscCall(MatFactorInfoInitialize(&tinfo));
3331: info = &tinfo;
3332: }
3334: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3335: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3336: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3337: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3338: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3339: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3340: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3341: PetscFunctionReturn(PETSC_SUCCESS);
3342: }
3344: /*@
3345: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3346: symmetric matrix.
3348: Collective
3350: Input Parameters:
3351: + mat - the matrix
3352: . perm - row and column permutations
3353: - info - expected fill as ratio of original fill
3355: Level: developer
3357: Notes:
3358: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3359: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3361: Most users should employ the `KSP` interface for linear solvers
3362: instead of working directly with matrix algebra routines such as this.
3363: See, e.g., `KSPCreate()`.
3365: Fortran Note:
3366: A valid (non-null) `info` argument must be provided
3368: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3369: `MatGetOrdering()`
3370: @*/
3371: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3372: {
3373: MatFactorInfo tinfo;
3375: PetscFunctionBegin;
3378: if (info) PetscAssertPointer(info, 3);
3380: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3381: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3382: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3383: MatCheckPreallocated(mat, 1);
3384: if (!info) {
3385: PetscCall(MatFactorInfoInitialize(&tinfo));
3386: info = &tinfo;
3387: }
3389: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3390: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3391: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3392: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3393: PetscFunctionReturn(PETSC_SUCCESS);
3394: }
3396: /*@
3397: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3398: of a symmetric matrix.
3400: Collective
3402: Input Parameters:
3403: + fact - the factor matrix obtained with `MatGetFactor()`
3404: . mat - the matrix
3405: . perm - row and column permutations
3406: - info - options for factorization, includes
3407: .vb
3408: fill - expected fill as ratio of original fill.
3409: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3410: Run with the option -info to determine an optimal value to use
3411: .ve
3413: Level: developer
3415: Notes:
3416: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3417: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3419: Most users should employ the `KSP` interface for linear solvers
3420: instead of working directly with matrix algebra routines such as this.
3421: See, e.g., `KSPCreate()`.
3423: Fortran Note:
3424: A valid (non-null) `info` argument must be provided
3426: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3427: `MatGetOrdering()`
3428: @*/
3429: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3430: {
3431: MatFactorInfo tinfo;
3433: PetscFunctionBegin;
3437: if (info) PetscAssertPointer(info, 4);
3440: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3441: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3442: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3443: MatCheckPreallocated(mat, 2);
3444: if (!info) {
3445: PetscCall(MatFactorInfoInitialize(&tinfo));
3446: info = &tinfo;
3447: }
3449: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3450: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3451: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3452: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3453: PetscFunctionReturn(PETSC_SUCCESS);
3454: }
3456: /*@
3457: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3458: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3459: `MatCholeskyFactorSymbolic()`.
3461: Collective
3463: Input Parameters:
3464: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3465: . mat - the initial matrix that is to be factored
3466: - info - options for factorization
3468: Level: developer
3470: Note:
3471: Most users should employ the `KSP` interface for linear solvers
3472: instead of working directly with matrix algebra routines such as this.
3473: See, e.g., `KSPCreate()`.
3475: Fortran Note:
3476: A valid (non-null) `info` argument must be provided
3478: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3479: @*/
3480: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3481: {
3482: MatFactorInfo tinfo;
3484: PetscFunctionBegin;
3489: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3490: 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,
3491: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3492: MatCheckPreallocated(mat, 2);
3493: if (!info) {
3494: PetscCall(MatFactorInfoInitialize(&tinfo));
3495: info = &tinfo;
3496: }
3498: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3499: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3500: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3501: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3502: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3503: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3504: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3505: PetscFunctionReturn(PETSC_SUCCESS);
3506: }
3508: /*@
3509: MatQRFactor - Performs in-place QR factorization of matrix.
3511: Collective
3513: Input Parameters:
3514: + mat - the matrix
3515: . col - column permutation
3516: - info - options for factorization, includes
3517: .vb
3518: fill - expected fill as ratio of original fill.
3519: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3520: Run with the option -info to determine an optimal value to use
3521: .ve
3523: Level: developer
3525: Notes:
3526: Most users should employ the `KSP` interface for linear solvers
3527: instead of working directly with matrix algebra routines such as this.
3528: See, e.g., `KSPCreate()`.
3530: This changes the state of the matrix to a factored matrix; it cannot be used
3531: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3533: Fortran Note:
3534: A valid (non-null) `info` argument must be provided
3536: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3537: `MatSetUnfactored()`
3538: @*/
3539: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3540: {
3541: PetscFunctionBegin;
3544: if (info) PetscAssertPointer(info, 3);
3546: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3547: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3548: MatCheckPreallocated(mat, 1);
3549: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3550: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3551: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3552: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3553: PetscFunctionReturn(PETSC_SUCCESS);
3554: }
3556: /*@
3557: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3558: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3560: Collective
3562: Input Parameters:
3563: + fact - the factor matrix obtained with `MatGetFactor()`
3564: . mat - the matrix
3565: . col - column permutation
3566: - info - options for factorization, includes
3567: .vb
3568: fill - expected fill as ratio of original fill.
3569: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3570: Run with the option -info to determine an optimal value to use
3571: .ve
3573: Level: developer
3575: Note:
3576: Most users should employ the `KSP` interface for linear solvers
3577: instead of working directly with matrix algebra routines such as this.
3578: See, e.g., `KSPCreate()`.
3580: Fortran Note:
3581: A valid (non-null) `info` argument must be provided
3583: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3584: @*/
3585: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3586: {
3587: MatFactorInfo tinfo;
3589: PetscFunctionBegin;
3593: if (info) PetscAssertPointer(info, 4);
3596: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3597: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3598: MatCheckPreallocated(mat, 2);
3599: if (!info) {
3600: PetscCall(MatFactorInfoInitialize(&tinfo));
3601: info = &tinfo;
3602: }
3604: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3605: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3606: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3607: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3608: PetscFunctionReturn(PETSC_SUCCESS);
3609: }
3611: /*@
3612: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3613: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3615: Collective
3617: Input Parameters:
3618: + fact - the factor matrix obtained with `MatGetFactor()`
3619: . mat - the matrix
3620: - info - options for factorization
3622: Level: developer
3624: Notes:
3625: See `MatQRFactor()` for in-place factorization.
3627: Most users should employ the `KSP` interface for linear solvers
3628: instead of working directly with matrix algebra routines such as this.
3629: See, e.g., `KSPCreate()`.
3631: Fortran Note:
3632: A valid (non-null) `info` argument must be provided
3634: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3635: @*/
3636: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3637: {
3638: MatFactorInfo tinfo;
3640: PetscFunctionBegin;
3645: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3646: 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,
3647: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3649: MatCheckPreallocated(mat, 2);
3650: if (!info) {
3651: PetscCall(MatFactorInfoInitialize(&tinfo));
3652: info = &tinfo;
3653: }
3655: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3656: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3657: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3658: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3659: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3660: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3661: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3662: PetscFunctionReturn(PETSC_SUCCESS);
3663: }
3665: /*@
3666: MatSolve - Solves $A x = b$, given a factored matrix.
3668: Neighbor-wise Collective
3670: Input Parameters:
3671: + mat - the factored matrix
3672: - b - the right-hand-side vector
3674: Output Parameter:
3675: . x - the result vector
3677: Level: developer
3679: Notes:
3680: The vectors `b` and `x` cannot be the same. I.e., one cannot
3681: call `MatSolve`(A,x,x).
3683: Most users should employ the `KSP` interface for linear solvers
3684: instead of working directly with matrix algebra routines such as this.
3685: See, e.g., `KSPCreate()`.
3687: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3688: @*/
3689: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3690: {
3691: PetscFunctionBegin;
3696: PetscCheckSameComm(mat, 1, b, 2);
3697: PetscCheckSameComm(mat, 1, x, 3);
3698: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3699: 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);
3700: 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);
3701: 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);
3702: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3703: MatCheckPreallocated(mat, 1);
3705: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3706: PetscCall(VecFlag(x, mat->factorerrortype));
3707: if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3708: else PetscUseTypeMethod(mat, solve, b, x);
3709: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3710: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3711: PetscFunctionReturn(PETSC_SUCCESS);
3712: }
3714: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3715: {
3716: Vec b, x;
3717: PetscInt N, i;
3718: PetscErrorCode (*f)(Mat, Vec, Vec);
3719: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3721: PetscFunctionBegin;
3722: if (A->factorerrortype) {
3723: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3724: PetscCall(MatSetInf(X));
3725: PetscFunctionReturn(PETSC_SUCCESS);
3726: }
3727: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3728: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3729: PetscCall(MatBoundToCPU(A, &Abound));
3730: if (!Abound) {
3731: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3732: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3733: }
3734: #if PetscDefined(HAVE_CUDA)
3735: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3736: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3737: #elif PetscDefined(HAVE_HIP)
3738: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3739: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3740: #endif
3741: PetscCall(MatGetSize(B, NULL, &N));
3742: for (i = 0; i < N; i++) {
3743: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3744: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3745: PetscCall((*f)(A, b, x));
3746: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3747: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3748: }
3749: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3750: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3751: PetscFunctionReturn(PETSC_SUCCESS);
3752: }
3754: /*@
3755: MatMatSolve - Solves $A X = B$, given a factored matrix.
3757: Neighbor-wise Collective
3759: Input Parameters:
3760: + A - the factored matrix
3761: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3763: Output Parameter:
3764: . X - the result matrix (dense matrix)
3766: Level: developer
3768: Note:
3769: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3770: otherwise, `B` and `X` cannot be the same.
3772: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3773: @*/
3774: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3775: {
3776: PetscFunctionBegin;
3781: PetscCheckSameComm(A, 1, B, 2);
3782: PetscCheckSameComm(A, 1, X, 3);
3783: 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);
3784: 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);
3785: 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");
3786: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3787: MatCheckPreallocated(A, 1);
3789: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3790: if (!A->ops->matsolve) {
3791: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3792: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3793: } else PetscUseTypeMethod(A, matsolve, B, X);
3794: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3795: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3796: PetscFunctionReturn(PETSC_SUCCESS);
3797: }
3799: /*@
3800: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3802: Neighbor-wise Collective
3804: Input Parameters:
3805: + A - the factored matrix
3806: - B - the right-hand-side matrix (`MATDENSE` matrix)
3808: Output Parameter:
3809: . X - the result matrix (dense matrix)
3811: Level: developer
3813: Note:
3814: The matrices `B` and `X` cannot be the same. I.e., one cannot
3815: call `MatMatSolveTranspose`(A,X,X).
3817: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3818: @*/
3819: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3820: {
3821: PetscFunctionBegin;
3826: PetscCheckSameComm(A, 1, B, 2);
3827: PetscCheckSameComm(A, 1, X, 3);
3828: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3829: 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);
3830: 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);
3831: 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);
3832: 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");
3833: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3834: MatCheckPreallocated(A, 1);
3836: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3837: if (!A->ops->matsolvetranspose) {
3838: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3839: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3840: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3841: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3842: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3843: PetscFunctionReturn(PETSC_SUCCESS);
3844: }
3846: /*@
3847: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3849: Neighbor-wise Collective
3851: Input Parameters:
3852: + A - the factored matrix
3853: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3855: Output Parameter:
3856: . X - the result matrix (dense matrix)
3858: Level: developer
3860: Note:
3861: 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
3862: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3864: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3865: @*/
3866: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3867: {
3868: PetscFunctionBegin;
3873: PetscCheckSameComm(A, 1, Bt, 2);
3874: PetscCheckSameComm(A, 1, X, 3);
3876: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3877: 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);
3878: 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);
3879: 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");
3880: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3881: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3882: MatCheckPreallocated(A, 1);
3884: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3885: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3886: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3887: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3888: PetscFunctionReturn(PETSC_SUCCESS);
3889: }
3891: /*@
3892: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3893: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3895: Neighbor-wise Collective
3897: Input Parameters:
3898: + mat - the factored matrix
3899: - b - the right-hand-side vector
3901: Output Parameter:
3902: . x - the result vector
3904: Level: developer
3906: Notes:
3907: `MatSolve()` should be used for most applications, as it performs
3908: a forward solve followed by a backward solve.
3910: The vectors `b` and `x` cannot be the same, i.e., one cannot
3911: call `MatForwardSolve`(A,x,x).
3913: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3914: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3915: `MatForwardSolve()` solves $U^T*D y = b$, and
3916: `MatBackwardSolve()` solves $U x = y$.
3917: Thus they do not provide a symmetric preconditioner.
3919: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3920: @*/
3921: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3922: {
3923: PetscFunctionBegin;
3928: PetscCheckSameComm(mat, 1, b, 2);
3929: PetscCheckSameComm(mat, 1, x, 3);
3930: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3931: 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);
3932: 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);
3933: 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);
3934: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3935: MatCheckPreallocated(mat, 1);
3937: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3938: PetscUseTypeMethod(mat, forwardsolve, b, x);
3939: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3940: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3941: PetscFunctionReturn(PETSC_SUCCESS);
3942: }
3944: /*@
3945: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3946: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3948: Neighbor-wise Collective
3950: Input Parameters:
3951: + mat - the factored matrix
3952: - b - the right-hand-side vector
3954: Output Parameter:
3955: . x - the result vector
3957: Level: developer
3959: Notes:
3960: `MatSolve()` should be used for most applications, as it performs
3961: a forward solve followed by a backward solve.
3963: The vectors `b` and `x` cannot be the same. I.e., one cannot
3964: call `MatBackwardSolve`(A,x,x).
3966: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3967: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3968: `MatForwardSolve()` solves $U^T*D y = b$, and
3969: `MatBackwardSolve()` solves $U x = y$.
3970: Thus they do not provide a symmetric preconditioner.
3972: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3973: @*/
3974: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3975: {
3976: PetscFunctionBegin;
3981: PetscCheckSameComm(mat, 1, b, 2);
3982: PetscCheckSameComm(mat, 1, x, 3);
3983: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3984: 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);
3985: 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);
3986: 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);
3987: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3988: MatCheckPreallocated(mat, 1);
3990: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3991: PetscUseTypeMethod(mat, backwardsolve, b, x);
3992: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3993: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3994: PetscFunctionReturn(PETSC_SUCCESS);
3995: }
3997: /*@
3998: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
4000: Neighbor-wise Collective
4002: Input Parameters:
4003: + mat - the factored matrix
4004: . b - the right-hand-side vector
4005: - y - the vector to be added to
4007: Output Parameter:
4008: . x - the result vector
4010: Level: developer
4012: Note:
4013: The vectors `b` and `x` cannot be the same. I.e., one cannot
4014: call `MatSolveAdd`(A,x,y,x).
4016: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4017: @*/
4018: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4019: {
4020: PetscScalar one = 1.0;
4021: Vec tmp;
4023: PetscFunctionBegin;
4029: PetscCheckSameComm(mat, 1, b, 2);
4030: PetscCheckSameComm(mat, 1, y, 3);
4031: PetscCheckSameComm(mat, 1, x, 4);
4032: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4033: 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);
4034: 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);
4035: 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);
4036: 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);
4037: 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);
4038: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4039: MatCheckPreallocated(mat, 1);
4041: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4042: PetscCall(VecFlag(x, mat->factorerrortype));
4043: if (mat->factorerrortype) {
4044: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4045: } else if (mat->ops->solveadd) {
4046: PetscUseTypeMethod(mat, solveadd, b, y, x);
4047: } else {
4048: /* do the solve then the add manually */
4049: if (x != y) {
4050: PetscCall(MatSolve(mat, b, x));
4051: PetscCall(VecAXPY(x, one, y));
4052: } else {
4053: PetscCall(VecDuplicate(x, &tmp));
4054: PetscCall(VecCopy(x, tmp));
4055: PetscCall(MatSolve(mat, b, x));
4056: PetscCall(VecAXPY(x, one, tmp));
4057: PetscCall(VecDestroy(&tmp));
4058: }
4059: }
4060: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4061: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4062: PetscFunctionReturn(PETSC_SUCCESS);
4063: }
4065: /*@
4066: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4068: Neighbor-wise Collective
4070: Input Parameters:
4071: + mat - the factored matrix
4072: - b - the right-hand-side vector
4074: Output Parameter:
4075: . x - the result vector
4077: Level: developer
4079: Notes:
4080: The vectors `b` and `x` cannot be the same. I.e., one cannot
4081: call `MatSolveTranspose`(A,x,x).
4083: Most users should employ the `KSP` interface for linear solvers
4084: instead of working directly with matrix algebra routines such as this.
4085: See, e.g., `KSPCreate()`.
4087: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4088: @*/
4089: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4090: {
4091: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4093: PetscFunctionBegin;
4098: PetscCheckSameComm(mat, 1, b, 2);
4099: PetscCheckSameComm(mat, 1, x, 3);
4100: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4101: 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);
4102: 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);
4103: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4104: MatCheckPreallocated(mat, 1);
4105: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4106: PetscCall(VecFlag(x, mat->factorerrortype));
4107: if (mat->factorerrortype) {
4108: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4109: } else {
4110: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4111: PetscCall((*f)(mat, b, x));
4112: }
4113: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4114: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4115: PetscFunctionReturn(PETSC_SUCCESS);
4116: }
4118: /*@
4119: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4120: factored matrix.
4122: Neighbor-wise Collective
4124: Input Parameters:
4125: + mat - the factored matrix
4126: . b - the right-hand-side vector
4127: - y - the vector to be added to
4129: Output Parameter:
4130: . x - the result vector
4132: Level: developer
4134: Note:
4135: The vectors `b` and `x` cannot be the same. I.e., one cannot
4136: call `MatSolveTransposeAdd`(A,x,y,x).
4138: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4139: @*/
4140: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4141: {
4142: PetscScalar one = 1.0;
4143: Vec tmp;
4144: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4146: PetscFunctionBegin;
4152: PetscCheckSameComm(mat, 1, b, 2);
4153: PetscCheckSameComm(mat, 1, y, 3);
4154: PetscCheckSameComm(mat, 1, x, 4);
4155: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4156: 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);
4157: 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);
4158: 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);
4159: 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);
4160: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4161: MatCheckPreallocated(mat, 1);
4163: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4164: PetscCall(VecFlag(x, mat->factorerrortype));
4165: if (mat->factorerrortype) {
4166: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4167: } else if (f) {
4168: PetscCall((*f)(mat, b, y, x));
4169: } else {
4170: /* do the solve then the add manually */
4171: if (x != y) {
4172: PetscCall(MatSolveTranspose(mat, b, x));
4173: PetscCall(VecAXPY(x, one, y));
4174: } else {
4175: PetscCall(VecDuplicate(x, &tmp));
4176: PetscCall(VecCopy(x, tmp));
4177: PetscCall(MatSolveTranspose(mat, b, x));
4178: PetscCall(VecAXPY(x, one, tmp));
4179: PetscCall(VecDestroy(&tmp));
4180: }
4181: }
4182: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4183: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4184: PetscFunctionReturn(PETSC_SUCCESS);
4185: }
4187: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4188: /*@
4189: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4191: Neighbor-wise Collective
4193: Input Parameters:
4194: + mat - the matrix
4195: . b - the right-hand side
4196: . omega - the relaxation factor
4197: . flag - flag indicating the type of SOR (see below)
4198: . shift - diagonal shift
4199: . its - the number of iterations
4200: - lits - the number of local iterations
4202: Output Parameter:
4203: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4205: SOR Flags:
4206: + `SOR_FORWARD_SWEEP` - forward SOR
4207: . `SOR_BACKWARD_SWEEP` - backward SOR
4208: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4209: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4210: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4211: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4212: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4213: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies upper/lower triangular part of matrix to vector (with `omega`)
4214: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4216: Level: developer
4218: Notes:
4219: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4220: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4221: on each processor.
4223: Application programmers will not generally use `MatSOR()` directly,
4224: but instead will employ `PCSOR` or `PCEISENSTAT`
4226: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with inodes, this does a block SOR smoothing, otherwise it does a pointwise smoothing.
4227: For `MATAIJ` matrices with inodes, the block sizes are determined by the inode sizes, not the block size set with `MatSetBlockSize()`
4229: Vectors `x` and `b` CANNOT be the same
4231: The flags are implemented as bitwise inclusive or operations.
4232: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4233: to specify a zero initial guess for SSOR.
4235: Developer Note:
4236: We should add block SOR support for `MATAIJ` matrices with block size set to greater than one and no inodes
4238: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4239: @*/
4240: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4241: {
4242: PetscFunctionBegin;
4247: PetscCheckSameComm(mat, 1, b, 2);
4248: PetscCheckSameComm(mat, 1, x, 8);
4249: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4250: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4251: 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);
4252: 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);
4253: 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);
4254: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4255: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4256: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4258: MatCheckPreallocated(mat, 1);
4259: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4260: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4261: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4262: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4263: PetscFunctionReturn(PETSC_SUCCESS);
4264: }
4266: /*
4267: Default matrix copy routine.
4268: */
4269: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4270: {
4271: PetscInt i, rstart = 0, rend = 0, nz;
4272: const PetscInt *cwork;
4273: const PetscScalar *vwork;
4275: PetscFunctionBegin;
4276: if (B->assembled) PetscCall(MatZeroEntries(B));
4277: if (str == SAME_NONZERO_PATTERN) {
4278: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4279: for (i = rstart; i < rend; i++) {
4280: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4281: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4282: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4283: }
4284: } else {
4285: PetscCall(MatAYPX(B, 0.0, A, str));
4286: }
4287: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4288: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4289: PetscFunctionReturn(PETSC_SUCCESS);
4290: }
4292: /*@
4293: MatCopy - Copies a matrix to another matrix.
4295: Collective
4297: Input Parameters:
4298: + A - the matrix
4299: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4301: Output Parameter:
4302: . B - where the copy is put
4304: Level: intermediate
4306: Notes:
4307: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4309: `MatCopy()` copies the matrix entries of a matrix to another existing
4310: matrix (after first zeroing the second matrix). A related routine is
4311: `MatConvert()`, which first creates a new matrix and then copies the data.
4313: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4314: @*/
4315: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4316: {
4317: PetscInt i;
4319: PetscFunctionBegin;
4324: PetscCheckSameComm(A, 1, B, 2);
4325: MatCheckPreallocated(B, 2);
4326: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4327: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4328: 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,
4329: A->cmap->N, B->cmap->N);
4330: MatCheckPreallocated(A, 1);
4331: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4333: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4334: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4335: else PetscCall(MatCopy_Basic(A, B, str));
4337: B->stencil.dim = A->stencil.dim;
4338: B->stencil.noc = A->stencil.noc;
4339: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4340: B->stencil.dims[i] = A->stencil.dims[i];
4341: B->stencil.starts[i] = A->stencil.starts[i];
4342: }
4344: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4345: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4346: PetscFunctionReturn(PETSC_SUCCESS);
4347: }
4349: /*@
4350: MatConvert - Converts a matrix to another matrix, either of the same
4351: or different type.
4353: Collective
4355: Input Parameters:
4356: + mat - the matrix
4357: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4358: same type as the original matrix.
4359: - reuse - denotes if the destination matrix is to be created or reused.
4360: 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
4361: `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).
4363: Output Parameter:
4364: . M - pointer to place new matrix
4366: Level: intermediate
4368: Notes:
4369: `MatConvert()` first creates a new matrix and then copies the data from
4370: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4371: entries of one matrix to another already existing matrix context.
4373: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4374: the MPI communicator of the generated matrix is always the same as the communicator
4375: of the input matrix.
4377: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4378: @*/
4379: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4380: {
4381: PetscBool sametype, issame, flg;
4382: PetscBool3 issymmetric, ishermitian;
4383: char convname[256], mtype[256];
4384: Mat B;
4386: PetscFunctionBegin;
4389: PetscAssertPointer(M, 4);
4390: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4391: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4392: MatCheckPreallocated(mat, 1);
4394: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4395: if (flg) newtype = mtype;
4397: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4398: PetscCall(PetscStrcmp(newtype, "same", &issame));
4399: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4400: if (reuse == MAT_REUSE_MATRIX) {
4402: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4403: }
4405: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4406: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4407: PetscFunctionReturn(PETSC_SUCCESS);
4408: }
4410: /* Cache Mat options because some converters use MatHeaderReplace */
4411: issymmetric = mat->symmetric;
4412: ishermitian = mat->hermitian;
4414: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4415: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4416: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4417: } else {
4418: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4419: const char *prefix[3] = {"seq", "mpi", ""};
4420: PetscInt i;
4421: /*
4422: Order of precedence:
4423: 0) See if newtype is a superclass of the current matrix.
4424: 1) See if a specialized converter is known to the current matrix.
4425: 2) See if a specialized converter is known to the desired matrix class.
4426: 3) See if a good general converter is registered for the desired class
4427: (as of 6/27/03 only MATMPIADJ falls into this category).
4428: 4) See if a good general converter is known for the current matrix.
4429: 5) Use a really basic converter.
4430: */
4432: /* 0) See if newtype is a superclass of the current matrix.
4433: i.e mat is mpiaij and newtype is aij */
4434: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4435: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4436: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4437: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4438: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4439: if (flg) {
4440: if (reuse == MAT_INPLACE_MATRIX) {
4441: PetscCall(PetscInfo(mat, "Early return\n"));
4442: PetscFunctionReturn(PETSC_SUCCESS);
4443: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4444: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4445: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4446: PetscFunctionReturn(PETSC_SUCCESS);
4447: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4448: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4449: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4450: PetscFunctionReturn(PETSC_SUCCESS);
4451: }
4452: }
4453: }
4454: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4455: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4456: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4457: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4458: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4459: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4460: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4461: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4462: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4463: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4464: if (conv) goto foundconv;
4465: }
4467: /* 2) See if a specialized converter is known to the desired matrix class. */
4468: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4469: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4470: PetscCall(MatSetType(B, newtype));
4471: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4472: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4473: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4474: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4475: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4476: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4477: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4478: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4479: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4480: if (conv) {
4481: PetscCall(MatDestroy(&B));
4482: goto foundconv;
4483: }
4484: }
4486: /* 3) See if a good general converter is registered for the desired class */
4487: conv = B->ops->convertfrom;
4488: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4489: PetscCall(MatDestroy(&B));
4490: if (conv) goto foundconv;
4492: /* 4) See if a good general converter is known for the current matrix */
4493: if (mat->ops->convert) conv = mat->ops->convert;
4494: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4495: if (conv) goto foundconv;
4497: /* 5) Use a really basic converter. */
4498: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4499: conv = MatConvert_Basic;
4501: foundconv:
4502: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4503: PetscCall((*conv)(mat, newtype, reuse, M));
4504: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4505: /* the block sizes must be same if the mappings are copied over */
4506: (*M)->rmap->bs = mat->rmap->bs;
4507: (*M)->cmap->bs = mat->cmap->bs;
4508: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4509: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4510: (*M)->rmap->mapping = mat->rmap->mapping;
4511: (*M)->cmap->mapping = mat->cmap->mapping;
4512: }
4513: (*M)->stencil.dim = mat->stencil.dim;
4514: (*M)->stencil.noc = mat->stencil.noc;
4515: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4516: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4517: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4518: }
4519: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4520: }
4521: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4523: /* Copy Mat options */
4524: if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4525: else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4526: if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4527: else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4528: PetscFunctionReturn(PETSC_SUCCESS);
4529: }
4531: /*@
4532: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4534: Not Collective
4536: Input Parameter:
4537: . mat - the matrix, must be a factored matrix
4539: Output Parameter:
4540: . type - the string name of the package (do not free this string)
4542: Level: intermediate
4544: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4545: @*/
4546: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4547: {
4548: PetscErrorCode (*conv)(Mat, MatSolverType *);
4550: PetscFunctionBegin;
4553: PetscAssertPointer(type, 2);
4554: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4555: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4556: if (conv) PetscCall((*conv)(mat, type));
4557: else *type = MATSOLVERPETSC;
4558: PetscFunctionReturn(PETSC_SUCCESS);
4559: }
4561: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4562: struct _MatSolverTypeForSpecifcType {
4563: MatType mtype;
4564: /* no entry for MAT_FACTOR_NONE */
4565: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4566: MatSolverTypeForSpecifcType next;
4567: };
4569: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4570: struct _MatSolverTypeHolder {
4571: char *name;
4572: MatSolverTypeForSpecifcType handlers;
4573: MatSolverTypeHolder next;
4574: };
4576: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4578: /*@C
4579: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4581: Logically Collective, No Fortran Support
4583: Input Parameters:
4584: + package - name of the package, for example `petsc` or `superlu`
4585: . mtype - the matrix type that works with this package
4586: . ftype - the type of factorization supported by the package
4587: - createfactor - routine that will create the factored matrix ready to be used
4589: Level: developer
4591: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4592: `MatGetFactor()`
4593: @*/
4594: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4595: {
4596: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4597: PetscBool flg;
4598: MatSolverTypeForSpecifcType inext, iprev = NULL;
4600: PetscFunctionBegin;
4601: PetscCall(MatInitializePackage());
4602: if (!next) {
4603: PetscCall(PetscNew(&MatSolverTypeHolders));
4604: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4605: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4606: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4607: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4608: PetscFunctionReturn(PETSC_SUCCESS);
4609: }
4610: while (next) {
4611: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4612: if (flg) {
4613: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4614: inext = next->handlers;
4615: while (inext) {
4616: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4617: if (flg) {
4618: inext->createfactor[(int)ftype - 1] = createfactor;
4619: PetscFunctionReturn(PETSC_SUCCESS);
4620: }
4621: iprev = inext;
4622: inext = inext->next;
4623: }
4624: PetscCall(PetscNew(&iprev->next));
4625: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4626: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4627: PetscFunctionReturn(PETSC_SUCCESS);
4628: }
4629: prev = next;
4630: next = next->next;
4631: }
4632: PetscCall(PetscNew(&prev->next));
4633: PetscCall(PetscStrallocpy(package, &prev->next->name));
4634: PetscCall(PetscNew(&prev->next->handlers));
4635: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4636: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4637: PetscFunctionReturn(PETSC_SUCCESS);
4638: }
4640: /*@C
4641: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4643: Input Parameters:
4644: + 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
4645: . ftype - the type of factorization supported by the type
4646: - mtype - the matrix type that works with this type
4648: Output Parameters:
4649: + foundtype - `PETSC_TRUE` if the type was registered
4650: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4651: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4653: Calling sequence of `createfactor`:
4654: + A - the matrix providing the factor matrix
4655: . ftype - the `MatFactorType` of the factor requested
4656: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4658: Level: developer
4660: Note:
4661: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4662: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4663: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4665: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4666: `MatInitializePackage()`
4667: @*/
4668: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4669: {
4670: MatSolverTypeHolder next = MatSolverTypeHolders;
4671: PetscBool flg;
4672: MatSolverTypeForSpecifcType inext;
4674: PetscFunctionBegin;
4675: if (foundtype) *foundtype = PETSC_FALSE;
4676: if (foundmtype) *foundmtype = PETSC_FALSE;
4677: if (createfactor) *createfactor = NULL;
4679: if (type) {
4680: while (next) {
4681: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4682: if (flg) {
4683: if (foundtype) *foundtype = PETSC_TRUE;
4684: inext = next->handlers;
4685: while (inext) {
4686: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4687: if (flg) {
4688: if (foundmtype) *foundmtype = PETSC_TRUE;
4689: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4690: PetscFunctionReturn(PETSC_SUCCESS);
4691: }
4692: inext = inext->next;
4693: }
4694: }
4695: next = next->next;
4696: }
4697: } else {
4698: while (next) {
4699: inext = next->handlers;
4700: while (inext) {
4701: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4702: if (flg && inext->createfactor[(int)ftype - 1]) {
4703: if (foundtype) *foundtype = PETSC_TRUE;
4704: if (foundmtype) *foundmtype = PETSC_TRUE;
4705: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4706: PetscFunctionReturn(PETSC_SUCCESS);
4707: }
4708: inext = inext->next;
4709: }
4710: next = next->next;
4711: }
4712: /* try with base classes inext->mtype */
4713: next = MatSolverTypeHolders;
4714: while (next) {
4715: inext = next->handlers;
4716: while (inext) {
4717: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4718: if (flg && inext->createfactor[(int)ftype - 1]) {
4719: if (foundtype) *foundtype = PETSC_TRUE;
4720: if (foundmtype) *foundmtype = PETSC_TRUE;
4721: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4722: PetscFunctionReturn(PETSC_SUCCESS);
4723: }
4724: inext = inext->next;
4725: }
4726: next = next->next;
4727: }
4728: }
4729: PetscFunctionReturn(PETSC_SUCCESS);
4730: }
4732: PetscErrorCode MatSolverTypeDestroy(void)
4733: {
4734: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4735: MatSolverTypeForSpecifcType inext, iprev;
4737: PetscFunctionBegin;
4738: while (next) {
4739: PetscCall(PetscFree(next->name));
4740: inext = next->handlers;
4741: while (inext) {
4742: PetscCall(PetscFree(inext->mtype));
4743: iprev = inext;
4744: inext = inext->next;
4745: PetscCall(PetscFree(iprev));
4746: }
4747: prev = next;
4748: next = next->next;
4749: PetscCall(PetscFree(prev));
4750: }
4751: MatSolverTypeHolders = NULL;
4752: PetscFunctionReturn(PETSC_SUCCESS);
4753: }
4755: /*@
4756: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4758: Logically Collective
4760: Input Parameter:
4761: . mat - the matrix
4763: Output Parameter:
4764: . flg - `PETSC_TRUE` if uses the ordering
4766: Level: developer
4768: Note:
4769: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4770: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4772: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4773: @*/
4774: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4775: {
4776: PetscFunctionBegin;
4777: *flg = mat->canuseordering;
4778: PetscFunctionReturn(PETSC_SUCCESS);
4779: }
4781: /*@
4782: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4784: Logically Collective
4786: Input Parameters:
4787: + mat - the matrix obtained with `MatGetFactor()`
4788: - ftype - the factorization type to be used
4790: Output Parameter:
4791: . otype - the preferred ordering type
4793: Level: developer
4795: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4796: @*/
4797: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4798: {
4799: PetscFunctionBegin;
4800: *otype = mat->preferredordering[ftype];
4801: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4802: PetscFunctionReturn(PETSC_SUCCESS);
4803: }
4805: /*@
4806: MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()
4808: Collective
4810: Input Parameters:
4811: + mat - the matrix
4812: . 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
4813: the other criteria is returned
4814: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4816: Output Parameter:
4817: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4819: Options Database Keys:
4820: + -pc_factor_mat_solver_type <type> - choose the type at run time. When using `KSP` solvers
4821: . -pc_factor_mat_factor_on_host <bool> - do mat factorization on host (with device matrices). Default is doing it on device
4822: - -pc_factor_mat_solve_on_host <bool> - do mat solve on host (with device matrices). Default is doing it on device
4824: Level: intermediate
4826: Notes:
4827: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4828: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4830: Users usually access the factorization solvers via `KSP`
4832: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4833: 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
4835: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4836: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4837: For example if one configuration had --download-mumps while a different one had --download-superlu_dist.
4839: Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4840: where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4841: call `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4843: Developer Note:
4844: This should actually be called `MatCreateFactor()` since it creates a new factor object
4846: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4847: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4848: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4849: @*/
4850: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4851: {
4852: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4853: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4855: PetscFunctionBegin;
4859: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4860: MatCheckPreallocated(mat, 1);
4862: PetscCall(MatIsShell(mat, &shell));
4863: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4864: if (hasop) {
4865: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4866: PetscFunctionReturn(PETSC_SUCCESS);
4867: }
4869: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4870: if (!foundtype) {
4871: if (type) {
4872: 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],
4873: ((PetscObject)mat)->type_name, type);
4874: } else {
4875: 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);
4876: }
4877: }
4878: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4879: 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);
4881: PetscCall((*conv)(mat, ftype, f));
4882: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4883: PetscFunctionReturn(PETSC_SUCCESS);
4884: }
4886: /*@
4887: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4889: Not Collective
4891: Input Parameters:
4892: + mat - the matrix
4893: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4894: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4896: Output Parameter:
4897: . flg - PETSC_TRUE if the factorization is available
4899: Level: intermediate
4901: Notes:
4902: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4903: such as pastix, superlu, mumps etc.
4905: PETSc must have been ./configure to use the external solver, using the option --download-package
4907: Developer Note:
4908: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
4910: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4911: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4912: @*/
4913: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4914: {
4915: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
4917: PetscFunctionBegin;
4919: PetscAssertPointer(flg, 4);
4921: *flg = PETSC_FALSE;
4922: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
4924: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4925: MatCheckPreallocated(mat, 1);
4927: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4928: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4929: PetscFunctionReturn(PETSC_SUCCESS);
4930: }
4932: /*@
4933: MatDuplicate - Duplicates a matrix including the non-zero structure.
4935: Collective
4937: Input Parameters:
4938: + mat - the matrix
4939: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4940: See the manual page for `MatDuplicateOption()` for an explanation of these options.
4942: Output Parameter:
4943: . M - pointer to place new matrix
4945: Level: intermediate
4947: Notes:
4948: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
4950: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
4952: 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.
4954: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4955: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4956: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
4958: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4959: @*/
4960: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4961: {
4962: Mat B;
4963: VecType vtype;
4964: PetscInt i;
4965: PetscObject dm, container_h, container_d;
4966: PetscErrorCodeFn *viewf;
4968: PetscFunctionBegin;
4971: PetscAssertPointer(M, 3);
4972: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4973: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4974: MatCheckPreallocated(mat, 1);
4976: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4977: PetscUseTypeMethod(mat, duplicate, op, M);
4978: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4979: B = *M;
4981: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4982: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4983: PetscCall(MatGetVecType(mat, &vtype));
4984: PetscCall(MatSetVecType(B, vtype));
4986: B->stencil.dim = mat->stencil.dim;
4987: B->stencil.noc = mat->stencil.noc;
4988: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4989: B->stencil.dims[i] = mat->stencil.dims[i];
4990: B->stencil.starts[i] = mat->stencil.starts[i];
4991: }
4993: B->nooffproczerorows = mat->nooffproczerorows;
4994: B->nooffprocentries = mat->nooffprocentries;
4996: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4997: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4998: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
4999: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5000: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5001: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5002: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5003: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5004: PetscFunctionReturn(PETSC_SUCCESS);
5005: }
5007: /*@
5008: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5010: Logically Collective
5012: Input Parameter:
5013: . mat - the matrix
5015: Output Parameter:
5016: . v - the diagonal of the matrix
5018: Level: intermediate
5020: Note:
5021: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5022: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5023: is larger than `ndiag`, the values of the remaining entries are unspecified.
5025: Currently only correct in parallel for square matrices.
5027: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5028: @*/
5029: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5030: {
5031: PetscFunctionBegin;
5035: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5036: MatCheckPreallocated(mat, 1);
5037: if (PetscDefined(USE_DEBUG)) {
5038: PetscInt nv, row, col, ndiag;
5040: PetscCall(VecGetLocalSize(v, &nv));
5041: PetscCall(MatGetLocalSize(mat, &row, &col));
5042: ndiag = PetscMin(row, col);
5043: 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);
5044: }
5046: PetscUseTypeMethod(mat, getdiagonal, v);
5047: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5048: PetscFunctionReturn(PETSC_SUCCESS);
5049: }
5051: /*@
5052: MatGetRowMin - Gets the minimum value (of the real part) of each
5053: row of the matrix
5055: Logically Collective
5057: Input Parameter:
5058: . mat - the matrix
5060: Output Parameters:
5061: + v - the vector for storing the maximums
5062: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5064: Level: intermediate
5066: Note:
5067: The result of this call are the same as if one converted the matrix to dense format
5068: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5070: This code is only implemented for a couple of matrix formats.
5072: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5073: `MatGetRowMax()`
5074: @*/
5075: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5076: {
5077: PetscFunctionBegin;
5081: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5083: if (!mat->cmap->N) {
5084: PetscCall(VecSet(v, PETSC_MAX_REAL));
5085: if (idx) {
5086: PetscInt i, m = mat->rmap->n;
5087: for (i = 0; i < m; i++) idx[i] = -1;
5088: }
5089: } else {
5090: MatCheckPreallocated(mat, 1);
5091: }
5092: PetscUseTypeMethod(mat, getrowmin, v, idx);
5093: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5094: PetscFunctionReturn(PETSC_SUCCESS);
5095: }
5097: /*@
5098: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5099: row of the matrix
5101: Logically Collective
5103: Input Parameter:
5104: . mat - the matrix
5106: Output Parameters:
5107: + v - the vector for storing the minimums
5108: - idx - the indices of the column found for each row (or `NULL` if not needed)
5110: Level: intermediate
5112: Notes:
5113: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5114: row is 0 (the first column).
5116: This code is only implemented for a couple of matrix formats.
5118: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5119: @*/
5120: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5121: {
5122: PetscFunctionBegin;
5126: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5127: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5129: if (!mat->cmap->N) {
5130: PetscCall(VecSet(v, 0.0));
5131: if (idx) {
5132: PetscInt i, m = mat->rmap->n;
5133: for (i = 0; i < m; i++) idx[i] = -1;
5134: }
5135: } else {
5136: MatCheckPreallocated(mat, 1);
5137: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5138: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5139: }
5140: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5141: PetscFunctionReturn(PETSC_SUCCESS);
5142: }
5144: /*@
5145: MatGetRowMax - Gets the maximum value (of the real part) of each
5146: row of the matrix
5148: Logically Collective
5150: Input Parameter:
5151: . mat - the matrix
5153: Output Parameters:
5154: + v - the vector for storing the maximums
5155: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5157: Level: intermediate
5159: Notes:
5160: The result of this call are the same as if one converted the matrix to dense format
5161: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5163: This code is only implemented for a couple of matrix formats.
5165: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5166: @*/
5167: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5168: {
5169: PetscFunctionBegin;
5173: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5175: if (!mat->cmap->N) {
5176: PetscCall(VecSet(v, PETSC_MIN_REAL));
5177: if (idx) {
5178: PetscInt i, m = mat->rmap->n;
5179: for (i = 0; i < m; i++) idx[i] = -1;
5180: }
5181: } else {
5182: MatCheckPreallocated(mat, 1);
5183: PetscUseTypeMethod(mat, getrowmax, v, idx);
5184: }
5185: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5186: PetscFunctionReturn(PETSC_SUCCESS);
5187: }
5189: /*@
5190: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5191: row of the matrix
5193: Logically Collective
5195: Input Parameter:
5196: . mat - the matrix
5198: Output Parameters:
5199: + v - the vector for storing the maximums
5200: - idx - the indices of the column found for each row (or `NULL` if not needed)
5202: Level: intermediate
5204: Notes:
5205: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5206: row is 0 (the first column).
5208: This code is only implemented for a couple of matrix formats.
5210: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5211: @*/
5212: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5213: {
5214: PetscFunctionBegin;
5218: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5220: if (!mat->cmap->N) {
5221: PetscCall(VecSet(v, 0.0));
5222: if (idx) {
5223: PetscInt i, m = mat->rmap->n;
5224: for (i = 0; i < m; i++) idx[i] = -1;
5225: }
5226: } else {
5227: MatCheckPreallocated(mat, 1);
5228: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5229: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5230: }
5231: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5232: PetscFunctionReturn(PETSC_SUCCESS);
5233: }
5235: /*@
5236: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5238: Logically Collective
5240: Input Parameter:
5241: . mat - the matrix
5243: Output Parameter:
5244: . v - the vector for storing the sum
5246: Level: intermediate
5248: This code is only implemented for a couple of matrix formats.
5250: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5251: @*/
5252: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5253: {
5254: PetscFunctionBegin;
5258: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5260: if (!mat->cmap->N) {
5261: PetscCall(VecSet(v, 0.0));
5262: } else {
5263: MatCheckPreallocated(mat, 1);
5264: PetscUseTypeMethod(mat, getrowsumabs, v);
5265: }
5266: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5267: PetscFunctionReturn(PETSC_SUCCESS);
5268: }
5270: /*@
5271: MatGetRowSum - Gets the sum of each row of the matrix
5273: Logically or Neighborhood Collective
5275: Input Parameter:
5276: . mat - the matrix
5278: Output Parameter:
5279: . v - the vector for storing the sum of rows
5281: Level: intermediate
5283: Note:
5284: This code is slow since it is not currently specialized for different formats
5286: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5287: @*/
5288: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5289: {
5290: Vec ones;
5292: PetscFunctionBegin;
5296: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5297: MatCheckPreallocated(mat, 1);
5298: PetscCall(MatCreateVecs(mat, &ones, NULL));
5299: PetscCall(VecSet(ones, 1.));
5300: PetscCall(MatMult(mat, ones, v));
5301: PetscCall(VecDestroy(&ones));
5302: PetscFunctionReturn(PETSC_SUCCESS);
5303: }
5305: /*@
5306: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5307: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5309: Collective
5311: Input Parameter:
5312: . mat - the matrix to provide the transpose
5314: Output Parameter:
5315: . 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
5317: Level: advanced
5319: Note:
5320: 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
5321: routine allows bypassing that call.
5323: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5324: @*/
5325: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5326: {
5327: MatParentState *rb = NULL;
5329: PetscFunctionBegin;
5330: PetscCall(PetscNew(&rb));
5331: rb->id = ((PetscObject)mat)->id;
5332: rb->state = 0;
5333: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5334: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5335: PetscFunctionReturn(PETSC_SUCCESS);
5336: }
5338: /*@
5339: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5341: Collective
5343: Input Parameters:
5344: + mat - the matrix to transpose
5345: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5347: Output Parameter:
5348: . B - the transpose of the matrix
5350: Level: intermediate
5352: Notes:
5353: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5355: `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
5356: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5358: 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.
5360: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5361: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5363: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5365: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5367: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5368: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5369: @*/
5370: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5371: {
5372: PetscContainer rB = NULL;
5373: MatParentState *rb = NULL;
5375: PetscFunctionBegin;
5378: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5379: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5380: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5381: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5382: MatCheckPreallocated(mat, 1);
5383: if (reuse == MAT_REUSE_MATRIX) {
5384: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5385: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5386: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5387: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5388: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5389: }
5391: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5392: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5393: PetscUseTypeMethod(mat, transpose, reuse, B);
5394: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5395: }
5396: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5398: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5399: if (reuse != MAT_INPLACE_MATRIX) {
5400: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5401: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5402: rb->state = ((PetscObject)mat)->state;
5403: rb->nonzerostate = mat->nonzerostate;
5404: }
5405: PetscFunctionReturn(PETSC_SUCCESS);
5406: }
5408: /*@
5409: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5411: Collective
5413: Input Parameter:
5414: . A - the matrix to transpose
5416: Output Parameter:
5417: . 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
5418: numerical portion.
5420: Level: intermediate
5422: Note:
5423: This is not supported for many matrix types, use `MatTranspose()` in those cases
5425: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5426: @*/
5427: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5428: {
5429: PetscFunctionBegin;
5432: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5433: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5434: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5435: PetscUseTypeMethod(A, transposesymbolic, B);
5436: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5438: PetscCall(MatTransposeSetPrecursor(A, *B));
5439: PetscFunctionReturn(PETSC_SUCCESS);
5440: }
5442: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5443: {
5444: PetscContainer rB;
5445: MatParentState *rb;
5447: PetscFunctionBegin;
5450: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5451: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5452: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5453: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5454: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5455: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5456: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5457: PetscFunctionReturn(PETSC_SUCCESS);
5458: }
5460: /*@
5461: MatIsTranspose - Test whether a matrix is another one's transpose,
5462: or its own, in which case it tests symmetry.
5464: Collective
5466: Input Parameters:
5467: + A - the matrix to test
5468: . B - the matrix to test against, this can equal the first parameter
5469: - tol - tolerance, differences between entries smaller than this are counted as zero
5471: Output Parameter:
5472: . flg - the result
5474: Level: intermediate
5476: Notes:
5477: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5478: test involves parallel copies of the block off-diagonal parts of the matrix.
5480: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5481: @*/
5482: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5483: {
5484: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5486: PetscFunctionBegin;
5489: PetscAssertPointer(flg, 4);
5490: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5491: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5492: *flg = PETSC_FALSE;
5493: if (f && g) {
5494: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5495: PetscCall((*f)(A, B, tol, flg));
5496: } else {
5497: MatType mattype;
5499: PetscCall(MatGetType(f ? B : A, &mattype));
5500: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5501: }
5502: PetscFunctionReturn(PETSC_SUCCESS);
5503: }
5505: /*@
5506: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5508: Collective
5510: Input Parameters:
5511: + mat - the matrix to transpose and complex conjugate
5512: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5514: Output Parameter:
5515: . B - the Hermitian transpose
5517: Level: intermediate
5519: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5520: @*/
5521: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5522: {
5523: PetscFunctionBegin;
5524: PetscCall(MatTranspose(mat, reuse, B));
5525: #if defined(PETSC_USE_COMPLEX)
5526: PetscCall(MatConjugate(*B));
5527: #endif
5528: PetscFunctionReturn(PETSC_SUCCESS);
5529: }
5531: /*@
5532: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5534: Collective
5536: Input Parameters:
5537: + A - the matrix to test
5538: . B - the matrix to test against, this can equal the first parameter
5539: - tol - tolerance, differences between entries smaller than this are counted as zero
5541: Output Parameter:
5542: . flg - the result
5544: Level: intermediate
5546: Notes:
5547: Only available for `MATAIJ` matrices.
5549: The sequential algorithm
5550: has a running time of the order of the number of nonzeros; the parallel
5551: test involves parallel copies of the block off-diagonal parts of the matrix.
5553: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5554: @*/
5555: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5556: {
5557: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5559: PetscFunctionBegin;
5562: PetscAssertPointer(flg, 4);
5563: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5564: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5565: if (f && g) {
5566: PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5567: PetscCall((*f)(A, B, tol, flg));
5568: }
5569: PetscFunctionReturn(PETSC_SUCCESS);
5570: }
5572: /*@
5573: MatPermute - Creates a new matrix with rows and columns permuted from the
5574: original.
5576: Collective
5578: Input Parameters:
5579: + mat - the matrix to permute
5580: . row - row permutation, each processor supplies only the permutation for its rows
5581: - col - column permutation, each processor supplies only the permutation for its columns
5583: Output Parameter:
5584: . B - the permuted matrix
5586: Level: advanced
5588: Note:
5589: The index sets map from row/col of permuted matrix to row/col of original matrix.
5590: The index sets should be on the same communicator as mat and have the same local sizes.
5592: Developer Note:
5593: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5594: exploit the fact that row and col are permutations, consider implementing the
5595: more general `MatCreateSubMatrix()` instead.
5597: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5598: @*/
5599: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5600: {
5601: PetscFunctionBegin;
5606: PetscAssertPointer(B, 4);
5607: PetscCheckSameComm(mat, 1, row, 2);
5608: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5609: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5610: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5611: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5612: MatCheckPreallocated(mat, 1);
5614: if (mat->ops->permute) {
5615: PetscUseTypeMethod(mat, permute, row, col, B);
5616: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5617: } else {
5618: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5619: }
5620: PetscFunctionReturn(PETSC_SUCCESS);
5621: }
5623: /*@
5624: MatEqual - Compares two matrices.
5626: Collective
5628: Input Parameters:
5629: + A - the first matrix
5630: - B - the second matrix
5632: Output Parameter:
5633: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5635: Level: intermediate
5637: Note:
5638: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5639: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5641: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5642: @*/
5643: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5644: {
5645: PetscFunctionBegin;
5650: PetscAssertPointer(flg, 3);
5651: PetscCheckSameComm(A, 1, B, 2);
5652: MatCheckPreallocated(A, 1);
5653: MatCheckPreallocated(B, 2);
5654: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5655: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5656: 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,
5657: B->cmap->N);
5658: if (A->ops->equal && A->ops->equal == B->ops->equal) {
5659: PetscUseTypeMethod(A, equal, B, flg);
5660: } else {
5661: PetscCall(MatMultEqual(A, B, 10, flg));
5662: }
5663: PetscFunctionReturn(PETSC_SUCCESS);
5664: }
5666: /*@
5667: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5668: matrices that are stored as vectors. Either of the two scaling
5669: matrices can be `NULL`.
5671: Collective
5673: Input Parameters:
5674: + mat - the matrix to be scaled
5675: . l - the left scaling vector (or `NULL`)
5676: - r - the right scaling vector (or `NULL`)
5678: Level: intermediate
5680: Note:
5681: `MatDiagonalScale()` computes $A = LAR$, where
5682: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5683: The L scales the rows of the matrix, the R scales the columns of the matrix.
5685: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5686: @*/
5687: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5688: {
5689: PetscFunctionBegin;
5692: if (l) {
5694: PetscCheckSameComm(mat, 1, l, 2);
5695: }
5696: if (r) {
5698: PetscCheckSameComm(mat, 1, r, 3);
5699: }
5700: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5701: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5702: MatCheckPreallocated(mat, 1);
5703: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5705: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5706: PetscUseTypeMethod(mat, diagonalscale, l, r);
5707: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5708: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5709: if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5710: PetscFunctionReturn(PETSC_SUCCESS);
5711: }
5713: /*@
5714: MatScale - Scales all elements of a matrix by a given number.
5716: Logically Collective
5718: Input Parameters:
5719: + mat - the matrix to be scaled
5720: - a - the scaling value
5722: Level: intermediate
5724: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5725: @*/
5726: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5727: {
5728: PetscFunctionBegin;
5731: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5732: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5734: MatCheckPreallocated(mat, 1);
5736: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5737: if (a != (PetscScalar)1.0) {
5738: PetscUseTypeMethod(mat, scale, a);
5739: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5740: }
5741: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5742: PetscFunctionReturn(PETSC_SUCCESS);
5743: }
5745: /*@
5746: MatNorm - Calculates various norms of a matrix.
5748: Collective
5750: Input Parameters:
5751: + mat - the matrix
5752: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5754: Output Parameter:
5755: . nrm - the resulting norm
5757: Level: intermediate
5759: .seealso: [](ch_matrices), `Mat`
5760: @*/
5761: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5762: {
5763: PetscFunctionBegin;
5766: PetscAssertPointer(nrm, 3);
5768: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5769: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5770: MatCheckPreallocated(mat, 1);
5772: PetscUseTypeMethod(mat, norm, type, nrm);
5773: PetscFunctionReturn(PETSC_SUCCESS);
5774: }
5776: /*
5777: This variable is used to prevent counting of MatAssemblyBegin() that
5778: are called from within a MatAssemblyEnd().
5779: */
5780: static PetscInt MatAssemblyEnd_InUse = 0;
5781: /*@
5782: MatAssemblyBegin - Begins assembling the matrix. This routine should
5783: be called after completing all calls to `MatSetValues()`.
5785: Collective
5787: Input Parameters:
5788: + mat - the matrix
5789: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5791: Level: beginner
5793: Notes:
5794: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5795: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5797: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5798: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5799: using the matrix.
5801: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5802: 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
5803: a global collective operation requiring all processes that share the matrix.
5805: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5806: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5807: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5809: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5810: @*/
5811: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5812: {
5813: PetscFunctionBegin;
5816: MatCheckPreallocated(mat, 1);
5817: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5818: if (mat->assembled) {
5819: mat->was_assembled = PETSC_TRUE;
5820: mat->assembled = PETSC_FALSE;
5821: }
5823: if (!MatAssemblyEnd_InUse) {
5824: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5825: PetscTryTypeMethod(mat, assemblybegin, type);
5826: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5827: } else PetscTryTypeMethod(mat, assemblybegin, type);
5828: PetscFunctionReturn(PETSC_SUCCESS);
5829: }
5831: /*@
5832: MatAssembled - Indicates if a matrix has been assembled and is ready for
5833: use; for example, in matrix-vector product.
5835: Not Collective
5837: Input Parameter:
5838: . mat - the matrix
5840: Output Parameter:
5841: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5843: Level: advanced
5845: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5846: @*/
5847: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5848: {
5849: PetscFunctionBegin;
5851: PetscAssertPointer(assembled, 2);
5852: *assembled = mat->assembled;
5853: PetscFunctionReturn(PETSC_SUCCESS);
5854: }
5856: /*@
5857: MatAssemblyEnd - Completes assembling the matrix. This routine should
5858: be called after `MatAssemblyBegin()`.
5860: Collective
5862: Input Parameters:
5863: + mat - the matrix
5864: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5866: Options Database Keys:
5867: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5868: . -mat_view ::ascii_info_detail - Prints more detailed info
5869: . -mat_view - Prints matrix in ASCII format
5870: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
5871: . -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5872: . -display <name> - Sets display name (default is host)
5873: . -draw_pause <sec> - Sets number of seconds to pause after display
5874: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5875: . -viewer_socket_machine <machine> - Machine to use for socket
5876: . -viewer_socket_port <port> - Port number to use for socket
5877: - -mat_view binary:filename[:append] - Save matrix to file in binary format
5879: Level: beginner
5881: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5882: @*/
5883: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5884: {
5885: static PetscInt inassm = 0;
5886: PetscBool flg = PETSC_FALSE;
5888: PetscFunctionBegin;
5892: inassm++;
5893: MatAssemblyEnd_InUse++;
5894: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5895: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5896: PetscTryTypeMethod(mat, assemblyend, type);
5897: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5898: } else PetscTryTypeMethod(mat, assemblyend, type);
5900: /* Flush assembly is not a true assembly */
5901: if (type != MAT_FLUSH_ASSEMBLY) {
5902: if (mat->num_ass) {
5903: if (!mat->symmetry_eternal) {
5904: mat->symmetric = PETSC_BOOL3_UNKNOWN;
5905: mat->hermitian = PETSC_BOOL3_UNKNOWN;
5906: }
5907: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5908: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5909: }
5910: mat->num_ass++;
5911: mat->assembled = PETSC_TRUE;
5912: mat->ass_nonzerostate = mat->nonzerostate;
5913: }
5915: mat->insertmode = NOT_SET_VALUES;
5916: MatAssemblyEnd_InUse--;
5917: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5918: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5919: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
5921: if (mat->checksymmetryonassembly) {
5922: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5923: if (flg) {
5924: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5925: } else {
5926: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5927: }
5928: }
5929: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5930: }
5931: inassm--;
5932: PetscFunctionReturn(PETSC_SUCCESS);
5933: }
5935: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5936: /*@
5937: MatSetOption - Sets a parameter option for a matrix. Some options
5938: may be specific to certain storage formats. Some options
5939: determine how values will be inserted (or added). Sorted,
5940: row-oriented input will generally assemble the fastest. The default
5941: is row-oriented.
5943: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
5945: Input Parameters:
5946: + mat - the matrix
5947: . op - the option, one of those listed below (and possibly others),
5948: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
5950: Options Describing Matrix Structure:
5951: + `MAT_SPD` - symmetric positive definite
5952: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
5953: . `MAT_HERMITIAN` - transpose is the complex conjugation
5954: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
5955: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5956: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5957: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
5959: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5960: do not need to be computed (usually at a high cost)
5962: Options For Use with `MatSetValues()`:
5963: Insert a logically dense subblock, which can be
5964: . `MAT_ROW_ORIENTED` - row-oriented (default)
5966: These options reflect the data you pass in with `MatSetValues()`; it has
5967: nothing to do with how the data is stored internally in the matrix
5968: data structure.
5970: When (re)assembling a matrix, we can restrict the input for
5971: efficiency/debugging purposes. These options include
5972: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
5973: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
5974: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
5975: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
5976: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
5977: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
5978: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5979: performance for very large process counts.
5980: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
5981: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5982: functions, instead sending only neighbor messages.
5984: Level: intermediate
5986: Notes:
5987: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
5989: Some options are relevant only for particular matrix types and
5990: are thus ignored by others. Other options are not supported by
5991: certain matrix types and will generate an error message if set.
5993: If using Fortran to compute a matrix, one may need to
5994: use the column-oriented option (or convert to the row-oriented
5995: format).
5997: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5998: that would generate a new entry in the nonzero structure is instead
5999: ignored. Thus, if memory has not already been allocated for this particular
6000: data, then the insertion is ignored. For dense matrices, in which
6001: the entire array is allocated, no entries are ever ignored.
6002: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6004: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6005: that would generate a new entry in the nonzero structure instead produces
6006: 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
6008: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6009: that would generate a new entry that has not been preallocated will
6010: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6011: only.) This is a useful flag when debugging matrix memory preallocation.
6012: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6014: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6015: other processors should be dropped, rather than stashed.
6016: This is useful if you know that the "owning" processor is also
6017: always generating the correct matrix entries, so that PETSc need
6018: not transfer duplicate entries generated on another processor.
6020: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6021: searches during matrix assembly. When this flag is set, the hash table
6022: is created during the first matrix assembly. This hash table is
6023: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6024: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6025: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6026: supported by `MATMPIBAIJ` format only.
6028: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6029: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6031: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6032: a zero location in the matrix
6034: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6036: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6037: zero row routines and thus improves performance for very large process counts.
6039: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6040: part of the matrix (since they should match the upper triangular part).
6042: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6043: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6044: with finite difference schemes with non-periodic boundary conditions.
6046: Developer Note:
6047: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6048: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6049: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6050: not changed.
6052: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6053: @*/
6054: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6055: {
6056: PetscFunctionBegin;
6058: if (op > 0) {
6061: }
6063: 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);
6065: switch (op) {
6066: case MAT_FORCE_DIAGONAL_ENTRIES:
6067: mat->force_diagonals = flg;
6068: PetscFunctionReturn(PETSC_SUCCESS);
6069: case MAT_NO_OFF_PROC_ENTRIES:
6070: mat->nooffprocentries = flg;
6071: PetscFunctionReturn(PETSC_SUCCESS);
6072: case MAT_SUBSET_OFF_PROC_ENTRIES:
6073: mat->assembly_subset = flg;
6074: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6075: #if !defined(PETSC_HAVE_MPIUNI)
6076: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6077: #endif
6078: mat->stash.first_assembly_done = PETSC_FALSE;
6079: }
6080: PetscFunctionReturn(PETSC_SUCCESS);
6081: case MAT_NO_OFF_PROC_ZERO_ROWS:
6082: mat->nooffproczerorows = flg;
6083: PetscFunctionReturn(PETSC_SUCCESS);
6084: case MAT_SPD:
6085: if (flg) {
6086: mat->spd = PETSC_BOOL3_TRUE;
6087: mat->symmetric = PETSC_BOOL3_TRUE;
6088: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6089: } else {
6090: mat->spd = PETSC_BOOL3_FALSE;
6091: }
6092: break;
6093: case MAT_SYMMETRIC:
6094: mat->symmetric = PetscBoolToBool3(flg);
6095: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6096: #if !defined(PETSC_USE_COMPLEX)
6097: mat->hermitian = PetscBoolToBool3(flg);
6098: #endif
6099: break;
6100: case MAT_HERMITIAN:
6101: mat->hermitian = PetscBoolToBool3(flg);
6102: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6103: #if !defined(PETSC_USE_COMPLEX)
6104: mat->symmetric = PetscBoolToBool3(flg);
6105: #endif
6106: break;
6107: case MAT_STRUCTURALLY_SYMMETRIC:
6108: mat->structurally_symmetric = PetscBoolToBool3(flg);
6109: break;
6110: case MAT_SYMMETRY_ETERNAL:
6111: 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");
6112: mat->symmetry_eternal = flg;
6113: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6114: break;
6115: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6116: 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");
6117: mat->structural_symmetry_eternal = flg;
6118: break;
6119: case MAT_SPD_ETERNAL:
6120: 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");
6121: mat->spd_eternal = flg;
6122: if (flg) {
6123: mat->structural_symmetry_eternal = PETSC_TRUE;
6124: mat->symmetry_eternal = PETSC_TRUE;
6125: }
6126: break;
6127: case MAT_STRUCTURE_ONLY:
6128: mat->structure_only = flg;
6129: break;
6130: case MAT_SORTED_FULL:
6131: mat->sortedfull = flg;
6132: break;
6133: default:
6134: break;
6135: }
6136: PetscTryTypeMethod(mat, setoption, op, flg);
6137: PetscFunctionReturn(PETSC_SUCCESS);
6138: }
6140: /*@
6141: MatGetOption - Gets a parameter option that has been set for a matrix.
6143: Logically Collective
6145: Input Parameters:
6146: + mat - the matrix
6147: - op - the option, this only responds to certain options, check the code for which ones
6149: Output Parameter:
6150: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6152: Level: intermediate
6154: Notes:
6155: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6157: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6158: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6160: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6161: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6162: @*/
6163: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6164: {
6165: PetscFunctionBegin;
6169: 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);
6170: 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()");
6172: switch (op) {
6173: case MAT_NO_OFF_PROC_ENTRIES:
6174: *flg = mat->nooffprocentries;
6175: break;
6176: case MAT_NO_OFF_PROC_ZERO_ROWS:
6177: *flg = mat->nooffproczerorows;
6178: break;
6179: case MAT_SYMMETRIC:
6180: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6181: break;
6182: case MAT_HERMITIAN:
6183: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6184: break;
6185: case MAT_STRUCTURALLY_SYMMETRIC:
6186: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6187: break;
6188: case MAT_SPD:
6189: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6190: break;
6191: case MAT_SYMMETRY_ETERNAL:
6192: *flg = mat->symmetry_eternal;
6193: break;
6194: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6195: *flg = mat->symmetry_eternal;
6196: break;
6197: default:
6198: break;
6199: }
6200: PetscFunctionReturn(PETSC_SUCCESS);
6201: }
6203: /*@
6204: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6205: this routine retains the old nonzero structure.
6207: Logically Collective
6209: Input Parameter:
6210: . mat - the matrix
6212: Level: intermediate
6214: Note:
6215: 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.
6216: See the Performance chapter of the users manual for information on preallocating matrices.
6218: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6219: @*/
6220: PetscErrorCode MatZeroEntries(Mat mat)
6221: {
6222: PetscFunctionBegin;
6225: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6226: 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");
6227: MatCheckPreallocated(mat, 1);
6229: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6230: PetscUseTypeMethod(mat, zeroentries);
6231: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6232: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6233: PetscFunctionReturn(PETSC_SUCCESS);
6234: }
6236: /*@
6237: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6238: of a set of rows and columns of a matrix.
6240: Collective
6242: Input Parameters:
6243: + mat - the matrix
6244: . numRows - the number of rows/columns to zero
6245: . rows - the global row indices
6246: . diag - value put in the diagonal of the eliminated rows
6247: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6248: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6250: Level: intermediate
6252: Notes:
6253: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6255: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6256: 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
6258: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6259: Krylov method to take advantage of the known solution on the zeroed rows.
6261: For the parallel case, all processes that share the matrix (i.e.,
6262: those in the communicator used for matrix creation) MUST call this
6263: routine, regardless of whether any rows being zeroed are owned by
6264: them.
6266: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6267: 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
6268: missing.
6270: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6271: list only rows local to itself).
6273: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6275: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6276: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6277: @*/
6278: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6279: {
6280: PetscFunctionBegin;
6283: if (numRows) PetscAssertPointer(rows, 3);
6284: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6285: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6286: MatCheckPreallocated(mat, 1);
6288: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6289: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6290: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6291: PetscFunctionReturn(PETSC_SUCCESS);
6292: }
6294: /*@
6295: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6296: of a set of rows and columns of a matrix.
6298: Collective
6300: Input Parameters:
6301: + mat - the matrix
6302: . is - the rows to zero
6303: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6304: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6305: - b - optional vector of right-hand side, that will be adjusted by provided solution
6307: Level: intermediate
6309: Note:
6310: See `MatZeroRowsColumns()` for details on how this routine operates.
6312: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6313: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6314: @*/
6315: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6316: {
6317: PetscInt numRows;
6318: const PetscInt *rows;
6320: PetscFunctionBegin;
6325: PetscCall(ISGetLocalSize(is, &numRows));
6326: PetscCall(ISGetIndices(is, &rows));
6327: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6328: PetscCall(ISRestoreIndices(is, &rows));
6329: PetscFunctionReturn(PETSC_SUCCESS);
6330: }
6332: /*@
6333: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6334: of a set of rows of a matrix.
6336: Collective
6338: Input Parameters:
6339: + mat - the matrix
6340: . numRows - the number of rows to zero
6341: . rows - the global row indices
6342: . diag - value put in the diagonal of the zeroed rows
6343: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6344: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6346: Level: intermediate
6348: Notes:
6349: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6351: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6353: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6354: Krylov method to take advantage of the known solution on the zeroed rows.
6356: 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)
6357: from the matrix.
6359: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6360: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6361: formats this does not alter the nonzero structure.
6363: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6364: of the matrix is not changed the values are
6365: merely zeroed.
6367: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6368: formats can optionally remove the main diagonal entry from the
6369: nonzero structure as well, by passing 0.0 as the final argument).
6371: For the parallel case, all processes that share the matrix (i.e.,
6372: those in the communicator used for matrix creation) MUST call this
6373: routine, regardless of whether any rows being zeroed are owned by
6374: them.
6376: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6377: list only rows local to itself).
6379: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6380: owns that are to be zeroed. This saves a global synchronization in the implementation.
6382: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6383: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6384: @*/
6385: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6386: {
6387: PetscFunctionBegin;
6390: if (numRows) PetscAssertPointer(rows, 3);
6391: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6392: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6393: MatCheckPreallocated(mat, 1);
6395: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6396: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6397: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6398: PetscFunctionReturn(PETSC_SUCCESS);
6399: }
6401: /*@
6402: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6403: of a set of rows of a matrix indicated by an `IS`
6405: Collective
6407: Input Parameters:
6408: + mat - the matrix
6409: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6410: . diag - value put in all diagonals of eliminated rows
6411: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6412: - b - optional vector of right-hand side, that will be adjusted by provided solution
6414: Level: intermediate
6416: Note:
6417: See `MatZeroRows()` for details on how this routine operates.
6419: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6420: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6421: @*/
6422: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6423: {
6424: PetscInt numRows = 0;
6425: const PetscInt *rows = NULL;
6427: PetscFunctionBegin;
6430: if (is) {
6432: PetscCall(ISGetLocalSize(is, &numRows));
6433: PetscCall(ISGetIndices(is, &rows));
6434: }
6435: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6436: if (is) PetscCall(ISRestoreIndices(is, &rows));
6437: PetscFunctionReturn(PETSC_SUCCESS);
6438: }
6440: /*@
6441: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6442: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6444: Collective
6446: Input Parameters:
6447: + mat - the matrix
6448: . numRows - the number of rows to remove
6449: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6450: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6451: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6452: - b - optional vector of right-hand side, that will be adjusted by provided solution
6454: Level: intermediate
6456: Notes:
6457: See `MatZeroRows()` for details on how this routine operates.
6459: The grid coordinates are across the entire grid, not just the local portion
6461: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6462: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6463: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6464: `DM_BOUNDARY_PERIODIC` boundary type.
6466: 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
6467: a single value per point) you can skip filling those indices.
6469: Fortran Note:
6470: `idxm` and `idxn` should be declared as
6471: .vb
6472: MatStencil idxm(4, m)
6473: .ve
6474: and the values inserted using
6475: .vb
6476: idxm(MatStencil_i, 1) = i
6477: idxm(MatStencil_j, 1) = j
6478: idxm(MatStencil_k, 1) = k
6479: idxm(MatStencil_c, 1) = c
6480: etc
6481: .ve
6483: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6484: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6485: @*/
6486: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6487: {
6488: PetscInt dim = mat->stencil.dim;
6489: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6490: PetscInt *dims = mat->stencil.dims + 1;
6491: PetscInt *starts = mat->stencil.starts;
6492: PetscInt *dxm = (PetscInt *)rows;
6493: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6495: PetscFunctionBegin;
6498: if (numRows) PetscAssertPointer(rows, 3);
6500: PetscCall(PetscMalloc1(numRows, &jdxm));
6501: for (i = 0; i < numRows; ++i) {
6502: /* Skip unused dimensions (they are ordered k, j, i, c) */
6503: for (j = 0; j < 3 - sdim; ++j) dxm++;
6504: /* Local index in X dir */
6505: tmp = *dxm++ - starts[0];
6506: /* Loop over remaining dimensions */
6507: for (j = 0; j < dim - 1; ++j) {
6508: /* If nonlocal, set index to be negative */
6509: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6510: /* Update local index */
6511: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6512: }
6513: /* Skip component slot if necessary */
6514: if (mat->stencil.noc) dxm++;
6515: /* Local row number */
6516: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6517: }
6518: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6519: PetscCall(PetscFree(jdxm));
6520: PetscFunctionReturn(PETSC_SUCCESS);
6521: }
6523: /*@
6524: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6525: of a set of rows and columns of a matrix.
6527: Collective
6529: Input Parameters:
6530: + mat - the matrix
6531: . numRows - the number of rows/columns to remove
6532: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6533: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6534: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6535: - b - optional vector of right-hand side, that will be adjusted by provided solution
6537: Level: intermediate
6539: Notes:
6540: See `MatZeroRowsColumns()` for details on how this routine operates.
6542: The grid coordinates are across the entire grid, not just the local portion
6544: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6545: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6546: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6547: `DM_BOUNDARY_PERIODIC` boundary type.
6549: 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
6550: a single value per point) you can skip filling those indices.
6552: Fortran Note:
6553: `idxm` and `idxn` should be declared as
6554: .vb
6555: MatStencil idxm(4, m)
6556: .ve
6557: and the values inserted using
6558: .vb
6559: idxm(MatStencil_i, 1) = i
6560: idxm(MatStencil_j, 1) = j
6561: idxm(MatStencil_k, 1) = k
6562: idxm(MatStencil_c, 1) = c
6563: etc
6564: .ve
6566: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6567: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6568: @*/
6569: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6570: {
6571: PetscInt dim = mat->stencil.dim;
6572: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6573: PetscInt *dims = mat->stencil.dims + 1;
6574: PetscInt *starts = mat->stencil.starts;
6575: PetscInt *dxm = (PetscInt *)rows;
6576: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6578: PetscFunctionBegin;
6581: if (numRows) PetscAssertPointer(rows, 3);
6583: PetscCall(PetscMalloc1(numRows, &jdxm));
6584: for (i = 0; i < numRows; ++i) {
6585: /* Skip unused dimensions (they are ordered k, j, i, c) */
6586: for (j = 0; j < 3 - sdim; ++j) dxm++;
6587: /* Local index in X dir */
6588: tmp = *dxm++ - starts[0];
6589: /* Loop over remaining dimensions */
6590: for (j = 0; j < dim - 1; ++j) {
6591: /* If nonlocal, set index to be negative */
6592: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6593: /* Update local index */
6594: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6595: }
6596: /* Skip component slot if necessary */
6597: if (mat->stencil.noc) dxm++;
6598: /* Local row number */
6599: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6600: }
6601: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6602: PetscCall(PetscFree(jdxm));
6603: PetscFunctionReturn(PETSC_SUCCESS);
6604: }
6606: /*@
6607: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6608: of a set of rows of a matrix; using local numbering of rows.
6610: Collective
6612: Input Parameters:
6613: + mat - the matrix
6614: . numRows - the number of rows to remove
6615: . rows - the local row indices
6616: . diag - value put in all diagonals of eliminated rows
6617: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6618: - b - optional vector of right-hand side, that will be adjusted by provided solution
6620: Level: intermediate
6622: Notes:
6623: Before calling `MatZeroRowsLocal()`, the user must first set the
6624: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6626: See `MatZeroRows()` for details on how this routine operates.
6628: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6629: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6630: @*/
6631: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6632: {
6633: PetscFunctionBegin;
6636: if (numRows) PetscAssertPointer(rows, 3);
6637: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6638: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6639: MatCheckPreallocated(mat, 1);
6641: if (mat->ops->zerorowslocal) {
6642: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6643: } else {
6644: IS is, newis;
6645: PetscInt *newRows, nl = 0;
6647: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6648: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6649: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6650: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6651: for (PetscInt i = 0; i < numRows; i++)
6652: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6653: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
6654: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6655: PetscCall(ISDestroy(&newis));
6656: PetscCall(ISDestroy(&is));
6657: }
6658: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6659: PetscFunctionReturn(PETSC_SUCCESS);
6660: }
6662: /*@
6663: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6664: of a set of rows of a matrix; using local numbering of rows.
6666: Collective
6668: Input Parameters:
6669: + mat - the matrix
6670: . is - index set of rows to remove
6671: . diag - value put in all diagonals of eliminated rows
6672: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6673: - b - optional vector of right-hand side, that will be adjusted by provided solution
6675: Level: intermediate
6677: Notes:
6678: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6679: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6681: See `MatZeroRows()` for details on how this routine operates.
6683: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6684: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6685: @*/
6686: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6687: {
6688: PetscInt numRows;
6689: const PetscInt *rows;
6691: PetscFunctionBegin;
6695: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6696: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6697: MatCheckPreallocated(mat, 1);
6699: PetscCall(ISGetLocalSize(is, &numRows));
6700: PetscCall(ISGetIndices(is, &rows));
6701: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6702: PetscCall(ISRestoreIndices(is, &rows));
6703: PetscFunctionReturn(PETSC_SUCCESS);
6704: }
6706: /*@
6707: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6708: of a set of rows and columns of a matrix; using local numbering of rows.
6710: Collective
6712: Input Parameters:
6713: + mat - the matrix
6714: . numRows - the number of rows to remove
6715: . rows - the global row indices
6716: . diag - value put in all diagonals of eliminated rows
6717: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6718: - b - optional vector of right-hand side, that will be adjusted by provided solution
6720: Level: intermediate
6722: Notes:
6723: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6724: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6726: See `MatZeroRowsColumns()` for details on how this routine operates.
6728: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6729: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6730: @*/
6731: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6732: {
6733: PetscFunctionBegin;
6736: if (numRows) PetscAssertPointer(rows, 3);
6737: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6738: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6739: MatCheckPreallocated(mat, 1);
6741: if (mat->ops->zerorowscolumnslocal) {
6742: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
6743: } else {
6744: IS is, newis;
6745: PetscInt *newRows, nl = 0;
6747: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6748: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6749: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6750: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6751: for (PetscInt i = 0; i < numRows; i++)
6752: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6753: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
6754: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6755: PetscCall(ISDestroy(&newis));
6756: PetscCall(ISDestroy(&is));
6757: }
6758: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6759: PetscFunctionReturn(PETSC_SUCCESS);
6760: }
6762: /*@
6763: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6764: of a set of rows and columns of a matrix; using local numbering of rows.
6766: Collective
6768: Input Parameters:
6769: + mat - the matrix
6770: . is - index set of rows to remove
6771: . diag - value put in all diagonals of eliminated rows
6772: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6773: - b - optional vector of right-hand side, that will be adjusted by provided solution
6775: Level: intermediate
6777: Notes:
6778: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6779: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6781: See `MatZeroRowsColumns()` for details on how this routine operates.
6783: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6784: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6785: @*/
6786: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6787: {
6788: PetscInt numRows;
6789: const PetscInt *rows;
6791: PetscFunctionBegin;
6795: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6796: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6797: MatCheckPreallocated(mat, 1);
6799: PetscCall(ISGetLocalSize(is, &numRows));
6800: PetscCall(ISGetIndices(is, &rows));
6801: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6802: PetscCall(ISRestoreIndices(is, &rows));
6803: PetscFunctionReturn(PETSC_SUCCESS);
6804: }
6806: /*@
6807: MatGetSize - Returns the numbers of rows and columns in a matrix.
6809: Not Collective
6811: Input Parameter:
6812: . mat - the matrix
6814: Output Parameters:
6815: + m - the number of global rows
6816: - n - the number of global columns
6818: Level: beginner
6820: Note:
6821: Both output parameters can be `NULL` on input.
6823: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6824: @*/
6825: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6826: {
6827: PetscFunctionBegin;
6829: if (m) *m = mat->rmap->N;
6830: if (n) *n = mat->cmap->N;
6831: PetscFunctionReturn(PETSC_SUCCESS);
6832: }
6834: /*@
6835: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6836: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6838: Not Collective
6840: Input Parameter:
6841: . mat - the matrix
6843: Output Parameters:
6844: + m - the number of local rows, use `NULL` to not obtain this value
6845: - n - the number of local columns, use `NULL` to not obtain this value
6847: Level: beginner
6849: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6850: @*/
6851: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6852: {
6853: PetscFunctionBegin;
6855: if (m) PetscAssertPointer(m, 2);
6856: if (n) PetscAssertPointer(n, 3);
6857: if (m) *m = mat->rmap->n;
6858: if (n) *n = mat->cmap->n;
6859: PetscFunctionReturn(PETSC_SUCCESS);
6860: }
6862: /*@
6863: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6864: vector one multiplies this matrix by that are owned by this processor.
6866: Not Collective, unless matrix has not been allocated, then collective
6868: Input Parameter:
6869: . mat - the matrix
6871: Output Parameters:
6872: + m - the global index of the first local column, use `NULL` to not obtain this value
6873: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
6875: Level: developer
6877: Notes:
6878: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6880: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6881: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6883: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6884: the local values in the matrix.
6886: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6887: Layouts](sec_matlayout) for details on matrix layouts.
6889: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6890: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6891: @*/
6892: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6893: {
6894: PetscFunctionBegin;
6897: if (m) PetscAssertPointer(m, 2);
6898: if (n) PetscAssertPointer(n, 3);
6899: MatCheckPreallocated(mat, 1);
6900: if (m) *m = mat->cmap->rstart;
6901: if (n) *n = mat->cmap->rend;
6902: PetscFunctionReturn(PETSC_SUCCESS);
6903: }
6905: /*@
6906: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6907: this MPI process.
6909: Not Collective
6911: Input Parameter:
6912: . mat - the matrix
6914: Output Parameters:
6915: + m - the global index of the first local row, use `NULL` to not obtain this value
6916: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
6918: Level: beginner
6920: Notes:
6921: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6923: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6924: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6926: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6927: the local values in the matrix.
6929: The high argument is one more than the last element stored locally.
6931: For all matrices it returns the range of matrix rows associated with rows of a vector that
6932: would contain the result of a matrix vector product with this matrix. See [Matrix
6933: Layouts](sec_matlayout) for details on matrix layouts.
6935: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6936: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6937: @*/
6938: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6939: {
6940: PetscFunctionBegin;
6943: if (m) PetscAssertPointer(m, 2);
6944: if (n) PetscAssertPointer(n, 3);
6945: MatCheckPreallocated(mat, 1);
6946: if (m) *m = mat->rmap->rstart;
6947: if (n) *n = mat->rmap->rend;
6948: PetscFunctionReturn(PETSC_SUCCESS);
6949: }
6951: /*@C
6952: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6953: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
6955: Not Collective, unless matrix has not been allocated
6957: Input Parameter:
6958: . mat - the matrix
6960: Output Parameter:
6961: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6962: where `size` is the number of MPI processes used by `mat`
6964: Level: beginner
6966: Notes:
6967: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6969: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6970: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6972: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6973: the local values in the matrix.
6975: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
6976: would contain the result of a matrix vector product with this matrix. See [Matrix
6977: Layouts](sec_matlayout) for details on matrix layouts.
6979: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6980: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6981: `DMDAGetGhostCorners()`, `DM`
6982: @*/
6983: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6984: {
6985: PetscFunctionBegin;
6988: MatCheckPreallocated(mat, 1);
6989: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6990: PetscFunctionReturn(PETSC_SUCCESS);
6991: }
6993: /*@C
6994: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6995: vector one multiplies this vector by that are owned by each processor.
6997: Not Collective, unless matrix has not been allocated
6999: Input Parameter:
7000: . mat - the matrix
7002: Output Parameter:
7003: . ranges - start of each processors portion plus one more than the total length at the end
7005: Level: beginner
7007: Notes:
7008: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7010: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7011: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7013: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7014: the local values in the matrix.
7016: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7017: Layouts](sec_matlayout) for details on matrix layouts.
7019: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7020: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7021: `DMDAGetGhostCorners()`, `DM`
7022: @*/
7023: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7024: {
7025: PetscFunctionBegin;
7028: MatCheckPreallocated(mat, 1);
7029: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7030: PetscFunctionReturn(PETSC_SUCCESS);
7031: }
7033: /*@
7034: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7036: Not Collective
7038: Input Parameter:
7039: . A - matrix
7041: Output Parameters:
7042: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7043: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7045: Level: intermediate
7047: Note:
7048: You should call `ISDestroy()` on the returned `IS`
7050: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7051: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7052: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7053: details on matrix layouts.
7055: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7056: @*/
7057: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7058: {
7059: PetscErrorCode (*f)(Mat, IS *, IS *);
7061: PetscFunctionBegin;
7064: MatCheckPreallocated(A, 1);
7065: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7066: if (f) {
7067: PetscCall((*f)(A, rows, cols));
7068: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7069: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7070: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7071: }
7072: PetscFunctionReturn(PETSC_SUCCESS);
7073: }
7075: /*@
7076: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7077: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7078: to complete the factorization.
7080: Collective
7082: Input Parameters:
7083: + fact - the factorized matrix obtained with `MatGetFactor()`
7084: . mat - the matrix
7085: . row - row permutation
7086: . col - column permutation
7087: - info - structure containing
7088: .vb
7089: levels - number of levels of fill.
7090: expected fill - as ratio of original fill.
7091: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7092: missing diagonal entries)
7093: .ve
7095: Level: developer
7097: Notes:
7098: See [Matrix Factorization](sec_matfactor) for additional information.
7100: Most users should employ the `KSP` interface for linear solvers
7101: instead of working directly with matrix algebra routines such as this.
7102: See, e.g., `KSPCreate()`.
7104: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7106: Fortran Note:
7107: A valid (non-null) `info` argument must be provided
7109: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7110: `MatGetOrdering()`, `MatFactorInfo`
7111: @*/
7112: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7113: {
7114: PetscFunctionBegin;
7119: PetscAssertPointer(info, 5);
7120: PetscAssertPointer(fact, 1);
7121: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7122: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7123: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7124: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7125: MatCheckPreallocated(mat, 2);
7127: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7128: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7129: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7130: PetscFunctionReturn(PETSC_SUCCESS);
7131: }
7133: /*@
7134: MatICCFactorSymbolic - Performs symbolic incomplete
7135: Cholesky factorization for a symmetric matrix. Use
7136: `MatCholeskyFactorNumeric()` to complete the factorization.
7138: Collective
7140: Input Parameters:
7141: + fact - the factorized matrix obtained with `MatGetFactor()`
7142: . mat - the matrix to be factored
7143: . perm - row and column permutation
7144: - info - structure containing
7145: .vb
7146: levels - number of levels of fill.
7147: expected fill - as ratio of original fill.
7148: .ve
7150: Level: developer
7152: Notes:
7153: Most users should employ the `KSP` interface for linear solvers
7154: instead of working directly with matrix algebra routines such as this.
7155: See, e.g., `KSPCreate()`.
7157: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7159: Fortran Note:
7160: A valid (non-null) `info` argument must be provided
7162: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7163: @*/
7164: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7165: {
7166: PetscFunctionBegin;
7170: PetscAssertPointer(info, 4);
7171: PetscAssertPointer(fact, 1);
7172: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7173: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7174: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7175: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7176: MatCheckPreallocated(mat, 2);
7178: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7179: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7180: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7181: PetscFunctionReturn(PETSC_SUCCESS);
7182: }
7184: /*@C
7185: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7186: points to an array of valid matrices, they may be reused to store the new
7187: submatrices.
7189: Collective
7191: Input Parameters:
7192: + mat - the matrix
7193: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7194: . irow - index set of rows to extract
7195: . icol - index set of columns to extract
7196: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7198: Output Parameter:
7199: . submat - the array of submatrices
7201: Level: advanced
7203: Notes:
7204: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7205: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7206: to extract a parallel submatrix.
7208: Some matrix types place restrictions on the row and column
7209: indices, such as that they be sorted or that they be equal to each other.
7211: The index sets may not have duplicate entries.
7213: When extracting submatrices from a parallel matrix, each processor can
7214: form a different submatrix by setting the rows and columns of its
7215: individual index sets according to the local submatrix desired.
7217: When finished using the submatrices, the user should destroy
7218: them with `MatDestroySubMatrices()`.
7220: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7221: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7223: This routine creates the matrices in submat; you should NOT create them before
7224: calling it. It also allocates the array of matrix pointers submat.
7226: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7227: request one row/column in a block, they must request all rows/columns that are in
7228: that block. For example, if the block size is 2 you cannot request just row 0 and
7229: column 0.
7231: Fortran Note:
7232: .vb
7233: Mat, pointer :: submat(:)
7234: .ve
7236: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7237: @*/
7238: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7239: {
7240: PetscInt i;
7241: PetscBool eq;
7243: PetscFunctionBegin;
7246: if (n) {
7247: PetscAssertPointer(irow, 3);
7249: PetscAssertPointer(icol, 4);
7251: }
7252: PetscAssertPointer(submat, 6);
7253: if (n && scall == MAT_REUSE_MATRIX) {
7254: PetscAssertPointer(*submat, 6);
7256: }
7257: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7258: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7259: MatCheckPreallocated(mat, 1);
7260: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7261: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7262: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7263: for (i = 0; i < n; i++) {
7264: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7265: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7266: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7267: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7268: if (mat->boundtocpu && mat->bindingpropagates) {
7269: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7270: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7271: }
7272: #endif
7273: }
7274: PetscFunctionReturn(PETSC_SUCCESS);
7275: }
7277: /*@C
7278: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7280: Collective
7282: Input Parameters:
7283: + mat - the matrix
7284: . n - the number of submatrixes to be extracted
7285: . irow - index set of rows to extract
7286: . icol - index set of columns to extract
7287: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7289: Output Parameter:
7290: . submat - the array of submatrices
7292: Level: advanced
7294: Note:
7295: This is used by `PCGASM`
7297: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7298: @*/
7299: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7300: {
7301: PetscInt i;
7302: PetscBool eq;
7304: PetscFunctionBegin;
7307: if (n) {
7308: PetscAssertPointer(irow, 3);
7310: PetscAssertPointer(icol, 4);
7312: }
7313: PetscAssertPointer(submat, 6);
7314: if (n && scall == MAT_REUSE_MATRIX) {
7315: PetscAssertPointer(*submat, 6);
7317: }
7318: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7319: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7320: MatCheckPreallocated(mat, 1);
7322: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7323: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7324: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7325: for (i = 0; i < n; i++) {
7326: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7327: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7328: }
7329: PetscFunctionReturn(PETSC_SUCCESS);
7330: }
7332: /*@C
7333: MatDestroyMatrices - Destroys an array of matrices
7335: Collective
7337: Input Parameters:
7338: + n - the number of local matrices
7339: - mat - the matrices (this is a pointer to the array of matrices)
7341: Level: advanced
7343: Notes:
7344: Frees not only the matrices, but also the array that contains the matrices
7346: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7348: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7349: @*/
7350: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7351: {
7352: PetscInt i;
7354: PetscFunctionBegin;
7355: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7356: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7357: PetscAssertPointer(mat, 2);
7359: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7361: /* memory is allocated even if n = 0 */
7362: PetscCall(PetscFree(*mat));
7363: PetscFunctionReturn(PETSC_SUCCESS);
7364: }
7366: /*@C
7367: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7369: Collective
7371: Input Parameters:
7372: + n - the number of local matrices
7373: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7375: Level: advanced
7377: Note:
7378: Frees not only the matrices, but also the array that contains the matrices
7380: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7381: @*/
7382: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7383: {
7384: Mat mat0;
7386: PetscFunctionBegin;
7387: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7388: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7389: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7390: PetscAssertPointer(mat, 2);
7392: mat0 = (*mat)[0];
7393: if (mat0 && mat0->ops->destroysubmatrices) {
7394: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7395: } else {
7396: PetscCall(MatDestroyMatrices(n, mat));
7397: }
7398: PetscFunctionReturn(PETSC_SUCCESS);
7399: }
7401: /*@
7402: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7404: Collective
7406: Input Parameter:
7407: . mat - the matrix
7409: Output Parameter:
7410: . matstruct - the sequential matrix with the nonzero structure of `mat`
7412: Level: developer
7414: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7415: @*/
7416: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7417: {
7418: PetscFunctionBegin;
7420: PetscAssertPointer(matstruct, 2);
7423: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7424: MatCheckPreallocated(mat, 1);
7426: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7427: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7428: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7429: PetscFunctionReturn(PETSC_SUCCESS);
7430: }
7432: /*@C
7433: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7435: Collective
7437: Input Parameter:
7438: . mat - the matrix
7440: Level: advanced
7442: Note:
7443: This is not needed, one can just call `MatDestroy()`
7445: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7446: @*/
7447: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7448: {
7449: PetscFunctionBegin;
7450: PetscAssertPointer(mat, 1);
7451: PetscCall(MatDestroy(mat));
7452: PetscFunctionReturn(PETSC_SUCCESS);
7453: }
7455: /*@
7456: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7457: replaces the index sets by larger ones that represent submatrices with
7458: additional overlap.
7460: Collective
7462: Input Parameters:
7463: + mat - the matrix
7464: . n - the number of index sets
7465: . is - the array of index sets (these index sets will changed during the call)
7466: - ov - the additional overlap requested
7468: Options Database Key:
7469: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7471: Level: developer
7473: Note:
7474: The computed overlap preserves the matrix block sizes when the blocks are square.
7475: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7476: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7478: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7479: @*/
7480: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7481: {
7482: PetscInt i, bs, cbs;
7484: PetscFunctionBegin;
7488: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7489: if (n) {
7490: PetscAssertPointer(is, 3);
7492: }
7493: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7494: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7495: MatCheckPreallocated(mat, 1);
7497: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7498: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7499: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7500: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7501: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7502: if (bs == cbs) {
7503: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7504: }
7505: PetscFunctionReturn(PETSC_SUCCESS);
7506: }
7508: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7510: /*@
7511: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7512: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7513: additional overlap.
7515: Collective
7517: Input Parameters:
7518: + mat - the matrix
7519: . n - the number of index sets
7520: . is - the array of index sets (these index sets will changed during the call)
7521: - ov - the additional overlap requested
7523: ` Options Database Key:
7524: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7526: Level: developer
7528: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7529: @*/
7530: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7531: {
7532: PetscInt i;
7534: PetscFunctionBegin;
7537: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7538: if (n) {
7539: PetscAssertPointer(is, 3);
7541: }
7542: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7543: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7544: MatCheckPreallocated(mat, 1);
7545: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7546: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7547: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7548: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7549: PetscFunctionReturn(PETSC_SUCCESS);
7550: }
7552: /*@
7553: MatGetBlockSize - Returns the matrix block size.
7555: Not Collective
7557: Input Parameter:
7558: . mat - the matrix
7560: Output Parameter:
7561: . bs - block size
7563: Level: intermediate
7565: Notes:
7566: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7568: If the block size has not been set yet this routine returns 1.
7570: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7571: @*/
7572: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7573: {
7574: PetscFunctionBegin;
7576: PetscAssertPointer(bs, 2);
7577: *bs = mat->rmap->bs;
7578: PetscFunctionReturn(PETSC_SUCCESS);
7579: }
7581: /*@
7582: MatGetBlockSizes - Returns the matrix block row and column sizes.
7584: Not Collective
7586: Input Parameter:
7587: . mat - the matrix
7589: Output Parameters:
7590: + rbs - row block size
7591: - cbs - column block size
7593: Level: intermediate
7595: Notes:
7596: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7597: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7599: If a block size has not been set yet this routine returns 1.
7601: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7602: @*/
7603: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7604: {
7605: PetscFunctionBegin;
7607: if (rbs) PetscAssertPointer(rbs, 2);
7608: if (cbs) PetscAssertPointer(cbs, 3);
7609: if (rbs) *rbs = mat->rmap->bs;
7610: if (cbs) *cbs = mat->cmap->bs;
7611: PetscFunctionReturn(PETSC_SUCCESS);
7612: }
7614: /*@
7615: MatSetBlockSize - Sets the matrix block size.
7617: Logically Collective
7619: Input Parameters:
7620: + mat - the matrix
7621: - bs - block size
7623: Level: intermediate
7625: Notes:
7626: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7627: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7629: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7630: is compatible with the matrix local sizes.
7632: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7633: @*/
7634: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7635: {
7636: PetscFunctionBegin;
7639: PetscCall(MatSetBlockSizes(mat, bs, bs));
7640: PetscFunctionReturn(PETSC_SUCCESS);
7641: }
7643: typedef struct {
7644: PetscInt n;
7645: IS *is;
7646: Mat *mat;
7647: PetscObjectState nonzerostate;
7648: Mat C;
7649: } EnvelopeData;
7651: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7652: {
7653: EnvelopeData *edata = (EnvelopeData *)*ptr;
7655: PetscFunctionBegin;
7656: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7657: PetscCall(PetscFree(edata->is));
7658: PetscCall(PetscFree(edata));
7659: PetscFunctionReturn(PETSC_SUCCESS);
7660: }
7662: /*@
7663: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7664: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7666: Collective
7668: Input Parameter:
7669: . mat - the matrix
7671: Level: intermediate
7673: Notes:
7674: There can be zeros within the blocks
7676: The blocks can overlap between processes, including laying on more than two processes
7678: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7679: @*/
7680: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7681: {
7682: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7683: PetscInt *diag, *odiag, sc;
7684: VecScatter scatter;
7685: PetscScalar *seqv;
7686: const PetscScalar *parv;
7687: const PetscInt *ia, *ja;
7688: PetscBool set, flag, done;
7689: Mat AA = mat, A;
7690: MPI_Comm comm;
7691: PetscMPIInt rank, size, tag;
7692: MPI_Status status;
7693: PetscContainer container;
7694: EnvelopeData *edata;
7695: Vec seq, par;
7696: IS isglobal;
7698: PetscFunctionBegin;
7700: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7701: if (!set || !flag) {
7702: /* TODO: only needs nonzero structure of transpose */
7703: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7704: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7705: }
7706: PetscCall(MatAIJGetLocalMat(AA, &A));
7707: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7708: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7710: PetscCall(MatGetLocalSize(mat, &n, NULL));
7711: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7712: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7713: PetscCallMPI(MPI_Comm_size(comm, &size));
7714: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7716: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7718: if (rank > 0) {
7719: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7720: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7721: }
7722: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7723: for (i = 0; i < n; i++) {
7724: env = PetscMax(env, ja[ia[i + 1] - 1]);
7725: II = rstart + i;
7726: if (env == II) {
7727: starts[lblocks] = tbs;
7728: sizes[lblocks++] = 1 + II - tbs;
7729: tbs = 1 + II;
7730: }
7731: }
7732: if (rank < size - 1) {
7733: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7734: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7735: }
7737: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7738: if (!set || !flag) PetscCall(MatDestroy(&AA));
7739: PetscCall(MatDestroy(&A));
7741: PetscCall(PetscNew(&edata));
7742: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7743: edata->n = lblocks;
7744: /* create IS needed for extracting blocks from the original matrix */
7745: PetscCall(PetscMalloc1(lblocks, &edata->is));
7746: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7748: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7749: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7750: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7751: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7752: PetscCall(MatSetType(edata->C, MATAIJ));
7754: /* Communicate the start and end of each row, from each block to the correct rank */
7755: /* TODO: Use PetscSF instead of VecScatter */
7756: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7757: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7758: PetscCall(VecGetArrayWrite(seq, &seqv));
7759: for (PetscInt i = 0; i < lblocks; i++) {
7760: for (PetscInt j = 0; j < sizes[i]; j++) {
7761: seqv[cnt] = starts[i];
7762: seqv[cnt + 1] = starts[i] + sizes[i];
7763: cnt += 2;
7764: }
7765: }
7766: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7767: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7768: sc -= cnt;
7769: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7770: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7771: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7772: PetscCall(ISDestroy(&isglobal));
7773: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7774: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7775: PetscCall(VecScatterDestroy(&scatter));
7776: PetscCall(VecDestroy(&seq));
7777: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7778: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7779: PetscCall(VecGetArrayRead(par, &parv));
7780: cnt = 0;
7781: PetscCall(MatGetSize(mat, NULL, &n));
7782: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7783: PetscInt start, end, d = 0, od = 0;
7785: start = (PetscInt)PetscRealPart(parv[cnt]);
7786: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7787: cnt += 2;
7789: if (start < cstart) {
7790: od += cstart - start + n - cend;
7791: d += cend - cstart;
7792: } else if (start < cend) {
7793: od += n - cend;
7794: d += cend - start;
7795: } else od += n - start;
7796: if (end <= cstart) {
7797: od -= cstart - end + n - cend;
7798: d -= cend - cstart;
7799: } else if (end < cend) {
7800: od -= n - cend;
7801: d -= cend - end;
7802: } else od -= n - end;
7804: odiag[i] = od;
7805: diag[i] = d;
7806: }
7807: PetscCall(VecRestoreArrayRead(par, &parv));
7808: PetscCall(VecDestroy(&par));
7809: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7810: PetscCall(PetscFree2(diag, odiag));
7811: PetscCall(PetscFree2(sizes, starts));
7813: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7814: PetscCall(PetscContainerSetPointer(container, edata));
7815: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7816: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7817: PetscCall(PetscObjectDereference((PetscObject)container));
7818: PetscFunctionReturn(PETSC_SUCCESS);
7819: }
7821: /*@
7822: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7824: Collective
7826: Input Parameters:
7827: + A - the matrix
7828: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7830: Output Parameter:
7831: . C - matrix with inverted block diagonal of `A`
7833: Level: advanced
7835: Note:
7836: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7838: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7839: @*/
7840: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7841: {
7842: PetscContainer container;
7843: EnvelopeData *edata;
7844: PetscObjectState nonzerostate;
7846: PetscFunctionBegin;
7847: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7848: if (!container) {
7849: PetscCall(MatComputeVariableBlockEnvelope(A));
7850: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7851: }
7852: PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7853: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7854: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7855: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7857: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7858: *C = edata->C;
7860: for (PetscInt i = 0; i < edata->n; i++) {
7861: Mat D;
7862: PetscScalar *dvalues;
7864: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7865: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7866: PetscCall(MatSeqDenseInvert(D));
7867: PetscCall(MatDenseGetArray(D, &dvalues));
7868: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7869: PetscCall(MatDestroy(&D));
7870: }
7871: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7872: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7873: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7874: PetscFunctionReturn(PETSC_SUCCESS);
7875: }
7877: /*@
7878: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
7880: Not Collective
7882: Input Parameters:
7883: + mat - the matrix
7884: . nblocks - the number of blocks on this process, each block can only exist on a single process
7885: - bsizes - the block sizes
7887: Level: intermediate
7889: Notes:
7890: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
7892: 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.
7894: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7895: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7896: @*/
7897: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7898: {
7899: PetscInt ncnt = 0, nlocal;
7901: PetscFunctionBegin;
7903: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7904: 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);
7905: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7906: 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);
7907: PetscCall(PetscFree(mat->bsizes));
7908: mat->nblocks = nblocks;
7909: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7910: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7911: PetscFunctionReturn(PETSC_SUCCESS);
7912: }
7914: /*@C
7915: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
7917: Not Collective; No Fortran Support
7919: Input Parameter:
7920: . mat - the matrix
7922: Output Parameters:
7923: + nblocks - the number of blocks on this process
7924: - bsizes - the block sizes
7926: Level: intermediate
7928: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7929: @*/
7930: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7931: {
7932: PetscFunctionBegin;
7934: if (nblocks) *nblocks = mat->nblocks;
7935: if (bsizes) *bsizes = mat->bsizes;
7936: PetscFunctionReturn(PETSC_SUCCESS);
7937: }
7939: /*@
7940: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
7942: Not Collective
7944: Input Parameter:
7945: + subA - the submatrix
7946: . A - the original matrix
7947: - isrow - The `IS` of selected rows for the submatrix, must be sorted
7949: Level: developer
7951: Notes:
7952: If the index set is not sorted or contains off-process entries, this function will do nothing.
7954: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7955: @*/
7956: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
7957: {
7958: const PetscInt *rows;
7959: PetscInt n, rStart, rEnd, Nb = 0;
7960: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
7962: PetscFunctionBegin;
7963: // The code for block size extraction does not support an unsorted IS
7964: if (flg) PetscCall(ISSorted(isrow, &flg));
7965: // We don't support originally off-diagonal blocks
7966: if (flg) {
7967: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
7968: PetscCall(ISGetLocalSize(isrow, &n));
7969: PetscCall(ISGetIndices(isrow, &rows));
7970: for (PetscInt i = 0; i < n && flg; ++i) {
7971: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
7972: }
7973: PetscCall(ISRestoreIndices(isrow, &rows));
7974: }
7975: // quiet return if we can't extract block size
7976: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
7977: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
7979: // extract block sizes
7980: PetscCall(ISGetIndices(isrow, &rows));
7981: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
7982: PetscBool occupied = PETSC_FALSE;
7984: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
7985: const PetscInt row = gr + br;
7987: if (i == n) break;
7988: if (rows[i] == row) {
7989: occupied = PETSC_TRUE;
7990: ++i;
7991: }
7992: while (i < n && rows[i] < row) ++i;
7993: }
7994: gr += A->bsizes[b];
7995: if (occupied) ++Nb;
7996: }
7997: subA->nblocks = Nb;
7998: PetscCall(PetscFree(subA->bsizes));
7999: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
8000: PetscInt sb = 0;
8001: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8002: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
8003: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8004: const PetscInt row = gr + br;
8006: if (i == n) break;
8007: if (rows[i] == row) {
8008: ++subA->bsizes[sb];
8009: ++i;
8010: }
8011: while (i < n && rows[i] < row) ++i;
8012: }
8013: gr += A->bsizes[b];
8014: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8015: }
8016: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8017: PetscInt nlocal, ncnt = 0;
8018: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8019: 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);
8020: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8021: 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);
8022: PetscCall(ISRestoreIndices(isrow, &rows));
8023: PetscFunctionReturn(PETSC_SUCCESS);
8024: }
8026: /*@
8027: MatSetBlockSizes - Sets the matrix block row and column sizes.
8029: Logically Collective
8031: Input Parameters:
8032: + mat - the matrix
8033: . rbs - row block size
8034: - cbs - column block size
8036: Level: intermediate
8038: Notes:
8039: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8040: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8041: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8043: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8044: are compatible with the matrix local sizes.
8046: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8048: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8049: @*/
8050: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8051: {
8052: PetscFunctionBegin;
8056: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8057: if (mat->rmap->refcnt) {
8058: ISLocalToGlobalMapping l2g = NULL;
8059: PetscLayout nmap = NULL;
8061: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8062: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8063: PetscCall(PetscLayoutDestroy(&mat->rmap));
8064: mat->rmap = nmap;
8065: mat->rmap->mapping = l2g;
8066: }
8067: if (mat->cmap->refcnt) {
8068: ISLocalToGlobalMapping l2g = NULL;
8069: PetscLayout nmap = NULL;
8071: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8072: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8073: PetscCall(PetscLayoutDestroy(&mat->cmap));
8074: mat->cmap = nmap;
8075: mat->cmap->mapping = l2g;
8076: }
8077: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8078: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8079: PetscFunctionReturn(PETSC_SUCCESS);
8080: }
8082: /*@
8083: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8085: Logically Collective
8087: Input Parameters:
8088: + mat - the matrix
8089: . fromRow - matrix from which to copy row block size
8090: - fromCol - matrix from which to copy column block size (can be same as fromRow)
8092: Level: developer
8094: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8095: @*/
8096: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8097: {
8098: PetscFunctionBegin;
8102: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8103: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8104: PetscFunctionReturn(PETSC_SUCCESS);
8105: }
8107: /*@
8108: MatResidual - Default routine to calculate the residual r = b - Ax
8110: Collective
8112: Input Parameters:
8113: + mat - the matrix
8114: . b - the right-hand-side
8115: - x - the approximate solution
8117: Output Parameter:
8118: . r - location to store the residual
8120: Level: developer
8122: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8123: @*/
8124: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8125: {
8126: PetscFunctionBegin;
8132: MatCheckPreallocated(mat, 1);
8133: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8134: if (!mat->ops->residual) {
8135: PetscCall(MatMult(mat, x, r));
8136: PetscCall(VecAYPX(r, -1.0, b));
8137: } else {
8138: PetscUseTypeMethod(mat, residual, b, x, r);
8139: }
8140: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8141: PetscFunctionReturn(PETSC_SUCCESS);
8142: }
8144: /*@C
8145: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8147: Collective
8149: Input Parameters:
8150: + mat - the matrix
8151: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8152: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8153: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8154: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8155: always used.
8157: Output Parameters:
8158: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8159: . 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
8160: . ja - the column indices, use `NULL` if not needed
8161: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8162: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8164: Level: developer
8166: Notes:
8167: You CANNOT change any of the ia[] or ja[] values.
8169: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8171: Fortran Notes:
8172: Use
8173: .vb
8174: PetscInt, pointer :: ia(:),ja(:)
8175: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8176: ! Access the ith and jth entries via ia(i) and ja(j)
8177: .ve
8179: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8180: @*/
8181: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8182: {
8183: PetscFunctionBegin;
8186: if (n) PetscAssertPointer(n, 5);
8187: if (ia) PetscAssertPointer(ia, 6);
8188: if (ja) PetscAssertPointer(ja, 7);
8189: if (done) PetscAssertPointer(done, 8);
8190: MatCheckPreallocated(mat, 1);
8191: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8192: else {
8193: if (done) *done = PETSC_TRUE;
8194: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8195: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8196: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8197: }
8198: PetscFunctionReturn(PETSC_SUCCESS);
8199: }
8201: /*@C
8202: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8204: Collective
8206: Input Parameters:
8207: + mat - the matrix
8208: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8209: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8210: symmetrized
8211: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8212: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8213: always used.
8215: Output Parameters:
8216: + n - number of columns in the (possibly compressed) matrix
8217: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8218: . ja - the row indices
8219: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8221: Level: developer
8223: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8224: @*/
8225: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8226: {
8227: PetscFunctionBegin;
8230: PetscAssertPointer(n, 5);
8231: if (ia) PetscAssertPointer(ia, 6);
8232: if (ja) PetscAssertPointer(ja, 7);
8233: PetscAssertPointer(done, 8);
8234: MatCheckPreallocated(mat, 1);
8235: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8236: else {
8237: *done = PETSC_TRUE;
8238: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8239: }
8240: PetscFunctionReturn(PETSC_SUCCESS);
8241: }
8243: /*@C
8244: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8246: Collective
8248: Input Parameters:
8249: + mat - the matrix
8250: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8251: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8252: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8253: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8254: always used.
8255: . n - size of (possibly compressed) matrix
8256: . ia - the row pointers
8257: - ja - the column indices
8259: Output Parameter:
8260: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8262: Level: developer
8264: Note:
8265: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8266: us of the array after it has been restored. If you pass `NULL`, it will
8267: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8269: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8270: @*/
8271: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8272: {
8273: PetscFunctionBegin;
8276: if (ia) PetscAssertPointer(ia, 6);
8277: if (ja) PetscAssertPointer(ja, 7);
8278: if (done) PetscAssertPointer(done, 8);
8279: MatCheckPreallocated(mat, 1);
8281: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8282: else {
8283: if (done) *done = PETSC_TRUE;
8284: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8285: if (n) *n = 0;
8286: if (ia) *ia = NULL;
8287: if (ja) *ja = NULL;
8288: }
8289: PetscFunctionReturn(PETSC_SUCCESS);
8290: }
8292: /*@C
8293: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8295: Collective
8297: Input Parameters:
8298: + mat - the matrix
8299: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8300: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8301: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8302: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8303: always used.
8305: Output Parameters:
8306: + n - size of (possibly compressed) matrix
8307: . ia - the column pointers
8308: . ja - the row indices
8309: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8311: Level: developer
8313: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8314: @*/
8315: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8316: {
8317: PetscFunctionBegin;
8320: if (ia) PetscAssertPointer(ia, 6);
8321: if (ja) PetscAssertPointer(ja, 7);
8322: PetscAssertPointer(done, 8);
8323: MatCheckPreallocated(mat, 1);
8325: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8326: else {
8327: *done = PETSC_TRUE;
8328: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8329: if (n) *n = 0;
8330: if (ia) *ia = NULL;
8331: if (ja) *ja = NULL;
8332: }
8333: PetscFunctionReturn(PETSC_SUCCESS);
8334: }
8336: /*@
8337: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8338: `MatGetColumnIJ()`.
8340: Collective
8342: Input Parameters:
8343: + mat - the matrix
8344: . ncolors - maximum color value
8345: . n - number of entries in colorarray
8346: - colorarray - array indicating color for each column
8348: Output Parameter:
8349: . iscoloring - coloring generated using colorarray information
8351: Level: developer
8353: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8354: @*/
8355: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8356: {
8357: PetscFunctionBegin;
8360: PetscAssertPointer(colorarray, 4);
8361: PetscAssertPointer(iscoloring, 5);
8362: MatCheckPreallocated(mat, 1);
8364: if (!mat->ops->coloringpatch) {
8365: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8366: } else {
8367: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8368: }
8369: PetscFunctionReturn(PETSC_SUCCESS);
8370: }
8372: /*@
8373: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8375: Logically Collective
8377: Input Parameter:
8378: . mat - the factored matrix to be reset
8380: Level: developer
8382: Notes:
8383: This routine should be used only with factored matrices formed by in-place
8384: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8385: format). This option can save memory, for example, when solving nonlinear
8386: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8387: ILU(0) preconditioner.
8389: One can specify in-place ILU(0) factorization by calling
8390: .vb
8391: PCType(pc,PCILU);
8392: PCFactorSeUseInPlace(pc);
8393: .ve
8394: or by using the options -pc_type ilu -pc_factor_in_place
8396: In-place factorization ILU(0) can also be used as a local
8397: solver for the blocks within the block Jacobi or additive Schwarz
8398: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8399: for details on setting local solver options.
8401: Most users should employ the `KSP` interface for linear solvers
8402: instead of working directly with matrix algebra routines such as this.
8403: See, e.g., `KSPCreate()`.
8405: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8406: @*/
8407: PetscErrorCode MatSetUnfactored(Mat mat)
8408: {
8409: PetscFunctionBegin;
8412: MatCheckPreallocated(mat, 1);
8413: mat->factortype = MAT_FACTOR_NONE;
8414: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8415: PetscUseTypeMethod(mat, setunfactored);
8416: PetscFunctionReturn(PETSC_SUCCESS);
8417: }
8419: /*@
8420: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8421: as the original matrix.
8423: Collective
8425: Input Parameters:
8426: + mat - the original matrix
8427: . isrow - parallel `IS` containing the rows this processor should obtain
8428: . 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.
8429: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8431: Output Parameter:
8432: . newmat - the new submatrix, of the same type as the original matrix
8434: Level: advanced
8436: Notes:
8437: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8439: Some matrix types place restrictions on the row and column indices, such
8440: 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;
8441: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8443: The index sets may not have duplicate entries.
8445: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8446: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8447: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8448: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8449: you are finished using it.
8451: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8452: the input matrix.
8454: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8456: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8457: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8459: Example usage:
8460: Consider the following 8x8 matrix with 34 non-zero values, that is
8461: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8462: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8463: as follows
8464: .vb
8465: 1 2 0 | 0 3 0 | 0 4
8466: Proc0 0 5 6 | 7 0 0 | 8 0
8467: 9 0 10 | 11 0 0 | 12 0
8468: -------------------------------------
8469: 13 0 14 | 15 16 17 | 0 0
8470: Proc1 0 18 0 | 19 20 21 | 0 0
8471: 0 0 0 | 22 23 0 | 24 0
8472: -------------------------------------
8473: Proc2 25 26 27 | 0 0 28 | 29 0
8474: 30 0 0 | 31 32 33 | 0 34
8475: .ve
8477: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8479: .vb
8480: 2 0 | 0 3 0 | 0
8481: Proc0 5 6 | 7 0 0 | 8
8482: -------------------------------
8483: Proc1 18 0 | 19 20 21 | 0
8484: -------------------------------
8485: Proc2 26 27 | 0 0 28 | 29
8486: 0 0 | 31 32 33 | 0
8487: .ve
8489: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8490: @*/
8491: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8492: {
8493: PetscMPIInt size;
8494: Mat *local;
8495: IS iscoltmp;
8496: PetscBool flg;
8498: PetscFunctionBegin;
8502: PetscAssertPointer(newmat, 5);
8505: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8506: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8508: MatCheckPreallocated(mat, 1);
8509: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8511: if (!iscol || isrow == iscol) {
8512: PetscBool stride;
8513: PetscMPIInt grabentirematrix = 0, grab;
8514: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8515: if (stride) {
8516: PetscInt first, step, n, rstart, rend;
8517: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8518: if (step == 1) {
8519: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8520: if (rstart == first) {
8521: PetscCall(ISGetLocalSize(isrow, &n));
8522: if (n == rend - rstart) grabentirematrix = 1;
8523: }
8524: }
8525: }
8526: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8527: if (grab) {
8528: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8529: if (cll == MAT_INITIAL_MATRIX) {
8530: *newmat = mat;
8531: PetscCall(PetscObjectReference((PetscObject)mat));
8532: }
8533: PetscFunctionReturn(PETSC_SUCCESS);
8534: }
8535: }
8537: if (!iscol) {
8538: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8539: } else {
8540: iscoltmp = iscol;
8541: }
8543: /* if original matrix is on just one processor then use submatrix generated */
8544: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8545: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8546: goto setproperties;
8547: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8548: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8549: *newmat = *local;
8550: PetscCall(PetscFree(local));
8551: goto setproperties;
8552: } else if (!mat->ops->createsubmatrix) {
8553: /* Create a new matrix type that implements the operation using the full matrix */
8554: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8555: switch (cll) {
8556: case MAT_INITIAL_MATRIX:
8557: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8558: break;
8559: case MAT_REUSE_MATRIX:
8560: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8561: break;
8562: default:
8563: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8564: }
8565: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8566: goto setproperties;
8567: }
8569: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8570: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8571: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8573: setproperties:
8574: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8575: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8576: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8577: }
8578: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8579: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8580: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
8581: PetscFunctionReturn(PETSC_SUCCESS);
8582: }
8584: /*@
8585: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8587: Not Collective
8589: Input Parameters:
8590: + A - the matrix we wish to propagate options from
8591: - B - the matrix we wish to propagate options to
8593: Level: beginner
8595: Note:
8596: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8598: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8599: @*/
8600: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8601: {
8602: PetscFunctionBegin;
8605: B->symmetry_eternal = A->symmetry_eternal;
8606: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8607: B->symmetric = A->symmetric;
8608: B->structurally_symmetric = A->structurally_symmetric;
8609: B->spd = A->spd;
8610: B->hermitian = A->hermitian;
8611: PetscFunctionReturn(PETSC_SUCCESS);
8612: }
8614: /*@
8615: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8616: used during the assembly process to store values that belong to
8617: other processors.
8619: Not Collective
8621: Input Parameters:
8622: + mat - the matrix
8623: . size - the initial size of the stash.
8624: - bsize - the initial size of the block-stash(if used).
8626: Options Database Keys:
8627: + -matstash_initial_size <size> or <size0,size1,...sizep-1> - set initial size
8628: - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> - set initial block size
8630: Level: intermediate
8632: Notes:
8633: The block-stash is used for values set with `MatSetValuesBlocked()` while
8634: the stash is used for values set with `MatSetValues()`
8636: Run with the option -info and look for output of the form
8637: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8638: to determine the appropriate value, MM, to use for size and
8639: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8640: to determine the value, BMM to use for bsize
8642: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8643: @*/
8644: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8645: {
8646: PetscFunctionBegin;
8649: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8650: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8651: PetscFunctionReturn(PETSC_SUCCESS);
8652: }
8654: /*@
8655: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8656: the matrix
8658: Neighbor-wise Collective
8660: Input Parameters:
8661: + A - the matrix
8662: . x - the vector to be multiplied by the interpolation operator
8663: - y - the vector to be added to the result
8665: Output Parameter:
8666: . w - the resulting vector
8668: Level: intermediate
8670: Notes:
8671: `w` may be the same vector as `y`.
8673: This allows one to use either the restriction or interpolation (its transpose)
8674: matrix to do the interpolation
8676: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8677: @*/
8678: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8679: {
8680: PetscInt M, N, Ny;
8682: PetscFunctionBegin;
8687: PetscCall(MatGetSize(A, &M, &N));
8688: PetscCall(VecGetSize(y, &Ny));
8689: if (M == Ny) {
8690: PetscCall(MatMultAdd(A, x, y, w));
8691: } else {
8692: PetscCall(MatMultTransposeAdd(A, x, y, w));
8693: }
8694: PetscFunctionReturn(PETSC_SUCCESS);
8695: }
8697: /*@
8698: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8699: the matrix
8701: Neighbor-wise Collective
8703: Input Parameters:
8704: + A - the matrix
8705: - x - the vector to be interpolated
8707: Output Parameter:
8708: . y - the resulting vector
8710: Level: intermediate
8712: Note:
8713: This allows one to use either the restriction or interpolation (its transpose)
8714: matrix to do the interpolation
8716: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8717: @*/
8718: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8719: {
8720: PetscInt M, N, Ny;
8722: PetscFunctionBegin;
8726: PetscCall(MatGetSize(A, &M, &N));
8727: PetscCall(VecGetSize(y, &Ny));
8728: if (M == Ny) {
8729: PetscCall(MatMult(A, x, y));
8730: } else {
8731: PetscCall(MatMultTranspose(A, x, y));
8732: }
8733: PetscFunctionReturn(PETSC_SUCCESS);
8734: }
8736: /*@
8737: MatRestrict - $y = A*x$ or $A^T*x$
8739: Neighbor-wise Collective
8741: Input Parameters:
8742: + A - the matrix
8743: - x - the vector to be restricted
8745: Output Parameter:
8746: . y - the resulting vector
8748: Level: intermediate
8750: Note:
8751: This allows one to use either the restriction or interpolation (its transpose)
8752: matrix to do the restriction
8754: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8755: @*/
8756: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8757: {
8758: PetscInt M, N, Nx;
8760: PetscFunctionBegin;
8764: PetscCall(MatGetSize(A, &M, &N));
8765: PetscCall(VecGetSize(x, &Nx));
8766: if (M == Nx) {
8767: PetscCall(MatMultTranspose(A, x, y));
8768: } else {
8769: PetscCall(MatMult(A, x, y));
8770: }
8771: PetscFunctionReturn(PETSC_SUCCESS);
8772: }
8774: /*@
8775: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8777: Neighbor-wise Collective
8779: Input Parameters:
8780: + A - the matrix
8781: . x - the input dense matrix to be multiplied
8782: - w - the input dense matrix to be added to the result
8784: Output Parameter:
8785: . y - the output dense matrix
8787: Level: intermediate
8789: Note:
8790: This allows one to use either the restriction or interpolation (its transpose)
8791: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8792: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8794: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8795: @*/
8796: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8797: {
8798: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8799: PetscBool trans = PETSC_TRUE;
8800: MatReuse reuse = MAT_INITIAL_MATRIX;
8802: PetscFunctionBegin;
8808: PetscCall(MatGetSize(A, &M, &N));
8809: PetscCall(MatGetSize(x, &Mx, &Nx));
8810: if (N == Mx) trans = PETSC_FALSE;
8811: 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);
8812: Mo = trans ? N : M;
8813: if (*y) {
8814: PetscCall(MatGetSize(*y, &My, &Ny));
8815: if (Mo == My && Nx == Ny) {
8816: reuse = MAT_REUSE_MATRIX;
8817: } else {
8818: 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);
8819: PetscCall(MatDestroy(y));
8820: }
8821: }
8823: if (w && *y == w) { /* this is to minimize changes in PCMG */
8824: PetscBool flg;
8826: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8827: if (w) {
8828: PetscInt My, Ny, Mw, Nw;
8830: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8831: PetscCall(MatGetSize(*y, &My, &Ny));
8832: PetscCall(MatGetSize(w, &Mw, &Nw));
8833: if (!flg || My != Mw || Ny != Nw) w = NULL;
8834: }
8835: if (!w) {
8836: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8837: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8838: PetscCall(PetscObjectDereference((PetscObject)w));
8839: } else {
8840: PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8841: }
8842: }
8843: if (!trans) {
8844: PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8845: } else {
8846: PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8847: }
8848: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8849: PetscFunctionReturn(PETSC_SUCCESS);
8850: }
8852: /*@
8853: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8855: Neighbor-wise Collective
8857: Input Parameters:
8858: + A - the matrix
8859: - x - the input dense matrix
8861: Output Parameter:
8862: . y - the output dense matrix
8864: Level: intermediate
8866: Note:
8867: This allows one to use either the restriction or interpolation (its transpose)
8868: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8869: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8871: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8872: @*/
8873: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8874: {
8875: PetscFunctionBegin;
8876: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8877: PetscFunctionReturn(PETSC_SUCCESS);
8878: }
8880: /*@
8881: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8883: Neighbor-wise Collective
8885: Input Parameters:
8886: + A - the matrix
8887: - x - the input dense matrix
8889: Output Parameter:
8890: . y - the output dense matrix
8892: Level: intermediate
8894: Note:
8895: This allows one to use either the restriction or interpolation (its transpose)
8896: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8897: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8899: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8900: @*/
8901: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8902: {
8903: PetscFunctionBegin;
8904: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8905: PetscFunctionReturn(PETSC_SUCCESS);
8906: }
8908: /*@
8909: MatGetNullSpace - retrieves the null space of a matrix.
8911: Logically Collective
8913: Input Parameters:
8914: + mat - the matrix
8915: - nullsp - the null space object
8917: Level: developer
8919: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8920: @*/
8921: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8922: {
8923: PetscFunctionBegin;
8925: PetscAssertPointer(nullsp, 2);
8926: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8927: PetscFunctionReturn(PETSC_SUCCESS);
8928: }
8930: /*@C
8931: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
8933: Logically Collective
8935: Input Parameters:
8936: + n - the number of matrices
8937: - mat - the array of matrices
8939: Output Parameters:
8940: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
8942: Level: developer
8944: Note:
8945: Call `MatRestoreNullspaces()` to provide these to another array of matrices
8947: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8948: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8949: @*/
8950: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8951: {
8952: PetscFunctionBegin;
8953: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8954: PetscAssertPointer(mat, 2);
8955: PetscAssertPointer(nullsp, 3);
8957: PetscCall(PetscCalloc1(3 * n, nullsp));
8958: for (PetscInt i = 0; i < n; i++) {
8960: (*nullsp)[i] = mat[i]->nullsp;
8961: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8962: (*nullsp)[n + i] = mat[i]->nearnullsp;
8963: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8964: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8965: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8966: }
8967: PetscFunctionReturn(PETSC_SUCCESS);
8968: }
8970: /*@C
8971: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
8973: Logically Collective
8975: Input Parameters:
8976: + n - the number of matrices
8977: . mat - the array of matrices
8978: - nullsp - an array of null spaces
8980: Level: developer
8982: Note:
8983: Call `MatGetNullSpaces()` to create `nullsp`
8985: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8986: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8987: @*/
8988: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8989: {
8990: PetscFunctionBegin;
8991: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8992: PetscAssertPointer(mat, 2);
8993: PetscAssertPointer(nullsp, 3);
8994: PetscAssertPointer(*nullsp, 3);
8996: for (PetscInt i = 0; i < n; i++) {
8998: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8999: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9000: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9001: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9002: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9003: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9004: }
9005: PetscCall(PetscFree(*nullsp));
9006: PetscFunctionReturn(PETSC_SUCCESS);
9007: }
9009: /*@
9010: MatSetNullSpace - attaches a null space to a matrix.
9012: Logically Collective
9014: Input Parameters:
9015: + mat - the matrix
9016: - nullsp - the null space object
9018: Level: advanced
9020: Notes:
9021: This null space is used by the `KSP` linear solvers to solve singular systems.
9023: 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`
9025: 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
9026: to zero but the linear system will still be solved in a least squares sense.
9028: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9029: 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 $A^T$, $R(A^T)$.
9030: 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
9031: $n(A^T))$ 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
9032: 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 $n(A^T)$.
9033: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9035: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9036: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9037: routine also automatically calls `MatSetTransposeNullSpace()`.
9039: The user should call `MatNullSpaceDestroy()`.
9041: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9042: `KSPSetPCSide()`
9043: @*/
9044: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9045: {
9046: PetscFunctionBegin;
9049: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9050: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9051: mat->nullsp = nullsp;
9052: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9053: PetscFunctionReturn(PETSC_SUCCESS);
9054: }
9056: /*@
9057: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
9059: Logically Collective
9061: Input Parameters:
9062: + mat - the matrix
9063: - nullsp - the null space object
9065: Level: developer
9067: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9068: @*/
9069: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9070: {
9071: PetscFunctionBegin;
9074: PetscAssertPointer(nullsp, 2);
9075: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9076: PetscFunctionReturn(PETSC_SUCCESS);
9077: }
9079: /*@
9080: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9082: Logically Collective
9084: Input Parameters:
9085: + mat - the matrix
9086: - nullsp - the null space object
9088: Level: advanced
9090: Notes:
9091: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9093: See `MatSetNullSpace()`
9095: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9096: @*/
9097: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9098: {
9099: PetscFunctionBegin;
9102: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9103: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9104: mat->transnullsp = nullsp;
9105: PetscFunctionReturn(PETSC_SUCCESS);
9106: }
9108: /*@
9109: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9110: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9112: Logically Collective
9114: Input Parameters:
9115: + mat - the matrix
9116: - nullsp - the null space object
9118: Level: advanced
9120: Notes:
9121: Overwrites any previous near null space that may have been attached
9123: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9125: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9126: @*/
9127: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9128: {
9129: PetscFunctionBegin;
9133: MatCheckPreallocated(mat, 1);
9134: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9135: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9136: mat->nearnullsp = nullsp;
9137: PetscFunctionReturn(PETSC_SUCCESS);
9138: }
9140: /*@
9141: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9143: Not Collective
9145: Input Parameter:
9146: . mat - the matrix
9148: Output Parameter:
9149: . nullsp - the null space object, `NULL` if not set
9151: Level: advanced
9153: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9154: @*/
9155: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9156: {
9157: PetscFunctionBegin;
9160: PetscAssertPointer(nullsp, 2);
9161: MatCheckPreallocated(mat, 1);
9162: *nullsp = mat->nearnullsp;
9163: PetscFunctionReturn(PETSC_SUCCESS);
9164: }
9166: /*@
9167: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9169: Collective
9171: Input Parameters:
9172: + mat - the matrix
9173: . row - row/column permutation
9174: - info - information on desired factorization process
9176: Level: developer
9178: Notes:
9179: Probably really in-place only when level of fill is zero, otherwise allocates
9180: new space to store factored matrix and deletes previous memory.
9182: Most users should employ the `KSP` interface for linear solvers
9183: instead of working directly with matrix algebra routines such as this.
9184: See, e.g., `KSPCreate()`.
9186: Fortran Note:
9187: A valid (non-null) `info` argument must be provided
9189: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9190: @*/
9191: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9192: {
9193: PetscFunctionBegin;
9197: PetscAssertPointer(info, 3);
9198: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9199: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9200: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9201: MatCheckPreallocated(mat, 1);
9202: PetscUseTypeMethod(mat, iccfactor, row, info);
9203: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9204: PetscFunctionReturn(PETSC_SUCCESS);
9205: }
9207: /*@
9208: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9209: ghosted ones.
9211: Not Collective
9213: Input Parameters:
9214: + mat - the matrix
9215: - diag - the diagonal values, including ghost ones
9217: Level: developer
9219: Notes:
9220: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9222: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9224: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9225: @*/
9226: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9227: {
9228: PetscMPIInt size;
9230: PetscFunctionBegin;
9235: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9236: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9237: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9238: if (size == 1) {
9239: PetscInt n, m;
9240: PetscCall(VecGetSize(diag, &n));
9241: PetscCall(MatGetSize(mat, NULL, &m));
9242: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9243: PetscCall(MatDiagonalScale(mat, NULL, diag));
9244: } else {
9245: PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9246: }
9247: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9248: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9249: PetscFunctionReturn(PETSC_SUCCESS);
9250: }
9252: /*@
9253: MatGetInertia - Gets the inertia from a factored matrix
9255: Collective
9257: Input Parameter:
9258: . mat - the matrix
9260: Output Parameters:
9261: + nneg - number of negative eigenvalues
9262: . nzero - number of zero eigenvalues
9263: - npos - number of positive eigenvalues
9265: Level: advanced
9267: Note:
9268: Matrix must have been factored by `MatCholeskyFactor()`
9270: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9271: @*/
9272: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9273: {
9274: PetscFunctionBegin;
9277: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9278: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9279: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9280: PetscFunctionReturn(PETSC_SUCCESS);
9281: }
9283: /*@C
9284: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9286: Neighbor-wise Collective
9288: Input Parameters:
9289: + mat - the factored matrix obtained with `MatGetFactor()`
9290: - b - the right-hand-side vectors
9292: Output Parameter:
9293: . x - the result vectors
9295: Level: developer
9297: Note:
9298: The vectors `b` and `x` cannot be the same. I.e., one cannot
9299: call `MatSolves`(A,x,x).
9301: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9302: @*/
9303: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9304: {
9305: PetscFunctionBegin;
9308: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9309: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9310: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9312: MatCheckPreallocated(mat, 1);
9313: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9314: PetscUseTypeMethod(mat, solves, b, x);
9315: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9316: PetscFunctionReturn(PETSC_SUCCESS);
9317: }
9319: /*@
9320: MatIsSymmetric - Test whether a matrix is symmetric
9322: Collective
9324: Input Parameters:
9325: + A - the matrix to test
9326: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9328: Output Parameter:
9329: . flg - the result
9331: Level: intermediate
9333: Notes:
9334: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9336: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9338: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9339: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9341: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9342: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9343: @*/
9344: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9345: {
9346: PetscFunctionBegin;
9348: PetscAssertPointer(flg, 3);
9349: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9350: else {
9351: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9352: else PetscCall(MatIsTranspose(A, A, tol, flg));
9353: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9354: }
9355: PetscFunctionReturn(PETSC_SUCCESS);
9356: }
9358: /*@
9359: MatIsHermitian - Test whether a matrix is Hermitian
9361: Collective
9363: Input Parameters:
9364: + A - the matrix to test
9365: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9367: Output Parameter:
9368: . flg - the result
9370: Level: intermediate
9372: Notes:
9373: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9375: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9377: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9378: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9380: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9381: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9382: @*/
9383: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9384: {
9385: PetscFunctionBegin;
9387: PetscAssertPointer(flg, 3);
9388: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9389: else {
9390: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9391: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9392: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9393: }
9394: PetscFunctionReturn(PETSC_SUCCESS);
9395: }
9397: /*@
9398: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9400: Not Collective
9402: Input Parameter:
9403: . A - the matrix to check
9405: Output Parameters:
9406: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9407: - flg - the result (only valid if set is `PETSC_TRUE`)
9409: Level: advanced
9411: Notes:
9412: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9413: if you want it explicitly checked
9415: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9416: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9418: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9419: @*/
9420: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9421: {
9422: PetscFunctionBegin;
9424: PetscAssertPointer(set, 2);
9425: PetscAssertPointer(flg, 3);
9426: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9427: *set = PETSC_TRUE;
9428: *flg = PetscBool3ToBool(A->symmetric);
9429: } else {
9430: *set = PETSC_FALSE;
9431: }
9432: PetscFunctionReturn(PETSC_SUCCESS);
9433: }
9435: /*@
9436: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9438: Not Collective
9440: Input Parameter:
9441: . A - the matrix to check
9443: Output Parameters:
9444: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9445: - flg - the result (only valid if set is `PETSC_TRUE`)
9447: Level: advanced
9449: Notes:
9450: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9452: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9453: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9455: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9456: @*/
9457: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9458: {
9459: PetscFunctionBegin;
9461: PetscAssertPointer(set, 2);
9462: PetscAssertPointer(flg, 3);
9463: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9464: *set = PETSC_TRUE;
9465: *flg = PetscBool3ToBool(A->spd);
9466: } else {
9467: *set = PETSC_FALSE;
9468: }
9469: PetscFunctionReturn(PETSC_SUCCESS);
9470: }
9472: /*@
9473: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9475: Not Collective
9477: Input Parameter:
9478: . A - the matrix to check
9480: Output Parameters:
9481: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9482: - flg - the result (only valid if set is `PETSC_TRUE`)
9484: Level: advanced
9486: Notes:
9487: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9488: if you want it explicitly checked
9490: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9491: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9493: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9494: @*/
9495: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9496: {
9497: PetscFunctionBegin;
9499: PetscAssertPointer(set, 2);
9500: PetscAssertPointer(flg, 3);
9501: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9502: *set = PETSC_TRUE;
9503: *flg = PetscBool3ToBool(A->hermitian);
9504: } else {
9505: *set = PETSC_FALSE;
9506: }
9507: PetscFunctionReturn(PETSC_SUCCESS);
9508: }
9510: /*@
9511: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9513: Collective
9515: Input Parameter:
9516: . A - the matrix to test
9518: Output Parameter:
9519: . flg - the result
9521: Level: intermediate
9523: Notes:
9524: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9526: 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
9527: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9529: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9530: @*/
9531: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9532: {
9533: PetscFunctionBegin;
9535: PetscAssertPointer(flg, 2);
9536: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9537: *flg = PetscBool3ToBool(A->structurally_symmetric);
9538: } else {
9539: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9540: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9541: }
9542: PetscFunctionReturn(PETSC_SUCCESS);
9543: }
9545: /*@
9546: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9548: Not Collective
9550: Input Parameter:
9551: . A - the matrix to check
9553: Output Parameters:
9554: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9555: - flg - the result (only valid if set is PETSC_TRUE)
9557: Level: advanced
9559: Notes:
9560: 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
9561: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9563: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9565: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9566: @*/
9567: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9568: {
9569: PetscFunctionBegin;
9571: PetscAssertPointer(set, 2);
9572: PetscAssertPointer(flg, 3);
9573: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9574: *set = PETSC_TRUE;
9575: *flg = PetscBool3ToBool(A->structurally_symmetric);
9576: } else {
9577: *set = PETSC_FALSE;
9578: }
9579: PetscFunctionReturn(PETSC_SUCCESS);
9580: }
9582: /*@
9583: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9584: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9586: Not Collective
9588: Input Parameter:
9589: . mat - the matrix
9591: Output Parameters:
9592: + nstash - the size of the stash
9593: . reallocs - the number of additional mallocs incurred.
9594: . bnstash - the size of the block stash
9595: - breallocs - the number of additional mallocs incurred.in the block stash
9597: Level: advanced
9599: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9600: @*/
9601: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9602: {
9603: PetscFunctionBegin;
9604: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9605: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9606: PetscFunctionReturn(PETSC_SUCCESS);
9607: }
9609: /*@
9610: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9611: parallel layout, `PetscLayout` for rows and columns
9613: Collective
9615: Input Parameter:
9616: . mat - the matrix
9618: Output Parameters:
9619: + right - (optional) vector that the matrix can be multiplied against
9620: - left - (optional) vector that the matrix vector product can be stored in
9622: Level: advanced
9624: Notes:
9625: 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()`.
9627: These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed
9629: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9630: @*/
9631: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9632: {
9633: PetscFunctionBegin;
9636: if (mat->ops->getvecs) {
9637: PetscUseTypeMethod(mat, getvecs, right, left);
9638: } else {
9639: if (right) {
9640: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9641: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9642: PetscCall(VecSetType(*right, mat->defaultvectype));
9643: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9644: if (mat->boundtocpu && mat->bindingpropagates) {
9645: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9646: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9647: }
9648: #endif
9649: }
9650: if (left) {
9651: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9652: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9653: PetscCall(VecSetType(*left, mat->defaultvectype));
9654: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9655: if (mat->boundtocpu && mat->bindingpropagates) {
9656: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9657: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9658: }
9659: #endif
9660: }
9661: }
9662: PetscFunctionReturn(PETSC_SUCCESS);
9663: }
9665: /*@
9666: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9667: with default values.
9669: Not Collective
9671: Input Parameter:
9672: . info - the `MatFactorInfo` data structure
9674: Level: developer
9676: Notes:
9677: The solvers are generally used through the `KSP` and `PC` objects, for example
9678: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9680: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9682: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9683: @*/
9684: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9685: {
9686: PetscFunctionBegin;
9687: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9688: PetscFunctionReturn(PETSC_SUCCESS);
9689: }
9691: /*@
9692: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9694: Collective
9696: Input Parameters:
9697: + mat - the factored matrix
9698: - is - the index set defining the Schur indices (0-based)
9700: Level: advanced
9702: Notes:
9703: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9705: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9707: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9709: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9710: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9711: @*/
9712: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9713: {
9714: PetscErrorCode (*f)(Mat, IS);
9716: PetscFunctionBegin;
9721: PetscCheckSameComm(mat, 1, is, 2);
9722: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9723: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9724: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9725: PetscCall(MatDestroy(&mat->schur));
9726: PetscCall((*f)(mat, is));
9727: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9728: PetscFunctionReturn(PETSC_SUCCESS);
9729: }
9731: /*@
9732: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9734: Logically Collective
9736: Input Parameters:
9737: + F - the factored matrix obtained by calling `MatGetFactor()`
9738: . S - location where to return the Schur complement, can be `NULL`
9739: - status - the status of the Schur complement matrix, can be `NULL`
9741: Level: advanced
9743: Notes:
9744: You must call `MatFactorSetSchurIS()` before calling this routine.
9746: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9748: The routine provides a copy of the Schur matrix stored within the solver data structures.
9749: The caller must destroy the object when it is no longer needed.
9750: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9752: 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)
9754: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9756: Developer Note:
9757: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9758: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9760: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9761: @*/
9762: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9763: {
9764: PetscFunctionBegin;
9766: if (S) PetscAssertPointer(S, 2);
9767: if (status) PetscAssertPointer(status, 3);
9768: if (S) {
9769: PetscErrorCode (*f)(Mat, Mat *);
9771: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9772: if (f) {
9773: PetscCall((*f)(F, S));
9774: } else {
9775: PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9776: }
9777: }
9778: if (status) *status = F->schur_status;
9779: PetscFunctionReturn(PETSC_SUCCESS);
9780: }
9782: /*@
9783: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9785: Logically Collective
9787: Input Parameters:
9788: + F - the factored matrix obtained by calling `MatGetFactor()`
9789: . S - location where to return the Schur complement, can be `NULL`
9790: - status - the status of the Schur complement matrix, can be `NULL`
9792: Level: advanced
9794: Notes:
9795: You must call `MatFactorSetSchurIS()` before calling this routine.
9797: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9799: The routine returns a the Schur Complement stored within the data structures of the solver.
9801: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9803: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9805: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9807: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9809: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9810: @*/
9811: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9812: {
9813: PetscFunctionBegin;
9815: if (S) {
9816: PetscAssertPointer(S, 2);
9817: *S = F->schur;
9818: }
9819: if (status) {
9820: PetscAssertPointer(status, 3);
9821: *status = F->schur_status;
9822: }
9823: PetscFunctionReturn(PETSC_SUCCESS);
9824: }
9826: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9827: {
9828: Mat S = F->schur;
9830: PetscFunctionBegin;
9831: switch (F->schur_status) {
9832: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9833: case MAT_FACTOR_SCHUR_INVERTED:
9834: if (S) {
9835: S->ops->solve = NULL;
9836: S->ops->matsolve = NULL;
9837: S->ops->solvetranspose = NULL;
9838: S->ops->matsolvetranspose = NULL;
9839: S->ops->solveadd = NULL;
9840: S->ops->solvetransposeadd = NULL;
9841: S->factortype = MAT_FACTOR_NONE;
9842: PetscCall(PetscFree(S->solvertype));
9843: }
9844: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9845: break;
9846: default:
9847: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9848: }
9849: PetscFunctionReturn(PETSC_SUCCESS);
9850: }
9852: /*@
9853: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9855: Logically Collective
9857: Input Parameters:
9858: + F - the factored matrix obtained by calling `MatGetFactor()`
9859: . S - location where the Schur complement is stored
9860: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9862: Level: advanced
9864: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9865: @*/
9866: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9867: {
9868: PetscFunctionBegin;
9870: if (S) {
9872: *S = NULL;
9873: }
9874: F->schur_status = status;
9875: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9876: PetscFunctionReturn(PETSC_SUCCESS);
9877: }
9879: /*@
9880: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9882: Logically Collective
9884: Input Parameters:
9885: + F - the factored matrix obtained by calling `MatGetFactor()`
9886: . rhs - location where the right-hand side of the Schur complement system is stored
9887: - sol - location where the solution of the Schur complement system has to be returned
9889: Level: advanced
9891: Notes:
9892: The sizes of the vectors should match the size of the Schur complement
9894: Must be called after `MatFactorSetSchurIS()`
9896: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9897: @*/
9898: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9899: {
9900: PetscFunctionBegin;
9907: PetscCheckSameComm(F, 1, rhs, 2);
9908: PetscCheckSameComm(F, 1, sol, 3);
9909: PetscCall(MatFactorFactorizeSchurComplement(F));
9910: switch (F->schur_status) {
9911: case MAT_FACTOR_SCHUR_FACTORED:
9912: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9913: break;
9914: case MAT_FACTOR_SCHUR_INVERTED:
9915: PetscCall(MatMultTranspose(F->schur, rhs, sol));
9916: break;
9917: default:
9918: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9919: }
9920: PetscFunctionReturn(PETSC_SUCCESS);
9921: }
9923: /*@
9924: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
9926: Logically Collective
9928: Input Parameters:
9929: + F - the factored matrix obtained by calling `MatGetFactor()`
9930: . rhs - location where the right-hand side of the Schur complement system is stored
9931: - sol - location where the solution of the Schur complement system has to be returned
9933: Level: advanced
9935: Notes:
9936: The sizes of the vectors should match the size of the Schur complement
9938: Must be called after `MatFactorSetSchurIS()`
9940: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9941: @*/
9942: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9943: {
9944: PetscFunctionBegin;
9951: PetscCheckSameComm(F, 1, rhs, 2);
9952: PetscCheckSameComm(F, 1, sol, 3);
9953: PetscCall(MatFactorFactorizeSchurComplement(F));
9954: switch (F->schur_status) {
9955: case MAT_FACTOR_SCHUR_FACTORED:
9956: PetscCall(MatSolve(F->schur, rhs, sol));
9957: break;
9958: case MAT_FACTOR_SCHUR_INVERTED:
9959: PetscCall(MatMult(F->schur, rhs, sol));
9960: break;
9961: default:
9962: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9963: }
9964: PetscFunctionReturn(PETSC_SUCCESS);
9965: }
9967: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9968: #if PetscDefined(HAVE_CUDA)
9969: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9970: #endif
9972: /* Schur status updated in the interface */
9973: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9974: {
9975: Mat S = F->schur;
9977: PetscFunctionBegin;
9978: if (S) {
9979: PetscMPIInt size;
9980: PetscBool isdense, isdensecuda;
9982: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9983: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9984: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9985: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9986: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9987: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9988: if (isdense) {
9989: PetscCall(MatSeqDenseInvertFactors_Private(S));
9990: } else if (isdensecuda) {
9991: #if defined(PETSC_HAVE_CUDA)
9992: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9993: #endif
9994: }
9995: // HIP??????????????
9996: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9997: }
9998: PetscFunctionReturn(PETSC_SUCCESS);
9999: }
10001: /*@
10002: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
10004: Logically Collective
10006: Input Parameter:
10007: . F - the factored matrix obtained by calling `MatGetFactor()`
10009: Level: advanced
10011: Notes:
10012: Must be called after `MatFactorSetSchurIS()`.
10014: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
10016: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10017: @*/
10018: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10019: {
10020: PetscFunctionBegin;
10023: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10024: PetscCall(MatFactorFactorizeSchurComplement(F));
10025: PetscCall(MatFactorInvertSchurComplement_Private(F));
10026: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10027: PetscFunctionReturn(PETSC_SUCCESS);
10028: }
10030: /*@
10031: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10033: Logically Collective
10035: Input Parameter:
10036: . F - the factored matrix obtained by calling `MatGetFactor()`
10038: Level: advanced
10040: Note:
10041: Must be called after `MatFactorSetSchurIS()`
10043: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10044: @*/
10045: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10046: {
10047: MatFactorInfo info;
10049: PetscFunctionBegin;
10052: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10053: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10054: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10055: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10056: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10057: } else {
10058: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10059: }
10060: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10061: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10062: PetscFunctionReturn(PETSC_SUCCESS);
10063: }
10065: /*@
10066: MatPtAP - Creates the matrix product $C = P^T * A * P$
10068: Neighbor-wise Collective
10070: Input Parameters:
10071: + A - the matrix
10072: . P - the projection matrix
10073: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10074: - 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
10075: if the result is a dense matrix this is irrelevant
10077: Output Parameter:
10078: . C - the product matrix
10080: Level: intermediate
10082: Notes:
10083: C will be created and must be destroyed by the user with `MatDestroy()`.
10085: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
10087: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10089: Developer Note:
10090: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10092: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10093: @*/
10094: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10095: {
10096: PetscFunctionBegin;
10097: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10098: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10100: if (scall == MAT_INITIAL_MATRIX) {
10101: PetscCall(MatProductCreate(A, P, NULL, C));
10102: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10103: PetscCall(MatProductSetAlgorithm(*C, "default"));
10104: PetscCall(MatProductSetFill(*C, fill));
10106: (*C)->product->api_user = PETSC_TRUE;
10107: PetscCall(MatProductSetFromOptions(*C));
10108: 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);
10109: PetscCall(MatProductSymbolic(*C));
10110: } else { /* scall == MAT_REUSE_MATRIX */
10111: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10112: }
10114: PetscCall(MatProductNumeric(*C));
10115: (*C)->symmetric = A->symmetric;
10116: (*C)->spd = A->spd;
10117: PetscFunctionReturn(PETSC_SUCCESS);
10118: }
10120: /*@
10121: MatRARt - Creates the matrix product $C = R * A * R^T$
10123: Neighbor-wise Collective
10125: Input Parameters:
10126: + A - the matrix
10127: . R - the projection matrix
10128: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10129: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10130: if the result is a dense matrix this is irrelevant
10132: Output Parameter:
10133: . C - the product matrix
10135: Level: intermediate
10137: Notes:
10138: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10140: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
10142: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10143: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10144: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10145: We recommend using `MatPtAP()` when possible.
10147: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10149: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10150: @*/
10151: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10152: {
10153: PetscFunctionBegin;
10154: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10155: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10157: if (scall == MAT_INITIAL_MATRIX) {
10158: PetscCall(MatProductCreate(A, R, NULL, C));
10159: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10160: PetscCall(MatProductSetAlgorithm(*C, "default"));
10161: PetscCall(MatProductSetFill(*C, fill));
10163: (*C)->product->api_user = PETSC_TRUE;
10164: PetscCall(MatProductSetFromOptions(*C));
10165: 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);
10166: PetscCall(MatProductSymbolic(*C));
10167: } else { /* scall == MAT_REUSE_MATRIX */
10168: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10169: }
10171: PetscCall(MatProductNumeric(*C));
10172: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10173: PetscFunctionReturn(PETSC_SUCCESS);
10174: }
10176: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10177: {
10178: PetscBool flg = PETSC_TRUE;
10180: PetscFunctionBegin;
10181: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10182: if (scall == MAT_INITIAL_MATRIX) {
10183: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10184: PetscCall(MatProductCreate(A, B, NULL, C));
10185: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10186: PetscCall(MatProductSetFill(*C, fill));
10187: } else { /* scall == MAT_REUSE_MATRIX */
10188: Mat_Product *product = (*C)->product;
10190: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10191: if (flg && product && product->type != ptype) {
10192: PetscCall(MatProductClear(*C));
10193: product = NULL;
10194: }
10195: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10196: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10197: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10198: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10199: product = (*C)->product;
10200: product->fill = fill;
10201: product->clear = PETSC_TRUE;
10202: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10203: flg = PETSC_FALSE;
10204: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10205: }
10206: }
10207: if (flg) {
10208: (*C)->product->api_user = PETSC_TRUE;
10209: PetscCall(MatProductSetType(*C, ptype));
10210: PetscCall(MatProductSetFromOptions(*C));
10211: PetscCall(MatProductSymbolic(*C));
10212: }
10213: PetscCall(MatProductNumeric(*C));
10214: PetscFunctionReturn(PETSC_SUCCESS);
10215: }
10217: /*@
10218: MatMatMult - Performs matrix-matrix multiplication C=A*B.
10220: Neighbor-wise Collective
10222: Input Parameters:
10223: + A - the left matrix
10224: . B - the right matrix
10225: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10226: - 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
10227: if the result is a dense matrix this is irrelevant
10229: Output Parameter:
10230: . C - the product matrix
10232: Notes:
10233: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10235: `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
10236: call to this function with `MAT_INITIAL_MATRIX`.
10238: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10240: 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`,
10241: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10243: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10245: Example of Usage:
10246: .vb
10247: MatProductCreate(A,B,NULL,&C);
10248: MatProductSetType(C,MATPRODUCT_AB);
10249: MatProductSymbolic(C);
10250: MatProductNumeric(C); // compute C=A * B
10251: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10252: MatProductNumeric(C);
10253: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10254: MatProductNumeric(C);
10255: .ve
10257: Level: intermediate
10259: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10260: @*/
10261: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10262: {
10263: PetscFunctionBegin;
10264: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10265: PetscFunctionReturn(PETSC_SUCCESS);
10266: }
10268: /*@
10269: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10271: Neighbor-wise Collective
10273: Input Parameters:
10274: + A - the left matrix
10275: . B - the right matrix
10276: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10277: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10279: Output Parameter:
10280: . C - the product matrix
10282: Options Database Key:
10283: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10284: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10285: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10287: Level: intermediate
10289: Notes:
10290: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10292: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10294: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10295: actually needed.
10297: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10298: and for pairs of `MATMPIDENSE` matrices.
10300: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`
10302: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10304: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10305: @*/
10306: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10307: {
10308: PetscFunctionBegin;
10309: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10310: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10311: PetscFunctionReturn(PETSC_SUCCESS);
10312: }
10314: /*@
10315: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10317: Neighbor-wise Collective
10319: Input Parameters:
10320: + A - the left matrix
10321: . B - the right matrix
10322: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10323: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10325: Output Parameter:
10326: . C - the product matrix
10328: Level: intermediate
10330: Notes:
10331: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10333: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.
10335: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`
10337: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10338: actually needed.
10340: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10341: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10343: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10345: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10346: @*/
10347: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10348: {
10349: PetscFunctionBegin;
10350: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10351: PetscFunctionReturn(PETSC_SUCCESS);
10352: }
10354: /*@
10355: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10357: Neighbor-wise Collective
10359: Input Parameters:
10360: + A - the left matrix
10361: . B - the middle matrix
10362: . C - the right matrix
10363: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10364: - 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
10365: if the result is a dense matrix this is irrelevant
10367: Output Parameter:
10368: . D - the product matrix
10370: Level: intermediate
10372: Notes:
10373: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10375: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10377: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`
10379: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10380: actually needed.
10382: If you have many matrices with the same non-zero structure to multiply, you
10383: should use `MAT_REUSE_MATRIX` in all calls but the first
10385: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10387: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10388: @*/
10389: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10390: {
10391: PetscFunctionBegin;
10392: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10393: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10395: if (scall == MAT_INITIAL_MATRIX) {
10396: PetscCall(MatProductCreate(A, B, C, D));
10397: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10398: PetscCall(MatProductSetAlgorithm(*D, "default"));
10399: PetscCall(MatProductSetFill(*D, fill));
10401: (*D)->product->api_user = PETSC_TRUE;
10402: PetscCall(MatProductSetFromOptions(*D));
10403: 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,
10404: ((PetscObject)C)->type_name);
10405: PetscCall(MatProductSymbolic(*D));
10406: } else { /* user may change input matrices when REUSE */
10407: PetscCall(MatProductReplaceMats(A, B, C, *D));
10408: }
10409: PetscCall(MatProductNumeric(*D));
10410: PetscFunctionReturn(PETSC_SUCCESS);
10411: }
10413: /*@
10414: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10416: Collective
10418: Input Parameters:
10419: + mat - the matrix
10420: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10421: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10422: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10424: Output Parameter:
10425: . matredundant - redundant matrix
10427: Level: advanced
10429: Notes:
10430: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10431: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10433: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10434: calling it.
10436: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10438: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10439: @*/
10440: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10441: {
10442: MPI_Comm comm;
10443: PetscMPIInt size;
10444: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10445: Mat_Redundant *redund = NULL;
10446: PetscSubcomm psubcomm = NULL;
10447: MPI_Comm subcomm_in = subcomm;
10448: Mat *matseq;
10449: IS isrow, iscol;
10450: PetscBool newsubcomm = PETSC_FALSE;
10452: PetscFunctionBegin;
10454: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10455: PetscAssertPointer(*matredundant, 5);
10457: }
10459: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10460: if (size == 1 || nsubcomm == 1) {
10461: if (reuse == MAT_INITIAL_MATRIX) {
10462: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10463: } else {
10464: 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");
10465: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10466: }
10467: PetscFunctionReturn(PETSC_SUCCESS);
10468: }
10470: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10471: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10472: MatCheckPreallocated(mat, 1);
10474: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10475: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10476: /* create psubcomm, then get subcomm */
10477: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10478: PetscCallMPI(MPI_Comm_size(comm, &size));
10479: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10481: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10482: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10483: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10484: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10485: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10486: newsubcomm = PETSC_TRUE;
10487: PetscCall(PetscSubcommDestroy(&psubcomm));
10488: }
10490: /* get isrow, iscol and a local sequential matrix matseq[0] */
10491: if (reuse == MAT_INITIAL_MATRIX) {
10492: mloc_sub = PETSC_DECIDE;
10493: nloc_sub = PETSC_DECIDE;
10494: if (bs < 1) {
10495: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10496: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10497: } else {
10498: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10499: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10500: }
10501: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10502: rstart = rend - mloc_sub;
10503: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10504: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10505: PetscCall(ISSetIdentity(iscol));
10506: } else { /* reuse == MAT_REUSE_MATRIX */
10507: 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");
10508: /* retrieve subcomm */
10509: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10510: redund = (*matredundant)->redundant;
10511: isrow = redund->isrow;
10512: iscol = redund->iscol;
10513: matseq = redund->matseq;
10514: }
10515: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10517: /* get matredundant over subcomm */
10518: if (reuse == MAT_INITIAL_MATRIX) {
10519: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10521: /* create a supporting struct and attach it to C for reuse */
10522: PetscCall(PetscNew(&redund));
10523: (*matredundant)->redundant = redund;
10524: redund->isrow = isrow;
10525: redund->iscol = iscol;
10526: redund->matseq = matseq;
10527: if (newsubcomm) {
10528: redund->subcomm = subcomm;
10529: } else {
10530: redund->subcomm = MPI_COMM_NULL;
10531: }
10532: } else {
10533: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10534: }
10535: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10536: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10537: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10538: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10539: }
10540: #endif
10541: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10542: PetscFunctionReturn(PETSC_SUCCESS);
10543: }
10545: /*@C
10546: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10547: a given `Mat`. Each submatrix can span multiple procs.
10549: Collective
10551: Input Parameters:
10552: + mat - the matrix
10553: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10554: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10556: Output Parameter:
10557: . subMat - parallel sub-matrices each spanning a given `subcomm`
10559: Level: advanced
10561: Notes:
10562: The submatrix partition across processors is dictated by `subComm` a
10563: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10564: is not restricted to be grouped with consecutive original MPI processes.
10566: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10567: map directly to the layout of the original matrix [wrt the local
10568: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10569: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10570: the `subMat`. However the offDiagMat looses some columns - and this is
10571: reconstructed with `MatSetValues()`
10573: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10575: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10576: @*/
10577: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10578: {
10579: PetscMPIInt commsize, subCommSize;
10581: PetscFunctionBegin;
10582: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10583: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10584: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10586: 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");
10587: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10588: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10589: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10590: PetscFunctionReturn(PETSC_SUCCESS);
10591: }
10593: /*@
10594: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10596: Not Collective
10598: Input Parameters:
10599: + mat - matrix to extract local submatrix from
10600: . isrow - local row indices for submatrix
10601: - iscol - local column indices for submatrix
10603: Output Parameter:
10604: . submat - the submatrix
10606: Level: intermediate
10608: Notes:
10609: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10611: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10612: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10614: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10615: `MatSetValuesBlockedLocal()` will also be implemented.
10617: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10618: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10620: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10621: @*/
10622: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10623: {
10624: PetscFunctionBegin;
10628: PetscCheckSameComm(isrow, 2, iscol, 3);
10629: PetscAssertPointer(submat, 4);
10630: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10632: if (mat->ops->getlocalsubmatrix) {
10633: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10634: } else {
10635: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10636: }
10637: (*submat)->assembled = mat->assembled;
10638: PetscFunctionReturn(PETSC_SUCCESS);
10639: }
10641: /*@
10642: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10644: Not Collective
10646: Input Parameters:
10647: + mat - matrix to extract local submatrix from
10648: . isrow - local row indices for submatrix
10649: . iscol - local column indices for submatrix
10650: - submat - the submatrix
10652: Level: intermediate
10654: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10655: @*/
10656: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10657: {
10658: PetscFunctionBegin;
10662: PetscCheckSameComm(isrow, 2, iscol, 3);
10663: PetscAssertPointer(submat, 4);
10666: if (mat->ops->restorelocalsubmatrix) {
10667: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10668: } else {
10669: PetscCall(MatDestroy(submat));
10670: }
10671: *submat = NULL;
10672: PetscFunctionReturn(PETSC_SUCCESS);
10673: }
10675: /*@
10676: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10678: Collective
10680: Input Parameter:
10681: . mat - the matrix
10683: Output Parameter:
10684: . is - if any rows have zero diagonals this contains the list of them
10686: Level: developer
10688: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10689: @*/
10690: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10691: {
10692: PetscFunctionBegin;
10695: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10696: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10698: if (!mat->ops->findzerodiagonals) {
10699: Vec diag;
10700: const PetscScalar *a;
10701: PetscInt *rows;
10702: PetscInt rStart, rEnd, r, nrow = 0;
10704: PetscCall(MatCreateVecs(mat, &diag, NULL));
10705: PetscCall(MatGetDiagonal(mat, diag));
10706: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10707: PetscCall(VecGetArrayRead(diag, &a));
10708: for (r = 0; r < rEnd - rStart; ++r)
10709: if (a[r] == 0.0) ++nrow;
10710: PetscCall(PetscMalloc1(nrow, &rows));
10711: nrow = 0;
10712: for (r = 0; r < rEnd - rStart; ++r)
10713: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10714: PetscCall(VecRestoreArrayRead(diag, &a));
10715: PetscCall(VecDestroy(&diag));
10716: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10717: } else {
10718: PetscUseTypeMethod(mat, findzerodiagonals, is);
10719: }
10720: PetscFunctionReturn(PETSC_SUCCESS);
10721: }
10723: /*@
10724: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10726: Collective
10728: Input Parameter:
10729: . mat - the matrix
10731: Output Parameter:
10732: . is - contains the list of rows with off block diagonal entries
10734: Level: developer
10736: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10737: @*/
10738: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10739: {
10740: PetscFunctionBegin;
10743: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10744: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10746: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10747: PetscFunctionReturn(PETSC_SUCCESS);
10748: }
10750: /*@C
10751: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10753: Collective; No Fortran Support
10755: Input Parameter:
10756: . mat - the matrix
10758: Output Parameter:
10759: . values - the block inverses in column major order (FORTRAN-like)
10761: Level: advanced
10763: Notes:
10764: The size of the blocks is determined by the block size of the matrix.
10766: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10768: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10770: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10771: @*/
10772: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10773: {
10774: PetscFunctionBegin;
10776: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10777: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10778: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10779: PetscFunctionReturn(PETSC_SUCCESS);
10780: }
10782: /*@
10783: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10785: Collective; No Fortran Support
10787: Input Parameters:
10788: + mat - the matrix
10789: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10790: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10792: Output Parameter:
10793: . values - the block inverses in column major order (FORTRAN-like)
10795: Level: advanced
10797: Notes:
10798: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10800: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10802: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10803: @*/
10804: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10805: {
10806: PetscFunctionBegin;
10808: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10809: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10810: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10811: PetscFunctionReturn(PETSC_SUCCESS);
10812: }
10814: /*@
10815: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10817: Collective
10819: Input Parameters:
10820: + A - the matrix
10821: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10823: Level: advanced
10825: Note:
10826: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10828: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10829: @*/
10830: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10831: {
10832: const PetscScalar *vals;
10833: PetscInt *dnnz;
10834: PetscInt m, rstart, rend, bs, i, j;
10836: PetscFunctionBegin;
10837: PetscCall(MatInvertBlockDiagonal(A, &vals));
10838: PetscCall(MatGetBlockSize(A, &bs));
10839: PetscCall(MatGetLocalSize(A, &m, NULL));
10840: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10841: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10842: PetscCall(PetscMalloc1(m / bs, &dnnz));
10843: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10844: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10845: PetscCall(PetscFree(dnnz));
10846: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10847: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10848: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10849: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10850: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10851: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10852: PetscFunctionReturn(PETSC_SUCCESS);
10853: }
10855: /*@
10856: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10857: via `MatTransposeColoringCreate()`.
10859: Collective
10861: Input Parameter:
10862: . c - coloring context
10864: Level: intermediate
10866: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10867: @*/
10868: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10869: {
10870: MatTransposeColoring matcolor = *c;
10872: PetscFunctionBegin;
10873: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10874: if (--((PetscObject)matcolor)->refct > 0) {
10875: matcolor = NULL;
10876: PetscFunctionReturn(PETSC_SUCCESS);
10877: }
10879: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10880: PetscCall(PetscFree(matcolor->rows));
10881: PetscCall(PetscFree(matcolor->den2sp));
10882: PetscCall(PetscFree(matcolor->colorforcol));
10883: PetscCall(PetscFree(matcolor->columns));
10884: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10885: PetscCall(PetscHeaderDestroy(c));
10886: PetscFunctionReturn(PETSC_SUCCESS);
10887: }
10889: /*@
10890: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10891: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10892: `MatTransposeColoring` to sparse `B`.
10894: Collective
10896: Input Parameters:
10897: + coloring - coloring context created with `MatTransposeColoringCreate()`
10898: - B - sparse matrix
10900: Output Parameter:
10901: . Btdense - dense matrix $B^T$
10903: Level: developer
10905: Note:
10906: These are used internally for some implementations of `MatRARt()`
10908: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10909: @*/
10910: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10911: {
10912: PetscFunctionBegin;
10917: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10918: PetscFunctionReturn(PETSC_SUCCESS);
10919: }
10921: /*@
10922: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10923: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10924: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10925: $C_{sp}$ from $C_{den}$.
10927: Collective
10929: Input Parameters:
10930: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10931: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
10933: Output Parameter:
10934: . Csp - sparse matrix
10936: Level: developer
10938: Note:
10939: These are used internally for some implementations of `MatRARt()`
10941: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10942: @*/
10943: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10944: {
10945: PetscFunctionBegin;
10950: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10951: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10952: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10953: PetscFunctionReturn(PETSC_SUCCESS);
10954: }
10956: /*@
10957: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
10959: Collective
10961: Input Parameters:
10962: + mat - the matrix product C
10963: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
10965: Output Parameter:
10966: . color - the new coloring context
10968: Level: intermediate
10970: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10971: `MatTransColoringApplyDenToSp()`
10972: @*/
10973: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10974: {
10975: MatTransposeColoring c;
10976: MPI_Comm comm;
10978: PetscFunctionBegin;
10979: PetscAssertPointer(color, 3);
10981: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10982: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10983: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10984: c->ctype = iscoloring->ctype;
10985: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10986: *color = c;
10987: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10988: PetscFunctionReturn(PETSC_SUCCESS);
10989: }
10991: /*@
10992: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10993: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
10995: Not Collective
10997: Input Parameter:
10998: . mat - the matrix
11000: Output Parameter:
11001: . state - the current state
11003: Level: intermediate
11005: Notes:
11006: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11007: different matrices
11009: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
11011: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
11013: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11014: @*/
11015: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11016: {
11017: PetscFunctionBegin;
11019: *state = mat->nonzerostate;
11020: PetscFunctionReturn(PETSC_SUCCESS);
11021: }
11023: /*@
11024: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11025: matrices from each processor
11027: Collective
11029: Input Parameters:
11030: + comm - the communicators the parallel matrix will live on
11031: . seqmat - the input sequential matrices
11032: . n - number of local columns (or `PETSC_DECIDE`)
11033: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11035: Output Parameter:
11036: . mpimat - the parallel matrix generated
11038: Level: developer
11040: Note:
11041: The number of columns of the matrix in EACH processor MUST be the same.
11043: .seealso: [](ch_matrices), `Mat`
11044: @*/
11045: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11046: {
11047: PetscMPIInt size;
11049: PetscFunctionBegin;
11050: PetscCallMPI(MPI_Comm_size(comm, &size));
11051: if (size == 1) {
11052: if (reuse == MAT_INITIAL_MATRIX) {
11053: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11054: } else {
11055: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11056: }
11057: PetscFunctionReturn(PETSC_SUCCESS);
11058: }
11060: 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");
11062: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11063: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11064: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11065: PetscFunctionReturn(PETSC_SUCCESS);
11066: }
11068: /*@
11069: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11071: Collective
11073: Input Parameters:
11074: + A - the matrix to create subdomains from
11075: - N - requested number of subdomains
11077: Output Parameters:
11078: + n - number of subdomains resulting on this MPI process
11079: - iss - `IS` list with indices of subdomains on this MPI process
11081: Level: advanced
11083: Note:
11084: The number of subdomains must be smaller than the communicator size
11086: .seealso: [](ch_matrices), `Mat`, `IS`
11087: @*/
11088: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11089: {
11090: MPI_Comm comm, subcomm;
11091: PetscMPIInt size, rank, color;
11092: PetscInt rstart, rend, k;
11094: PetscFunctionBegin;
11095: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11096: PetscCallMPI(MPI_Comm_size(comm, &size));
11097: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11098: 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);
11099: *n = 1;
11100: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11101: color = rank / k;
11102: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11103: PetscCall(PetscMalloc1(1, iss));
11104: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11105: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11106: PetscCallMPI(MPI_Comm_free(&subcomm));
11107: PetscFunctionReturn(PETSC_SUCCESS);
11108: }
11110: /*@
11111: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11113: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11114: If they are not the same, uses `MatMatMatMult()`.
11116: Once the coarse grid problem is constructed, correct for interpolation operators
11117: that are not of full rank, which can legitimately happen in the case of non-nested
11118: geometric multigrid.
11120: Input Parameters:
11121: + restrct - restriction operator
11122: . dA - fine grid matrix
11123: . interpolate - interpolation operator
11124: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11125: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11127: Output Parameter:
11128: . A - the Galerkin coarse matrix
11130: Options Database Key:
11131: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used
11133: Level: developer
11135: Note:
11136: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11138: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11139: @*/
11140: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11141: {
11142: IS zerorows;
11143: Vec diag;
11145: PetscFunctionBegin;
11146: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11147: /* Construct the coarse grid matrix */
11148: if (interpolate == restrct) {
11149: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11150: } else {
11151: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11152: }
11154: /* If the interpolation matrix is not of full rank, A will have zero rows.
11155: This can legitimately happen in the case of non-nested geometric multigrid.
11156: In that event, we set the rows of the matrix to the rows of the identity,
11157: ignoring the equations (as the RHS will also be zero). */
11159: PetscCall(MatFindZeroRows(*A, &zerorows));
11161: if (zerorows != NULL) { /* if there are any zero rows */
11162: PetscCall(MatCreateVecs(*A, &diag, NULL));
11163: PetscCall(MatGetDiagonal(*A, diag));
11164: PetscCall(VecISSet(diag, zerorows, 1.0));
11165: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11166: PetscCall(VecDestroy(&diag));
11167: PetscCall(ISDestroy(&zerorows));
11168: }
11169: PetscFunctionReturn(PETSC_SUCCESS);
11170: }
11172: /*@C
11173: MatSetOperation - Allows user to set a matrix operation for any matrix type
11175: Logically Collective
11177: Input Parameters:
11178: + mat - the matrix
11179: . op - the name of the operation
11180: - f - the function that provides the operation
11182: Level: developer
11184: Example Usage:
11185: .vb
11186: extern PetscErrorCode usermult(Mat, Vec, Vec);
11188: PetscCall(MatCreateXXX(comm, ..., &A));
11189: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11190: .ve
11192: Notes:
11193: See the file `include/petscmat.h` for a complete list of matrix
11194: operations, which all have the form MATOP_<OPERATION>, where
11195: <OPERATION> is the name (in all capital letters) of the
11196: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11198: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11199: sequence as the usual matrix interface routines, since they
11200: are intended to be accessed via the usual matrix interface
11201: routines, e.g.,
11202: .vb
11203: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11204: .ve
11206: In particular each function MUST return `PETSC_SUCCESS` on success and
11207: nonzero on failure.
11209: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11211: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11212: @*/
11213: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11214: {
11215: PetscFunctionBegin;
11217: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11218: (((PetscErrorCodeFn **)mat->ops)[op]) = f;
11219: PetscFunctionReturn(PETSC_SUCCESS);
11220: }
11222: /*@C
11223: MatGetOperation - Gets a matrix operation for any matrix type.
11225: Not Collective
11227: Input Parameters:
11228: + mat - the matrix
11229: - op - the name of the operation
11231: Output Parameter:
11232: . f - the function that provides the operation
11234: Level: developer
11236: Example Usage:
11237: .vb
11238: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11240: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11241: .ve
11243: Notes:
11244: See the file `include/petscmat.h` for a complete list of matrix
11245: operations, which all have the form MATOP_<OPERATION>, where
11246: <OPERATION> is the name (in all capital letters) of the
11247: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11249: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11251: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11252: @*/
11253: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11254: {
11255: PetscFunctionBegin;
11257: *f = (((PetscErrorCodeFn **)mat->ops)[op]);
11258: PetscFunctionReturn(PETSC_SUCCESS);
11259: }
11261: /*@
11262: MatHasOperation - Determines whether the given matrix supports the particular operation.
11264: Not Collective
11266: Input Parameters:
11267: + mat - the matrix
11268: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11270: Output Parameter:
11271: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11273: Level: advanced
11275: Note:
11276: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11278: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11279: @*/
11280: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11281: {
11282: PetscFunctionBegin;
11284: PetscAssertPointer(has, 3);
11285: if (mat->ops->hasoperation) {
11286: PetscUseTypeMethod(mat, hasoperation, op, has);
11287: } else {
11288: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11289: else {
11290: *has = PETSC_FALSE;
11291: if (op == MATOP_CREATE_SUBMATRIX) {
11292: PetscMPIInt size;
11294: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11295: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11296: }
11297: }
11298: }
11299: PetscFunctionReturn(PETSC_SUCCESS);
11300: }
11302: /*@
11303: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11305: Collective
11307: Input Parameter:
11308: . mat - the matrix
11310: Output Parameter:
11311: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11313: Level: beginner
11315: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11316: @*/
11317: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11318: {
11319: PetscFunctionBegin;
11322: PetscAssertPointer(cong, 2);
11323: if (!mat->rmap || !mat->cmap) {
11324: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11325: PetscFunctionReturn(PETSC_SUCCESS);
11326: }
11327: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11328: PetscCall(PetscLayoutSetUp(mat->rmap));
11329: PetscCall(PetscLayoutSetUp(mat->cmap));
11330: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11331: if (*cong) mat->congruentlayouts = 1;
11332: else mat->congruentlayouts = 0;
11333: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11334: PetscFunctionReturn(PETSC_SUCCESS);
11335: }
11337: PetscErrorCode MatSetInf(Mat A)
11338: {
11339: PetscFunctionBegin;
11340: PetscUseTypeMethod(A, setinf);
11341: PetscFunctionReturn(PETSC_SUCCESS);
11342: }
11344: /*@
11345: 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
11346: and possibly removes small values from the graph structure.
11348: Collective
11350: Input Parameters:
11351: + A - the matrix
11352: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11353: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11354: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11355: . num_idx - size of 'index' array
11356: - index - array of block indices to use for graph strength of connection weight
11358: Output Parameter:
11359: . graph - the resulting graph
11361: Level: advanced
11363: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11364: @*/
11365: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11366: {
11367: PetscFunctionBegin;
11371: PetscAssertPointer(graph, 7);
11372: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11373: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11374: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11375: PetscFunctionReturn(PETSC_SUCCESS);
11376: }
11378: /*@
11379: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11380: meaning the same memory is used for the matrix, and no new memory is allocated.
11382: Collective
11384: Input Parameters:
11385: + A - the matrix
11386: - 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
11388: Level: intermediate
11390: Developer Note:
11391: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11392: of the arrays in the data structure are unneeded.
11394: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11395: @*/
11396: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11397: {
11398: PetscFunctionBegin;
11400: PetscUseTypeMethod(A, eliminatezeros, keep);
11401: PetscFunctionReturn(PETSC_SUCCESS);
11402: }
11404: /*@C
11405: MatGetCurrentMemType - Get the memory location of the matrix
11407: Not Collective, but the result will be the same on all MPI processes
11409: Input Parameter:
11410: . A - the matrix whose memory type we are checking
11412: Output Parameter:
11413: . m - the memory type
11415: Level: intermediate
11417: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11418: @*/
11419: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11420: {
11421: PetscFunctionBegin;
11423: PetscAssertPointer(m, 2);
11424: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11425: else *m = PETSC_MEMTYPE_HOST;
11426: PetscFunctionReturn(PETSC_SUCCESS);
11427: }