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: PetscFunctionReturn(PETSC_SUCCESS);
759: }
761: /*@
762: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
763: for matrices created with `MatGetFactor()`
765: Logically Collective
767: Input Parameters:
768: + A - the matrix
769: - prefix - the prefix to prepend to all option names for the factored matrix
771: Level: developer
773: Notes:
774: A hyphen (-) must NOT be given at the beginning of the prefix name.
775: The first character of all runtime options is AUTOMATICALLY the hyphen.
777: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
778: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
780: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
781: @*/
782: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
783: {
784: PetscFunctionBegin;
786: if (prefix) {
787: PetscAssertPointer(prefix, 2);
788: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
789: if (prefix != A->factorprefix) {
790: PetscCall(PetscFree(A->factorprefix));
791: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
792: }
793: } else PetscCall(PetscFree(A->factorprefix));
794: PetscFunctionReturn(PETSC_SUCCESS);
795: }
797: /*@
798: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
799: for matrices created with `MatGetFactor()`
801: Logically Collective
803: Input Parameters:
804: + A - the matrix
805: - prefix - the prefix to prepend to all option names for the factored matrix
807: Level: developer
809: Notes:
810: A hyphen (-) must NOT be given at the beginning of the prefix name.
811: The first character of all runtime options is AUTOMATICALLY the hyphen.
813: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
814: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
816: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
817: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
818: `MatSetOptionsPrefix()`
819: @*/
820: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
821: {
822: size_t len1, len2, new_len;
824: PetscFunctionBegin;
826: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
827: if (!A->factorprefix) {
828: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
829: PetscFunctionReturn(PETSC_SUCCESS);
830: }
831: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
833: PetscCall(PetscStrlen(A->factorprefix, &len1));
834: PetscCall(PetscStrlen(prefix, &len2));
835: new_len = len1 + len2 + 1;
836: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
837: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
838: PetscFunctionReturn(PETSC_SUCCESS);
839: }
841: /*@
842: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
843: matrix options in the database.
845: Logically Collective
847: Input Parameters:
848: + A - the matrix
849: - prefix - the prefix to prepend to all option names
851: Level: advanced
853: Note:
854: A hyphen (-) must NOT be given at the beginning of the prefix name.
855: The first character of all runtime options is AUTOMATICALLY the hyphen.
857: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
858: @*/
859: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
860: {
861: PetscFunctionBegin;
863: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
864: PetscFunctionReturn(PETSC_SUCCESS);
865: }
867: /*@
868: MatGetOptionsPrefix - Gets the prefix used for searching for all
869: matrix options in the database.
871: Not Collective
873: Input Parameter:
874: . A - the matrix
876: Output Parameter:
877: . prefix - pointer to the prefix string used
879: Level: advanced
881: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
882: @*/
883: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
884: {
885: PetscFunctionBegin;
887: PetscAssertPointer(prefix, 2);
888: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
889: PetscFunctionReturn(PETSC_SUCCESS);
890: }
892: /*@
893: MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`
895: Not Collective
897: Input Parameter:
898: . A - the matrix
900: Output Parameter:
901: . state - the object state
903: Level: advanced
905: Note:
906: Object state is an integer which gets increased every time
907: the object is changed. By saving and later querying the object state
908: one can determine whether information about the object is still current.
910: See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.
912: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
913: @*/
914: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
915: {
916: PetscFunctionBegin;
918: PetscAssertPointer(state, 2);
919: PetscCall(PetscObjectStateGet((PetscObject)A, state));
920: PetscFunctionReturn(PETSC_SUCCESS);
921: }
923: /*@
924: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
926: Collective
928: Input Parameter:
929: . A - the matrix
931: Level: beginner
933: Notes:
934: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
935: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
936: makes all of the preallocation space available
938: Current values in the matrix are lost in this call
940: Currently only supported for `MATAIJ` matrices.
942: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
943: @*/
944: PetscErrorCode MatResetPreallocation(Mat A)
945: {
946: PetscFunctionBegin;
949: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
950: PetscFunctionReturn(PETSC_SUCCESS);
951: }
953: /*@
954: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
956: Collective
958: Input Parameter:
959: . A - the matrix
961: Level: intermediate
963: Notes:
964: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
966: Currently only supported for `MATAIJ` matrices.
968: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
969: @*/
970: PetscErrorCode MatResetHash(Mat A)
971: {
972: PetscFunctionBegin;
975: 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()");
976: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
977: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
978: /* These flags are used to determine whether certain setups occur */
979: A->was_assembled = PETSC_FALSE;
980: A->assembled = PETSC_FALSE;
981: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
982: PetscCall(PetscObjectStateIncrease((PetscObject)A));
983: PetscFunctionReturn(PETSC_SUCCESS);
984: }
986: /*@
987: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
989: Collective
991: Input Parameter:
992: . A - the matrix
994: Level: advanced
996: Notes:
997: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
998: setting values in the matrix.
1000: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
1002: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1003: @*/
1004: PetscErrorCode MatSetUp(Mat A)
1005: {
1006: PetscFunctionBegin;
1008: if (!((PetscObject)A)->type_name) {
1009: PetscMPIInt size;
1011: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1012: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1013: }
1014: if (!A->preallocated) PetscTryTypeMethod(A, setup);
1015: PetscCall(PetscLayoutSetUp(A->rmap));
1016: PetscCall(PetscLayoutSetUp(A->cmap));
1017: A->preallocated = PETSC_TRUE;
1018: PetscFunctionReturn(PETSC_SUCCESS);
1019: }
1021: #if defined(PETSC_HAVE_SAWS)
1022: #include <petscviewersaws.h>
1023: #endif
1025: /*
1026: If threadsafety is on extraneous matrices may be printed
1028: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1029: */
1030: #if !defined(PETSC_HAVE_THREADSAFETY)
1031: static PetscInt insidematview = 0;
1032: #endif
1034: /*@
1035: MatViewFromOptions - View properties of the matrix based on options set in the options database
1037: Collective
1039: Input Parameters:
1040: + A - the matrix
1041: . obj - optional additional object that provides the options prefix to use
1042: - name - command line option
1044: Options Database Key:
1045: . -mat_view [viewertype]:... - the viewer and its options
1047: Level: intermediate
1049: Note:
1050: .vb
1051: If no value is provided ascii:stdout is used
1052: ascii[:[filename][:[format][:append]]] defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
1053: for example ascii::ascii_info prints just the information about the object not all details
1054: unless :append is given filename opens in write mode, overwriting what was already there
1055: binary[:[filename][:[format][:append]]] defaults to the file binaryoutput
1056: draw[:drawtype[:filename]] for example, draw:tikz, draw:tikz:figure.tex or draw:x
1057: socket[:port] defaults to the standard output port
1058: saws[:communicatorname] publishes object to the Scientific Application Webserver (SAWs)
1059: .ve
1061: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1062: @*/
1063: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1064: {
1065: PetscFunctionBegin;
1067: #if !defined(PETSC_HAVE_THREADSAFETY)
1068: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1069: #endif
1070: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1071: PetscFunctionReturn(PETSC_SUCCESS);
1072: }
1074: /*@
1075: MatView - display information about a matrix in a variety ways
1077: Collective on viewer
1079: Input Parameters:
1080: + mat - the matrix
1081: - viewer - visualization context
1083: Options Database Keys:
1084: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1085: . -mat_view ::ascii_info_detail - Prints more detailed info
1086: . -mat_view - Prints matrix in ASCII format
1087: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1088: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1089: . -display <name> - Sets display name (default is host)
1090: . -draw_pause <sec> - Sets number of seconds to pause after display
1091: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1092: . -viewer_socket_machine <machine> - -
1093: . -viewer_socket_port <port> - -
1094: . -mat_view binary - save matrix to file in binary format
1095: - -viewer_binary_filename <name> - -
1097: Level: beginner
1099: Notes:
1100: The available visualization contexts include
1101: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1102: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1103: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1104: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1106: The user can open alternative visualization contexts with
1107: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1108: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1109: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1110: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1112: The user can call `PetscViewerPushFormat()` to specify the output
1113: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1114: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1115: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1116: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1117: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1118: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1119: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1120: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1121: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1123: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1124: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1126: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1128: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1129: viewer is used.
1131: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1132: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1134: One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1135: and then use the following mouse functions.
1136: .vb
1137: left mouse: zoom in
1138: middle mouse: zoom out
1139: right mouse: continue with the simulation
1140: .ve
1142: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1143: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1144: @*/
1145: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1146: {
1147: PetscInt rows, cols, rbs, cbs;
1148: PetscBool isascii, isstring, issaws;
1149: PetscViewerFormat format;
1150: PetscMPIInt size;
1152: PetscFunctionBegin;
1155: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1158: PetscCall(PetscViewerGetFormat(viewer, &format));
1159: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1160: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1162: #if !defined(PETSC_HAVE_THREADSAFETY)
1163: insidematview++;
1164: #endif
1165: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1166: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1167: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1168: 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");
1170: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1171: if (isascii) {
1172: if (!mat->preallocated) {
1173: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1174: #if !defined(PETSC_HAVE_THREADSAFETY)
1175: insidematview--;
1176: #endif
1177: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1178: PetscFunctionReturn(PETSC_SUCCESS);
1179: }
1180: if (!mat->assembled) {
1181: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1182: #if !defined(PETSC_HAVE_THREADSAFETY)
1183: insidematview--;
1184: #endif
1185: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1186: PetscFunctionReturn(PETSC_SUCCESS);
1187: }
1188: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1189: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1190: MatNullSpace nullsp, transnullsp;
1192: PetscCall(PetscViewerASCIIPushTab(viewer));
1193: PetscCall(MatGetSize(mat, &rows, &cols));
1194: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1195: if (rbs != 1 || cbs != 1) {
1196: 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" : ""));
1197: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1198: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1199: if (mat->factortype) {
1200: MatSolverType solver;
1201: PetscCall(MatFactorGetSolverType(mat, &solver));
1202: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1203: }
1204: if (mat->ops->getinfo) {
1205: MatInfo info;
1206: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1207: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1208: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1209: }
1210: PetscCall(MatGetNullSpace(mat, &nullsp));
1211: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1212: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1213: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1214: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1215: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1216: PetscCall(PetscViewerASCIIPushTab(viewer));
1217: PetscCall(MatProductView(mat, viewer));
1218: PetscCall(PetscViewerASCIIPopTab(viewer));
1219: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1220: IS tmp;
1222: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1223: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1224: PetscCall(PetscViewerASCIIPushTab(viewer));
1225: PetscCall(ISView(tmp, viewer));
1226: PetscCall(PetscViewerASCIIPopTab(viewer));
1227: PetscCall(ISDestroy(&tmp));
1228: }
1229: }
1230: } else if (issaws) {
1231: #if defined(PETSC_HAVE_SAWS)
1232: PetscMPIInt rank;
1234: PetscCall(PetscObjectName((PetscObject)mat));
1235: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1236: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1237: #endif
1238: } else if (isstring) {
1239: const char *type;
1240: PetscCall(MatGetType(mat, &type));
1241: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1242: PetscTryTypeMethod(mat, view, viewer);
1243: }
1244: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1245: PetscCall(PetscViewerASCIIPushTab(viewer));
1246: PetscUseTypeMethod(mat, viewnative, viewer);
1247: PetscCall(PetscViewerASCIIPopTab(viewer));
1248: } else if (mat->ops->view) {
1249: PetscCall(PetscViewerASCIIPushTab(viewer));
1250: PetscUseTypeMethod(mat, view, viewer);
1251: PetscCall(PetscViewerASCIIPopTab(viewer));
1252: }
1253: if (isascii) {
1254: PetscCall(PetscViewerGetFormat(viewer, &format));
1255: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1256: }
1257: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1258: #if !defined(PETSC_HAVE_THREADSAFETY)
1259: insidematview--;
1260: #endif
1261: PetscFunctionReturn(PETSC_SUCCESS);
1262: }
1264: #if defined(PETSC_USE_DEBUG)
1265: #include <../src/sys/totalview/tv_data_display.h>
1266: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1267: {
1268: TV_add_row("Local rows", "int", &mat->rmap->n);
1269: TV_add_row("Local columns", "int", &mat->cmap->n);
1270: TV_add_row("Global rows", "int", &mat->rmap->N);
1271: TV_add_row("Global columns", "int", &mat->cmap->N);
1272: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1273: return TV_format_OK;
1274: }
1275: #endif
1277: /*@
1278: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1279: with `MatView()`. The matrix format is determined from the options database.
1280: Generates a parallel MPI matrix if the communicator has more than one
1281: processor. The default matrix type is `MATAIJ`.
1283: Collective
1285: Input Parameters:
1286: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1287: or some related function before a call to `MatLoad()`
1288: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1290: Options Database Key:
1291: . -matload_block_size <bs> - set block size
1293: Level: beginner
1295: Notes:
1296: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1297: `Mat` before calling this routine if you wish to set it from the options database.
1299: `MatLoad()` automatically loads into the options database any options
1300: given in the file filename.info where filename is the name of the file
1301: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1302: file will be ignored if you use the -viewer_binary_skip_info option.
1304: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1305: sets the default matrix type AIJ and sets the local and global sizes.
1306: If type and/or size is already set, then the same are used.
1308: In parallel, each processor can load a subset of rows (or the
1309: entire matrix). This routine is especially useful when a large
1310: matrix is stored on disk and only part of it is desired on each
1311: processor. For example, a parallel solver may access only some of
1312: the rows from each processor. The algorithm used here reads
1313: relatively small blocks of data rather than reading the entire
1314: matrix and then subsetting it.
1316: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1317: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1318: or the sequence like
1319: .vb
1320: `PetscViewer` v;
1321: `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1322: `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1323: `PetscViewerSetFromOptions`(v);
1324: `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1325: `PetscViewerFileSetName`(v,"datafile");
1326: .ve
1327: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1328: .vb
1329: -viewer_type {binary, hdf5}
1330: .ve
1332: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1333: and src/mat/tutorials/ex10.c with the second approach.
1335: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1336: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1337: Multiple objects, both matrices and vectors, can be stored within the same file.
1338: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1340: Most users should not need to know the details of the binary storage
1341: format, since `MatLoad()` and `MatView()` completely hide these details.
1342: But for anyone who is interested, the standard binary matrix storage
1343: format is
1345: .vb
1346: PetscInt MAT_FILE_CLASSID
1347: PetscInt number of rows
1348: PetscInt number of columns
1349: PetscInt total number of nonzeros
1350: PetscInt *number nonzeros in each row
1351: PetscInt *column indices of all nonzeros (starting index is zero)
1352: PetscScalar *values of all nonzeros
1353: .ve
1354: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1355: 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
1356: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1358: PETSc automatically does the byte swapping for
1359: machines that store the bytes reversed. Thus if you write your own binary
1360: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1361: and `PetscBinaryWrite()` to see how this may be done.
1363: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1364: Each processor's chunk is loaded independently by its owning MPI process.
1365: Multiple objects, both matrices and vectors, can be stored within the same file.
1366: They are looked up by their PetscObject name.
1368: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1369: by default the same structure and naming of the AIJ arrays and column count
1370: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1371: .vb
1372: save example.mat A b -v7.3
1373: .ve
1374: can be directly read by this routine (see Reference 1 for details).
1376: Depending on your MATLAB version, this format might be a default,
1377: otherwise you can set it as default in Preferences.
1379: Unless -nocompression flag is used to save the file in MATLAB,
1380: PETSc must be configured with ZLIB package.
1382: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1384: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1386: Corresponding `MatView()` is not yet implemented.
1388: The loaded matrix is actually a transpose of the original one in MATLAB,
1389: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1390: With this format, matrix is automatically transposed by PETSc,
1391: unless the matrix is marked as SPD or symmetric
1392: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1394: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1396: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1397: @*/
1398: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1399: {
1400: PetscBool flg;
1402: PetscFunctionBegin;
1406: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1408: flg = PETSC_FALSE;
1409: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1410: if (flg) {
1411: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1412: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1413: }
1414: flg = PETSC_FALSE;
1415: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1416: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1418: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1419: PetscUseTypeMethod(mat, load, viewer);
1420: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1421: PetscFunctionReturn(PETSC_SUCCESS);
1422: }
1424: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1425: {
1426: Mat_Redundant *redund = *redundant;
1428: PetscFunctionBegin;
1429: if (redund) {
1430: if (redund->matseq) { /* via MatCreateSubMatrices() */
1431: PetscCall(ISDestroy(&redund->isrow));
1432: PetscCall(ISDestroy(&redund->iscol));
1433: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1434: } else {
1435: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1436: PetscCall(PetscFree(redund->sbuf_j));
1437: PetscCall(PetscFree(redund->sbuf_a));
1438: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1439: PetscCall(PetscFree(redund->rbuf_j[i]));
1440: PetscCall(PetscFree(redund->rbuf_a[i]));
1441: }
1442: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1443: }
1445: if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1446: PetscCall(PetscFree(redund));
1447: }
1448: PetscFunctionReturn(PETSC_SUCCESS);
1449: }
1451: /*@
1452: MatDestroy - Frees space taken by a matrix.
1454: Collective
1456: Input Parameter:
1457: . A - the matrix
1459: Level: beginner
1461: Developer Note:
1462: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1463: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1464: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1465: if changes are needed here.
1467: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1468: @*/
1469: PetscErrorCode MatDestroy(Mat *A)
1470: {
1471: PetscFunctionBegin;
1472: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1474: if (--((PetscObject)*A)->refct > 0) {
1475: *A = NULL;
1476: PetscFunctionReturn(PETSC_SUCCESS);
1477: }
1479: /* if memory was published with SAWs then destroy it */
1480: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1481: PetscTryTypeMethod(*A, destroy);
1483: PetscCall(PetscFree((*A)->factorprefix));
1484: PetscCall(PetscFree((*A)->defaultvectype));
1485: PetscCall(PetscFree((*A)->defaultrandtype));
1486: PetscCall(PetscFree((*A)->bsizes));
1487: PetscCall(PetscFree((*A)->solvertype));
1488: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1489: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1490: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1491: PetscCall(MatProductClear(*A));
1492: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1493: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1494: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1495: PetscCall(MatDestroy(&(*A)->schur));
1496: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1497: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1498: PetscCall(PetscHeaderDestroy(A));
1499: PetscFunctionReturn(PETSC_SUCCESS);
1500: }
1502: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1503: /*@
1504: MatSetValues - Inserts or adds a block of values into a matrix.
1505: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1506: MUST be called after all calls to `MatSetValues()` have been completed.
1508: Not Collective
1510: Input Parameters:
1511: + mat - the matrix
1512: . v - a logically two-dimensional array of values
1513: . m - the number of rows
1514: . idxm - the global indices of the rows
1515: . n - the number of columns
1516: . idxn - the global indices of the columns
1517: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1519: Level: beginner
1521: Notes:
1522: By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.
1524: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1525: options cannot be mixed without intervening calls to the assembly
1526: routines.
1528: `MatSetValues()` uses 0-based row and column numbers in Fortran
1529: as well as in C.
1531: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1532: simply ignored. This allows easily inserting element stiffness matrices
1533: with homogeneous Dirichlet boundary conditions that you don't want represented
1534: in the matrix.
1536: Efficiency Alert:
1537: The routine `MatSetValuesBlocked()` may offer much better efficiency
1538: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1540: Fortran Notes:
1541: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1542: .vb
1543: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1544: .ve
1546: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1548: Developer Note:
1549: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1550: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1552: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1553: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1554: @*/
1555: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1556: {
1557: PetscFunctionBeginHot;
1560: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1561: PetscAssertPointer(idxm, 3);
1562: PetscAssertPointer(idxn, 5);
1563: MatCheckPreallocated(mat, 1);
1565: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1566: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1568: if (PetscDefined(USE_DEBUG)) {
1569: PetscInt i, j;
1571: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1572: if (v) {
1573: for (i = 0; i < m; i++) {
1574: for (j = 0; j < n; j++) {
1575: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1576: #if defined(PETSC_USE_COMPLEX)
1577: 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]);
1578: #else
1579: 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]);
1580: #endif
1581: }
1582: }
1583: }
1584: 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);
1585: 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);
1586: }
1588: if (mat->assembled) {
1589: mat->was_assembled = PETSC_TRUE;
1590: mat->assembled = PETSC_FALSE;
1591: }
1592: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1593: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1594: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1595: PetscFunctionReturn(PETSC_SUCCESS);
1596: }
1598: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1599: /*@
1600: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1601: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1602: MUST be called after all calls to `MatSetValues()` have been completed.
1604: Not Collective
1606: Input Parameters:
1607: + mat - the matrix
1608: . v - a logically two-dimensional array of values
1609: . ism - the rows to provide
1610: . isn - the columns to provide
1611: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1613: Level: beginner
1615: Notes:
1616: By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.
1618: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1619: options cannot be mixed without intervening calls to the assembly
1620: routines.
1622: `MatSetValues()` uses 0-based row and column numbers in Fortran
1623: as well as in C.
1625: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1626: simply ignored. This allows easily inserting element stiffness matrices
1627: with homogeneous Dirichlet boundary conditions that you don't want represented
1628: in the matrix.
1630: Efficiency Alert:
1631: The routine `MatSetValuesBlocked()` may offer much better efficiency
1632: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1634: This is currently not optimized for any particular `ISType`
1636: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1637: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1638: @*/
1639: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1640: {
1641: PetscInt m, n;
1642: const PetscInt *rows, *cols;
1644: PetscFunctionBeginHot;
1646: PetscCall(ISGetIndices(ism, &rows));
1647: PetscCall(ISGetIndices(isn, &cols));
1648: PetscCall(ISGetLocalSize(ism, &m));
1649: PetscCall(ISGetLocalSize(isn, &n));
1650: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1651: PetscCall(ISRestoreIndices(ism, &rows));
1652: PetscCall(ISRestoreIndices(isn, &cols));
1653: PetscFunctionReturn(PETSC_SUCCESS);
1654: }
1656: /*@
1657: MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1658: values into a matrix
1660: Not Collective
1662: Input Parameters:
1663: + mat - the matrix
1664: . row - the (block) row to set
1665: - v - a logically two-dimensional array of values
1667: Level: intermediate
1669: Notes:
1670: The values, `v`, are column-oriented (for the block version) and sorted
1672: All the nonzero values in `row` must be provided
1674: The matrix must have previously had its column indices set, likely by having been assembled.
1676: `row` must belong to this MPI process
1678: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1679: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1680: @*/
1681: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1682: {
1683: PetscInt globalrow;
1685: PetscFunctionBegin;
1688: PetscAssertPointer(v, 3);
1689: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1690: PetscCall(MatSetValuesRow(mat, globalrow, v));
1691: PetscFunctionReturn(PETSC_SUCCESS);
1692: }
1694: /*@
1695: MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1696: values into a matrix
1698: Not Collective
1700: Input Parameters:
1701: + mat - the matrix
1702: . row - the (block) row to set
1703: - 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
1705: Level: advanced
1707: Notes:
1708: The values, `v`, are column-oriented for the block version.
1710: All the nonzeros in `row` must be provided
1712: THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.
1714: `row` must belong to this process
1716: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1717: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1718: @*/
1719: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1720: {
1721: PetscFunctionBeginHot;
1724: MatCheckPreallocated(mat, 1);
1725: PetscAssertPointer(v, 3);
1726: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1727: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1728: mat->insertmode = INSERT_VALUES;
1730: if (mat->assembled) {
1731: mat->was_assembled = PETSC_TRUE;
1732: mat->assembled = PETSC_FALSE;
1733: }
1734: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1735: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1736: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1737: PetscFunctionReturn(PETSC_SUCCESS);
1738: }
1740: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1741: /*@
1742: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1743: Using structured grid indexing
1745: Not Collective
1747: Input Parameters:
1748: + mat - the matrix
1749: . m - number of rows being entered
1750: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1751: . n - number of columns being entered
1752: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1753: . v - a logically two-dimensional array of values
1754: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1756: Level: beginner
1758: Notes:
1759: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1761: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1762: options cannot be mixed without intervening calls to the assembly
1763: routines.
1765: The grid coordinates are across the entire grid, not just the local portion
1767: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1768: as well as in C.
1770: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1772: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1773: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1775: The columns and rows in the stencil passed in MUST be contained within the
1776: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1777: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1778: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1779: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1781: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1782: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1783: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1784: `DM_BOUNDARY_PERIODIC` boundary type.
1786: 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
1787: a single value per point) you can skip filling those indices.
1789: Inspired by the structured grid interface to the HYPRE package
1790: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1792: Efficiency Alert:
1793: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1794: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1796: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1797: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1798: @*/
1799: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1800: {
1801: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1802: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1803: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1805: PetscFunctionBegin;
1806: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1809: PetscAssertPointer(idxm, 3);
1810: PetscAssertPointer(idxn, 5);
1812: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1813: jdxm = buf;
1814: jdxn = buf + m;
1815: } else {
1816: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1817: jdxm = bufm;
1818: jdxn = bufn;
1819: }
1820: for (i = 0; i < m; i++) {
1821: for (j = 0; j < 3 - sdim; j++) dxm++;
1822: tmp = *dxm++ - starts[0];
1823: for (j = 0; j < dim - 1; j++) {
1824: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1825: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1826: }
1827: if (mat->stencil.noc) dxm++;
1828: jdxm[i] = tmp;
1829: }
1830: for (i = 0; i < n; i++) {
1831: for (j = 0; j < 3 - sdim; j++) dxn++;
1832: tmp = *dxn++ - starts[0];
1833: for (j = 0; j < dim - 1; j++) {
1834: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1835: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1836: }
1837: if (mat->stencil.noc) dxn++;
1838: jdxn[i] = tmp;
1839: }
1840: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1841: PetscCall(PetscFree2(bufm, bufn));
1842: PetscFunctionReturn(PETSC_SUCCESS);
1843: }
1845: /*@
1846: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1847: Using structured grid indexing
1849: Not Collective
1851: Input Parameters:
1852: + mat - the matrix
1853: . m - number of rows being entered
1854: . idxm - grid coordinates for matrix rows being entered
1855: . n - number of columns being entered
1856: . idxn - grid coordinates for matrix columns being entered
1857: . v - a logically two-dimensional array of values
1858: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1860: Level: beginner
1862: Notes:
1863: By default the values, `v`, are row-oriented and unsorted.
1864: See `MatSetOption()` for other options.
1866: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1867: options cannot be mixed without intervening calls to the assembly
1868: routines.
1870: The grid coordinates are across the entire grid, not just the local portion
1872: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1873: as well as in C.
1875: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1877: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1878: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1880: The columns and rows in the stencil passed in MUST be contained within the
1881: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1882: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1883: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1884: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1886: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1887: simply ignored. This allows easily inserting element stiffness matrices
1888: with homogeneous Dirichlet boundary conditions that you don't want represented
1889: in the matrix.
1891: Inspired by the structured grid interface to the HYPRE package
1892: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1894: Fortran Note:
1895: `idxm` and `idxn` should be declared as
1896: .vb
1897: MatStencil idxm(4,m),idxn(4,n)
1898: .ve
1899: and the values inserted using
1900: .vb
1901: idxm(MatStencil_i,1) = i
1902: idxm(MatStencil_j,1) = j
1903: idxm(MatStencil_k,1) = k
1904: etc
1905: .ve
1907: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1908: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1909: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1910: @*/
1911: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1912: {
1913: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1914: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1915: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1917: PetscFunctionBegin;
1918: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1921: PetscAssertPointer(idxm, 3);
1922: PetscAssertPointer(idxn, 5);
1923: PetscAssertPointer(v, 6);
1925: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1926: jdxm = buf;
1927: jdxn = buf + m;
1928: } else {
1929: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1930: jdxm = bufm;
1931: jdxn = bufn;
1932: }
1933: for (i = 0; i < m; i++) {
1934: for (j = 0; j < 3 - sdim; j++) dxm++;
1935: tmp = *dxm++ - starts[0];
1936: for (j = 0; j < sdim - 1; j++) {
1937: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1938: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1939: }
1940: dxm++;
1941: jdxm[i] = tmp;
1942: }
1943: for (i = 0; i < n; i++) {
1944: for (j = 0; j < 3 - sdim; j++) dxn++;
1945: tmp = *dxn++ - starts[0];
1946: for (j = 0; j < sdim - 1; j++) {
1947: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1948: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1949: }
1950: dxn++;
1951: jdxn[i] = tmp;
1952: }
1953: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1954: PetscCall(PetscFree2(bufm, bufn));
1955: PetscFunctionReturn(PETSC_SUCCESS);
1956: }
1958: /*@
1959: MatSetStencil - Sets the grid information for setting values into a matrix via
1960: `MatSetValuesStencil()`
1962: Not Collective
1964: Input Parameters:
1965: + mat - the matrix
1966: . dim - dimension of the grid 1, 2, or 3
1967: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1968: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1969: - dof - number of degrees of freedom per node
1971: Level: beginner
1973: Notes:
1974: Inspired by the structured grid interface to the HYPRE package
1975: (www.llnl.gov/CASC/hyper)
1977: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1978: user.
1980: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1981: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1982: @*/
1983: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1984: {
1985: PetscFunctionBegin;
1987: PetscAssertPointer(dims, 3);
1988: PetscAssertPointer(starts, 4);
1990: mat->stencil.dim = dim + (dof > 1);
1991: for (PetscInt i = 0; i < dim; i++) {
1992: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
1993: mat->stencil.starts[i] = starts[dim - i - 1];
1994: }
1995: mat->stencil.dims[dim] = dof;
1996: mat->stencil.starts[dim] = 0;
1997: mat->stencil.noc = (PetscBool)(dof == 1);
1998: PetscFunctionReturn(PETSC_SUCCESS);
1999: }
2001: /*@
2002: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
2004: Not Collective
2006: Input Parameters:
2007: + mat - the matrix
2008: . v - a logically two-dimensional array of values
2009: . m - the number of block rows
2010: . idxm - the global block indices
2011: . n - the number of block columns
2012: . idxn - the global block indices
2013: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
2015: Level: intermediate
2017: Notes:
2018: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2019: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2021: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2022: NOT the total number of rows/columns; for example, if the block size is 2 and
2023: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2024: The values in `idxm` would be 1 2; that is the first index for each block divided by
2025: the block size.
2027: You must call `MatSetBlockSize()` when constructing this matrix (before
2028: preallocating it).
2030: By default the values, `v`, are row-oriented, so the layout of
2031: `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.
2033: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2034: options cannot be mixed without intervening calls to the assembly
2035: routines.
2037: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2038: as well as in C.
2040: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2041: simply ignored. This allows easily inserting element stiffness matrices
2042: with homogeneous Dirichlet boundary conditions that you don't want represented
2043: in the matrix.
2045: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2046: internal searching must be done to determine where to place the
2047: data in the matrix storage space. By instead inserting blocks of
2048: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2049: reduced.
2051: Example:
2052: .vb
2053: Suppose m=n=2 and block size(bs) = 2 The array is
2055: 1 2 | 3 4
2056: 5 6 | 7 8
2057: - - - | - - -
2058: 9 10 | 11 12
2059: 13 14 | 15 16
2061: v[] should be passed in like
2062: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2064: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2065: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2066: .ve
2068: Fortran Notes:
2069: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2070: .vb
2071: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2072: .ve
2074: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2076: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2077: @*/
2078: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2079: {
2080: PetscFunctionBeginHot;
2083: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2084: PetscAssertPointer(idxm, 3);
2085: PetscAssertPointer(idxn, 5);
2086: MatCheckPreallocated(mat, 1);
2087: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2088: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2089: if (PetscDefined(USE_DEBUG)) {
2090: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2091: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2092: }
2093: if (PetscDefined(USE_DEBUG)) {
2094: PetscInt rbs, cbs, M, N, i;
2095: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2096: PetscCall(MatGetSize(mat, &M, &N));
2097: 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);
2098: for (i = 0; i < n; i++)
2099: 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);
2100: }
2101: if (mat->assembled) {
2102: mat->was_assembled = PETSC_TRUE;
2103: mat->assembled = PETSC_FALSE;
2104: }
2105: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2106: if (mat->ops->setvaluesblocked) {
2107: PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2108: } else {
2109: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2110: PetscInt i, j, bs, cbs;
2112: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2113: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2114: iidxm = buf;
2115: iidxn = buf + m * bs;
2116: } else {
2117: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2118: iidxm = bufr;
2119: iidxn = bufc;
2120: }
2121: for (i = 0; i < m; i++) {
2122: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2123: }
2124: if (m != n || bs != cbs || idxm != idxn) {
2125: for (i = 0; i < n; i++) {
2126: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2127: }
2128: } else iidxn = iidxm;
2129: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2130: PetscCall(PetscFree2(bufr, bufc));
2131: }
2132: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2133: PetscFunctionReturn(PETSC_SUCCESS);
2134: }
2136: /*@
2137: MatGetValues - Gets a block of local values from a matrix.
2139: Not Collective; can only return values that are owned by the give process
2141: Input Parameters:
2142: + mat - the matrix
2143: . v - a logically two-dimensional array for storing the values
2144: . m - the number of rows
2145: . idxm - the global indices of the rows
2146: . n - the number of columns
2147: - idxn - the global indices of the columns
2149: Level: advanced
2151: Notes:
2152: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2153: The values, `v`, are then returned in a row-oriented format,
2154: analogous to that used by default in `MatSetValues()`.
2156: `MatGetValues()` uses 0-based row and column numbers in
2157: Fortran as well as in C.
2159: `MatGetValues()` requires that the matrix has been assembled
2160: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2161: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2162: without intermediate matrix assembly.
2164: Negative row or column indices will be ignored and those locations in `v` will be
2165: left unchanged.
2167: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2168: That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2169: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2171: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2172: @*/
2173: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2174: {
2175: PetscFunctionBegin;
2178: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2179: PetscAssertPointer(idxm, 3);
2180: PetscAssertPointer(idxn, 5);
2181: PetscAssertPointer(v, 6);
2182: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2183: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2184: MatCheckPreallocated(mat, 1);
2186: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2187: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2188: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2189: PetscFunctionReturn(PETSC_SUCCESS);
2190: }
2192: /*@
2193: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2194: defined previously by `MatSetLocalToGlobalMapping()`
2196: Not Collective
2198: Input Parameters:
2199: + mat - the matrix
2200: . nrow - number of rows
2201: . irow - the row local indices
2202: . ncol - number of columns
2203: - icol - the column local indices
2205: Output Parameter:
2206: . y - a logically two-dimensional array of values
2208: Level: advanced
2210: Notes:
2211: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2213: 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,
2214: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2215: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2216: with `MatSetLocalToGlobalMapping()`.
2218: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2219: `MatSetValuesLocal()`, `MatGetValues()`
2220: @*/
2221: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2222: {
2223: PetscFunctionBeginHot;
2226: MatCheckPreallocated(mat, 1);
2227: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2228: PetscAssertPointer(irow, 3);
2229: PetscAssertPointer(icol, 5);
2230: if (PetscDefined(USE_DEBUG)) {
2231: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2232: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2233: }
2234: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2235: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2236: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2237: else {
2238: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2239: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2240: irowm = buf;
2241: icolm = buf + nrow;
2242: } else {
2243: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2244: irowm = bufr;
2245: icolm = bufc;
2246: }
2247: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2248: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2249: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2250: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2251: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2252: PetscCall(PetscFree2(bufr, bufc));
2253: }
2254: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2255: PetscFunctionReturn(PETSC_SUCCESS);
2256: }
2258: /*@
2259: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2260: the same size. Currently, this can only be called once and creates the given matrix.
2262: Not Collective
2264: Input Parameters:
2265: + mat - the matrix
2266: . nb - the number of blocks
2267: . bs - the number of rows (and columns) in each block
2268: . rows - a concatenation of the rows for each block
2269: - v - a concatenation of logically two-dimensional arrays of values
2271: Level: advanced
2273: Notes:
2274: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2276: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2278: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2279: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2280: @*/
2281: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2282: {
2283: PetscFunctionBegin;
2286: PetscAssertPointer(rows, 4);
2287: PetscAssertPointer(v, 5);
2288: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2290: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2291: if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2292: else {
2293: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2294: }
2295: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2296: PetscFunctionReturn(PETSC_SUCCESS);
2297: }
2299: /*@
2300: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2301: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2302: using a local (per-processor) numbering.
2304: Not Collective
2306: Input Parameters:
2307: + x - the matrix
2308: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2309: - cmapping - column mapping
2311: Level: intermediate
2313: Note:
2314: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2316: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2317: @*/
2318: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2319: {
2320: PetscFunctionBegin;
2325: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2326: else {
2327: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2328: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2329: }
2330: PetscFunctionReturn(PETSC_SUCCESS);
2331: }
2333: /*@
2334: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2336: Not Collective
2338: Input Parameter:
2339: . A - the matrix
2341: Output Parameters:
2342: + rmapping - row mapping
2343: - cmapping - column mapping
2345: Level: advanced
2347: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2348: @*/
2349: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2350: {
2351: PetscFunctionBegin;
2354: if (rmapping) {
2355: PetscAssertPointer(rmapping, 2);
2356: *rmapping = A->rmap->mapping;
2357: }
2358: if (cmapping) {
2359: PetscAssertPointer(cmapping, 3);
2360: *cmapping = A->cmap->mapping;
2361: }
2362: PetscFunctionReturn(PETSC_SUCCESS);
2363: }
2365: /*@
2366: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2368: Logically Collective
2370: Input Parameters:
2371: + A - the matrix
2372: . rmap - row layout
2373: - cmap - column layout
2375: Level: advanced
2377: Note:
2378: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2380: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2381: @*/
2382: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2383: {
2384: PetscFunctionBegin;
2386: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2387: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2388: PetscFunctionReturn(PETSC_SUCCESS);
2389: }
2391: /*@
2392: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2394: Not Collective
2396: Input Parameter:
2397: . A - the matrix
2399: Output Parameters:
2400: + rmap - row layout
2401: - cmap - column layout
2403: Level: advanced
2405: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2406: @*/
2407: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2408: {
2409: PetscFunctionBegin;
2412: if (rmap) {
2413: PetscAssertPointer(rmap, 2);
2414: *rmap = A->rmap;
2415: }
2416: if (cmap) {
2417: PetscAssertPointer(cmap, 3);
2418: *cmap = A->cmap;
2419: }
2420: PetscFunctionReturn(PETSC_SUCCESS);
2421: }
2423: /*@
2424: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2425: using a local numbering of the rows and columns.
2427: Not Collective
2429: Input Parameters:
2430: + mat - the matrix
2431: . nrow - number of rows
2432: . irow - the row local indices
2433: . ncol - number of columns
2434: . icol - the column local indices
2435: . y - a logically two-dimensional array of values
2436: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2438: Level: intermediate
2440: Notes:
2441: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2443: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2444: options cannot be mixed without intervening calls to the assembly
2445: routines.
2447: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2448: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2450: Fortran Notes:
2451: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2452: .vb
2453: call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2454: .ve
2456: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2458: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2459: `MatGetValuesLocal()`
2460: @*/
2461: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2462: {
2463: PetscFunctionBeginHot;
2466: MatCheckPreallocated(mat, 1);
2467: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2468: PetscAssertPointer(irow, 3);
2469: PetscAssertPointer(icol, 5);
2470: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2471: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2472: if (PetscDefined(USE_DEBUG)) {
2473: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2474: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2475: }
2477: if (mat->assembled) {
2478: mat->was_assembled = PETSC_TRUE;
2479: mat->assembled = PETSC_FALSE;
2480: }
2481: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2482: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2483: else {
2484: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2485: const PetscInt *irowm, *icolm;
2487: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2488: bufr = buf;
2489: bufc = buf + nrow;
2490: irowm = bufr;
2491: icolm = bufc;
2492: } else {
2493: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2494: irowm = bufr;
2495: icolm = bufc;
2496: }
2497: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2498: else irowm = irow;
2499: if (mat->cmap->mapping) {
2500: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2501: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2502: } else icolm = irowm;
2503: } else icolm = icol;
2504: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2505: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2506: }
2507: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2508: PetscFunctionReturn(PETSC_SUCCESS);
2509: }
2511: /*@
2512: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2513: using a local ordering of the nodes a block at a time.
2515: Not Collective
2517: Input Parameters:
2518: + mat - the matrix
2519: . nrow - number of rows
2520: . irow - the row local indices
2521: . ncol - number of columns
2522: . icol - the column local indices
2523: . y - a logically two-dimensional array of values
2524: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2526: Level: intermediate
2528: Notes:
2529: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2530: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2532: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2533: options cannot be mixed without intervening calls to the assembly
2534: routines.
2536: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2537: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2539: Fortran Notes:
2540: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2541: .vb
2542: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2543: .ve
2545: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2547: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2548: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2549: @*/
2550: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2551: {
2552: PetscFunctionBeginHot;
2555: MatCheckPreallocated(mat, 1);
2556: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2557: PetscAssertPointer(irow, 3);
2558: PetscAssertPointer(icol, 5);
2559: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2560: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2561: if (PetscDefined(USE_DEBUG)) {
2562: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2563: 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);
2564: }
2566: if (mat->assembled) {
2567: mat->was_assembled = PETSC_TRUE;
2568: mat->assembled = PETSC_FALSE;
2569: }
2570: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2571: PetscInt irbs, rbs;
2572: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2573: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2574: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2575: }
2576: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2577: PetscInt icbs, cbs;
2578: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2579: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2580: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2581: }
2582: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2583: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2584: else {
2585: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2586: const PetscInt *irowm, *icolm;
2588: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2589: bufr = buf;
2590: bufc = buf + nrow;
2591: irowm = bufr;
2592: icolm = bufc;
2593: } else {
2594: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2595: irowm = bufr;
2596: icolm = bufc;
2597: }
2598: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2599: else irowm = irow;
2600: if (mat->cmap->mapping) {
2601: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2602: PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2603: } else icolm = irowm;
2604: } else icolm = icol;
2605: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2606: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2607: }
2608: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2609: PetscFunctionReturn(PETSC_SUCCESS);
2610: }
2612: /*@
2613: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2615: Collective
2617: Input Parameters:
2618: + mat - the matrix
2619: - x - the vector to be multiplied
2621: Output Parameter:
2622: . y - the result
2624: Level: developer
2626: Note:
2627: The vectors `x` and `y` cannot be the same. I.e., one cannot
2628: call `MatMultDiagonalBlock`(A,y,y).
2630: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2631: @*/
2632: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2633: {
2634: PetscFunctionBegin;
2640: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2641: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2642: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2643: MatCheckPreallocated(mat, 1);
2645: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2646: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2647: PetscFunctionReturn(PETSC_SUCCESS);
2648: }
2650: /*@
2651: MatMult - Computes the matrix-vector product, $y = Ax$.
2653: Neighbor-wise Collective
2655: Input Parameters:
2656: + mat - the matrix
2657: - x - the vector to be multiplied
2659: Output Parameter:
2660: . y - the result
2662: Level: beginner
2664: Note:
2665: The vectors `x` and `y` cannot be the same. I.e., one cannot
2666: call `MatMult`(A,y,y).
2668: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2669: @*/
2670: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2671: {
2672: PetscFunctionBegin;
2676: VecCheckAssembled(x);
2678: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2679: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2680: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2681: 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);
2682: 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);
2683: 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);
2684: 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);
2685: PetscCall(VecSetErrorIfLocked(y, 3));
2686: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2687: MatCheckPreallocated(mat, 1);
2689: PetscCall(VecLockReadPush(x));
2690: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2691: PetscUseTypeMethod(mat, mult, x, y);
2692: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2693: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2694: PetscCall(VecLockReadPop(x));
2695: PetscFunctionReturn(PETSC_SUCCESS);
2696: }
2698: /*@
2699: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2701: Neighbor-wise Collective
2703: Input Parameters:
2704: + mat - the matrix
2705: - x - the vector to be multiplied
2707: Output Parameter:
2708: . y - the result
2710: Level: beginner
2712: Notes:
2713: The vectors `x` and `y` cannot be the same. I.e., one cannot
2714: call `MatMultTranspose`(A,y,y).
2716: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2717: use `MatMultHermitianTranspose()`
2719: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2720: @*/
2721: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2722: {
2723: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2725: PetscFunctionBegin;
2729: VecCheckAssembled(x);
2732: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2733: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2734: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2735: 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);
2736: 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);
2737: 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);
2738: 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);
2739: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2740: MatCheckPreallocated(mat, 1);
2742: if (!mat->ops->multtranspose) {
2743: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2744: 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);
2745: } else op = mat->ops->multtranspose;
2746: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2747: PetscCall(VecLockReadPush(x));
2748: PetscCall((*op)(mat, x, y));
2749: PetscCall(VecLockReadPop(x));
2750: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2751: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2752: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2753: PetscFunctionReturn(PETSC_SUCCESS);
2754: }
2756: /*@
2757: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2759: Neighbor-wise Collective
2761: Input Parameters:
2762: + mat - the matrix
2763: - x - the vector to be multiplied
2765: Output Parameter:
2766: . y - the result
2768: Level: beginner
2770: Notes:
2771: The vectors `x` and `y` cannot be the same. I.e., one cannot
2772: call `MatMultHermitianTranspose`(A,y,y).
2774: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2776: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2778: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2779: @*/
2780: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2781: {
2782: PetscFunctionBegin;
2788: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2789: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2790: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2791: 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);
2792: 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);
2793: 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);
2794: 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);
2795: MatCheckPreallocated(mat, 1);
2797: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2798: #if defined(PETSC_USE_COMPLEX)
2799: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2800: PetscCall(VecLockReadPush(x));
2801: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2802: else PetscUseTypeMethod(mat, mult, x, y);
2803: PetscCall(VecLockReadPop(x));
2804: } else {
2805: Vec w;
2806: PetscCall(VecDuplicate(x, &w));
2807: PetscCall(VecCopy(x, w));
2808: PetscCall(VecConjugate(w));
2809: PetscCall(MatMultTranspose(mat, w, y));
2810: PetscCall(VecDestroy(&w));
2811: PetscCall(VecConjugate(y));
2812: }
2813: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2814: #else
2815: PetscCall(MatMultTranspose(mat, x, y));
2816: #endif
2817: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2818: PetscFunctionReturn(PETSC_SUCCESS);
2819: }
2821: /*@
2822: MatMultAdd - Computes $v3 = v2 + A * v1$.
2824: Neighbor-wise Collective
2826: Input Parameters:
2827: + mat - the matrix
2828: . v1 - the vector to be multiplied by `mat`
2829: - v2 - the vector to be added to the result
2831: Output Parameter:
2832: . v3 - the result
2834: Level: beginner
2836: Note:
2837: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2838: call `MatMultAdd`(A,v1,v2,v1).
2840: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2841: @*/
2842: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2843: {
2844: PetscFunctionBegin;
2851: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2852: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2853: 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);
2854: /* 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);
2855: 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); */
2856: 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);
2857: 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);
2858: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2859: MatCheckPreallocated(mat, 1);
2861: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2862: PetscCall(VecLockReadPush(v1));
2863: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2864: PetscCall(VecLockReadPop(v1));
2865: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2866: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2867: PetscFunctionReturn(PETSC_SUCCESS);
2868: }
2870: /*@
2871: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2873: Neighbor-wise Collective
2875: Input Parameters:
2876: + mat - the matrix
2877: . v1 - the vector to be multiplied by the transpose of the matrix
2878: - v2 - the vector to be added to the result
2880: Output Parameter:
2881: . v3 - the result
2883: Level: beginner
2885: Note:
2886: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2887: call `MatMultTransposeAdd`(A,v1,v2,v1).
2889: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2890: @*/
2891: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2892: {
2893: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2895: PetscFunctionBegin;
2902: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2903: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2904: 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);
2905: 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);
2906: 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);
2907: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2908: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2909: MatCheckPreallocated(mat, 1);
2911: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2912: PetscCall(VecLockReadPush(v1));
2913: PetscCall((*op)(mat, v1, v2, v3));
2914: PetscCall(VecLockReadPop(v1));
2915: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2916: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2917: PetscFunctionReturn(PETSC_SUCCESS);
2918: }
2920: /*@
2921: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2923: Neighbor-wise Collective
2925: Input Parameters:
2926: + mat - the matrix
2927: . v1 - the vector to be multiplied by the Hermitian transpose
2928: - v2 - the vector to be added to the result
2930: Output Parameter:
2931: . v3 - the result
2933: Level: beginner
2935: Note:
2936: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2937: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2939: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2940: @*/
2941: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2942: {
2943: PetscFunctionBegin;
2950: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2951: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2952: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2953: 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);
2954: 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);
2955: 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);
2956: MatCheckPreallocated(mat, 1);
2958: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2959: PetscCall(VecLockReadPush(v1));
2960: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2961: else {
2962: Vec w, z;
2963: PetscCall(VecDuplicate(v1, &w));
2964: PetscCall(VecCopy(v1, w));
2965: PetscCall(VecConjugate(w));
2966: PetscCall(VecDuplicate(v3, &z));
2967: PetscCall(MatMultTranspose(mat, w, z));
2968: PetscCall(VecDestroy(&w));
2969: PetscCall(VecConjugate(z));
2970: if (v2 != v3) {
2971: PetscCall(VecWAXPY(v3, 1.0, v2, z));
2972: } else {
2973: PetscCall(VecAXPY(v3, 1.0, z));
2974: }
2975: PetscCall(VecDestroy(&z));
2976: }
2977: PetscCall(VecLockReadPop(v1));
2978: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2979: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2980: PetscFunctionReturn(PETSC_SUCCESS);
2981: }
2983: /*@
2984: MatGetFactorType - gets the type of factorization a matrix is
2986: Not Collective
2988: Input Parameter:
2989: . mat - the matrix
2991: Output Parameter:
2992: . 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`
2994: Level: intermediate
2996: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
2997: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
2998: @*/
2999: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3000: {
3001: PetscFunctionBegin;
3004: PetscAssertPointer(t, 2);
3005: *t = mat->factortype;
3006: PetscFunctionReturn(PETSC_SUCCESS);
3007: }
3009: /*@
3010: MatSetFactorType - sets the type of factorization a matrix is
3012: Logically Collective
3014: Input Parameters:
3015: + mat - the matrix
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()`, `MatGetFactorType()`, `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 MatSetFactorType(Mat mat, MatFactorType t)
3024: {
3025: PetscFunctionBegin;
3028: mat->factortype = t;
3029: PetscFunctionReturn(PETSC_SUCCESS);
3030: }
3032: /*@
3033: MatGetInfo - Returns information about matrix storage (number of
3034: nonzeros, memory, etc.).
3036: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3038: Input Parameters:
3039: + mat - the matrix
3040: - 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)
3042: Output Parameter:
3043: . info - matrix information context
3045: Options Database Key:
3046: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3048: Level: intermediate
3050: Notes:
3051: The `MatInfo` context contains a variety of matrix data, including
3052: number of nonzeros allocated and used, number of mallocs during
3053: matrix assembly, etc. Additional information for factored matrices
3054: is provided (such as the fill ratio, number of mallocs during
3055: factorization, etc.).
3057: Example:
3058: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3059: data within the `MatInfo` context. For example,
3060: .vb
3061: MatInfo info;
3062: Mat A;
3063: double mal, nz_a, nz_u;
3065: MatGetInfo(A, MAT_LOCAL, &info);
3066: mal = info.mallocs;
3067: nz_a = info.nz_allocated;
3068: .ve
3070: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3071: @*/
3072: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3073: {
3074: PetscFunctionBegin;
3077: PetscAssertPointer(info, 3);
3078: MatCheckPreallocated(mat, 1);
3079: PetscUseTypeMethod(mat, getinfo, flag, info);
3080: PetscFunctionReturn(PETSC_SUCCESS);
3081: }
3083: /*
3084: This is used by external packages where it is not easy to get the info from the actual
3085: matrix factorization.
3086: */
3087: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3088: {
3089: PetscFunctionBegin;
3090: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3091: PetscFunctionReturn(PETSC_SUCCESS);
3092: }
3094: /*@
3095: MatLUFactor - Performs in-place LU factorization of matrix.
3097: Collective
3099: Input Parameters:
3100: + mat - the matrix
3101: . row - row permutation
3102: . col - column permutation
3103: - info - options for factorization, includes
3104: .vb
3105: fill - expected fill as ratio of original fill.
3106: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3107: Run with the option -info to determine an optimal value to use
3108: .ve
3110: Level: developer
3112: Notes:
3113: Most users should employ the `KSP` interface for linear solvers
3114: instead of working directly with matrix algebra routines such as this.
3115: See, e.g., `KSPCreate()`.
3117: This changes the state of the matrix to a factored matrix; it cannot be used
3118: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3120: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3121: when not using `KSP`.
3123: Fortran Note:
3124: A valid (non-null) `info` argument must be provided
3126: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3127: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3128: @*/
3129: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3130: {
3131: MatFactorInfo tinfo;
3133: PetscFunctionBegin;
3137: if (info) PetscAssertPointer(info, 4);
3139: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3140: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3141: MatCheckPreallocated(mat, 1);
3142: if (!info) {
3143: PetscCall(MatFactorInfoInitialize(&tinfo));
3144: info = &tinfo;
3145: }
3147: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3148: PetscUseTypeMethod(mat, lufactor, row, col, info);
3149: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3150: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3151: PetscFunctionReturn(PETSC_SUCCESS);
3152: }
3154: /*@
3155: MatILUFactor - Performs in-place ILU factorization of matrix.
3157: Collective
3159: Input Parameters:
3160: + mat - the matrix
3161: . row - row permutation
3162: . col - column permutation
3163: - info - structure containing
3164: .vb
3165: levels - number of levels of fill.
3166: expected fill - as ratio of original fill.
3167: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3168: missing diagonal entries)
3169: .ve
3171: Level: developer
3173: Notes:
3174: Most users should employ the `KSP` interface for linear solvers
3175: instead of working directly with matrix algebra routines such as this.
3176: See, e.g., `KSPCreate()`.
3178: Probably really in-place only when level of fill is zero, otherwise allocates
3179: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3180: when not using `KSP`.
3182: Fortran Note:
3183: A valid (non-null) `info` argument must be provided
3185: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3186: @*/
3187: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3188: {
3189: PetscFunctionBegin;
3193: PetscAssertPointer(info, 4);
3195: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3196: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3197: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3198: MatCheckPreallocated(mat, 1);
3200: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3201: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3202: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3203: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3204: PetscFunctionReturn(PETSC_SUCCESS);
3205: }
3207: /*@
3208: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3209: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3211: Collective
3213: Input Parameters:
3214: + fact - the factor matrix obtained with `MatGetFactor()`
3215: . mat - the matrix
3216: . row - the row permutation
3217: . col - the column permutation
3218: - info - options for factorization, includes
3219: .vb
3220: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3221: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3222: .ve
3224: Level: developer
3226: Notes:
3227: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3229: Most users should employ the simplified `KSP` interface for linear solvers
3230: instead of working directly with matrix algebra routines such as this.
3231: See, e.g., `KSPCreate()`.
3233: Fortran Note:
3234: A valid (non-null) `info` argument must be provided
3236: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3237: @*/
3238: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3239: {
3240: MatFactorInfo tinfo;
3242: PetscFunctionBegin;
3247: if (info) PetscAssertPointer(info, 5);
3250: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3251: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3252: MatCheckPreallocated(mat, 2);
3253: if (!info) {
3254: PetscCall(MatFactorInfoInitialize(&tinfo));
3255: info = &tinfo;
3256: }
3258: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3259: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3260: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3261: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3262: PetscFunctionReturn(PETSC_SUCCESS);
3263: }
3265: /*@
3266: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3267: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3269: Collective
3271: Input Parameters:
3272: + fact - the factor matrix obtained with `MatGetFactor()`
3273: . mat - the matrix
3274: - info - options for factorization
3276: Level: developer
3278: Notes:
3279: See `MatLUFactor()` for in-place factorization. See
3280: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3282: Most users should employ the `KSP` interface for linear solvers
3283: instead of working directly with matrix algebra routines such as this.
3284: See, e.g., `KSPCreate()`.
3286: Fortran Note:
3287: A valid (non-null) `info` argument must be provided
3289: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3290: @*/
3291: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3292: {
3293: MatFactorInfo tinfo;
3295: PetscFunctionBegin;
3300: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3301: 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,
3302: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3304: MatCheckPreallocated(mat, 2);
3305: if (!info) {
3306: PetscCall(MatFactorInfoInitialize(&tinfo));
3307: info = &tinfo;
3308: }
3310: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3311: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3312: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3313: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3314: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3315: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3316: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3317: PetscFunctionReturn(PETSC_SUCCESS);
3318: }
3320: /*@
3321: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3322: symmetric matrix.
3324: Collective
3326: Input Parameters:
3327: + mat - the matrix
3328: . perm - row and column permutations
3329: - info - expected fill as ratio of original fill
3331: Level: developer
3333: Notes:
3334: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3335: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3337: Most users should employ the `KSP` interface for linear solvers
3338: instead of working directly with matrix algebra routines such as this.
3339: See, e.g., `KSPCreate()`.
3341: Fortran Note:
3342: A valid (non-null) `info` argument must be provided
3344: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3345: `MatGetOrdering()`
3346: @*/
3347: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3348: {
3349: MatFactorInfo tinfo;
3351: PetscFunctionBegin;
3354: if (info) PetscAssertPointer(info, 3);
3356: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3357: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3358: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3359: MatCheckPreallocated(mat, 1);
3360: if (!info) {
3361: PetscCall(MatFactorInfoInitialize(&tinfo));
3362: info = &tinfo;
3363: }
3365: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3366: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3367: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3368: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3369: PetscFunctionReturn(PETSC_SUCCESS);
3370: }
3372: /*@
3373: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3374: of a symmetric matrix.
3376: Collective
3378: Input Parameters:
3379: + fact - the factor matrix obtained with `MatGetFactor()`
3380: . mat - the matrix
3381: . perm - row and column permutations
3382: - info - options for factorization, includes
3383: .vb
3384: fill - expected fill as ratio of original fill.
3385: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3386: Run with the option -info to determine an optimal value to use
3387: .ve
3389: Level: developer
3391: Notes:
3392: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3393: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3395: Most users should employ the `KSP` interface for linear solvers
3396: instead of working directly with matrix algebra routines such as this.
3397: See, e.g., `KSPCreate()`.
3399: Fortran Note:
3400: A valid (non-null) `info` argument must be provided
3402: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3403: `MatGetOrdering()`
3404: @*/
3405: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3406: {
3407: MatFactorInfo tinfo;
3409: PetscFunctionBegin;
3413: if (info) PetscAssertPointer(info, 4);
3416: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3417: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3418: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3419: MatCheckPreallocated(mat, 2);
3420: if (!info) {
3421: PetscCall(MatFactorInfoInitialize(&tinfo));
3422: info = &tinfo;
3423: }
3425: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3426: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3427: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3428: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3429: PetscFunctionReturn(PETSC_SUCCESS);
3430: }
3432: /*@
3433: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3434: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3435: `MatCholeskyFactorSymbolic()`.
3437: Collective
3439: Input Parameters:
3440: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3441: . mat - the initial matrix that is to be factored
3442: - info - options for factorization
3444: Level: developer
3446: Note:
3447: Most users should employ the `KSP` interface for linear solvers
3448: instead of working directly with matrix algebra routines such as this.
3449: See, e.g., `KSPCreate()`.
3451: Fortran Note:
3452: A valid (non-null) `info` argument must be provided
3454: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3455: @*/
3456: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3457: {
3458: MatFactorInfo tinfo;
3460: PetscFunctionBegin;
3465: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3466: 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,
3467: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3468: MatCheckPreallocated(mat, 2);
3469: if (!info) {
3470: PetscCall(MatFactorInfoInitialize(&tinfo));
3471: info = &tinfo;
3472: }
3474: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3475: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3476: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3477: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3478: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3479: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3480: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3481: PetscFunctionReturn(PETSC_SUCCESS);
3482: }
3484: /*@
3485: MatQRFactor - Performs in-place QR factorization of matrix.
3487: Collective
3489: Input Parameters:
3490: + mat - the matrix
3491: . col - column permutation
3492: - info - options for factorization, includes
3493: .vb
3494: fill - expected fill as ratio of original fill.
3495: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3496: Run with the option -info to determine an optimal value to use
3497: .ve
3499: Level: developer
3501: Notes:
3502: Most users should employ the `KSP` interface for linear solvers
3503: instead of working directly with matrix algebra routines such as this.
3504: See, e.g., `KSPCreate()`.
3506: This changes the state of the matrix to a factored matrix; it cannot be used
3507: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3509: Fortran Note:
3510: A valid (non-null) `info` argument must be provided
3512: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3513: `MatSetUnfactored()`
3514: @*/
3515: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3516: {
3517: PetscFunctionBegin;
3520: if (info) PetscAssertPointer(info, 3);
3522: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3523: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3524: MatCheckPreallocated(mat, 1);
3525: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3526: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3527: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3528: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3529: PetscFunctionReturn(PETSC_SUCCESS);
3530: }
3532: /*@
3533: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3534: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3536: Collective
3538: Input Parameters:
3539: + fact - the factor matrix obtained with `MatGetFactor()`
3540: . mat - the matrix
3541: . col - column permutation
3542: - info - options for factorization, includes
3543: .vb
3544: fill - expected fill as ratio of original fill.
3545: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3546: Run with the option -info to determine an optimal value to use
3547: .ve
3549: Level: developer
3551: Note:
3552: Most users should employ the `KSP` interface for linear solvers
3553: instead of working directly with matrix algebra routines such as this.
3554: See, e.g., `KSPCreate()`.
3556: Fortran Note:
3557: A valid (non-null) `info` argument must be provided
3559: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3560: @*/
3561: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3562: {
3563: MatFactorInfo tinfo;
3565: PetscFunctionBegin;
3569: if (info) PetscAssertPointer(info, 4);
3572: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3573: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3574: MatCheckPreallocated(mat, 2);
3575: if (!info) {
3576: PetscCall(MatFactorInfoInitialize(&tinfo));
3577: info = &tinfo;
3578: }
3580: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3581: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3582: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3583: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3584: PetscFunctionReturn(PETSC_SUCCESS);
3585: }
3587: /*@
3588: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3589: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3591: Collective
3593: Input Parameters:
3594: + fact - the factor matrix obtained with `MatGetFactor()`
3595: . mat - the matrix
3596: - info - options for factorization
3598: Level: developer
3600: Notes:
3601: See `MatQRFactor()` for in-place factorization.
3603: Most users should employ the `KSP` interface for linear solvers
3604: instead of working directly with matrix algebra routines such as this.
3605: See, e.g., `KSPCreate()`.
3607: Fortran Note:
3608: A valid (non-null) `info` argument must be provided
3610: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3611: @*/
3612: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3613: {
3614: MatFactorInfo tinfo;
3616: PetscFunctionBegin;
3621: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3622: 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,
3623: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3625: MatCheckPreallocated(mat, 2);
3626: if (!info) {
3627: PetscCall(MatFactorInfoInitialize(&tinfo));
3628: info = &tinfo;
3629: }
3631: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3632: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3633: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3634: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3635: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3636: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3637: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3638: PetscFunctionReturn(PETSC_SUCCESS);
3639: }
3641: /*@
3642: MatSolve - Solves $A x = b$, given a factored matrix.
3644: Neighbor-wise Collective
3646: Input Parameters:
3647: + mat - the factored matrix
3648: - b - the right-hand-side vector
3650: Output Parameter:
3651: . x - the result vector
3653: Level: developer
3655: Notes:
3656: The vectors `b` and `x` cannot be the same. I.e., one cannot
3657: call `MatSolve`(A,x,x).
3659: Most users should employ the `KSP` interface for linear solvers
3660: instead of working directly with matrix algebra routines such as this.
3661: See, e.g., `KSPCreate()`.
3663: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3664: @*/
3665: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3666: {
3667: PetscFunctionBegin;
3672: PetscCheckSameComm(mat, 1, b, 2);
3673: PetscCheckSameComm(mat, 1, x, 3);
3674: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3675: 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);
3676: 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);
3677: 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);
3678: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3679: MatCheckPreallocated(mat, 1);
3681: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3682: PetscCall(VecFlag(x, mat->factorerrortype));
3683: if (mat->factorerrortype) {
3684: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3685: } else PetscUseTypeMethod(mat, solve, b, x);
3686: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3687: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3688: PetscFunctionReturn(PETSC_SUCCESS);
3689: }
3691: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3692: {
3693: Vec b, x;
3694: PetscInt N, i;
3695: PetscErrorCode (*f)(Mat, Vec, Vec);
3696: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3698: PetscFunctionBegin;
3699: if (A->factorerrortype) {
3700: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3701: PetscCall(MatSetInf(X));
3702: PetscFunctionReturn(PETSC_SUCCESS);
3703: }
3704: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3705: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3706: PetscCall(MatBoundToCPU(A, &Abound));
3707: if (!Abound) {
3708: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3709: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3710: }
3711: #if PetscDefined(HAVE_CUDA)
3712: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3713: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3714: #elif PetscDefined(HAVE_HIP)
3715: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3716: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3717: #endif
3718: PetscCall(MatGetSize(B, NULL, &N));
3719: for (i = 0; i < N; i++) {
3720: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3721: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3722: PetscCall((*f)(A, b, x));
3723: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3724: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3725: }
3726: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3727: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3728: PetscFunctionReturn(PETSC_SUCCESS);
3729: }
3731: /*@
3732: MatMatSolve - Solves $A X = B$, given a factored matrix.
3734: Neighbor-wise Collective
3736: Input Parameters:
3737: + A - the factored matrix
3738: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3740: Output Parameter:
3741: . X - the result matrix (dense matrix)
3743: Level: developer
3745: Note:
3746: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3747: otherwise, `B` and `X` cannot be the same.
3749: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3750: @*/
3751: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3752: {
3753: PetscFunctionBegin;
3758: PetscCheckSameComm(A, 1, B, 2);
3759: PetscCheckSameComm(A, 1, X, 3);
3760: 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);
3761: 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);
3762: 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");
3763: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3764: MatCheckPreallocated(A, 1);
3766: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3767: if (!A->ops->matsolve) {
3768: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3769: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3770: } else PetscUseTypeMethod(A, matsolve, B, X);
3771: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3772: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3773: PetscFunctionReturn(PETSC_SUCCESS);
3774: }
3776: /*@
3777: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3779: Neighbor-wise Collective
3781: Input Parameters:
3782: + A - the factored matrix
3783: - B - the right-hand-side matrix (`MATDENSE` matrix)
3785: Output Parameter:
3786: . X - the result matrix (dense matrix)
3788: Level: developer
3790: Note:
3791: The matrices `B` and `X` cannot be the same. I.e., one cannot
3792: call `MatMatSolveTranspose`(A,X,X).
3794: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3795: @*/
3796: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3797: {
3798: PetscFunctionBegin;
3803: PetscCheckSameComm(A, 1, B, 2);
3804: PetscCheckSameComm(A, 1, X, 3);
3805: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3806: 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);
3807: 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);
3808: 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);
3809: 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");
3810: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3811: MatCheckPreallocated(A, 1);
3813: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3814: if (!A->ops->matsolvetranspose) {
3815: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3816: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3817: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3818: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3819: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3820: PetscFunctionReturn(PETSC_SUCCESS);
3821: }
3823: /*@
3824: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3826: Neighbor-wise Collective
3828: Input Parameters:
3829: + A - the factored matrix
3830: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3832: Output Parameter:
3833: . X - the result matrix (dense matrix)
3835: Level: developer
3837: Note:
3838: 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
3839: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3841: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3842: @*/
3843: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3844: {
3845: PetscFunctionBegin;
3850: PetscCheckSameComm(A, 1, Bt, 2);
3851: PetscCheckSameComm(A, 1, X, 3);
3853: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3854: 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);
3855: 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);
3856: 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");
3857: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3858: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3859: MatCheckPreallocated(A, 1);
3861: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3862: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3863: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3864: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3865: PetscFunctionReturn(PETSC_SUCCESS);
3866: }
3868: /*@
3869: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3870: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3872: Neighbor-wise Collective
3874: Input Parameters:
3875: + mat - the factored matrix
3876: - b - the right-hand-side vector
3878: Output Parameter:
3879: . x - the result vector
3881: Level: developer
3883: Notes:
3884: `MatSolve()` should be used for most applications, as it performs
3885: a forward solve followed by a backward solve.
3887: The vectors `b` and `x` cannot be the same, i.e., one cannot
3888: call `MatForwardSolve`(A,x,x).
3890: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3891: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3892: `MatForwardSolve()` solves $U^T*D y = b$, and
3893: `MatBackwardSolve()` solves $U x = y$.
3894: Thus they do not provide a symmetric preconditioner.
3896: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3897: @*/
3898: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3899: {
3900: PetscFunctionBegin;
3905: PetscCheckSameComm(mat, 1, b, 2);
3906: PetscCheckSameComm(mat, 1, x, 3);
3907: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3908: 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);
3909: 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);
3910: 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);
3911: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3912: MatCheckPreallocated(mat, 1);
3914: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3915: PetscUseTypeMethod(mat, forwardsolve, b, x);
3916: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3917: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3918: PetscFunctionReturn(PETSC_SUCCESS);
3919: }
3921: /*@
3922: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3923: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3925: Neighbor-wise Collective
3927: Input Parameters:
3928: + mat - the factored matrix
3929: - b - the right-hand-side vector
3931: Output Parameter:
3932: . x - the result vector
3934: Level: developer
3936: Notes:
3937: `MatSolve()` should be used for most applications, as it performs
3938: a forward solve followed by a backward solve.
3940: The vectors `b` and `x` cannot be the same. I.e., one cannot
3941: call `MatBackwardSolve`(A,x,x).
3943: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3944: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3945: `MatForwardSolve()` solves $U^T*D y = b$, and
3946: `MatBackwardSolve()` solves $U x = y$.
3947: Thus they do not provide a symmetric preconditioner.
3949: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3950: @*/
3951: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3952: {
3953: PetscFunctionBegin;
3958: PetscCheckSameComm(mat, 1, b, 2);
3959: PetscCheckSameComm(mat, 1, x, 3);
3960: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3961: 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);
3962: 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);
3963: 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);
3964: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3965: MatCheckPreallocated(mat, 1);
3967: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3968: PetscUseTypeMethod(mat, backwardsolve, b, x);
3969: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3970: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3971: PetscFunctionReturn(PETSC_SUCCESS);
3972: }
3974: /*@
3975: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
3977: Neighbor-wise Collective
3979: Input Parameters:
3980: + mat - the factored matrix
3981: . b - the right-hand-side vector
3982: - y - the vector to be added to
3984: Output Parameter:
3985: . x - the result vector
3987: Level: developer
3989: Note:
3990: The vectors `b` and `x` cannot be the same. I.e., one cannot
3991: call `MatSolveAdd`(A,x,y,x).
3993: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3994: @*/
3995: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
3996: {
3997: PetscScalar one = 1.0;
3998: Vec tmp;
4000: PetscFunctionBegin;
4006: PetscCheckSameComm(mat, 1, b, 2);
4007: PetscCheckSameComm(mat, 1, y, 3);
4008: PetscCheckSameComm(mat, 1, x, 4);
4009: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4010: 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);
4011: 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);
4012: 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);
4013: 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);
4014: 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);
4015: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4016: MatCheckPreallocated(mat, 1);
4018: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4019: PetscCall(VecFlag(x, mat->factorerrortype));
4020: if (mat->factorerrortype) {
4021: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4022: } else if (mat->ops->solveadd) {
4023: PetscUseTypeMethod(mat, solveadd, b, y, x);
4024: } else {
4025: /* do the solve then the add manually */
4026: if (x != y) {
4027: PetscCall(MatSolve(mat, b, x));
4028: PetscCall(VecAXPY(x, one, y));
4029: } else {
4030: PetscCall(VecDuplicate(x, &tmp));
4031: PetscCall(VecCopy(x, tmp));
4032: PetscCall(MatSolve(mat, b, x));
4033: PetscCall(VecAXPY(x, one, tmp));
4034: PetscCall(VecDestroy(&tmp));
4035: }
4036: }
4037: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4038: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4039: PetscFunctionReturn(PETSC_SUCCESS);
4040: }
4042: /*@
4043: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4045: Neighbor-wise Collective
4047: Input Parameters:
4048: + mat - the factored matrix
4049: - b - the right-hand-side vector
4051: Output Parameter:
4052: . x - the result vector
4054: Level: developer
4056: Notes:
4057: The vectors `b` and `x` cannot be the same. I.e., one cannot
4058: call `MatSolveTranspose`(A,x,x).
4060: Most users should employ the `KSP` interface for linear solvers
4061: instead of working directly with matrix algebra routines such as this.
4062: See, e.g., `KSPCreate()`.
4064: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4065: @*/
4066: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4067: {
4068: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4070: PetscFunctionBegin;
4075: PetscCheckSameComm(mat, 1, b, 2);
4076: PetscCheckSameComm(mat, 1, x, 3);
4077: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4078: 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);
4079: 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);
4080: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4081: MatCheckPreallocated(mat, 1);
4082: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4083: PetscCall(VecFlag(x, mat->factorerrortype));
4084: if (mat->factorerrortype) {
4085: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4086: } else {
4087: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4088: PetscCall((*f)(mat, b, x));
4089: }
4090: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4091: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4092: PetscFunctionReturn(PETSC_SUCCESS);
4093: }
4095: /*@
4096: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4097: factored matrix.
4099: Neighbor-wise Collective
4101: Input Parameters:
4102: + mat - the factored matrix
4103: . b - the right-hand-side vector
4104: - y - the vector to be added to
4106: Output Parameter:
4107: . x - the result vector
4109: Level: developer
4111: Note:
4112: The vectors `b` and `x` cannot be the same. I.e., one cannot
4113: call `MatSolveTransposeAdd`(A,x,y,x).
4115: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4116: @*/
4117: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4118: {
4119: PetscScalar one = 1.0;
4120: Vec tmp;
4121: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4123: PetscFunctionBegin;
4129: PetscCheckSameComm(mat, 1, b, 2);
4130: PetscCheckSameComm(mat, 1, y, 3);
4131: PetscCheckSameComm(mat, 1, x, 4);
4132: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4133: 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);
4134: 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);
4135: 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);
4136: 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);
4137: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4138: MatCheckPreallocated(mat, 1);
4140: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4141: PetscCall(VecFlag(x, mat->factorerrortype));
4142: if (mat->factorerrortype) {
4143: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4144: } else if (f) {
4145: PetscCall((*f)(mat, b, y, x));
4146: } else {
4147: /* do the solve then the add manually */
4148: if (x != y) {
4149: PetscCall(MatSolveTranspose(mat, b, x));
4150: PetscCall(VecAXPY(x, one, y));
4151: } else {
4152: PetscCall(VecDuplicate(x, &tmp));
4153: PetscCall(VecCopy(x, tmp));
4154: PetscCall(MatSolveTranspose(mat, b, x));
4155: PetscCall(VecAXPY(x, one, tmp));
4156: PetscCall(VecDestroy(&tmp));
4157: }
4158: }
4159: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4160: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4161: PetscFunctionReturn(PETSC_SUCCESS);
4162: }
4164: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4165: /*@
4166: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4168: Neighbor-wise Collective
4170: Input Parameters:
4171: + mat - the matrix
4172: . b - the right-hand side
4173: . omega - the relaxation factor
4174: . flag - flag indicating the type of SOR (see below)
4175: . shift - diagonal shift
4176: . its - the number of iterations
4177: - lits - the number of local iterations
4179: Output Parameter:
4180: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4182: SOR Flags:
4183: + `SOR_FORWARD_SWEEP` - forward SOR
4184: . `SOR_BACKWARD_SWEEP` - backward SOR
4185: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4186: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4187: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4188: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4189: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4190: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4191: upper/lower triangular part of matrix to
4192: vector (with omega)
4193: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4195: Level: developer
4197: Notes:
4198: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4199: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4200: on each processor.
4202: Application programmers will not generally use `MatSOR()` directly,
4203: but instead will employ the `KSP`/`PC` interface.
4205: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing
4207: Most users should employ the `KSP` interface for linear solvers
4208: instead of working directly with matrix algebra routines such as this.
4209: See, e.g., `KSPCreate()`.
4211: Vectors `x` and `b` CANNOT be the same
4213: The flags are implemented as bitwise inclusive or operations.
4214: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4215: to specify a zero initial guess for SSOR.
4217: Developer Note:
4218: We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes
4220: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4221: @*/
4222: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4223: {
4224: PetscFunctionBegin;
4229: PetscCheckSameComm(mat, 1, b, 2);
4230: PetscCheckSameComm(mat, 1, x, 8);
4231: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4232: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4233: 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);
4234: 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);
4235: 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);
4236: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4237: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4238: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4240: MatCheckPreallocated(mat, 1);
4241: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4242: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4243: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4244: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4245: PetscFunctionReturn(PETSC_SUCCESS);
4246: }
4248: /*
4249: Default matrix copy routine.
4250: */
4251: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4252: {
4253: PetscInt i, rstart = 0, rend = 0, nz;
4254: const PetscInt *cwork;
4255: const PetscScalar *vwork;
4257: PetscFunctionBegin;
4258: if (B->assembled) PetscCall(MatZeroEntries(B));
4259: if (str == SAME_NONZERO_PATTERN) {
4260: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4261: for (i = rstart; i < rend; i++) {
4262: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4263: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4264: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4265: }
4266: } else {
4267: PetscCall(MatAYPX(B, 0.0, A, str));
4268: }
4269: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4270: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4271: PetscFunctionReturn(PETSC_SUCCESS);
4272: }
4274: /*@
4275: MatCopy - Copies a matrix to another matrix.
4277: Collective
4279: Input Parameters:
4280: + A - the matrix
4281: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4283: Output Parameter:
4284: . B - where the copy is put
4286: Level: intermediate
4288: Notes:
4289: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4291: `MatCopy()` copies the matrix entries of a matrix to another existing
4292: matrix (after first zeroing the second matrix). A related routine is
4293: `MatConvert()`, which first creates a new matrix and then copies the data.
4295: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4296: @*/
4297: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4298: {
4299: PetscInt i;
4301: PetscFunctionBegin;
4306: PetscCheckSameComm(A, 1, B, 2);
4307: MatCheckPreallocated(B, 2);
4308: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4309: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4310: 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,
4311: A->cmap->N, B->cmap->N);
4312: MatCheckPreallocated(A, 1);
4313: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4315: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4316: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4317: else PetscCall(MatCopy_Basic(A, B, str));
4319: B->stencil.dim = A->stencil.dim;
4320: B->stencil.noc = A->stencil.noc;
4321: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4322: B->stencil.dims[i] = A->stencil.dims[i];
4323: B->stencil.starts[i] = A->stencil.starts[i];
4324: }
4326: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4327: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4328: PetscFunctionReturn(PETSC_SUCCESS);
4329: }
4331: /*@
4332: MatConvert - Converts a matrix to another matrix, either of the same
4333: or different type.
4335: Collective
4337: Input Parameters:
4338: + mat - the matrix
4339: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4340: same type as the original matrix.
4341: - reuse - denotes if the destination matrix is to be created or reused.
4342: 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
4343: `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).
4345: Output Parameter:
4346: . M - pointer to place new matrix
4348: Level: intermediate
4350: Notes:
4351: `MatConvert()` first creates a new matrix and then copies the data from
4352: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4353: entries of one matrix to another already existing matrix context.
4355: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4356: the MPI communicator of the generated matrix is always the same as the communicator
4357: of the input matrix.
4359: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4360: @*/
4361: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4362: {
4363: PetscBool sametype, issame, flg;
4364: PetscBool3 issymmetric, ishermitian;
4365: char convname[256], mtype[256];
4366: Mat B;
4368: PetscFunctionBegin;
4371: PetscAssertPointer(M, 4);
4372: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4373: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4374: MatCheckPreallocated(mat, 1);
4376: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4377: if (flg) newtype = mtype;
4379: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4380: PetscCall(PetscStrcmp(newtype, "same", &issame));
4381: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4382: if (reuse == MAT_REUSE_MATRIX) {
4384: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4385: }
4387: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4388: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4389: PetscFunctionReturn(PETSC_SUCCESS);
4390: }
4392: /* Cache Mat options because some converters use MatHeaderReplace */
4393: issymmetric = mat->symmetric;
4394: ishermitian = mat->hermitian;
4396: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4397: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4398: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4399: } else {
4400: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4401: const char *prefix[3] = {"seq", "mpi", ""};
4402: PetscInt i;
4403: /*
4404: Order of precedence:
4405: 0) See if newtype is a superclass of the current matrix.
4406: 1) See if a specialized converter is known to the current matrix.
4407: 2) See if a specialized converter is known to the desired matrix class.
4408: 3) See if a good general converter is registered for the desired class
4409: (as of 6/27/03 only MATMPIADJ falls into this category).
4410: 4) See if a good general converter is known for the current matrix.
4411: 5) Use a really basic converter.
4412: */
4414: /* 0) See if newtype is a superclass of the current matrix.
4415: i.e mat is mpiaij and newtype is aij */
4416: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4417: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4418: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4419: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4420: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4421: if (flg) {
4422: if (reuse == MAT_INPLACE_MATRIX) {
4423: PetscCall(PetscInfo(mat, "Early return\n"));
4424: PetscFunctionReturn(PETSC_SUCCESS);
4425: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4426: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4427: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4428: PetscFunctionReturn(PETSC_SUCCESS);
4429: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4430: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4431: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4432: PetscFunctionReturn(PETSC_SUCCESS);
4433: }
4434: }
4435: }
4436: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4437: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4438: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4439: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4440: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4441: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4442: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4443: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4444: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4445: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4446: if (conv) goto foundconv;
4447: }
4449: /* 2) See if a specialized converter is known to the desired matrix class. */
4450: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4451: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4452: PetscCall(MatSetType(B, newtype));
4453: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4454: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4455: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4456: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4457: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4458: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4459: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4460: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4461: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4462: if (conv) {
4463: PetscCall(MatDestroy(&B));
4464: goto foundconv;
4465: }
4466: }
4468: /* 3) See if a good general converter is registered for the desired class */
4469: conv = B->ops->convertfrom;
4470: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4471: PetscCall(MatDestroy(&B));
4472: if (conv) goto foundconv;
4474: /* 4) See if a good general converter is known for the current matrix */
4475: if (mat->ops->convert) conv = mat->ops->convert;
4476: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4477: if (conv) goto foundconv;
4479: /* 5) Use a really basic converter. */
4480: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4481: conv = MatConvert_Basic;
4483: foundconv:
4484: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4485: PetscCall((*conv)(mat, newtype, reuse, M));
4486: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4487: /* the block sizes must be same if the mappings are copied over */
4488: (*M)->rmap->bs = mat->rmap->bs;
4489: (*M)->cmap->bs = mat->cmap->bs;
4490: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4491: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4492: (*M)->rmap->mapping = mat->rmap->mapping;
4493: (*M)->cmap->mapping = mat->cmap->mapping;
4494: }
4495: (*M)->stencil.dim = mat->stencil.dim;
4496: (*M)->stencil.noc = mat->stencil.noc;
4497: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4498: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4499: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4500: }
4501: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4502: }
4503: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4505: /* Copy Mat options */
4506: if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4507: else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4508: if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4509: else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4510: PetscFunctionReturn(PETSC_SUCCESS);
4511: }
4513: /*@
4514: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4516: Not Collective
4518: Input Parameter:
4519: . mat - the matrix, must be a factored matrix
4521: Output Parameter:
4522: . type - the string name of the package (do not free this string)
4524: Level: intermediate
4526: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4527: @*/
4528: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4529: {
4530: PetscErrorCode (*conv)(Mat, MatSolverType *);
4532: PetscFunctionBegin;
4535: PetscAssertPointer(type, 2);
4536: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4537: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4538: if (conv) PetscCall((*conv)(mat, type));
4539: else *type = MATSOLVERPETSC;
4540: PetscFunctionReturn(PETSC_SUCCESS);
4541: }
4543: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4544: struct _MatSolverTypeForSpecifcType {
4545: MatType mtype;
4546: /* no entry for MAT_FACTOR_NONE */
4547: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4548: MatSolverTypeForSpecifcType next;
4549: };
4551: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4552: struct _MatSolverTypeHolder {
4553: char *name;
4554: MatSolverTypeForSpecifcType handlers;
4555: MatSolverTypeHolder next;
4556: };
4558: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4560: /*@C
4561: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4563: Logically Collective, No Fortran Support
4565: Input Parameters:
4566: + package - name of the package, for example `petsc` or `superlu`
4567: . mtype - the matrix type that works with this package
4568: . ftype - the type of factorization supported by the package
4569: - createfactor - routine that will create the factored matrix ready to be used
4571: Level: developer
4573: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4574: `MatGetFactor()`
4575: @*/
4576: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4577: {
4578: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4579: PetscBool flg;
4580: MatSolverTypeForSpecifcType inext, iprev = NULL;
4582: PetscFunctionBegin;
4583: PetscCall(MatInitializePackage());
4584: if (!next) {
4585: PetscCall(PetscNew(&MatSolverTypeHolders));
4586: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4587: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4588: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4589: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4590: PetscFunctionReturn(PETSC_SUCCESS);
4591: }
4592: while (next) {
4593: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4594: if (flg) {
4595: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4596: inext = next->handlers;
4597: while (inext) {
4598: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4599: if (flg) {
4600: inext->createfactor[(int)ftype - 1] = createfactor;
4601: PetscFunctionReturn(PETSC_SUCCESS);
4602: }
4603: iprev = inext;
4604: inext = inext->next;
4605: }
4606: PetscCall(PetscNew(&iprev->next));
4607: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4608: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4609: PetscFunctionReturn(PETSC_SUCCESS);
4610: }
4611: prev = next;
4612: next = next->next;
4613: }
4614: PetscCall(PetscNew(&prev->next));
4615: PetscCall(PetscStrallocpy(package, &prev->next->name));
4616: PetscCall(PetscNew(&prev->next->handlers));
4617: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4618: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4619: PetscFunctionReturn(PETSC_SUCCESS);
4620: }
4622: /*@C
4623: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4625: Input Parameters:
4626: + 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
4627: . ftype - the type of factorization supported by the type
4628: - mtype - the matrix type that works with this type
4630: Output Parameters:
4631: + foundtype - `PETSC_TRUE` if the type was registered
4632: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4633: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4635: Calling sequence of `createfactor`:
4636: + A - the matrix providing the factor matrix
4637: . ftype - the `MatFactorType` of the factor requested
4638: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4640: Level: developer
4642: Note:
4643: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4644: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4645: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4647: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4648: `MatInitializePackage()`
4649: @*/
4650: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4651: {
4652: MatSolverTypeHolder next = MatSolverTypeHolders;
4653: PetscBool flg;
4654: MatSolverTypeForSpecifcType inext;
4656: PetscFunctionBegin;
4657: if (foundtype) *foundtype = PETSC_FALSE;
4658: if (foundmtype) *foundmtype = PETSC_FALSE;
4659: if (createfactor) *createfactor = NULL;
4661: if (type) {
4662: while (next) {
4663: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4664: if (flg) {
4665: if (foundtype) *foundtype = PETSC_TRUE;
4666: inext = next->handlers;
4667: while (inext) {
4668: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4669: if (flg) {
4670: if (foundmtype) *foundmtype = PETSC_TRUE;
4671: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4672: PetscFunctionReturn(PETSC_SUCCESS);
4673: }
4674: inext = inext->next;
4675: }
4676: }
4677: next = next->next;
4678: }
4679: } else {
4680: while (next) {
4681: inext = next->handlers;
4682: while (inext) {
4683: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4684: if (flg && inext->createfactor[(int)ftype - 1]) {
4685: if (foundtype) *foundtype = PETSC_TRUE;
4686: if (foundmtype) *foundmtype = PETSC_TRUE;
4687: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4688: PetscFunctionReturn(PETSC_SUCCESS);
4689: }
4690: inext = inext->next;
4691: }
4692: next = next->next;
4693: }
4694: /* try with base classes inext->mtype */
4695: next = MatSolverTypeHolders;
4696: while (next) {
4697: inext = next->handlers;
4698: while (inext) {
4699: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4700: if (flg && inext->createfactor[(int)ftype - 1]) {
4701: if (foundtype) *foundtype = PETSC_TRUE;
4702: if (foundmtype) *foundmtype = PETSC_TRUE;
4703: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4704: PetscFunctionReturn(PETSC_SUCCESS);
4705: }
4706: inext = inext->next;
4707: }
4708: next = next->next;
4709: }
4710: }
4711: PetscFunctionReturn(PETSC_SUCCESS);
4712: }
4714: PetscErrorCode MatSolverTypeDestroy(void)
4715: {
4716: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4717: MatSolverTypeForSpecifcType inext, iprev;
4719: PetscFunctionBegin;
4720: while (next) {
4721: PetscCall(PetscFree(next->name));
4722: inext = next->handlers;
4723: while (inext) {
4724: PetscCall(PetscFree(inext->mtype));
4725: iprev = inext;
4726: inext = inext->next;
4727: PetscCall(PetscFree(iprev));
4728: }
4729: prev = next;
4730: next = next->next;
4731: PetscCall(PetscFree(prev));
4732: }
4733: MatSolverTypeHolders = NULL;
4734: PetscFunctionReturn(PETSC_SUCCESS);
4735: }
4737: /*@
4738: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4740: Logically Collective
4742: Input Parameter:
4743: . mat - the matrix
4745: Output Parameter:
4746: . flg - `PETSC_TRUE` if uses the ordering
4748: Level: developer
4750: Note:
4751: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4752: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4754: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4755: @*/
4756: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4757: {
4758: PetscFunctionBegin;
4759: *flg = mat->canuseordering;
4760: PetscFunctionReturn(PETSC_SUCCESS);
4761: }
4763: /*@
4764: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4766: Logically Collective
4768: Input Parameters:
4769: + mat - the matrix obtained with `MatGetFactor()`
4770: - ftype - the factorization type to be used
4772: Output Parameter:
4773: . otype - the preferred ordering type
4775: Level: developer
4777: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4778: @*/
4779: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4780: {
4781: PetscFunctionBegin;
4782: *otype = mat->preferredordering[ftype];
4783: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4784: PetscFunctionReturn(PETSC_SUCCESS);
4785: }
4787: /*@
4788: MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()
4790: Collective
4792: Input Parameters:
4793: + mat - the matrix
4794: . 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
4795: the other criteria is returned
4796: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4798: Output Parameter:
4799: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4801: Options Database Keys:
4802: + -pc_factor_mat_solver_type <type> - choose the type at run time. When using `KSP` solvers
4803: . -pc_factor_mat_factor_on_host <bool> - do mat factorization on host (with device matrices). Default is doing it on device
4804: - -pc_factor_mat_solve_on_host <bool> - do mat solve on host (with device matrices). Default is doing it on device
4806: Level: intermediate
4808: Notes:
4809: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4810: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4812: Users usually access the factorization solvers via `KSP`
4814: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4815: 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
4817: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4818: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4819: For example if one configuration had --download-mumps while a different one had --download-superlu_dist.
4821: Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4822: where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4823: call `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4825: Developer Note:
4826: This should actually be called `MatCreateFactor()` since it creates a new factor object
4828: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4829: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4830: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4831: @*/
4832: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4833: {
4834: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4835: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4837: PetscFunctionBegin;
4841: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4842: MatCheckPreallocated(mat, 1);
4844: PetscCall(MatIsShell(mat, &shell));
4845: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4846: if (hasop) {
4847: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4848: PetscFunctionReturn(PETSC_SUCCESS);
4849: }
4851: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4852: if (!foundtype) {
4853: if (type) {
4854: 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],
4855: ((PetscObject)mat)->type_name, type);
4856: } else {
4857: 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);
4858: }
4859: }
4860: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4861: 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);
4863: PetscCall((*conv)(mat, ftype, f));
4864: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4865: PetscFunctionReturn(PETSC_SUCCESS);
4866: }
4868: /*@
4869: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4871: Not Collective
4873: Input Parameters:
4874: + mat - the matrix
4875: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4876: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4878: Output Parameter:
4879: . flg - PETSC_TRUE if the factorization is available
4881: Level: intermediate
4883: Notes:
4884: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4885: such as pastix, superlu, mumps etc.
4887: PETSc must have been ./configure to use the external solver, using the option --download-package
4889: Developer Note:
4890: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
4892: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4893: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4894: @*/
4895: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4896: {
4897: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
4899: PetscFunctionBegin;
4901: PetscAssertPointer(flg, 4);
4903: *flg = PETSC_FALSE;
4904: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
4906: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4907: MatCheckPreallocated(mat, 1);
4909: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4910: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4911: PetscFunctionReturn(PETSC_SUCCESS);
4912: }
4914: /*@
4915: MatDuplicate - Duplicates a matrix including the non-zero structure.
4917: Collective
4919: Input Parameters:
4920: + mat - the matrix
4921: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4922: See the manual page for `MatDuplicateOption()` for an explanation of these options.
4924: Output Parameter:
4925: . M - pointer to place new matrix
4927: Level: intermediate
4929: Notes:
4930: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
4932: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
4934: 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.
4936: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4937: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4938: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
4940: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4941: @*/
4942: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4943: {
4944: Mat B;
4945: VecType vtype;
4946: PetscInt i;
4947: PetscObject dm, container_h, container_d;
4948: void (*viewf)(void);
4950: PetscFunctionBegin;
4953: PetscAssertPointer(M, 3);
4954: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4955: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4956: MatCheckPreallocated(mat, 1);
4958: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4959: PetscUseTypeMethod(mat, duplicate, op, M);
4960: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4961: B = *M;
4963: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4964: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4965: PetscCall(MatGetVecType(mat, &vtype));
4966: PetscCall(MatSetVecType(B, vtype));
4968: B->stencil.dim = mat->stencil.dim;
4969: B->stencil.noc = mat->stencil.noc;
4970: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4971: B->stencil.dims[i] = mat->stencil.dims[i];
4972: B->stencil.starts[i] = mat->stencil.starts[i];
4973: }
4975: B->nooffproczerorows = mat->nooffproczerorows;
4976: B->nooffprocentries = mat->nooffprocentries;
4978: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4979: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4980: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
4981: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
4982: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
4983: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
4984: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
4985: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4986: PetscFunctionReturn(PETSC_SUCCESS);
4987: }
4989: /*@
4990: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
4992: Logically Collective
4994: Input Parameter:
4995: . mat - the matrix
4997: Output Parameter:
4998: . v - the diagonal of the matrix
5000: Level: intermediate
5002: Note:
5003: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5004: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5005: is larger than `ndiag`, the values of the remaining entries are unspecified.
5007: Currently only correct in parallel for square matrices.
5009: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5010: @*/
5011: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5012: {
5013: PetscFunctionBegin;
5017: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5018: MatCheckPreallocated(mat, 1);
5019: if (PetscDefined(USE_DEBUG)) {
5020: PetscInt nv, row, col, ndiag;
5022: PetscCall(VecGetLocalSize(v, &nv));
5023: PetscCall(MatGetLocalSize(mat, &row, &col));
5024: ndiag = PetscMin(row, col);
5025: 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);
5026: }
5028: PetscUseTypeMethod(mat, getdiagonal, v);
5029: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5030: PetscFunctionReturn(PETSC_SUCCESS);
5031: }
5033: /*@
5034: MatGetRowMin - Gets the minimum value (of the real part) of each
5035: row of the matrix
5037: Logically Collective
5039: Input Parameter:
5040: . mat - the matrix
5042: Output Parameters:
5043: + v - the vector for storing the maximums
5044: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5046: Level: intermediate
5048: Note:
5049: The result of this call are the same as if one converted the matrix to dense format
5050: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5052: This code is only implemented for a couple of matrix formats.
5054: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5055: `MatGetRowMax()`
5056: @*/
5057: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5058: {
5059: PetscFunctionBegin;
5063: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5065: if (!mat->cmap->N) {
5066: PetscCall(VecSet(v, PETSC_MAX_REAL));
5067: if (idx) {
5068: PetscInt i, m = mat->rmap->n;
5069: for (i = 0; i < m; i++) idx[i] = -1;
5070: }
5071: } else {
5072: MatCheckPreallocated(mat, 1);
5073: }
5074: PetscUseTypeMethod(mat, getrowmin, v, idx);
5075: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5076: PetscFunctionReturn(PETSC_SUCCESS);
5077: }
5079: /*@
5080: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5081: row of the matrix
5083: Logically Collective
5085: Input Parameter:
5086: . mat - the matrix
5088: Output Parameters:
5089: + v - the vector for storing the minimums
5090: - idx - the indices of the column found for each row (or `NULL` if not needed)
5092: Level: intermediate
5094: Notes:
5095: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5096: row is 0 (the first column).
5098: This code is only implemented for a couple of matrix formats.
5100: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5101: @*/
5102: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5103: {
5104: PetscFunctionBegin;
5108: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5109: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5111: if (!mat->cmap->N) {
5112: PetscCall(VecSet(v, 0.0));
5113: if (idx) {
5114: PetscInt i, m = mat->rmap->n;
5115: for (i = 0; i < m; i++) idx[i] = -1;
5116: }
5117: } else {
5118: MatCheckPreallocated(mat, 1);
5119: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5120: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5121: }
5122: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5123: PetscFunctionReturn(PETSC_SUCCESS);
5124: }
5126: /*@
5127: MatGetRowMax - Gets the maximum value (of the real part) of each
5128: row of the matrix
5130: Logically Collective
5132: Input Parameter:
5133: . mat - the matrix
5135: Output Parameters:
5136: + v - the vector for storing the maximums
5137: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5139: Level: intermediate
5141: Notes:
5142: The result of this call are the same as if one converted the matrix to dense format
5143: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5145: This code is only implemented for a couple of matrix formats.
5147: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5148: @*/
5149: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5150: {
5151: PetscFunctionBegin;
5155: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5157: if (!mat->cmap->N) {
5158: PetscCall(VecSet(v, PETSC_MIN_REAL));
5159: if (idx) {
5160: PetscInt i, m = mat->rmap->n;
5161: for (i = 0; i < m; i++) idx[i] = -1;
5162: }
5163: } else {
5164: MatCheckPreallocated(mat, 1);
5165: PetscUseTypeMethod(mat, getrowmax, v, idx);
5166: }
5167: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5168: PetscFunctionReturn(PETSC_SUCCESS);
5169: }
5171: /*@
5172: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5173: row of the matrix
5175: Logically Collective
5177: Input Parameter:
5178: . mat - the matrix
5180: Output Parameters:
5181: + v - the vector for storing the maximums
5182: - idx - the indices of the column found for each row (or `NULL` if not needed)
5184: Level: intermediate
5186: Notes:
5187: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5188: row is 0 (the first column).
5190: This code is only implemented for a couple of matrix formats.
5192: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5193: @*/
5194: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5195: {
5196: PetscFunctionBegin;
5200: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5202: if (!mat->cmap->N) {
5203: PetscCall(VecSet(v, 0.0));
5204: if (idx) {
5205: PetscInt i, m = mat->rmap->n;
5206: for (i = 0; i < m; i++) idx[i] = -1;
5207: }
5208: } else {
5209: MatCheckPreallocated(mat, 1);
5210: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5211: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5212: }
5213: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5214: PetscFunctionReturn(PETSC_SUCCESS);
5215: }
5217: /*@
5218: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5220: Logically Collective
5222: Input Parameter:
5223: . mat - the matrix
5225: Output Parameter:
5226: . v - the vector for storing the sum
5228: Level: intermediate
5230: This code is only implemented for a couple of matrix formats.
5232: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5233: @*/
5234: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5235: {
5236: PetscFunctionBegin;
5240: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5242: if (!mat->cmap->N) {
5243: PetscCall(VecSet(v, 0.0));
5244: } else {
5245: MatCheckPreallocated(mat, 1);
5246: PetscUseTypeMethod(mat, getrowsumabs, v);
5247: }
5248: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5249: PetscFunctionReturn(PETSC_SUCCESS);
5250: }
5252: /*@
5253: MatGetRowSum - Gets the sum of each row of the matrix
5255: Logically or Neighborhood Collective
5257: Input Parameter:
5258: . mat - the matrix
5260: Output Parameter:
5261: . v - the vector for storing the sum of rows
5263: Level: intermediate
5265: Note:
5266: This code is slow since it is not currently specialized for different formats
5268: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5269: @*/
5270: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5271: {
5272: Vec ones;
5274: PetscFunctionBegin;
5278: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5279: MatCheckPreallocated(mat, 1);
5280: PetscCall(MatCreateVecs(mat, &ones, NULL));
5281: PetscCall(VecSet(ones, 1.));
5282: PetscCall(MatMult(mat, ones, v));
5283: PetscCall(VecDestroy(&ones));
5284: PetscFunctionReturn(PETSC_SUCCESS);
5285: }
5287: /*@
5288: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5289: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5291: Collective
5293: Input Parameter:
5294: . mat - the matrix to provide the transpose
5296: Output Parameter:
5297: . 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
5299: Level: advanced
5301: Note:
5302: 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
5303: routine allows bypassing that call.
5305: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5306: @*/
5307: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5308: {
5309: MatParentState *rb = NULL;
5311: PetscFunctionBegin;
5312: PetscCall(PetscNew(&rb));
5313: rb->id = ((PetscObject)mat)->id;
5314: rb->state = 0;
5315: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5316: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5317: PetscFunctionReturn(PETSC_SUCCESS);
5318: }
5320: /*@
5321: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5323: Collective
5325: Input Parameters:
5326: + mat - the matrix to transpose
5327: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5329: Output Parameter:
5330: . B - the transpose of the matrix
5332: Level: intermediate
5334: Notes:
5335: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5337: `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
5338: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5340: 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.
5342: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5343: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5345: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5347: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5349: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5350: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5351: @*/
5352: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5353: {
5354: PetscContainer rB = NULL;
5355: MatParentState *rb = NULL;
5357: PetscFunctionBegin;
5360: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5361: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5362: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5363: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5364: MatCheckPreallocated(mat, 1);
5365: if (reuse == MAT_REUSE_MATRIX) {
5366: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5367: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5368: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5369: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5370: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5371: }
5373: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5374: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5375: PetscUseTypeMethod(mat, transpose, reuse, B);
5376: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5377: }
5378: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5380: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5381: if (reuse != MAT_INPLACE_MATRIX) {
5382: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5383: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5384: rb->state = ((PetscObject)mat)->state;
5385: rb->nonzerostate = mat->nonzerostate;
5386: }
5387: PetscFunctionReturn(PETSC_SUCCESS);
5388: }
5390: /*@
5391: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5393: Collective
5395: Input Parameter:
5396: . A - the matrix to transpose
5398: Output Parameter:
5399: . 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
5400: numerical portion.
5402: Level: intermediate
5404: Note:
5405: This is not supported for many matrix types, use `MatTranspose()` in those cases
5407: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5408: @*/
5409: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5410: {
5411: PetscFunctionBegin;
5414: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5415: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5416: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5417: PetscUseTypeMethod(A, transposesymbolic, B);
5418: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5420: PetscCall(MatTransposeSetPrecursor(A, *B));
5421: PetscFunctionReturn(PETSC_SUCCESS);
5422: }
5424: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5425: {
5426: PetscContainer rB;
5427: MatParentState *rb;
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(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5435: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5436: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5437: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5438: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5439: PetscFunctionReturn(PETSC_SUCCESS);
5440: }
5442: /*@
5443: MatIsTranspose - Test whether a matrix is another one's transpose,
5444: or its own, in which case it tests symmetry.
5446: Collective
5448: Input Parameters:
5449: + A - the matrix to test
5450: . B - the matrix to test against, this can equal the first parameter
5451: - tol - tolerance, differences between entries smaller than this are counted as zero
5453: Output Parameter:
5454: . flg - the result
5456: Level: intermediate
5458: Notes:
5459: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5460: test involves parallel copies of the block off-diagonal parts of the matrix.
5462: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5463: @*/
5464: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5465: {
5466: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5468: PetscFunctionBegin;
5471: PetscAssertPointer(flg, 4);
5472: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5473: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5474: *flg = PETSC_FALSE;
5475: if (f && g) {
5476: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5477: PetscCall((*f)(A, B, tol, flg));
5478: } else {
5479: MatType mattype;
5481: PetscCall(MatGetType(f ? B : A, &mattype));
5482: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5483: }
5484: PetscFunctionReturn(PETSC_SUCCESS);
5485: }
5487: /*@
5488: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5490: Collective
5492: Input Parameters:
5493: + mat - the matrix to transpose and complex conjugate
5494: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5496: Output Parameter:
5497: . B - the Hermitian transpose
5499: Level: intermediate
5501: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5502: @*/
5503: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5504: {
5505: PetscFunctionBegin;
5506: PetscCall(MatTranspose(mat, reuse, B));
5507: #if defined(PETSC_USE_COMPLEX)
5508: PetscCall(MatConjugate(*B));
5509: #endif
5510: PetscFunctionReturn(PETSC_SUCCESS);
5511: }
5513: /*@
5514: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5516: Collective
5518: Input Parameters:
5519: + A - the matrix to test
5520: . B - the matrix to test against, this can equal the first parameter
5521: - tol - tolerance, differences between entries smaller than this are counted as zero
5523: Output Parameter:
5524: . flg - the result
5526: Level: intermediate
5528: Notes:
5529: Only available for `MATAIJ` matrices.
5531: The sequential algorithm
5532: has a running time of the order of the number of nonzeros; the parallel
5533: test involves parallel copies of the block off-diagonal parts of the matrix.
5535: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5536: @*/
5537: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5538: {
5539: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5541: PetscFunctionBegin;
5544: PetscAssertPointer(flg, 4);
5545: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5546: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5547: if (f && g) {
5548: PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5549: PetscCall((*f)(A, B, tol, flg));
5550: }
5551: PetscFunctionReturn(PETSC_SUCCESS);
5552: }
5554: /*@
5555: MatPermute - Creates a new matrix with rows and columns permuted from the
5556: original.
5558: Collective
5560: Input Parameters:
5561: + mat - the matrix to permute
5562: . row - row permutation, each processor supplies only the permutation for its rows
5563: - col - column permutation, each processor supplies only the permutation for its columns
5565: Output Parameter:
5566: . B - the permuted matrix
5568: Level: advanced
5570: Note:
5571: The index sets map from row/col of permuted matrix to row/col of original matrix.
5572: The index sets should be on the same communicator as mat and have the same local sizes.
5574: Developer Note:
5575: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5576: exploit the fact that row and col are permutations, consider implementing the
5577: more general `MatCreateSubMatrix()` instead.
5579: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5580: @*/
5581: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5582: {
5583: PetscFunctionBegin;
5588: PetscAssertPointer(B, 4);
5589: PetscCheckSameComm(mat, 1, row, 2);
5590: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5591: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5592: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5593: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5594: MatCheckPreallocated(mat, 1);
5596: if (mat->ops->permute) {
5597: PetscUseTypeMethod(mat, permute, row, col, B);
5598: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5599: } else {
5600: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5601: }
5602: PetscFunctionReturn(PETSC_SUCCESS);
5603: }
5605: /*@
5606: MatEqual - Compares two matrices.
5608: Collective
5610: Input Parameters:
5611: + A - the first matrix
5612: - B - the second matrix
5614: Output Parameter:
5615: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5617: Level: intermediate
5619: Note:
5620: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing the results of several matrix-vector product
5621: using several randomly created vectors, see `MatMultEqual()`.
5623: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5624: @*/
5625: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5626: {
5627: PetscFunctionBegin;
5632: PetscAssertPointer(flg, 3);
5633: PetscCheckSameComm(A, 1, B, 2);
5634: MatCheckPreallocated(A, 1);
5635: MatCheckPreallocated(B, 2);
5636: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5637: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5638: 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,
5639: B->cmap->N);
5640: if (A->ops->equal && A->ops->equal == B->ops->equal) {
5641: PetscUseTypeMethod(A, equal, B, flg);
5642: } else {
5643: PetscCall(MatMultEqual(A, B, 10, flg));
5644: }
5645: PetscFunctionReturn(PETSC_SUCCESS);
5646: }
5648: /*@
5649: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5650: matrices that are stored as vectors. Either of the two scaling
5651: matrices can be `NULL`.
5653: Collective
5655: Input Parameters:
5656: + mat - the matrix to be scaled
5657: . l - the left scaling vector (or `NULL`)
5658: - r - the right scaling vector (or `NULL`)
5660: Level: intermediate
5662: Note:
5663: `MatDiagonalScale()` computes $A = LAR$, where
5664: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5665: The L scales the rows of the matrix, the R scales the columns of the matrix.
5667: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5668: @*/
5669: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5670: {
5671: PetscFunctionBegin;
5674: if (l) {
5676: PetscCheckSameComm(mat, 1, l, 2);
5677: }
5678: if (r) {
5680: PetscCheckSameComm(mat, 1, r, 3);
5681: }
5682: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5683: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5684: MatCheckPreallocated(mat, 1);
5685: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5687: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5688: PetscUseTypeMethod(mat, diagonalscale, l, r);
5689: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5690: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5691: if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5692: PetscFunctionReturn(PETSC_SUCCESS);
5693: }
5695: /*@
5696: MatScale - Scales all elements of a matrix by a given number.
5698: Logically Collective
5700: Input Parameters:
5701: + mat - the matrix to be scaled
5702: - a - the scaling value
5704: Level: intermediate
5706: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5707: @*/
5708: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5709: {
5710: PetscFunctionBegin;
5713: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5714: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5716: MatCheckPreallocated(mat, 1);
5718: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5719: if (a != (PetscScalar)1.0) {
5720: PetscUseTypeMethod(mat, scale, a);
5721: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5722: }
5723: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5724: PetscFunctionReturn(PETSC_SUCCESS);
5725: }
5727: /*@
5728: MatNorm - Calculates various norms of a matrix.
5730: Collective
5732: Input Parameters:
5733: + mat - the matrix
5734: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5736: Output Parameter:
5737: . nrm - the resulting norm
5739: Level: intermediate
5741: .seealso: [](ch_matrices), `Mat`
5742: @*/
5743: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5744: {
5745: PetscFunctionBegin;
5748: PetscAssertPointer(nrm, 3);
5750: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5751: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5752: MatCheckPreallocated(mat, 1);
5754: PetscUseTypeMethod(mat, norm, type, nrm);
5755: PetscFunctionReturn(PETSC_SUCCESS);
5756: }
5758: /*
5759: This variable is used to prevent counting of MatAssemblyBegin() that
5760: are called from within a MatAssemblyEnd().
5761: */
5762: static PetscInt MatAssemblyEnd_InUse = 0;
5763: /*@
5764: MatAssemblyBegin - Begins assembling the matrix. This routine should
5765: be called after completing all calls to `MatSetValues()`.
5767: Collective
5769: Input Parameters:
5770: + mat - the matrix
5771: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5773: Level: beginner
5775: Notes:
5776: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5777: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5779: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5780: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5781: using the matrix.
5783: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5784: 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
5785: a global collective operation requiring all processes that share the matrix.
5787: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5788: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5789: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5791: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5792: @*/
5793: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5794: {
5795: PetscFunctionBegin;
5798: MatCheckPreallocated(mat, 1);
5799: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5800: if (mat->assembled) {
5801: mat->was_assembled = PETSC_TRUE;
5802: mat->assembled = PETSC_FALSE;
5803: }
5805: if (!MatAssemblyEnd_InUse) {
5806: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5807: PetscTryTypeMethod(mat, assemblybegin, type);
5808: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5809: } else PetscTryTypeMethod(mat, assemblybegin, type);
5810: PetscFunctionReturn(PETSC_SUCCESS);
5811: }
5813: /*@
5814: MatAssembled - Indicates if a matrix has been assembled and is ready for
5815: use; for example, in matrix-vector product.
5817: Not Collective
5819: Input Parameter:
5820: . mat - the matrix
5822: Output Parameter:
5823: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5825: Level: advanced
5827: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5828: @*/
5829: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5830: {
5831: PetscFunctionBegin;
5833: PetscAssertPointer(assembled, 2);
5834: *assembled = mat->assembled;
5835: PetscFunctionReturn(PETSC_SUCCESS);
5836: }
5838: /*@
5839: MatAssemblyEnd - Completes assembling the matrix. This routine should
5840: be called after `MatAssemblyBegin()`.
5842: Collective
5844: Input Parameters:
5845: + mat - the matrix
5846: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5848: Options Database Keys:
5849: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5850: . -mat_view ::ascii_info_detail - Prints more detailed info
5851: . -mat_view - Prints matrix in ASCII format
5852: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
5853: . -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5854: . -display <name> - Sets display name (default is host)
5855: . -draw_pause <sec> - Sets number of seconds to pause after display
5856: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5857: . -viewer_socket_machine <machine> - Machine to use for socket
5858: . -viewer_socket_port <port> - Port number to use for socket
5859: - -mat_view binary:filename[:append] - Save matrix to file in binary format
5861: Level: beginner
5863: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5864: @*/
5865: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5866: {
5867: static PetscInt inassm = 0;
5868: PetscBool flg = PETSC_FALSE;
5870: PetscFunctionBegin;
5874: inassm++;
5875: MatAssemblyEnd_InUse++;
5876: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5877: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5878: PetscTryTypeMethod(mat, assemblyend, type);
5879: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5880: } else PetscTryTypeMethod(mat, assemblyend, type);
5882: /* Flush assembly is not a true assembly */
5883: if (type != MAT_FLUSH_ASSEMBLY) {
5884: if (mat->num_ass) {
5885: if (!mat->symmetry_eternal) {
5886: mat->symmetric = PETSC_BOOL3_UNKNOWN;
5887: mat->hermitian = PETSC_BOOL3_UNKNOWN;
5888: }
5889: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5890: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5891: }
5892: mat->num_ass++;
5893: mat->assembled = PETSC_TRUE;
5894: mat->ass_nonzerostate = mat->nonzerostate;
5895: }
5897: mat->insertmode = NOT_SET_VALUES;
5898: MatAssemblyEnd_InUse--;
5899: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5900: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5901: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
5903: if (mat->checksymmetryonassembly) {
5904: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5905: if (flg) {
5906: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5907: } else {
5908: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5909: }
5910: }
5911: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5912: }
5913: inassm--;
5914: PetscFunctionReturn(PETSC_SUCCESS);
5915: }
5917: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5918: /*@
5919: MatSetOption - Sets a parameter option for a matrix. Some options
5920: may be specific to certain storage formats. Some options
5921: determine how values will be inserted (or added). Sorted,
5922: row-oriented input will generally assemble the fastest. The default
5923: is row-oriented.
5925: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
5927: Input Parameters:
5928: + mat - the matrix
5929: . op - the option, one of those listed below (and possibly others),
5930: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
5932: Options Describing Matrix Structure:
5933: + `MAT_SPD` - symmetric positive definite
5934: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
5935: . `MAT_HERMITIAN` - transpose is the complex conjugation
5936: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
5937: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5938: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5939: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
5941: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5942: do not need to be computed (usually at a high cost)
5944: Options For Use with `MatSetValues()`:
5945: Insert a logically dense subblock, which can be
5946: . `MAT_ROW_ORIENTED` - row-oriented (default)
5948: These options reflect the data you pass in with `MatSetValues()`; it has
5949: nothing to do with how the data is stored internally in the matrix
5950: data structure.
5952: When (re)assembling a matrix, we can restrict the input for
5953: efficiency/debugging purposes. These options include
5954: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
5955: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
5956: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
5957: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
5958: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
5959: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
5960: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5961: performance for very large process counts.
5962: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
5963: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5964: functions, instead sending only neighbor messages.
5966: Level: intermediate
5968: Notes:
5969: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
5971: Some options are relevant only for particular matrix types and
5972: are thus ignored by others. Other options are not supported by
5973: certain matrix types and will generate an error message if set.
5975: If using Fortran to compute a matrix, one may need to
5976: use the column-oriented option (or convert to the row-oriented
5977: format).
5979: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5980: that would generate a new entry in the nonzero structure is instead
5981: ignored. Thus, if memory has not already been allocated for this particular
5982: data, then the insertion is ignored. For dense matrices, in which
5983: the entire array is allocated, no entries are ever ignored.
5984: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
5986: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
5987: that would generate a new entry in the nonzero structure instead produces
5988: 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
5990: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
5991: that would generate a new entry that has not been preallocated will
5992: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
5993: only.) This is a useful flag when debugging matrix memory preallocation.
5994: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
5996: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
5997: other processors should be dropped, rather than stashed.
5998: This is useful if you know that the "owning" processor is also
5999: always generating the correct matrix entries, so that PETSc need
6000: not transfer duplicate entries generated on another processor.
6002: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6003: searches during matrix assembly. When this flag is set, the hash table
6004: is created during the first matrix assembly. This hash table is
6005: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6006: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6007: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6008: supported by `MATMPIBAIJ` format only.
6010: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6011: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6013: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6014: a zero location in the matrix
6016: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6018: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6019: zero row routines and thus improves performance for very large process counts.
6021: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6022: part of the matrix (since they should match the upper triangular part).
6024: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6025: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6026: with finite difference schemes with non-periodic boundary conditions.
6028: Developer Note:
6029: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6030: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6031: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6032: not changed.
6034: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6035: @*/
6036: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6037: {
6038: PetscFunctionBegin;
6040: if (op > 0) {
6043: }
6045: 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);
6047: switch (op) {
6048: case MAT_FORCE_DIAGONAL_ENTRIES:
6049: mat->force_diagonals = flg;
6050: PetscFunctionReturn(PETSC_SUCCESS);
6051: case MAT_NO_OFF_PROC_ENTRIES:
6052: mat->nooffprocentries = flg;
6053: PetscFunctionReturn(PETSC_SUCCESS);
6054: case MAT_SUBSET_OFF_PROC_ENTRIES:
6055: mat->assembly_subset = flg;
6056: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6057: #if !defined(PETSC_HAVE_MPIUNI)
6058: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6059: #endif
6060: mat->stash.first_assembly_done = PETSC_FALSE;
6061: }
6062: PetscFunctionReturn(PETSC_SUCCESS);
6063: case MAT_NO_OFF_PROC_ZERO_ROWS:
6064: mat->nooffproczerorows = flg;
6065: PetscFunctionReturn(PETSC_SUCCESS);
6066: case MAT_SPD:
6067: if (flg) {
6068: mat->spd = PETSC_BOOL3_TRUE;
6069: mat->symmetric = PETSC_BOOL3_TRUE;
6070: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6071: } else {
6072: mat->spd = PETSC_BOOL3_FALSE;
6073: }
6074: break;
6075: case MAT_SYMMETRIC:
6076: mat->symmetric = PetscBoolToBool3(flg);
6077: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6078: #if !defined(PETSC_USE_COMPLEX)
6079: mat->hermitian = PetscBoolToBool3(flg);
6080: #endif
6081: break;
6082: case MAT_HERMITIAN:
6083: mat->hermitian = PetscBoolToBool3(flg);
6084: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6085: #if !defined(PETSC_USE_COMPLEX)
6086: mat->symmetric = PetscBoolToBool3(flg);
6087: #endif
6088: break;
6089: case MAT_STRUCTURALLY_SYMMETRIC:
6090: mat->structurally_symmetric = PetscBoolToBool3(flg);
6091: break;
6092: case MAT_SYMMETRY_ETERNAL:
6093: 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");
6094: mat->symmetry_eternal = flg;
6095: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6096: break;
6097: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6098: 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");
6099: mat->structural_symmetry_eternal = flg;
6100: break;
6101: case MAT_SPD_ETERNAL:
6102: 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");
6103: mat->spd_eternal = flg;
6104: if (flg) {
6105: mat->structural_symmetry_eternal = PETSC_TRUE;
6106: mat->symmetry_eternal = PETSC_TRUE;
6107: }
6108: break;
6109: case MAT_STRUCTURE_ONLY:
6110: mat->structure_only = flg;
6111: break;
6112: case MAT_SORTED_FULL:
6113: mat->sortedfull = flg;
6114: break;
6115: default:
6116: break;
6117: }
6118: PetscTryTypeMethod(mat, setoption, op, flg);
6119: PetscFunctionReturn(PETSC_SUCCESS);
6120: }
6122: /*@
6123: MatGetOption - Gets a parameter option that has been set for a matrix.
6125: Logically Collective
6127: Input Parameters:
6128: + mat - the matrix
6129: - op - the option, this only responds to certain options, check the code for which ones
6131: Output Parameter:
6132: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6134: Level: intermediate
6136: Notes:
6137: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6139: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6140: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6142: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6143: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6144: @*/
6145: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6146: {
6147: PetscFunctionBegin;
6151: 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);
6152: 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()");
6154: switch (op) {
6155: case MAT_NO_OFF_PROC_ENTRIES:
6156: *flg = mat->nooffprocentries;
6157: break;
6158: case MAT_NO_OFF_PROC_ZERO_ROWS:
6159: *flg = mat->nooffproczerorows;
6160: break;
6161: case MAT_SYMMETRIC:
6162: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6163: break;
6164: case MAT_HERMITIAN:
6165: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6166: break;
6167: case MAT_STRUCTURALLY_SYMMETRIC:
6168: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6169: break;
6170: case MAT_SPD:
6171: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6172: break;
6173: case MAT_SYMMETRY_ETERNAL:
6174: *flg = mat->symmetry_eternal;
6175: break;
6176: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6177: *flg = mat->symmetry_eternal;
6178: break;
6179: default:
6180: break;
6181: }
6182: PetscFunctionReturn(PETSC_SUCCESS);
6183: }
6185: /*@
6186: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6187: this routine retains the old nonzero structure.
6189: Logically Collective
6191: Input Parameter:
6192: . mat - the matrix
6194: Level: intermediate
6196: Note:
6197: 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.
6198: See the Performance chapter of the users manual for information on preallocating matrices.
6200: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6201: @*/
6202: PetscErrorCode MatZeroEntries(Mat mat)
6203: {
6204: PetscFunctionBegin;
6207: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6208: 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");
6209: MatCheckPreallocated(mat, 1);
6211: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6212: PetscUseTypeMethod(mat, zeroentries);
6213: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6214: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6215: PetscFunctionReturn(PETSC_SUCCESS);
6216: }
6218: /*@
6219: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6220: of a set of rows and columns of a matrix.
6222: Collective
6224: Input Parameters:
6225: + mat - the matrix
6226: . numRows - the number of rows/columns to zero
6227: . rows - the global row indices
6228: . diag - value put in the diagonal of the eliminated rows
6229: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6230: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6232: Level: intermediate
6234: Notes:
6235: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6237: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6238: 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
6240: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6241: Krylov method to take advantage of the known solution on the zeroed rows.
6243: For the parallel case, all processes that share the matrix (i.e.,
6244: those in the communicator used for matrix creation) MUST call this
6245: routine, regardless of whether any rows being zeroed are owned by
6246: them.
6248: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6249: 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
6250: missing.
6252: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6253: list only rows local to itself).
6255: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6257: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6258: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6259: @*/
6260: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6261: {
6262: PetscFunctionBegin;
6265: if (numRows) PetscAssertPointer(rows, 3);
6266: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6267: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6268: MatCheckPreallocated(mat, 1);
6270: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6271: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6272: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6273: PetscFunctionReturn(PETSC_SUCCESS);
6274: }
6276: /*@
6277: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6278: of a set of rows and columns of a matrix.
6280: Collective
6282: Input Parameters:
6283: + mat - the matrix
6284: . is - the rows to zero
6285: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6286: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6287: - b - optional vector of right-hand side, that will be adjusted by provided solution
6289: Level: intermediate
6291: Note:
6292: See `MatZeroRowsColumns()` for details on how this routine operates.
6294: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6295: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6296: @*/
6297: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6298: {
6299: PetscInt numRows;
6300: const PetscInt *rows;
6302: PetscFunctionBegin;
6307: PetscCall(ISGetLocalSize(is, &numRows));
6308: PetscCall(ISGetIndices(is, &rows));
6309: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6310: PetscCall(ISRestoreIndices(is, &rows));
6311: PetscFunctionReturn(PETSC_SUCCESS);
6312: }
6314: /*@
6315: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6316: of a set of rows of a matrix.
6318: Collective
6320: Input Parameters:
6321: + mat - the matrix
6322: . numRows - the number of rows to zero
6323: . rows - the global row indices
6324: . diag - value put in the diagonal of the zeroed rows
6325: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6326: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6328: Level: intermediate
6330: Notes:
6331: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6333: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6335: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6336: Krylov method to take advantage of the known solution on the zeroed rows.
6338: 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)
6339: from the matrix.
6341: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6342: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6343: formats this does not alter the nonzero structure.
6345: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6346: of the matrix is not changed the values are
6347: merely zeroed.
6349: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6350: formats can optionally remove the main diagonal entry from the
6351: nonzero structure as well, by passing 0.0 as the final argument).
6353: For the parallel case, all processes that share the matrix (i.e.,
6354: those in the communicator used for matrix creation) MUST call this
6355: routine, regardless of whether any rows being zeroed are owned by
6356: them.
6358: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6359: list only rows local to itself).
6361: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6362: owns that are to be zeroed. This saves a global synchronization in the implementation.
6364: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6365: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6366: @*/
6367: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6368: {
6369: PetscFunctionBegin;
6372: if (numRows) PetscAssertPointer(rows, 3);
6373: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6374: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6375: MatCheckPreallocated(mat, 1);
6377: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6378: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6379: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6380: PetscFunctionReturn(PETSC_SUCCESS);
6381: }
6383: /*@
6384: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6385: of a set of rows of a matrix indicated by an `IS`
6387: Collective
6389: Input Parameters:
6390: + mat - the matrix
6391: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6392: . diag - value put in all diagonals of eliminated rows
6393: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6394: - b - optional vector of right-hand side, that will be adjusted by provided solution
6396: Level: intermediate
6398: Note:
6399: See `MatZeroRows()` for details on how this routine operates.
6401: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6402: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6403: @*/
6404: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6405: {
6406: PetscInt numRows = 0;
6407: const PetscInt *rows = NULL;
6409: PetscFunctionBegin;
6412: if (is) {
6414: PetscCall(ISGetLocalSize(is, &numRows));
6415: PetscCall(ISGetIndices(is, &rows));
6416: }
6417: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6418: if (is) PetscCall(ISRestoreIndices(is, &rows));
6419: PetscFunctionReturn(PETSC_SUCCESS);
6420: }
6422: /*@
6423: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6424: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6426: Collective
6428: Input Parameters:
6429: + mat - the matrix
6430: . numRows - the number of rows to remove
6431: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6432: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6433: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6434: - b - optional vector of right-hand side, that will be adjusted by provided solution
6436: Level: intermediate
6438: Notes:
6439: See `MatZeroRows()` for details on how this routine operates.
6441: The grid coordinates are across the entire grid, not just the local portion
6443: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6444: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6445: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6446: `DM_BOUNDARY_PERIODIC` boundary type.
6448: 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
6449: a single value per point) you can skip filling those indices.
6451: Fortran Note:
6452: `idxm` and `idxn` should be declared as
6453: .vb
6454: MatStencil idxm(4, m)
6455: .ve
6456: and the values inserted using
6457: .vb
6458: idxm(MatStencil_i, 1) = i
6459: idxm(MatStencil_j, 1) = j
6460: idxm(MatStencil_k, 1) = k
6461: idxm(MatStencil_c, 1) = c
6462: etc
6463: .ve
6465: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6466: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6467: @*/
6468: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6469: {
6470: PetscInt dim = mat->stencil.dim;
6471: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6472: PetscInt *dims = mat->stencil.dims + 1;
6473: PetscInt *starts = mat->stencil.starts;
6474: PetscInt *dxm = (PetscInt *)rows;
6475: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6477: PetscFunctionBegin;
6480: if (numRows) PetscAssertPointer(rows, 3);
6482: PetscCall(PetscMalloc1(numRows, &jdxm));
6483: for (i = 0; i < numRows; ++i) {
6484: /* Skip unused dimensions (they are ordered k, j, i, c) */
6485: for (j = 0; j < 3 - sdim; ++j) dxm++;
6486: /* Local index in X dir */
6487: tmp = *dxm++ - starts[0];
6488: /* Loop over remaining dimensions */
6489: for (j = 0; j < dim - 1; ++j) {
6490: /* If nonlocal, set index to be negative */
6491: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6492: /* Update local index */
6493: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6494: }
6495: /* Skip component slot if necessary */
6496: if (mat->stencil.noc) dxm++;
6497: /* Local row number */
6498: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6499: }
6500: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6501: PetscCall(PetscFree(jdxm));
6502: PetscFunctionReturn(PETSC_SUCCESS);
6503: }
6505: /*@
6506: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6507: of a set of rows and columns of a matrix.
6509: Collective
6511: Input Parameters:
6512: + mat - the matrix
6513: . numRows - the number of rows/columns to remove
6514: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6515: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6516: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6517: - b - optional vector of right-hand side, that will be adjusted by provided solution
6519: Level: intermediate
6521: Notes:
6522: See `MatZeroRowsColumns()` for details on how this routine operates.
6524: The grid coordinates are across the entire grid, not just the local portion
6526: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6527: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6528: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6529: `DM_BOUNDARY_PERIODIC` boundary type.
6531: 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
6532: a single value per point) you can skip filling those indices.
6534: Fortran Note:
6535: `idxm` and `idxn` should be declared as
6536: .vb
6537: MatStencil idxm(4, m)
6538: .ve
6539: and the values inserted using
6540: .vb
6541: idxm(MatStencil_i, 1) = i
6542: idxm(MatStencil_j, 1) = j
6543: idxm(MatStencil_k, 1) = k
6544: idxm(MatStencil_c, 1) = c
6545: etc
6546: .ve
6548: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6549: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6550: @*/
6551: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6552: {
6553: PetscInt dim = mat->stencil.dim;
6554: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6555: PetscInt *dims = mat->stencil.dims + 1;
6556: PetscInt *starts = mat->stencil.starts;
6557: PetscInt *dxm = (PetscInt *)rows;
6558: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6560: PetscFunctionBegin;
6563: if (numRows) PetscAssertPointer(rows, 3);
6565: PetscCall(PetscMalloc1(numRows, &jdxm));
6566: for (i = 0; i < numRows; ++i) {
6567: /* Skip unused dimensions (they are ordered k, j, i, c) */
6568: for (j = 0; j < 3 - sdim; ++j) dxm++;
6569: /* Local index in X dir */
6570: tmp = *dxm++ - starts[0];
6571: /* Loop over remaining dimensions */
6572: for (j = 0; j < dim - 1; ++j) {
6573: /* If nonlocal, set index to be negative */
6574: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6575: /* Update local index */
6576: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6577: }
6578: /* Skip component slot if necessary */
6579: if (mat->stencil.noc) dxm++;
6580: /* Local row number */
6581: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6582: }
6583: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6584: PetscCall(PetscFree(jdxm));
6585: PetscFunctionReturn(PETSC_SUCCESS);
6586: }
6588: /*@
6589: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6590: of a set of rows of a matrix; using local numbering of rows.
6592: Collective
6594: Input Parameters:
6595: + mat - the matrix
6596: . numRows - the number of rows to remove
6597: . rows - the local row indices
6598: . diag - value put in all diagonals of eliminated rows
6599: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6600: - b - optional vector of right-hand side, that will be adjusted by provided solution
6602: Level: intermediate
6604: Notes:
6605: Before calling `MatZeroRowsLocal()`, the user must first set the
6606: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6608: See `MatZeroRows()` for details on how this routine operates.
6610: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6611: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6612: @*/
6613: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6614: {
6615: PetscFunctionBegin;
6618: if (numRows) PetscAssertPointer(rows, 3);
6619: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6620: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6621: MatCheckPreallocated(mat, 1);
6623: if (mat->ops->zerorowslocal) {
6624: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6625: } else {
6626: IS is, newis;
6627: const PetscInt *newRows;
6629: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6630: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6631: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6632: PetscCall(ISGetIndices(newis, &newRows));
6633: PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6634: PetscCall(ISRestoreIndices(newis, &newRows));
6635: PetscCall(ISDestroy(&newis));
6636: PetscCall(ISDestroy(&is));
6637: }
6638: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6639: PetscFunctionReturn(PETSC_SUCCESS);
6640: }
6642: /*@
6643: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6644: of a set of rows of a matrix; using local numbering of rows.
6646: Collective
6648: Input Parameters:
6649: + mat - the matrix
6650: . is - index set of rows to remove
6651: . diag - value put in all diagonals of eliminated rows
6652: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6653: - b - optional vector of right-hand side, that will be adjusted by provided solution
6655: Level: intermediate
6657: Notes:
6658: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6659: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6661: See `MatZeroRows()` for details on how this routine operates.
6663: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6664: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6665: @*/
6666: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6667: {
6668: PetscInt numRows;
6669: const PetscInt *rows;
6671: PetscFunctionBegin;
6675: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6676: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6677: MatCheckPreallocated(mat, 1);
6679: PetscCall(ISGetLocalSize(is, &numRows));
6680: PetscCall(ISGetIndices(is, &rows));
6681: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6682: PetscCall(ISRestoreIndices(is, &rows));
6683: PetscFunctionReturn(PETSC_SUCCESS);
6684: }
6686: /*@
6687: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6688: of a set of rows and columns of a matrix; using local numbering of rows.
6690: Collective
6692: Input Parameters:
6693: + mat - the matrix
6694: . numRows - the number of rows to remove
6695: . rows - the global row indices
6696: . diag - value put in all diagonals of eliminated rows
6697: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6698: - b - optional vector of right-hand side, that will be adjusted by provided solution
6700: Level: intermediate
6702: Notes:
6703: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6704: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6706: See `MatZeroRowsColumns()` for details on how this routine operates.
6708: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6709: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6710: @*/
6711: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6712: {
6713: IS is, newis;
6714: const PetscInt *newRows;
6716: PetscFunctionBegin;
6719: if (numRows) PetscAssertPointer(rows, 3);
6720: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6721: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6722: MatCheckPreallocated(mat, 1);
6724: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6725: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6726: PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6727: PetscCall(ISGetIndices(newis, &newRows));
6728: PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6729: PetscCall(ISRestoreIndices(newis, &newRows));
6730: PetscCall(ISDestroy(&newis));
6731: PetscCall(ISDestroy(&is));
6732: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6733: PetscFunctionReturn(PETSC_SUCCESS);
6734: }
6736: /*@
6737: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6738: of a set of rows and columns of a matrix; using local numbering of rows.
6740: Collective
6742: Input Parameters:
6743: + mat - the matrix
6744: . is - index set of rows to remove
6745: . diag - value put in all diagonals of eliminated rows
6746: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6747: - b - optional vector of right-hand side, that will be adjusted by provided solution
6749: Level: intermediate
6751: Notes:
6752: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6753: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6755: See `MatZeroRowsColumns()` for details on how this routine operates.
6757: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6758: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6759: @*/
6760: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6761: {
6762: PetscInt numRows;
6763: const PetscInt *rows;
6765: PetscFunctionBegin;
6769: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6770: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6771: MatCheckPreallocated(mat, 1);
6773: PetscCall(ISGetLocalSize(is, &numRows));
6774: PetscCall(ISGetIndices(is, &rows));
6775: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6776: PetscCall(ISRestoreIndices(is, &rows));
6777: PetscFunctionReturn(PETSC_SUCCESS);
6778: }
6780: /*@
6781: MatGetSize - Returns the numbers of rows and columns in a matrix.
6783: Not Collective
6785: Input Parameter:
6786: . mat - the matrix
6788: Output Parameters:
6789: + m - the number of global rows
6790: - n - the number of global columns
6792: Level: beginner
6794: Note:
6795: Both output parameters can be `NULL` on input.
6797: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6798: @*/
6799: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6800: {
6801: PetscFunctionBegin;
6803: if (m) *m = mat->rmap->N;
6804: if (n) *n = mat->cmap->N;
6805: PetscFunctionReturn(PETSC_SUCCESS);
6806: }
6808: /*@
6809: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6810: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6812: Not Collective
6814: Input Parameter:
6815: . mat - the matrix
6817: Output Parameters:
6818: + m - the number of local rows, use `NULL` to not obtain this value
6819: - n - the number of local columns, use `NULL` to not obtain this value
6821: Level: beginner
6823: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6824: @*/
6825: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6826: {
6827: PetscFunctionBegin;
6829: if (m) PetscAssertPointer(m, 2);
6830: if (n) PetscAssertPointer(n, 3);
6831: if (m) *m = mat->rmap->n;
6832: if (n) *n = mat->cmap->n;
6833: PetscFunctionReturn(PETSC_SUCCESS);
6834: }
6836: /*@
6837: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6838: vector one multiplies this matrix by that are owned by this processor.
6840: Not Collective, unless matrix has not been allocated, then collective
6842: Input Parameter:
6843: . mat - the matrix
6845: Output Parameters:
6846: + m - the global index of the first local column, use `NULL` to not obtain this value
6847: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
6849: Level: developer
6851: Notes:
6852: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6854: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6855: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6857: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6858: the local values in the matrix.
6860: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6861: Layouts](sec_matlayout) for details on matrix layouts.
6863: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6864: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6865: @*/
6866: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6867: {
6868: PetscFunctionBegin;
6871: if (m) PetscAssertPointer(m, 2);
6872: if (n) PetscAssertPointer(n, 3);
6873: MatCheckPreallocated(mat, 1);
6874: if (m) *m = mat->cmap->rstart;
6875: if (n) *n = mat->cmap->rend;
6876: PetscFunctionReturn(PETSC_SUCCESS);
6877: }
6879: /*@
6880: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6881: this MPI process.
6883: Not Collective
6885: Input Parameter:
6886: . mat - the matrix
6888: Output Parameters:
6889: + m - the global index of the first local row, use `NULL` to not obtain this value
6890: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
6892: Level: beginner
6894: Notes:
6895: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6897: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6898: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6900: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6901: the local values in the matrix.
6903: The high argument is one more than the last element stored locally.
6905: For all matrices it returns the range of matrix rows associated with rows of a vector that
6906: would contain the result of a matrix vector product with this matrix. See [Matrix
6907: Layouts](sec_matlayout) for details on matrix layouts.
6909: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6910: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6911: @*/
6912: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6913: {
6914: PetscFunctionBegin;
6917: if (m) PetscAssertPointer(m, 2);
6918: if (n) PetscAssertPointer(n, 3);
6919: MatCheckPreallocated(mat, 1);
6920: if (m) *m = mat->rmap->rstart;
6921: if (n) *n = mat->rmap->rend;
6922: PetscFunctionReturn(PETSC_SUCCESS);
6923: }
6925: /*@C
6926: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6927: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
6929: Not Collective, unless matrix has not been allocated
6931: Input Parameter:
6932: . mat - the matrix
6934: Output Parameter:
6935: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6936: where `size` is the number of MPI processes used by `mat`
6938: Level: beginner
6940: Notes:
6941: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6943: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6944: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6946: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6947: the local values in the matrix.
6949: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
6950: would contain the result of a matrix vector product with this matrix. See [Matrix
6951: Layouts](sec_matlayout) for details on matrix layouts.
6953: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6954: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6955: `DMDAGetGhostCorners()`, `DM`
6956: @*/
6957: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6958: {
6959: PetscFunctionBegin;
6962: MatCheckPreallocated(mat, 1);
6963: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6964: PetscFunctionReturn(PETSC_SUCCESS);
6965: }
6967: /*@C
6968: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6969: vector one multiplies this vector by that are owned by each processor.
6971: Not Collective, unless matrix has not been allocated
6973: Input Parameter:
6974: . mat - the matrix
6976: Output Parameter:
6977: . ranges - start of each processors portion plus one more than the total length at the end
6979: Level: beginner
6981: Notes:
6982: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6984: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6985: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6987: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6988: the local values in the matrix.
6990: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
6991: Layouts](sec_matlayout) for details on matrix layouts.
6993: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
6994: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
6995: `DMDAGetGhostCorners()`, `DM`
6996: @*/
6997: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
6998: {
6999: PetscFunctionBegin;
7002: MatCheckPreallocated(mat, 1);
7003: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7004: PetscFunctionReturn(PETSC_SUCCESS);
7005: }
7007: /*@
7008: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7010: Not Collective
7012: Input Parameter:
7013: . A - matrix
7015: Output Parameters:
7016: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7017: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7019: Level: intermediate
7021: Note:
7022: You should call `ISDestroy()` on the returned `IS`
7024: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7025: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7026: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7027: details on matrix layouts.
7029: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7030: @*/
7031: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7032: {
7033: PetscErrorCode (*f)(Mat, IS *, IS *);
7035: PetscFunctionBegin;
7038: MatCheckPreallocated(A, 1);
7039: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7040: if (f) {
7041: PetscCall((*f)(A, rows, cols));
7042: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7043: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7044: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7045: }
7046: PetscFunctionReturn(PETSC_SUCCESS);
7047: }
7049: /*@
7050: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7051: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7052: to complete the factorization.
7054: Collective
7056: Input Parameters:
7057: + fact - the factorized matrix obtained with `MatGetFactor()`
7058: . mat - the matrix
7059: . row - row permutation
7060: . col - column permutation
7061: - info - structure containing
7062: .vb
7063: levels - number of levels of fill.
7064: expected fill - as ratio of original fill.
7065: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7066: missing diagonal entries)
7067: .ve
7069: Level: developer
7071: Notes:
7072: See [Matrix Factorization](sec_matfactor) for additional information.
7074: Most users should employ the `KSP` interface for linear solvers
7075: instead of working directly with matrix algebra routines such as this.
7076: See, e.g., `KSPCreate()`.
7078: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7080: Fortran Note:
7081: A valid (non-null) `info` argument must be provided
7083: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7084: `MatGetOrdering()`, `MatFactorInfo`
7085: @*/
7086: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7087: {
7088: PetscFunctionBegin;
7093: PetscAssertPointer(info, 5);
7094: PetscAssertPointer(fact, 1);
7095: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7096: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7097: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7098: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7099: MatCheckPreallocated(mat, 2);
7101: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7102: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7103: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7104: PetscFunctionReturn(PETSC_SUCCESS);
7105: }
7107: /*@
7108: MatICCFactorSymbolic - Performs symbolic incomplete
7109: Cholesky factorization for a symmetric matrix. Use
7110: `MatCholeskyFactorNumeric()` to complete the factorization.
7112: Collective
7114: Input Parameters:
7115: + fact - the factorized matrix obtained with `MatGetFactor()`
7116: . mat - the matrix to be factored
7117: . perm - row and column permutation
7118: - info - structure containing
7119: .vb
7120: levels - number of levels of fill.
7121: expected fill - as ratio of original fill.
7122: .ve
7124: Level: developer
7126: Notes:
7127: Most users should employ the `KSP` interface for linear solvers
7128: instead of working directly with matrix algebra routines such as this.
7129: See, e.g., `KSPCreate()`.
7131: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7133: Fortran Note:
7134: A valid (non-null) `info` argument must be provided
7136: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7137: @*/
7138: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7139: {
7140: PetscFunctionBegin;
7144: PetscAssertPointer(info, 4);
7145: PetscAssertPointer(fact, 1);
7146: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7147: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7148: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7149: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7150: MatCheckPreallocated(mat, 2);
7152: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7153: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7154: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7155: PetscFunctionReturn(PETSC_SUCCESS);
7156: }
7158: /*@C
7159: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7160: points to an array of valid matrices, they may be reused to store the new
7161: submatrices.
7163: Collective
7165: Input Parameters:
7166: + mat - the matrix
7167: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7168: . irow - index set of rows to extract
7169: . icol - index set of columns to extract
7170: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7172: Output Parameter:
7173: . submat - the array of submatrices
7175: Level: advanced
7177: Notes:
7178: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7179: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7180: to extract a parallel submatrix.
7182: Some matrix types place restrictions on the row and column
7183: indices, such as that they be sorted or that they be equal to each other.
7185: The index sets may not have duplicate entries.
7187: When extracting submatrices from a parallel matrix, each processor can
7188: form a different submatrix by setting the rows and columns of its
7189: individual index sets according to the local submatrix desired.
7191: When finished using the submatrices, the user should destroy
7192: them with `MatDestroySubMatrices()`.
7194: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7195: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7197: This routine creates the matrices in submat; you should NOT create them before
7198: calling it. It also allocates the array of matrix pointers submat.
7200: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7201: request one row/column in a block, they must request all rows/columns that are in
7202: that block. For example, if the block size is 2 you cannot request just row 0 and
7203: column 0.
7205: Fortran Note:
7206: .vb
7207: Mat, pointer :: submat(:)
7208: .ve
7210: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7211: @*/
7212: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7213: {
7214: PetscInt i;
7215: PetscBool eq;
7217: PetscFunctionBegin;
7220: if (n) {
7221: PetscAssertPointer(irow, 3);
7223: PetscAssertPointer(icol, 4);
7225: }
7226: PetscAssertPointer(submat, 6);
7227: if (n && scall == MAT_REUSE_MATRIX) {
7228: PetscAssertPointer(*submat, 6);
7230: }
7231: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7232: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7233: MatCheckPreallocated(mat, 1);
7234: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7235: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7236: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7237: for (i = 0; i < n; i++) {
7238: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7239: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7240: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7241: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7242: if (mat->boundtocpu && mat->bindingpropagates) {
7243: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7244: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7245: }
7246: #endif
7247: }
7248: PetscFunctionReturn(PETSC_SUCCESS);
7249: }
7251: /*@C
7252: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7254: Collective
7256: Input Parameters:
7257: + mat - the matrix
7258: . n - the number of submatrixes to be extracted
7259: . irow - index set of rows to extract
7260: . icol - index set of columns to extract
7261: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7263: Output Parameter:
7264: . submat - the array of submatrices
7266: Level: advanced
7268: Note:
7269: This is used by `PCGASM`
7271: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7272: @*/
7273: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7274: {
7275: PetscInt i;
7276: PetscBool eq;
7278: PetscFunctionBegin;
7281: if (n) {
7282: PetscAssertPointer(irow, 3);
7284: PetscAssertPointer(icol, 4);
7286: }
7287: PetscAssertPointer(submat, 6);
7288: if (n && scall == MAT_REUSE_MATRIX) {
7289: PetscAssertPointer(*submat, 6);
7291: }
7292: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7293: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7294: MatCheckPreallocated(mat, 1);
7296: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7297: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7298: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7299: for (i = 0; i < n; i++) {
7300: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7301: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7302: }
7303: PetscFunctionReturn(PETSC_SUCCESS);
7304: }
7306: /*@C
7307: MatDestroyMatrices - Destroys an array of matrices
7309: Collective
7311: Input Parameters:
7312: + n - the number of local matrices
7313: - mat - the matrices (this is a pointer to the array of matrices)
7315: Level: advanced
7317: Notes:
7318: Frees not only the matrices, but also the array that contains the matrices
7320: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7322: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7323: @*/
7324: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7325: {
7326: PetscInt i;
7328: PetscFunctionBegin;
7329: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7330: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7331: PetscAssertPointer(mat, 2);
7333: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7335: /* memory is allocated even if n = 0 */
7336: PetscCall(PetscFree(*mat));
7337: PetscFunctionReturn(PETSC_SUCCESS);
7338: }
7340: /*@C
7341: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7343: Collective
7345: Input Parameters:
7346: + n - the number of local matrices
7347: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7349: Level: advanced
7351: Note:
7352: Frees not only the matrices, but also the array that contains the matrices
7354: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7355: @*/
7356: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7357: {
7358: Mat mat0;
7360: PetscFunctionBegin;
7361: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7362: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7363: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7364: PetscAssertPointer(mat, 2);
7366: mat0 = (*mat)[0];
7367: if (mat0 && mat0->ops->destroysubmatrices) {
7368: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7369: } else {
7370: PetscCall(MatDestroyMatrices(n, mat));
7371: }
7372: PetscFunctionReturn(PETSC_SUCCESS);
7373: }
7375: /*@
7376: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7378: Collective
7380: Input Parameter:
7381: . mat - the matrix
7383: Output Parameter:
7384: . matstruct - the sequential matrix with the nonzero structure of `mat`
7386: Level: developer
7388: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7389: @*/
7390: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7391: {
7392: PetscFunctionBegin;
7394: PetscAssertPointer(matstruct, 2);
7397: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7398: MatCheckPreallocated(mat, 1);
7400: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7401: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7402: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7403: PetscFunctionReturn(PETSC_SUCCESS);
7404: }
7406: /*@C
7407: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7409: Collective
7411: Input Parameter:
7412: . mat - the matrix
7414: Level: advanced
7416: Note:
7417: This is not needed, one can just call `MatDestroy()`
7419: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7420: @*/
7421: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7422: {
7423: PetscFunctionBegin;
7424: PetscAssertPointer(mat, 1);
7425: PetscCall(MatDestroy(mat));
7426: PetscFunctionReturn(PETSC_SUCCESS);
7427: }
7429: /*@
7430: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7431: replaces the index sets by larger ones that represent submatrices with
7432: additional overlap.
7434: Collective
7436: Input Parameters:
7437: + mat - the matrix
7438: . n - the number of index sets
7439: . is - the array of index sets (these index sets will changed during the call)
7440: - ov - the additional overlap requested
7442: Options Database Key:
7443: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7445: Level: developer
7447: Note:
7448: The computed overlap preserves the matrix block sizes when the blocks are square.
7449: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7450: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7452: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7453: @*/
7454: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7455: {
7456: PetscInt i, bs, cbs;
7458: PetscFunctionBegin;
7462: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7463: if (n) {
7464: PetscAssertPointer(is, 3);
7466: }
7467: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7468: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7469: MatCheckPreallocated(mat, 1);
7471: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7472: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7473: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7474: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7475: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7476: if (bs == cbs) {
7477: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7478: }
7479: PetscFunctionReturn(PETSC_SUCCESS);
7480: }
7482: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7484: /*@
7485: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7486: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7487: additional overlap.
7489: Collective
7491: Input Parameters:
7492: + mat - the matrix
7493: . n - the number of index sets
7494: . is - the array of index sets (these index sets will changed during the call)
7495: - ov - the additional overlap requested
7497: ` Options Database Key:
7498: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7500: Level: developer
7502: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7503: @*/
7504: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7505: {
7506: PetscInt i;
7508: PetscFunctionBegin;
7511: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7512: if (n) {
7513: PetscAssertPointer(is, 3);
7515: }
7516: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7517: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7518: MatCheckPreallocated(mat, 1);
7519: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7520: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7521: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7522: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7523: PetscFunctionReturn(PETSC_SUCCESS);
7524: }
7526: /*@
7527: MatGetBlockSize - Returns the matrix block size.
7529: Not Collective
7531: Input Parameter:
7532: . mat - the matrix
7534: Output Parameter:
7535: . bs - block size
7537: Level: intermediate
7539: Notes:
7540: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7542: If the block size has not been set yet this routine returns 1.
7544: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7545: @*/
7546: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7547: {
7548: PetscFunctionBegin;
7550: PetscAssertPointer(bs, 2);
7551: *bs = mat->rmap->bs;
7552: PetscFunctionReturn(PETSC_SUCCESS);
7553: }
7555: /*@
7556: MatGetBlockSizes - Returns the matrix block row and column sizes.
7558: Not Collective
7560: Input Parameter:
7561: . mat - the matrix
7563: Output Parameters:
7564: + rbs - row block size
7565: - cbs - column block size
7567: Level: intermediate
7569: Notes:
7570: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7571: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7573: If a block size has not been set yet this routine returns 1.
7575: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7576: @*/
7577: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7578: {
7579: PetscFunctionBegin;
7581: if (rbs) PetscAssertPointer(rbs, 2);
7582: if (cbs) PetscAssertPointer(cbs, 3);
7583: if (rbs) *rbs = mat->rmap->bs;
7584: if (cbs) *cbs = mat->cmap->bs;
7585: PetscFunctionReturn(PETSC_SUCCESS);
7586: }
7588: /*@
7589: MatSetBlockSize - Sets the matrix block size.
7591: Logically Collective
7593: Input Parameters:
7594: + mat - the matrix
7595: - bs - block size
7597: Level: intermediate
7599: Notes:
7600: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7601: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7603: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7604: is compatible with the matrix local sizes.
7606: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7607: @*/
7608: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7609: {
7610: PetscFunctionBegin;
7613: PetscCall(MatSetBlockSizes(mat, bs, bs));
7614: PetscFunctionReturn(PETSC_SUCCESS);
7615: }
7617: typedef struct {
7618: PetscInt n;
7619: IS *is;
7620: Mat *mat;
7621: PetscObjectState nonzerostate;
7622: Mat C;
7623: } EnvelopeData;
7625: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7626: {
7627: EnvelopeData *edata = (EnvelopeData *)*ptr;
7629: PetscFunctionBegin;
7630: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7631: PetscCall(PetscFree(edata->is));
7632: PetscCall(PetscFree(edata));
7633: PetscFunctionReturn(PETSC_SUCCESS);
7634: }
7636: /*@
7637: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7638: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7640: Collective
7642: Input Parameter:
7643: . mat - the matrix
7645: Level: intermediate
7647: Notes:
7648: There can be zeros within the blocks
7650: The blocks can overlap between processes, including laying on more than two processes
7652: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7653: @*/
7654: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7655: {
7656: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7657: PetscInt *diag, *odiag, sc;
7658: VecScatter scatter;
7659: PetscScalar *seqv;
7660: const PetscScalar *parv;
7661: const PetscInt *ia, *ja;
7662: PetscBool set, flag, done;
7663: Mat AA = mat, A;
7664: MPI_Comm comm;
7665: PetscMPIInt rank, size, tag;
7666: MPI_Status status;
7667: PetscContainer container;
7668: EnvelopeData *edata;
7669: Vec seq, par;
7670: IS isglobal;
7672: PetscFunctionBegin;
7674: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7675: if (!set || !flag) {
7676: /* TODO: only needs nonzero structure of transpose */
7677: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7678: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7679: }
7680: PetscCall(MatAIJGetLocalMat(AA, &A));
7681: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7682: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7684: PetscCall(MatGetLocalSize(mat, &n, NULL));
7685: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7686: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7687: PetscCallMPI(MPI_Comm_size(comm, &size));
7688: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7690: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7692: if (rank > 0) {
7693: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7694: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7695: }
7696: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7697: for (i = 0; i < n; i++) {
7698: env = PetscMax(env, ja[ia[i + 1] - 1]);
7699: II = rstart + i;
7700: if (env == II) {
7701: starts[lblocks] = tbs;
7702: sizes[lblocks++] = 1 + II - tbs;
7703: tbs = 1 + II;
7704: }
7705: }
7706: if (rank < size - 1) {
7707: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7708: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7709: }
7711: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7712: if (!set || !flag) PetscCall(MatDestroy(&AA));
7713: PetscCall(MatDestroy(&A));
7715: PetscCall(PetscNew(&edata));
7716: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7717: edata->n = lblocks;
7718: /* create IS needed for extracting blocks from the original matrix */
7719: PetscCall(PetscMalloc1(lblocks, &edata->is));
7720: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7722: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7723: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7724: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7725: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7726: PetscCall(MatSetType(edata->C, MATAIJ));
7728: /* Communicate the start and end of each row, from each block to the correct rank */
7729: /* TODO: Use PetscSF instead of VecScatter */
7730: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7731: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7732: PetscCall(VecGetArrayWrite(seq, &seqv));
7733: for (PetscInt i = 0; i < lblocks; i++) {
7734: for (PetscInt j = 0; j < sizes[i]; j++) {
7735: seqv[cnt] = starts[i];
7736: seqv[cnt + 1] = starts[i] + sizes[i];
7737: cnt += 2;
7738: }
7739: }
7740: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7741: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7742: sc -= cnt;
7743: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7744: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7745: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7746: PetscCall(ISDestroy(&isglobal));
7747: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7748: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7749: PetscCall(VecScatterDestroy(&scatter));
7750: PetscCall(VecDestroy(&seq));
7751: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7752: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7753: PetscCall(VecGetArrayRead(par, &parv));
7754: cnt = 0;
7755: PetscCall(MatGetSize(mat, NULL, &n));
7756: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7757: PetscInt start, end, d = 0, od = 0;
7759: start = (PetscInt)PetscRealPart(parv[cnt]);
7760: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7761: cnt += 2;
7763: if (start < cstart) {
7764: od += cstart - start + n - cend;
7765: d += cend - cstart;
7766: } else if (start < cend) {
7767: od += n - cend;
7768: d += cend - start;
7769: } else od += n - start;
7770: if (end <= cstart) {
7771: od -= cstart - end + n - cend;
7772: d -= cend - cstart;
7773: } else if (end < cend) {
7774: od -= n - cend;
7775: d -= cend - end;
7776: } else od -= n - end;
7778: odiag[i] = od;
7779: diag[i] = d;
7780: }
7781: PetscCall(VecRestoreArrayRead(par, &parv));
7782: PetscCall(VecDestroy(&par));
7783: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7784: PetscCall(PetscFree2(diag, odiag));
7785: PetscCall(PetscFree2(sizes, starts));
7787: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7788: PetscCall(PetscContainerSetPointer(container, edata));
7789: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7790: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7791: PetscCall(PetscObjectDereference((PetscObject)container));
7792: PetscFunctionReturn(PETSC_SUCCESS);
7793: }
7795: /*@
7796: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7798: Collective
7800: Input Parameters:
7801: + A - the matrix
7802: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7804: Output Parameter:
7805: . C - matrix with inverted block diagonal of `A`
7807: Level: advanced
7809: Note:
7810: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7812: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7813: @*/
7814: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7815: {
7816: PetscContainer container;
7817: EnvelopeData *edata;
7818: PetscObjectState nonzerostate;
7820: PetscFunctionBegin;
7821: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7822: if (!container) {
7823: PetscCall(MatComputeVariableBlockEnvelope(A));
7824: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7825: }
7826: PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7827: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7828: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7829: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7831: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7832: *C = edata->C;
7834: for (PetscInt i = 0; i < edata->n; i++) {
7835: Mat D;
7836: PetscScalar *dvalues;
7838: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7839: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7840: PetscCall(MatSeqDenseInvert(D));
7841: PetscCall(MatDenseGetArray(D, &dvalues));
7842: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7843: PetscCall(MatDestroy(&D));
7844: }
7845: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7846: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7847: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7848: PetscFunctionReturn(PETSC_SUCCESS);
7849: }
7851: /*@
7852: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
7854: Not Collective
7856: Input Parameters:
7857: + mat - the matrix
7858: . nblocks - the number of blocks on this process, each block can only exist on a single process
7859: - bsizes - the block sizes
7861: Level: intermediate
7863: Notes:
7864: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
7866: 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.
7868: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7869: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7870: @*/
7871: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7872: {
7873: PetscInt ncnt = 0, nlocal;
7875: PetscFunctionBegin;
7877: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7878: 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);
7879: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7880: 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);
7881: PetscCall(PetscFree(mat->bsizes));
7882: mat->nblocks = nblocks;
7883: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7884: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7885: PetscFunctionReturn(PETSC_SUCCESS);
7886: }
7888: /*@C
7889: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
7891: Not Collective; No Fortran Support
7893: Input Parameter:
7894: . mat - the matrix
7896: Output Parameters:
7897: + nblocks - the number of blocks on this process
7898: - bsizes - the block sizes
7900: Level: intermediate
7902: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7903: @*/
7904: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7905: {
7906: PetscFunctionBegin;
7908: if (nblocks) *nblocks = mat->nblocks;
7909: if (bsizes) *bsizes = mat->bsizes;
7910: PetscFunctionReturn(PETSC_SUCCESS);
7911: }
7913: /*@
7914: MatSetBlockSizes - Sets the matrix block row and column sizes.
7916: Logically Collective
7918: Input Parameters:
7919: + mat - the matrix
7920: . rbs - row block size
7921: - cbs - column block size
7923: Level: intermediate
7925: Notes:
7926: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7927: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7928: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7930: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7931: are compatible with the matrix local sizes.
7933: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
7935: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7936: @*/
7937: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7938: {
7939: PetscFunctionBegin;
7943: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7944: if (mat->rmap->refcnt) {
7945: ISLocalToGlobalMapping l2g = NULL;
7946: PetscLayout nmap = NULL;
7948: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7949: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7950: PetscCall(PetscLayoutDestroy(&mat->rmap));
7951: mat->rmap = nmap;
7952: mat->rmap->mapping = l2g;
7953: }
7954: if (mat->cmap->refcnt) {
7955: ISLocalToGlobalMapping l2g = NULL;
7956: PetscLayout nmap = NULL;
7958: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7959: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7960: PetscCall(PetscLayoutDestroy(&mat->cmap));
7961: mat->cmap = nmap;
7962: mat->cmap->mapping = l2g;
7963: }
7964: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7965: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7966: PetscFunctionReturn(PETSC_SUCCESS);
7967: }
7969: /*@
7970: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
7972: Logically Collective
7974: Input Parameters:
7975: + mat - the matrix
7976: . fromRow - matrix from which to copy row block size
7977: - fromCol - matrix from which to copy column block size (can be same as fromRow)
7979: Level: developer
7981: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
7982: @*/
7983: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
7984: {
7985: PetscFunctionBegin;
7989: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
7990: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
7991: PetscFunctionReturn(PETSC_SUCCESS);
7992: }
7994: /*@
7995: MatResidual - Default routine to calculate the residual r = b - Ax
7997: Collective
7999: Input Parameters:
8000: + mat - the matrix
8001: . b - the right-hand-side
8002: - x - the approximate solution
8004: Output Parameter:
8005: . r - location to store the residual
8007: Level: developer
8009: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8010: @*/
8011: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8012: {
8013: PetscFunctionBegin;
8019: MatCheckPreallocated(mat, 1);
8020: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8021: if (!mat->ops->residual) {
8022: PetscCall(MatMult(mat, x, r));
8023: PetscCall(VecAYPX(r, -1.0, b));
8024: } else {
8025: PetscUseTypeMethod(mat, residual, b, x, r);
8026: }
8027: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8028: PetscFunctionReturn(PETSC_SUCCESS);
8029: }
8031: /*@C
8032: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8034: Collective
8036: Input Parameters:
8037: + mat - the matrix
8038: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8039: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8040: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8041: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8042: always used.
8044: Output Parameters:
8045: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8046: . 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
8047: . ja - the column indices, use `NULL` if not needed
8048: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8049: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8051: Level: developer
8053: Notes:
8054: You CANNOT change any of the ia[] or ja[] values.
8056: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8058: Fortran Notes:
8059: Use
8060: .vb
8061: PetscInt, pointer :: ia(:),ja(:)
8062: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8063: ! Access the ith and jth entries via ia(i) and ja(j)
8064: .ve
8066: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8067: @*/
8068: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8069: {
8070: PetscFunctionBegin;
8073: if (n) PetscAssertPointer(n, 5);
8074: if (ia) PetscAssertPointer(ia, 6);
8075: if (ja) PetscAssertPointer(ja, 7);
8076: if (done) PetscAssertPointer(done, 8);
8077: MatCheckPreallocated(mat, 1);
8078: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8079: else {
8080: if (done) *done = PETSC_TRUE;
8081: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8082: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8083: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8084: }
8085: PetscFunctionReturn(PETSC_SUCCESS);
8086: }
8088: /*@C
8089: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8091: Collective
8093: Input Parameters:
8094: + mat - the matrix
8095: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8096: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8097: symmetrized
8098: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8099: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8100: always used.
8101: . n - number of columns in the (possibly compressed) matrix
8102: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8103: - ja - the row indices
8105: Output Parameter:
8106: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8108: Level: developer
8110: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8111: @*/
8112: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8113: {
8114: PetscFunctionBegin;
8117: PetscAssertPointer(n, 5);
8118: if (ia) PetscAssertPointer(ia, 6);
8119: if (ja) PetscAssertPointer(ja, 7);
8120: PetscAssertPointer(done, 8);
8121: MatCheckPreallocated(mat, 1);
8122: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8123: else {
8124: *done = PETSC_TRUE;
8125: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8126: }
8127: PetscFunctionReturn(PETSC_SUCCESS);
8128: }
8130: /*@C
8131: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8133: Collective
8135: Input Parameters:
8136: + mat - the matrix
8137: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8138: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8139: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8140: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8141: always used.
8142: . n - size of (possibly compressed) matrix
8143: . ia - the row pointers
8144: - ja - the column indices
8146: Output Parameter:
8147: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8149: Level: developer
8151: Note:
8152: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8153: us of the array after it has been restored. If you pass `NULL`, it will
8154: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8156: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8157: @*/
8158: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8159: {
8160: PetscFunctionBegin;
8163: if (ia) PetscAssertPointer(ia, 6);
8164: if (ja) PetscAssertPointer(ja, 7);
8165: if (done) PetscAssertPointer(done, 8);
8166: MatCheckPreallocated(mat, 1);
8168: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8169: else {
8170: if (done) *done = PETSC_TRUE;
8171: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8172: if (n) *n = 0;
8173: if (ia) *ia = NULL;
8174: if (ja) *ja = NULL;
8175: }
8176: PetscFunctionReturn(PETSC_SUCCESS);
8177: }
8179: /*@C
8180: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8182: Collective
8184: Input Parameters:
8185: + mat - the matrix
8186: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8187: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8188: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8189: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8190: always used.
8192: Output Parameters:
8193: + n - size of (possibly compressed) matrix
8194: . ia - the column pointers
8195: . ja - the row indices
8196: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8198: Level: developer
8200: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8201: @*/
8202: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8203: {
8204: PetscFunctionBegin;
8207: if (ia) PetscAssertPointer(ia, 6);
8208: if (ja) PetscAssertPointer(ja, 7);
8209: PetscAssertPointer(done, 8);
8210: MatCheckPreallocated(mat, 1);
8212: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8213: else {
8214: *done = PETSC_TRUE;
8215: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8216: if (n) *n = 0;
8217: if (ia) *ia = NULL;
8218: if (ja) *ja = NULL;
8219: }
8220: PetscFunctionReturn(PETSC_SUCCESS);
8221: }
8223: /*@
8224: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8225: `MatGetColumnIJ()`.
8227: Collective
8229: Input Parameters:
8230: + mat - the matrix
8231: . ncolors - maximum color value
8232: . n - number of entries in colorarray
8233: - colorarray - array indicating color for each column
8235: Output Parameter:
8236: . iscoloring - coloring generated using colorarray information
8238: Level: developer
8240: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8241: @*/
8242: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8243: {
8244: PetscFunctionBegin;
8247: PetscAssertPointer(colorarray, 4);
8248: PetscAssertPointer(iscoloring, 5);
8249: MatCheckPreallocated(mat, 1);
8251: if (!mat->ops->coloringpatch) {
8252: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8253: } else {
8254: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8255: }
8256: PetscFunctionReturn(PETSC_SUCCESS);
8257: }
8259: /*@
8260: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8262: Logically Collective
8264: Input Parameter:
8265: . mat - the factored matrix to be reset
8267: Level: developer
8269: Notes:
8270: This routine should be used only with factored matrices formed by in-place
8271: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8272: format). This option can save memory, for example, when solving nonlinear
8273: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8274: ILU(0) preconditioner.
8276: One can specify in-place ILU(0) factorization by calling
8277: .vb
8278: PCType(pc,PCILU);
8279: PCFactorSeUseInPlace(pc);
8280: .ve
8281: or by using the options -pc_type ilu -pc_factor_in_place
8283: In-place factorization ILU(0) can also be used as a local
8284: solver for the blocks within the block Jacobi or additive Schwarz
8285: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8286: for details on setting local solver options.
8288: Most users should employ the `KSP` interface for linear solvers
8289: instead of working directly with matrix algebra routines such as this.
8290: See, e.g., `KSPCreate()`.
8292: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8293: @*/
8294: PetscErrorCode MatSetUnfactored(Mat mat)
8295: {
8296: PetscFunctionBegin;
8299: MatCheckPreallocated(mat, 1);
8300: mat->factortype = MAT_FACTOR_NONE;
8301: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8302: PetscUseTypeMethod(mat, setunfactored);
8303: PetscFunctionReturn(PETSC_SUCCESS);
8304: }
8306: /*@
8307: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8308: as the original matrix.
8310: Collective
8312: Input Parameters:
8313: + mat - the original matrix
8314: . isrow - parallel `IS` containing the rows this processor should obtain
8315: . 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.
8316: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8318: Output Parameter:
8319: . newmat - the new submatrix, of the same type as the original matrix
8321: Level: advanced
8323: Notes:
8324: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8326: Some matrix types place restrictions on the row and column indices, such
8327: 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;
8328: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8330: The index sets may not have duplicate entries.
8332: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8333: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8334: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8335: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8336: you are finished using it.
8338: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8339: the input matrix.
8341: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8343: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8344: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8346: Example usage:
8347: Consider the following 8x8 matrix with 34 non-zero values, that is
8348: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8349: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8350: as follows
8351: .vb
8352: 1 2 0 | 0 3 0 | 0 4
8353: Proc0 0 5 6 | 7 0 0 | 8 0
8354: 9 0 10 | 11 0 0 | 12 0
8355: -------------------------------------
8356: 13 0 14 | 15 16 17 | 0 0
8357: Proc1 0 18 0 | 19 20 21 | 0 0
8358: 0 0 0 | 22 23 0 | 24 0
8359: -------------------------------------
8360: Proc2 25 26 27 | 0 0 28 | 29 0
8361: 30 0 0 | 31 32 33 | 0 34
8362: .ve
8364: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8366: .vb
8367: 2 0 | 0 3 0 | 0
8368: Proc0 5 6 | 7 0 0 | 8
8369: -------------------------------
8370: Proc1 18 0 | 19 20 21 | 0
8371: -------------------------------
8372: Proc2 26 27 | 0 0 28 | 29
8373: 0 0 | 31 32 33 | 0
8374: .ve
8376: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8377: @*/
8378: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8379: {
8380: PetscMPIInt size;
8381: Mat *local;
8382: IS iscoltmp;
8383: PetscBool flg;
8385: PetscFunctionBegin;
8389: PetscAssertPointer(newmat, 5);
8392: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8393: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8395: MatCheckPreallocated(mat, 1);
8396: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8398: if (!iscol || isrow == iscol) {
8399: PetscBool stride;
8400: PetscMPIInt grabentirematrix = 0, grab;
8401: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8402: if (stride) {
8403: PetscInt first, step, n, rstart, rend;
8404: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8405: if (step == 1) {
8406: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8407: if (rstart == first) {
8408: PetscCall(ISGetLocalSize(isrow, &n));
8409: if (n == rend - rstart) grabentirematrix = 1;
8410: }
8411: }
8412: }
8413: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8414: if (grab) {
8415: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8416: if (cll == MAT_INITIAL_MATRIX) {
8417: *newmat = mat;
8418: PetscCall(PetscObjectReference((PetscObject)mat));
8419: }
8420: PetscFunctionReturn(PETSC_SUCCESS);
8421: }
8422: }
8424: if (!iscol) {
8425: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8426: } else {
8427: iscoltmp = iscol;
8428: }
8430: /* if original matrix is on just one processor then use submatrix generated */
8431: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8432: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8433: goto setproperties;
8434: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8435: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8436: *newmat = *local;
8437: PetscCall(PetscFree(local));
8438: goto setproperties;
8439: } else if (!mat->ops->createsubmatrix) {
8440: /* Create a new matrix type that implements the operation using the full matrix */
8441: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8442: switch (cll) {
8443: case MAT_INITIAL_MATRIX:
8444: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8445: break;
8446: case MAT_REUSE_MATRIX:
8447: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8448: break;
8449: default:
8450: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8451: }
8452: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8453: goto setproperties;
8454: }
8456: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8457: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8458: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8460: setproperties:
8461: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8462: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8463: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8464: }
8465: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8466: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8467: PetscFunctionReturn(PETSC_SUCCESS);
8468: }
8470: /*@
8471: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8473: Not Collective
8475: Input Parameters:
8476: + A - the matrix we wish to propagate options from
8477: - B - the matrix we wish to propagate options to
8479: Level: beginner
8481: Note:
8482: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8484: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8485: @*/
8486: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8487: {
8488: PetscFunctionBegin;
8491: B->symmetry_eternal = A->symmetry_eternal;
8492: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8493: B->symmetric = A->symmetric;
8494: B->structurally_symmetric = A->structurally_symmetric;
8495: B->spd = A->spd;
8496: B->hermitian = A->hermitian;
8497: PetscFunctionReturn(PETSC_SUCCESS);
8498: }
8500: /*@
8501: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8502: used during the assembly process to store values that belong to
8503: other processors.
8505: Not Collective
8507: Input Parameters:
8508: + mat - the matrix
8509: . size - the initial size of the stash.
8510: - bsize - the initial size of the block-stash(if used).
8512: Options Database Keys:
8513: + -matstash_initial_size <size> or <size0,size1,...sizep-1> - set initial size
8514: - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> - set initial block size
8516: Level: intermediate
8518: Notes:
8519: The block-stash is used for values set with `MatSetValuesBlocked()` while
8520: the stash is used for values set with `MatSetValues()`
8522: Run with the option -info and look for output of the form
8523: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8524: to determine the appropriate value, MM, to use for size and
8525: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8526: to determine the value, BMM to use for bsize
8528: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8529: @*/
8530: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8531: {
8532: PetscFunctionBegin;
8535: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8536: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8537: PetscFunctionReturn(PETSC_SUCCESS);
8538: }
8540: /*@
8541: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8542: the matrix
8544: Neighbor-wise Collective
8546: Input Parameters:
8547: + A - the matrix
8548: . x - the vector to be multiplied by the interpolation operator
8549: - y - the vector to be added to the result
8551: Output Parameter:
8552: . w - the resulting vector
8554: Level: intermediate
8556: Notes:
8557: `w` may be the same vector as `y`.
8559: This allows one to use either the restriction or interpolation (its transpose)
8560: matrix to do the interpolation
8562: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8563: @*/
8564: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8565: {
8566: PetscInt M, N, Ny;
8568: PetscFunctionBegin;
8573: PetscCall(MatGetSize(A, &M, &N));
8574: PetscCall(VecGetSize(y, &Ny));
8575: if (M == Ny) {
8576: PetscCall(MatMultAdd(A, x, y, w));
8577: } else {
8578: PetscCall(MatMultTransposeAdd(A, x, y, w));
8579: }
8580: PetscFunctionReturn(PETSC_SUCCESS);
8581: }
8583: /*@
8584: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8585: the matrix
8587: Neighbor-wise Collective
8589: Input Parameters:
8590: + A - the matrix
8591: - x - the vector to be interpolated
8593: Output Parameter:
8594: . y - the resulting vector
8596: Level: intermediate
8598: Note:
8599: This allows one to use either the restriction or interpolation (its transpose)
8600: matrix to do the interpolation
8602: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8603: @*/
8604: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8605: {
8606: PetscInt M, N, Ny;
8608: PetscFunctionBegin;
8612: PetscCall(MatGetSize(A, &M, &N));
8613: PetscCall(VecGetSize(y, &Ny));
8614: if (M == Ny) {
8615: PetscCall(MatMult(A, x, y));
8616: } else {
8617: PetscCall(MatMultTranspose(A, x, y));
8618: }
8619: PetscFunctionReturn(PETSC_SUCCESS);
8620: }
8622: /*@
8623: MatRestrict - $y = A*x$ or $A^T*x$
8625: Neighbor-wise Collective
8627: Input Parameters:
8628: + A - the matrix
8629: - x - the vector to be restricted
8631: Output Parameter:
8632: . y - the resulting vector
8634: Level: intermediate
8636: Note:
8637: This allows one to use either the restriction or interpolation (its transpose)
8638: matrix to do the restriction
8640: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8641: @*/
8642: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8643: {
8644: PetscInt M, N, Nx;
8646: PetscFunctionBegin;
8650: PetscCall(MatGetSize(A, &M, &N));
8651: PetscCall(VecGetSize(x, &Nx));
8652: if (M == Nx) {
8653: PetscCall(MatMultTranspose(A, x, y));
8654: } else {
8655: PetscCall(MatMult(A, x, y));
8656: }
8657: PetscFunctionReturn(PETSC_SUCCESS);
8658: }
8660: /*@
8661: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8663: Neighbor-wise Collective
8665: Input Parameters:
8666: + A - the matrix
8667: . x - the input dense matrix to be multiplied
8668: - w - the input dense matrix to be added to the result
8670: Output Parameter:
8671: . y - the output dense matrix
8673: Level: intermediate
8675: Note:
8676: This allows one to use either the restriction or interpolation (its transpose)
8677: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8678: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8680: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8681: @*/
8682: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8683: {
8684: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8685: PetscBool trans = PETSC_TRUE;
8686: MatReuse reuse = MAT_INITIAL_MATRIX;
8688: PetscFunctionBegin;
8694: PetscCall(MatGetSize(A, &M, &N));
8695: PetscCall(MatGetSize(x, &Mx, &Nx));
8696: if (N == Mx) trans = PETSC_FALSE;
8697: 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);
8698: Mo = trans ? N : M;
8699: if (*y) {
8700: PetscCall(MatGetSize(*y, &My, &Ny));
8701: if (Mo == My && Nx == Ny) {
8702: reuse = MAT_REUSE_MATRIX;
8703: } else {
8704: 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);
8705: PetscCall(MatDestroy(y));
8706: }
8707: }
8709: if (w && *y == w) { /* this is to minimize changes in PCMG */
8710: PetscBool flg;
8712: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8713: if (w) {
8714: PetscInt My, Ny, Mw, Nw;
8716: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8717: PetscCall(MatGetSize(*y, &My, &Ny));
8718: PetscCall(MatGetSize(w, &Mw, &Nw));
8719: if (!flg || My != Mw || Ny != Nw) w = NULL;
8720: }
8721: if (!w) {
8722: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8723: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8724: PetscCall(PetscObjectDereference((PetscObject)w));
8725: } else {
8726: PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8727: }
8728: }
8729: if (!trans) {
8730: PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8731: } else {
8732: PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8733: }
8734: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8735: PetscFunctionReturn(PETSC_SUCCESS);
8736: }
8738: /*@
8739: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8741: Neighbor-wise Collective
8743: Input Parameters:
8744: + A - the matrix
8745: - x - the input dense matrix
8747: Output Parameter:
8748: . y - the output dense matrix
8750: Level: intermediate
8752: Note:
8753: This allows one to use either the restriction or interpolation (its transpose)
8754: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8755: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8757: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8758: @*/
8759: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8760: {
8761: PetscFunctionBegin;
8762: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8763: PetscFunctionReturn(PETSC_SUCCESS);
8764: }
8766: /*@
8767: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8769: Neighbor-wise Collective
8771: Input Parameters:
8772: + A - the matrix
8773: - x - the input dense matrix
8775: Output Parameter:
8776: . y - the output dense matrix
8778: Level: intermediate
8780: Note:
8781: This allows one to use either the restriction or interpolation (its transpose)
8782: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8783: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8785: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8786: @*/
8787: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8788: {
8789: PetscFunctionBegin;
8790: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8791: PetscFunctionReturn(PETSC_SUCCESS);
8792: }
8794: /*@
8795: MatGetNullSpace - retrieves the null space of a matrix.
8797: Logically Collective
8799: Input Parameters:
8800: + mat - the matrix
8801: - nullsp - the null space object
8803: Level: developer
8805: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8806: @*/
8807: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8808: {
8809: PetscFunctionBegin;
8811: PetscAssertPointer(nullsp, 2);
8812: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8813: PetscFunctionReturn(PETSC_SUCCESS);
8814: }
8816: /*@C
8817: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
8819: Logically Collective
8821: Input Parameters:
8822: + n - the number of matrices
8823: - mat - the array of matrices
8825: Output Parameters:
8826: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
8828: Level: developer
8830: Note:
8831: Call `MatRestoreNullspaces()` to provide these to another array of matrices
8833: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8834: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8835: @*/
8836: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8837: {
8838: PetscFunctionBegin;
8839: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8840: PetscAssertPointer(mat, 2);
8841: PetscAssertPointer(nullsp, 3);
8843: PetscCall(PetscCalloc1(3 * n, nullsp));
8844: for (PetscInt i = 0; i < n; i++) {
8846: (*nullsp)[i] = mat[i]->nullsp;
8847: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8848: (*nullsp)[n + i] = mat[i]->nearnullsp;
8849: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8850: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8851: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8852: }
8853: PetscFunctionReturn(PETSC_SUCCESS);
8854: }
8856: /*@C
8857: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
8859: Logically Collective
8861: Input Parameters:
8862: + n - the number of matrices
8863: . mat - the array of matrices
8864: - nullsp - an array of null spaces
8866: Level: developer
8868: Note:
8869: Call `MatGetNullSpaces()` to create `nullsp`
8871: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8872: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8873: @*/
8874: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8875: {
8876: PetscFunctionBegin;
8877: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8878: PetscAssertPointer(mat, 2);
8879: PetscAssertPointer(nullsp, 3);
8880: PetscAssertPointer(*nullsp, 3);
8882: for (PetscInt i = 0; i < n; i++) {
8884: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8885: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
8886: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
8887: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
8888: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
8889: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
8890: }
8891: PetscCall(PetscFree(*nullsp));
8892: PetscFunctionReturn(PETSC_SUCCESS);
8893: }
8895: /*@
8896: MatSetNullSpace - attaches a null space to a matrix.
8898: Logically Collective
8900: Input Parameters:
8901: + mat - the matrix
8902: - nullsp - the null space object
8904: Level: advanced
8906: Notes:
8907: This null space is used by the `KSP` linear solvers to solve singular systems.
8909: 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`
8911: 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
8912: to zero but the linear system will still be solved in a least squares sense.
8914: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8915: 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 $A^T$, $R(A^T)$.
8916: 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
8917: $n(A^T))$ 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
8918: 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 $n(A^T)$.
8919: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
8921: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
8922: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
8923: routine also automatically calls `MatSetTransposeNullSpace()`.
8925: The user should call `MatNullSpaceDestroy()`.
8927: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
8928: `KSPSetPCSide()`
8929: @*/
8930: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
8931: {
8932: PetscFunctionBegin;
8935: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8936: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
8937: mat->nullsp = nullsp;
8938: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
8939: PetscFunctionReturn(PETSC_SUCCESS);
8940: }
8942: /*@
8943: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
8945: Logically Collective
8947: Input Parameters:
8948: + mat - the matrix
8949: - nullsp - the null space object
8951: Level: developer
8953: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
8954: @*/
8955: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8956: {
8957: PetscFunctionBegin;
8960: PetscAssertPointer(nullsp, 2);
8961: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
8962: PetscFunctionReturn(PETSC_SUCCESS);
8963: }
8965: /*@
8966: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
8968: Logically Collective
8970: Input Parameters:
8971: + mat - the matrix
8972: - nullsp - the null space object
8974: Level: advanced
8976: Notes:
8977: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
8979: See `MatSetNullSpace()`
8981: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
8982: @*/
8983: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
8984: {
8985: PetscFunctionBegin;
8988: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8989: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
8990: mat->transnullsp = nullsp;
8991: PetscFunctionReturn(PETSC_SUCCESS);
8992: }
8994: /*@
8995: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
8996: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
8998: Logically Collective
9000: Input Parameters:
9001: + mat - the matrix
9002: - nullsp - the null space object
9004: Level: advanced
9006: Notes:
9007: Overwrites any previous near null space that may have been attached
9009: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9011: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9012: @*/
9013: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9014: {
9015: PetscFunctionBegin;
9019: MatCheckPreallocated(mat, 1);
9020: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9021: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9022: mat->nearnullsp = nullsp;
9023: PetscFunctionReturn(PETSC_SUCCESS);
9024: }
9026: /*@
9027: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9029: Not Collective
9031: Input Parameter:
9032: . mat - the matrix
9034: Output Parameter:
9035: . nullsp - the null space object, `NULL` if not set
9037: Level: advanced
9039: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9040: @*/
9041: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9042: {
9043: PetscFunctionBegin;
9046: PetscAssertPointer(nullsp, 2);
9047: MatCheckPreallocated(mat, 1);
9048: *nullsp = mat->nearnullsp;
9049: PetscFunctionReturn(PETSC_SUCCESS);
9050: }
9052: /*@
9053: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9055: Collective
9057: Input Parameters:
9058: + mat - the matrix
9059: . row - row/column permutation
9060: - info - information on desired factorization process
9062: Level: developer
9064: Notes:
9065: Probably really in-place only when level of fill is zero, otherwise allocates
9066: new space to store factored matrix and deletes previous memory.
9068: Most users should employ the `KSP` interface for linear solvers
9069: instead of working directly with matrix algebra routines such as this.
9070: See, e.g., `KSPCreate()`.
9072: Fortran Note:
9073: A valid (non-null) `info` argument must be provided
9075: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9076: @*/
9077: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9078: {
9079: PetscFunctionBegin;
9083: PetscAssertPointer(info, 3);
9084: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9085: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9086: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9087: MatCheckPreallocated(mat, 1);
9088: PetscUseTypeMethod(mat, iccfactor, row, info);
9089: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9090: PetscFunctionReturn(PETSC_SUCCESS);
9091: }
9093: /*@
9094: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9095: ghosted ones.
9097: Not Collective
9099: Input Parameters:
9100: + mat - the matrix
9101: - diag - the diagonal values, including ghost ones
9103: Level: developer
9105: Notes:
9106: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9108: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9110: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9111: @*/
9112: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9113: {
9114: PetscMPIInt size;
9116: PetscFunctionBegin;
9121: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9122: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9123: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9124: if (size == 1) {
9125: PetscInt n, m;
9126: PetscCall(VecGetSize(diag, &n));
9127: PetscCall(MatGetSize(mat, NULL, &m));
9128: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9129: PetscCall(MatDiagonalScale(mat, NULL, diag));
9130: } else {
9131: PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9132: }
9133: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9134: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9135: PetscFunctionReturn(PETSC_SUCCESS);
9136: }
9138: /*@
9139: MatGetInertia - Gets the inertia from a factored matrix
9141: Collective
9143: Input Parameter:
9144: . mat - the matrix
9146: Output Parameters:
9147: + nneg - number of negative eigenvalues
9148: . nzero - number of zero eigenvalues
9149: - npos - number of positive eigenvalues
9151: Level: advanced
9153: Note:
9154: Matrix must have been factored by `MatCholeskyFactor()`
9156: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9157: @*/
9158: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9159: {
9160: PetscFunctionBegin;
9163: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9164: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9165: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9166: PetscFunctionReturn(PETSC_SUCCESS);
9167: }
9169: /*@C
9170: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9172: Neighbor-wise Collective
9174: Input Parameters:
9175: + mat - the factored matrix obtained with `MatGetFactor()`
9176: - b - the right-hand-side vectors
9178: Output Parameter:
9179: . x - the result vectors
9181: Level: developer
9183: Note:
9184: The vectors `b` and `x` cannot be the same. I.e., one cannot
9185: call `MatSolves`(A,x,x).
9187: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9188: @*/
9189: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9190: {
9191: PetscFunctionBegin;
9194: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9195: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9196: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9198: MatCheckPreallocated(mat, 1);
9199: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9200: PetscUseTypeMethod(mat, solves, b, x);
9201: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9202: PetscFunctionReturn(PETSC_SUCCESS);
9203: }
9205: /*@
9206: MatIsSymmetric - Test whether a matrix is symmetric
9208: Collective
9210: Input Parameters:
9211: + A - the matrix to test
9212: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9214: Output Parameter:
9215: . flg - the result
9217: Level: intermediate
9219: Notes:
9220: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9222: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9224: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9225: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9227: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9228: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9229: @*/
9230: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9231: {
9232: PetscFunctionBegin;
9234: PetscAssertPointer(flg, 3);
9235: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9236: else {
9237: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9238: else PetscCall(MatIsTranspose(A, A, tol, flg));
9239: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9240: }
9241: PetscFunctionReturn(PETSC_SUCCESS);
9242: }
9244: /*@
9245: MatIsHermitian - Test whether a matrix is Hermitian
9247: Collective
9249: Input Parameters:
9250: + A - the matrix to test
9251: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9253: Output Parameter:
9254: . flg - the result
9256: Level: intermediate
9258: Notes:
9259: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9261: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9263: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9264: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9266: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9267: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9268: @*/
9269: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9270: {
9271: PetscFunctionBegin;
9273: PetscAssertPointer(flg, 3);
9274: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9275: else {
9276: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9277: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9278: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9279: }
9280: PetscFunctionReturn(PETSC_SUCCESS);
9281: }
9283: /*@
9284: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9286: Not Collective
9288: Input Parameter:
9289: . A - the matrix to check
9291: Output Parameters:
9292: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9293: - flg - the result (only valid if set is `PETSC_TRUE`)
9295: Level: advanced
9297: Notes:
9298: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9299: if you want it explicitly checked
9301: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9302: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9304: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9305: @*/
9306: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9307: {
9308: PetscFunctionBegin;
9310: PetscAssertPointer(set, 2);
9311: PetscAssertPointer(flg, 3);
9312: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9313: *set = PETSC_TRUE;
9314: *flg = PetscBool3ToBool(A->symmetric);
9315: } else {
9316: *set = PETSC_FALSE;
9317: }
9318: PetscFunctionReturn(PETSC_SUCCESS);
9319: }
9321: /*@
9322: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9324: Not Collective
9326: Input Parameter:
9327: . A - the matrix to check
9329: Output Parameters:
9330: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9331: - flg - the result (only valid if set is `PETSC_TRUE`)
9333: Level: advanced
9335: Notes:
9336: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9338: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9339: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9341: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9342: @*/
9343: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9344: {
9345: PetscFunctionBegin;
9347: PetscAssertPointer(set, 2);
9348: PetscAssertPointer(flg, 3);
9349: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9350: *set = PETSC_TRUE;
9351: *flg = PetscBool3ToBool(A->spd);
9352: } else {
9353: *set = PETSC_FALSE;
9354: }
9355: PetscFunctionReturn(PETSC_SUCCESS);
9356: }
9358: /*@
9359: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9361: Not Collective
9363: Input Parameter:
9364: . A - the matrix to check
9366: Output Parameters:
9367: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9368: - flg - the result (only valid if set is `PETSC_TRUE`)
9370: Level: advanced
9372: Notes:
9373: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9374: if you want it explicitly checked
9376: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9377: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9379: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9380: @*/
9381: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9382: {
9383: PetscFunctionBegin;
9385: PetscAssertPointer(set, 2);
9386: PetscAssertPointer(flg, 3);
9387: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9388: *set = PETSC_TRUE;
9389: *flg = PetscBool3ToBool(A->hermitian);
9390: } else {
9391: *set = PETSC_FALSE;
9392: }
9393: PetscFunctionReturn(PETSC_SUCCESS);
9394: }
9396: /*@
9397: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9399: Collective
9401: Input Parameter:
9402: . A - the matrix to test
9404: Output Parameter:
9405: . flg - the result
9407: Level: intermediate
9409: Notes:
9410: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9412: 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
9413: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9415: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9416: @*/
9417: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9418: {
9419: PetscFunctionBegin;
9421: PetscAssertPointer(flg, 2);
9422: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9423: *flg = PetscBool3ToBool(A->structurally_symmetric);
9424: } else {
9425: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9426: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9427: }
9428: PetscFunctionReturn(PETSC_SUCCESS);
9429: }
9431: /*@
9432: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9434: Not Collective
9436: Input Parameter:
9437: . A - the matrix to check
9439: Output Parameters:
9440: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9441: - flg - the result (only valid if set is PETSC_TRUE)
9443: Level: advanced
9445: Notes:
9446: 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
9447: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9449: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9451: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9452: @*/
9453: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9454: {
9455: PetscFunctionBegin;
9457: PetscAssertPointer(set, 2);
9458: PetscAssertPointer(flg, 3);
9459: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9460: *set = PETSC_TRUE;
9461: *flg = PetscBool3ToBool(A->structurally_symmetric);
9462: } else {
9463: *set = PETSC_FALSE;
9464: }
9465: PetscFunctionReturn(PETSC_SUCCESS);
9466: }
9468: /*@
9469: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9470: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9472: Not Collective
9474: Input Parameter:
9475: . mat - the matrix
9477: Output Parameters:
9478: + nstash - the size of the stash
9479: . reallocs - the number of additional mallocs incurred.
9480: . bnstash - the size of the block stash
9481: - breallocs - the number of additional mallocs incurred.in the block stash
9483: Level: advanced
9485: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9486: @*/
9487: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9488: {
9489: PetscFunctionBegin;
9490: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9491: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9492: PetscFunctionReturn(PETSC_SUCCESS);
9493: }
9495: /*@
9496: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9497: parallel layout, `PetscLayout` for rows and columns
9499: Collective
9501: Input Parameter:
9502: . mat - the matrix
9504: Output Parameters:
9505: + right - (optional) vector that the matrix can be multiplied against
9506: - left - (optional) vector that the matrix vector product can be stored in
9508: Level: advanced
9510: Notes:
9511: 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()`.
9513: These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed
9515: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9516: @*/
9517: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9518: {
9519: PetscFunctionBegin;
9522: if (mat->ops->getvecs) {
9523: PetscUseTypeMethod(mat, getvecs, right, left);
9524: } else {
9525: if (right) {
9526: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9527: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9528: PetscCall(VecSetType(*right, mat->defaultvectype));
9529: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9530: if (mat->boundtocpu && mat->bindingpropagates) {
9531: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9532: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9533: }
9534: #endif
9535: }
9536: if (left) {
9537: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9538: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9539: PetscCall(VecSetType(*left, mat->defaultvectype));
9540: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9541: if (mat->boundtocpu && mat->bindingpropagates) {
9542: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9543: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9544: }
9545: #endif
9546: }
9547: }
9548: PetscFunctionReturn(PETSC_SUCCESS);
9549: }
9551: /*@
9552: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9553: with default values.
9555: Not Collective
9557: Input Parameter:
9558: . info - the `MatFactorInfo` data structure
9560: Level: developer
9562: Notes:
9563: The solvers are generally used through the `KSP` and `PC` objects, for example
9564: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9566: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9568: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9569: @*/
9570: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9571: {
9572: PetscFunctionBegin;
9573: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9574: PetscFunctionReturn(PETSC_SUCCESS);
9575: }
9577: /*@
9578: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9580: Collective
9582: Input Parameters:
9583: + mat - the factored matrix
9584: - is - the index set defining the Schur indices (0-based)
9586: Level: advanced
9588: Notes:
9589: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9591: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9593: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9595: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9596: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9597: @*/
9598: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9599: {
9600: PetscErrorCode (*f)(Mat, IS);
9602: PetscFunctionBegin;
9607: PetscCheckSameComm(mat, 1, is, 2);
9608: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9609: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9610: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9611: PetscCall(MatDestroy(&mat->schur));
9612: PetscCall((*f)(mat, is));
9613: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9614: PetscFunctionReturn(PETSC_SUCCESS);
9615: }
9617: /*@
9618: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9620: Logically Collective
9622: Input Parameters:
9623: + F - the factored matrix obtained by calling `MatGetFactor()`
9624: . S - location where to return the Schur complement, can be `NULL`
9625: - status - the status of the Schur complement matrix, can be `NULL`
9627: Level: advanced
9629: Notes:
9630: You must call `MatFactorSetSchurIS()` before calling this routine.
9632: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9634: The routine provides a copy of the Schur matrix stored within the solver data structures.
9635: The caller must destroy the object when it is no longer needed.
9636: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9638: 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)
9640: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9642: Developer Note:
9643: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9644: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9646: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9647: @*/
9648: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9649: {
9650: PetscFunctionBegin;
9652: if (S) PetscAssertPointer(S, 2);
9653: if (status) PetscAssertPointer(status, 3);
9654: if (S) {
9655: PetscErrorCode (*f)(Mat, Mat *);
9657: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9658: if (f) {
9659: PetscCall((*f)(F, S));
9660: } else {
9661: PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9662: }
9663: }
9664: if (status) *status = F->schur_status;
9665: PetscFunctionReturn(PETSC_SUCCESS);
9666: }
9668: /*@
9669: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9671: Logically Collective
9673: Input Parameters:
9674: + F - the factored matrix obtained by calling `MatGetFactor()`
9675: . S - location where to return the Schur complement, can be `NULL`
9676: - status - the status of the Schur complement matrix, can be `NULL`
9678: Level: advanced
9680: Notes:
9681: You must call `MatFactorSetSchurIS()` before calling this routine.
9683: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9685: The routine returns a the Schur Complement stored within the data structures of the solver.
9687: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9689: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9691: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9693: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9695: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9696: @*/
9697: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9698: {
9699: PetscFunctionBegin;
9701: if (S) {
9702: PetscAssertPointer(S, 2);
9703: *S = F->schur;
9704: }
9705: if (status) {
9706: PetscAssertPointer(status, 3);
9707: *status = F->schur_status;
9708: }
9709: PetscFunctionReturn(PETSC_SUCCESS);
9710: }
9712: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9713: {
9714: Mat S = F->schur;
9716: PetscFunctionBegin;
9717: switch (F->schur_status) {
9718: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9719: case MAT_FACTOR_SCHUR_INVERTED:
9720: if (S) {
9721: S->ops->solve = NULL;
9722: S->ops->matsolve = NULL;
9723: S->ops->solvetranspose = NULL;
9724: S->ops->matsolvetranspose = NULL;
9725: S->ops->solveadd = NULL;
9726: S->ops->solvetransposeadd = NULL;
9727: S->factortype = MAT_FACTOR_NONE;
9728: PetscCall(PetscFree(S->solvertype));
9729: }
9730: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9731: break;
9732: default:
9733: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9734: }
9735: PetscFunctionReturn(PETSC_SUCCESS);
9736: }
9738: /*@
9739: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9741: Logically Collective
9743: Input Parameters:
9744: + F - the factored matrix obtained by calling `MatGetFactor()`
9745: . S - location where the Schur complement is stored
9746: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9748: Level: advanced
9750: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9751: @*/
9752: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9753: {
9754: PetscFunctionBegin;
9756: if (S) {
9758: *S = NULL;
9759: }
9760: F->schur_status = status;
9761: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9762: PetscFunctionReturn(PETSC_SUCCESS);
9763: }
9765: /*@
9766: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9768: Logically Collective
9770: Input Parameters:
9771: + F - the factored matrix obtained by calling `MatGetFactor()`
9772: . rhs - location where the right-hand side of the Schur complement system is stored
9773: - sol - location where the solution of the Schur complement system has to be returned
9775: Level: advanced
9777: Notes:
9778: The sizes of the vectors should match the size of the Schur complement
9780: Must be called after `MatFactorSetSchurIS()`
9782: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9783: @*/
9784: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9785: {
9786: PetscFunctionBegin;
9793: PetscCheckSameComm(F, 1, rhs, 2);
9794: PetscCheckSameComm(F, 1, sol, 3);
9795: PetscCall(MatFactorFactorizeSchurComplement(F));
9796: switch (F->schur_status) {
9797: case MAT_FACTOR_SCHUR_FACTORED:
9798: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9799: break;
9800: case MAT_FACTOR_SCHUR_INVERTED:
9801: PetscCall(MatMultTranspose(F->schur, rhs, sol));
9802: break;
9803: default:
9804: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9805: }
9806: PetscFunctionReturn(PETSC_SUCCESS);
9807: }
9809: /*@
9810: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
9812: Logically Collective
9814: Input Parameters:
9815: + F - the factored matrix obtained by calling `MatGetFactor()`
9816: . rhs - location where the right-hand side of the Schur complement system is stored
9817: - sol - location where the solution of the Schur complement system has to be returned
9819: Level: advanced
9821: Notes:
9822: The sizes of the vectors should match the size of the Schur complement
9824: Must be called after `MatFactorSetSchurIS()`
9826: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9827: @*/
9828: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9829: {
9830: PetscFunctionBegin;
9837: PetscCheckSameComm(F, 1, rhs, 2);
9838: PetscCheckSameComm(F, 1, sol, 3);
9839: PetscCall(MatFactorFactorizeSchurComplement(F));
9840: switch (F->schur_status) {
9841: case MAT_FACTOR_SCHUR_FACTORED:
9842: PetscCall(MatSolve(F->schur, rhs, sol));
9843: break;
9844: case MAT_FACTOR_SCHUR_INVERTED:
9845: PetscCall(MatMult(F->schur, rhs, sol));
9846: break;
9847: default:
9848: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9849: }
9850: PetscFunctionReturn(PETSC_SUCCESS);
9851: }
9853: PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9854: #if PetscDefined(HAVE_CUDA)
9855: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9856: #endif
9858: /* Schur status updated in the interface */
9859: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9860: {
9861: Mat S = F->schur;
9863: PetscFunctionBegin;
9864: if (S) {
9865: PetscMPIInt size;
9866: PetscBool isdense, isdensecuda;
9868: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9869: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9870: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9871: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9872: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9873: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9874: if (isdense) {
9875: PetscCall(MatSeqDenseInvertFactors_Private(S));
9876: } else if (isdensecuda) {
9877: #if defined(PETSC_HAVE_CUDA)
9878: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9879: #endif
9880: }
9881: // HIP??????????????
9882: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9883: }
9884: PetscFunctionReturn(PETSC_SUCCESS);
9885: }
9887: /*@
9888: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
9890: Logically Collective
9892: Input Parameter:
9893: . F - the factored matrix obtained by calling `MatGetFactor()`
9895: Level: advanced
9897: Notes:
9898: Must be called after `MatFactorSetSchurIS()`.
9900: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
9902: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9903: @*/
9904: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9905: {
9906: PetscFunctionBegin;
9909: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9910: PetscCall(MatFactorFactorizeSchurComplement(F));
9911: PetscCall(MatFactorInvertSchurComplement_Private(F));
9912: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9913: PetscFunctionReturn(PETSC_SUCCESS);
9914: }
9916: /*@
9917: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
9919: Logically Collective
9921: Input Parameter:
9922: . F - the factored matrix obtained by calling `MatGetFactor()`
9924: Level: advanced
9926: Note:
9927: Must be called after `MatFactorSetSchurIS()`
9929: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
9930: @*/
9931: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9932: {
9933: MatFactorInfo info;
9935: PetscFunctionBegin;
9938: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
9939: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
9940: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
9941: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
9942: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
9943: } else {
9944: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
9945: }
9946: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
9947: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9948: PetscFunctionReturn(PETSC_SUCCESS);
9949: }
9951: /*@
9952: MatPtAP - Creates the matrix product $C = P^T * A * P$
9954: Neighbor-wise Collective
9956: Input Parameters:
9957: + A - the matrix
9958: . P - the projection matrix
9959: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
9960: - 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
9961: if the result is a dense matrix this is irrelevant
9963: Output Parameter:
9964: . C - the product matrix
9966: Level: intermediate
9968: Notes:
9969: C will be created and must be destroyed by the user with `MatDestroy()`.
9971: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
9973: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
9975: Developer Note:
9976: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
9978: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
9979: @*/
9980: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
9981: {
9982: PetscFunctionBegin;
9983: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
9984: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
9986: if (scall == MAT_INITIAL_MATRIX) {
9987: PetscCall(MatProductCreate(A, P, NULL, C));
9988: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
9989: PetscCall(MatProductSetAlgorithm(*C, "default"));
9990: PetscCall(MatProductSetFill(*C, fill));
9992: (*C)->product->api_user = PETSC_TRUE;
9993: PetscCall(MatProductSetFromOptions(*C));
9994: 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);
9995: PetscCall(MatProductSymbolic(*C));
9996: } else { /* scall == MAT_REUSE_MATRIX */
9997: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
9998: }
10000: PetscCall(MatProductNumeric(*C));
10001: (*C)->symmetric = A->symmetric;
10002: (*C)->spd = A->spd;
10003: PetscFunctionReturn(PETSC_SUCCESS);
10004: }
10006: /*@
10007: MatRARt - Creates the matrix product $C = R * A * R^T$
10009: Neighbor-wise Collective
10011: Input Parameters:
10012: + A - the matrix
10013: . R - the projection matrix
10014: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10015: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10016: if the result is a dense matrix this is irrelevant
10018: Output Parameter:
10019: . C - the product matrix
10021: Level: intermediate
10023: Notes:
10024: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10026: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
10028: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10029: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10030: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10031: We recommend using `MatPtAP()` when possible.
10033: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10035: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10036: @*/
10037: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10038: {
10039: PetscFunctionBegin;
10040: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10041: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10043: if (scall == MAT_INITIAL_MATRIX) {
10044: PetscCall(MatProductCreate(A, R, NULL, C));
10045: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10046: PetscCall(MatProductSetAlgorithm(*C, "default"));
10047: PetscCall(MatProductSetFill(*C, fill));
10049: (*C)->product->api_user = PETSC_TRUE;
10050: PetscCall(MatProductSetFromOptions(*C));
10051: 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);
10052: PetscCall(MatProductSymbolic(*C));
10053: } else { /* scall == MAT_REUSE_MATRIX */
10054: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10055: }
10057: PetscCall(MatProductNumeric(*C));
10058: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10059: PetscFunctionReturn(PETSC_SUCCESS);
10060: }
10062: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10063: {
10064: PetscBool flg = PETSC_TRUE;
10066: PetscFunctionBegin;
10067: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10068: if (scall == MAT_INITIAL_MATRIX) {
10069: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10070: PetscCall(MatProductCreate(A, B, NULL, C));
10071: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10072: PetscCall(MatProductSetFill(*C, fill));
10073: } else { /* scall == MAT_REUSE_MATRIX */
10074: Mat_Product *product = (*C)->product;
10076: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10077: if (flg && product && product->type != ptype) {
10078: PetscCall(MatProductClear(*C));
10079: product = NULL;
10080: }
10081: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10082: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10083: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10084: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10085: product = (*C)->product;
10086: product->fill = fill;
10087: product->clear = PETSC_TRUE;
10088: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10089: flg = PETSC_FALSE;
10090: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10091: }
10092: }
10093: if (flg) {
10094: (*C)->product->api_user = PETSC_TRUE;
10095: PetscCall(MatProductSetType(*C, ptype));
10096: PetscCall(MatProductSetFromOptions(*C));
10097: PetscCall(MatProductSymbolic(*C));
10098: }
10099: PetscCall(MatProductNumeric(*C));
10100: PetscFunctionReturn(PETSC_SUCCESS);
10101: }
10103: /*@
10104: MatMatMult - Performs matrix-matrix multiplication C=A*B.
10106: Neighbor-wise Collective
10108: Input Parameters:
10109: + A - the left matrix
10110: . B - the right matrix
10111: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10112: - 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
10113: if the result is a dense matrix this is irrelevant
10115: Output Parameter:
10116: . C - the product matrix
10118: Notes:
10119: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10121: `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
10122: call to this function with `MAT_INITIAL_MATRIX`.
10124: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10126: 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`,
10127: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10129: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10131: Example of Usage:
10132: .vb
10133: MatProductCreate(A,B,NULL,&C);
10134: MatProductSetType(C,MATPRODUCT_AB);
10135: MatProductSymbolic(C);
10136: MatProductNumeric(C); // compute C=A * B
10137: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10138: MatProductNumeric(C);
10139: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10140: MatProductNumeric(C);
10141: .ve
10143: Level: intermediate
10145: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10146: @*/
10147: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10148: {
10149: PetscFunctionBegin;
10150: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10151: PetscFunctionReturn(PETSC_SUCCESS);
10152: }
10154: /*@
10155: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10157: Neighbor-wise Collective
10159: Input Parameters:
10160: + A - the left matrix
10161: . B - the right matrix
10162: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10163: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10165: Output Parameter:
10166: . C - the product matrix
10168: Options Database Key:
10169: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10170: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10171: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10173: Level: intermediate
10175: Notes:
10176: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10178: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10180: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10181: actually needed.
10183: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10184: and for pairs of `MATMPIDENSE` matrices.
10186: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`
10188: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10190: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10191: @*/
10192: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10193: {
10194: PetscFunctionBegin;
10195: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10196: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10197: PetscFunctionReturn(PETSC_SUCCESS);
10198: }
10200: /*@
10201: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10203: Neighbor-wise Collective
10205: Input Parameters:
10206: + A - the left matrix
10207: . B - the right matrix
10208: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10209: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10211: Output Parameter:
10212: . C - the product matrix
10214: Level: intermediate
10216: Notes:
10217: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10219: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.
10221: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`
10223: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10224: actually needed.
10226: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10227: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10229: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10231: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10232: @*/
10233: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10234: {
10235: PetscFunctionBegin;
10236: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10237: PetscFunctionReturn(PETSC_SUCCESS);
10238: }
10240: /*@
10241: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10243: Neighbor-wise Collective
10245: Input Parameters:
10246: + A - the left matrix
10247: . B - the middle matrix
10248: . C - the right matrix
10249: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10250: - 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
10251: if the result is a dense matrix this is irrelevant
10253: Output Parameter:
10254: . D - the product matrix
10256: Level: intermediate
10258: Notes:
10259: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10261: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10263: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`
10265: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10266: actually needed.
10268: If you have many matrices with the same non-zero structure to multiply, you
10269: should use `MAT_REUSE_MATRIX` in all calls but the first
10271: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10273: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10274: @*/
10275: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10276: {
10277: PetscFunctionBegin;
10278: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10279: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10281: if (scall == MAT_INITIAL_MATRIX) {
10282: PetscCall(MatProductCreate(A, B, C, D));
10283: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10284: PetscCall(MatProductSetAlgorithm(*D, "default"));
10285: PetscCall(MatProductSetFill(*D, fill));
10287: (*D)->product->api_user = PETSC_TRUE;
10288: PetscCall(MatProductSetFromOptions(*D));
10289: 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,
10290: ((PetscObject)C)->type_name);
10291: PetscCall(MatProductSymbolic(*D));
10292: } else { /* user may change input matrices when REUSE */
10293: PetscCall(MatProductReplaceMats(A, B, C, *D));
10294: }
10295: PetscCall(MatProductNumeric(*D));
10296: PetscFunctionReturn(PETSC_SUCCESS);
10297: }
10299: /*@
10300: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10302: Collective
10304: Input Parameters:
10305: + mat - the matrix
10306: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10307: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10308: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10310: Output Parameter:
10311: . matredundant - redundant matrix
10313: Level: advanced
10315: Notes:
10316: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10317: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10319: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10320: calling it.
10322: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10324: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10325: @*/
10326: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10327: {
10328: MPI_Comm comm;
10329: PetscMPIInt size;
10330: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10331: Mat_Redundant *redund = NULL;
10332: PetscSubcomm psubcomm = NULL;
10333: MPI_Comm subcomm_in = subcomm;
10334: Mat *matseq;
10335: IS isrow, iscol;
10336: PetscBool newsubcomm = PETSC_FALSE;
10338: PetscFunctionBegin;
10340: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10341: PetscAssertPointer(*matredundant, 5);
10343: }
10345: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10346: if (size == 1 || nsubcomm == 1) {
10347: if (reuse == MAT_INITIAL_MATRIX) {
10348: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10349: } else {
10350: 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");
10351: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10352: }
10353: PetscFunctionReturn(PETSC_SUCCESS);
10354: }
10356: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10357: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10358: MatCheckPreallocated(mat, 1);
10360: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10361: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10362: /* create psubcomm, then get subcomm */
10363: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10364: PetscCallMPI(MPI_Comm_size(comm, &size));
10365: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10367: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10368: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10369: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10370: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10371: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10372: newsubcomm = PETSC_TRUE;
10373: PetscCall(PetscSubcommDestroy(&psubcomm));
10374: }
10376: /* get isrow, iscol and a local sequential matrix matseq[0] */
10377: if (reuse == MAT_INITIAL_MATRIX) {
10378: mloc_sub = PETSC_DECIDE;
10379: nloc_sub = PETSC_DECIDE;
10380: if (bs < 1) {
10381: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10382: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10383: } else {
10384: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10385: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10386: }
10387: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10388: rstart = rend - mloc_sub;
10389: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10390: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10391: PetscCall(ISSetIdentity(iscol));
10392: } else { /* reuse == MAT_REUSE_MATRIX */
10393: 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");
10394: /* retrieve subcomm */
10395: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10396: redund = (*matredundant)->redundant;
10397: isrow = redund->isrow;
10398: iscol = redund->iscol;
10399: matseq = redund->matseq;
10400: }
10401: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10403: /* get matredundant over subcomm */
10404: if (reuse == MAT_INITIAL_MATRIX) {
10405: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10407: /* create a supporting struct and attach it to C for reuse */
10408: PetscCall(PetscNew(&redund));
10409: (*matredundant)->redundant = redund;
10410: redund->isrow = isrow;
10411: redund->iscol = iscol;
10412: redund->matseq = matseq;
10413: if (newsubcomm) {
10414: redund->subcomm = subcomm;
10415: } else {
10416: redund->subcomm = MPI_COMM_NULL;
10417: }
10418: } else {
10419: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10420: }
10421: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10422: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10423: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10424: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10425: }
10426: #endif
10427: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10428: PetscFunctionReturn(PETSC_SUCCESS);
10429: }
10431: /*@C
10432: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10433: a given `Mat`. Each submatrix can span multiple procs.
10435: Collective
10437: Input Parameters:
10438: + mat - the matrix
10439: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10440: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10442: Output Parameter:
10443: . subMat - parallel sub-matrices each spanning a given `subcomm`
10445: Level: advanced
10447: Notes:
10448: The submatrix partition across processors is dictated by `subComm` a
10449: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10450: is not restricted to be grouped with consecutive original MPI processes.
10452: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10453: map directly to the layout of the original matrix [wrt the local
10454: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10455: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10456: the `subMat`. However the offDiagMat looses some columns - and this is
10457: reconstructed with `MatSetValues()`
10459: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10461: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10462: @*/
10463: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10464: {
10465: PetscMPIInt commsize, subCommSize;
10467: PetscFunctionBegin;
10468: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10469: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10470: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10472: 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");
10473: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10474: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10475: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10476: PetscFunctionReturn(PETSC_SUCCESS);
10477: }
10479: /*@
10480: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10482: Not Collective
10484: Input Parameters:
10485: + mat - matrix to extract local submatrix from
10486: . isrow - local row indices for submatrix
10487: - iscol - local column indices for submatrix
10489: Output Parameter:
10490: . submat - the submatrix
10492: Level: intermediate
10494: Notes:
10495: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10497: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10498: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10500: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10501: `MatSetValuesBlockedLocal()` will also be implemented.
10503: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10504: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10506: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10507: @*/
10508: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10509: {
10510: PetscFunctionBegin;
10514: PetscCheckSameComm(isrow, 2, iscol, 3);
10515: PetscAssertPointer(submat, 4);
10516: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10518: if (mat->ops->getlocalsubmatrix) {
10519: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10520: } else {
10521: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10522: }
10523: PetscFunctionReturn(PETSC_SUCCESS);
10524: }
10526: /*@
10527: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10529: Not Collective
10531: Input Parameters:
10532: + mat - matrix to extract local submatrix from
10533: . isrow - local row indices for submatrix
10534: . iscol - local column indices for submatrix
10535: - submat - the submatrix
10537: Level: intermediate
10539: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10540: @*/
10541: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10542: {
10543: PetscFunctionBegin;
10547: PetscCheckSameComm(isrow, 2, iscol, 3);
10548: PetscAssertPointer(submat, 4);
10551: if (mat->ops->restorelocalsubmatrix) {
10552: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10553: } else {
10554: PetscCall(MatDestroy(submat));
10555: }
10556: *submat = NULL;
10557: PetscFunctionReturn(PETSC_SUCCESS);
10558: }
10560: /*@
10561: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10563: Collective
10565: Input Parameter:
10566: . mat - the matrix
10568: Output Parameter:
10569: . is - if any rows have zero diagonals this contains the list of them
10571: Level: developer
10573: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10574: @*/
10575: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10576: {
10577: PetscFunctionBegin;
10580: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10581: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10583: if (!mat->ops->findzerodiagonals) {
10584: Vec diag;
10585: const PetscScalar *a;
10586: PetscInt *rows;
10587: PetscInt rStart, rEnd, r, nrow = 0;
10589: PetscCall(MatCreateVecs(mat, &diag, NULL));
10590: PetscCall(MatGetDiagonal(mat, diag));
10591: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10592: PetscCall(VecGetArrayRead(diag, &a));
10593: for (r = 0; r < rEnd - rStart; ++r)
10594: if (a[r] == 0.0) ++nrow;
10595: PetscCall(PetscMalloc1(nrow, &rows));
10596: nrow = 0;
10597: for (r = 0; r < rEnd - rStart; ++r)
10598: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10599: PetscCall(VecRestoreArrayRead(diag, &a));
10600: PetscCall(VecDestroy(&diag));
10601: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10602: } else {
10603: PetscUseTypeMethod(mat, findzerodiagonals, is);
10604: }
10605: PetscFunctionReturn(PETSC_SUCCESS);
10606: }
10608: /*@
10609: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10611: Collective
10613: Input Parameter:
10614: . mat - the matrix
10616: Output Parameter:
10617: . is - contains the list of rows with off block diagonal entries
10619: Level: developer
10621: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10622: @*/
10623: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10624: {
10625: PetscFunctionBegin;
10628: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10629: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10631: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10632: PetscFunctionReturn(PETSC_SUCCESS);
10633: }
10635: /*@C
10636: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10638: Collective; No Fortran Support
10640: Input Parameter:
10641: . mat - the matrix
10643: Output Parameter:
10644: . values - the block inverses in column major order (FORTRAN-like)
10646: Level: advanced
10648: Notes:
10649: The size of the blocks is determined by the block size of the matrix.
10651: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10653: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10655: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10656: @*/
10657: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10658: {
10659: PetscFunctionBegin;
10661: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10662: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10663: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10664: PetscFunctionReturn(PETSC_SUCCESS);
10665: }
10667: /*@
10668: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10670: Collective; No Fortran Support
10672: Input Parameters:
10673: + mat - the matrix
10674: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10675: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10677: Output Parameter:
10678: . values - the block inverses in column major order (FORTRAN-like)
10680: Level: advanced
10682: Notes:
10683: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10685: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10687: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10688: @*/
10689: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10690: {
10691: PetscFunctionBegin;
10693: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10694: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10695: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10696: PetscFunctionReturn(PETSC_SUCCESS);
10697: }
10699: /*@
10700: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10702: Collective
10704: Input Parameters:
10705: + A - the matrix
10706: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10708: Level: advanced
10710: Note:
10711: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10713: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10714: @*/
10715: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10716: {
10717: const PetscScalar *vals;
10718: PetscInt *dnnz;
10719: PetscInt m, rstart, rend, bs, i, j;
10721: PetscFunctionBegin;
10722: PetscCall(MatInvertBlockDiagonal(A, &vals));
10723: PetscCall(MatGetBlockSize(A, &bs));
10724: PetscCall(MatGetLocalSize(A, &m, NULL));
10725: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10726: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10727: PetscCall(PetscMalloc1(m / bs, &dnnz));
10728: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10729: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10730: PetscCall(PetscFree(dnnz));
10731: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10732: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10733: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10734: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10735: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10736: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10737: PetscFunctionReturn(PETSC_SUCCESS);
10738: }
10740: /*@
10741: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10742: via `MatTransposeColoringCreate()`.
10744: Collective
10746: Input Parameter:
10747: . c - coloring context
10749: Level: intermediate
10751: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10752: @*/
10753: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10754: {
10755: MatTransposeColoring matcolor = *c;
10757: PetscFunctionBegin;
10758: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10759: if (--((PetscObject)matcolor)->refct > 0) {
10760: matcolor = NULL;
10761: PetscFunctionReturn(PETSC_SUCCESS);
10762: }
10764: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10765: PetscCall(PetscFree(matcolor->rows));
10766: PetscCall(PetscFree(matcolor->den2sp));
10767: PetscCall(PetscFree(matcolor->colorforcol));
10768: PetscCall(PetscFree(matcolor->columns));
10769: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10770: PetscCall(PetscHeaderDestroy(c));
10771: PetscFunctionReturn(PETSC_SUCCESS);
10772: }
10774: /*@
10775: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10776: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10777: `MatTransposeColoring` to sparse `B`.
10779: Collective
10781: Input Parameters:
10782: + coloring - coloring context created with `MatTransposeColoringCreate()`
10783: - B - sparse matrix
10785: Output Parameter:
10786: . Btdense - dense matrix $B^T$
10788: Level: developer
10790: Note:
10791: These are used internally for some implementations of `MatRARt()`
10793: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10794: @*/
10795: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10796: {
10797: PetscFunctionBegin;
10802: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10803: PetscFunctionReturn(PETSC_SUCCESS);
10804: }
10806: /*@
10807: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10808: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10809: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10810: $C_{sp}$ from $C_{den}$.
10812: Collective
10814: Input Parameters:
10815: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10816: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
10818: Output Parameter:
10819: . Csp - sparse matrix
10821: Level: developer
10823: Note:
10824: These are used internally for some implementations of `MatRARt()`
10826: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10827: @*/
10828: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10829: {
10830: PetscFunctionBegin;
10835: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10836: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10837: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10838: PetscFunctionReturn(PETSC_SUCCESS);
10839: }
10841: /*@
10842: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
10844: Collective
10846: Input Parameters:
10847: + mat - the matrix product C
10848: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
10850: Output Parameter:
10851: . color - the new coloring context
10853: Level: intermediate
10855: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10856: `MatTransColoringApplyDenToSp()`
10857: @*/
10858: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10859: {
10860: MatTransposeColoring c;
10861: MPI_Comm comm;
10863: PetscFunctionBegin;
10864: PetscAssertPointer(color, 3);
10866: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10867: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10868: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10869: c->ctype = iscoloring->ctype;
10870: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10871: *color = c;
10872: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10873: PetscFunctionReturn(PETSC_SUCCESS);
10874: }
10876: /*@
10877: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10878: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
10880: Not Collective
10882: Input Parameter:
10883: . mat - the matrix
10885: Output Parameter:
10886: . state - the current state
10888: Level: intermediate
10890: Notes:
10891: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10892: different matrices
10894: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
10896: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
10898: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10899: @*/
10900: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10901: {
10902: PetscFunctionBegin;
10904: *state = mat->nonzerostate;
10905: PetscFunctionReturn(PETSC_SUCCESS);
10906: }
10908: /*@
10909: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10910: matrices from each processor
10912: Collective
10914: Input Parameters:
10915: + comm - the communicators the parallel matrix will live on
10916: . seqmat - the input sequential matrices
10917: . n - number of local columns (or `PETSC_DECIDE`)
10918: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10920: Output Parameter:
10921: . mpimat - the parallel matrix generated
10923: Level: developer
10925: Note:
10926: The number of columns of the matrix in EACH processor MUST be the same.
10928: .seealso: [](ch_matrices), `Mat`
10929: @*/
10930: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
10931: {
10932: PetscMPIInt size;
10934: PetscFunctionBegin;
10935: PetscCallMPI(MPI_Comm_size(comm, &size));
10936: if (size == 1) {
10937: if (reuse == MAT_INITIAL_MATRIX) {
10938: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
10939: } else {
10940: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
10941: }
10942: PetscFunctionReturn(PETSC_SUCCESS);
10943: }
10945: 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");
10947: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
10948: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
10949: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
10950: PetscFunctionReturn(PETSC_SUCCESS);
10951: }
10953: /*@
10954: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
10956: Collective
10958: Input Parameters:
10959: + A - the matrix to create subdomains from
10960: - N - requested number of subdomains
10962: Output Parameters:
10963: + n - number of subdomains resulting on this MPI process
10964: - iss - `IS` list with indices of subdomains on this MPI process
10966: Level: advanced
10968: Note:
10969: The number of subdomains must be smaller than the communicator size
10971: .seealso: [](ch_matrices), `Mat`, `IS`
10972: @*/
10973: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
10974: {
10975: MPI_Comm comm, subcomm;
10976: PetscMPIInt size, rank, color;
10977: PetscInt rstart, rend, k;
10979: PetscFunctionBegin;
10980: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
10981: PetscCallMPI(MPI_Comm_size(comm, &size));
10982: PetscCallMPI(MPI_Comm_rank(comm, &rank));
10983: 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);
10984: *n = 1;
10985: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
10986: color = rank / k;
10987: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
10988: PetscCall(PetscMalloc1(1, iss));
10989: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
10990: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
10991: PetscCallMPI(MPI_Comm_free(&subcomm));
10992: PetscFunctionReturn(PETSC_SUCCESS);
10993: }
10995: /*@
10996: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
10998: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
10999: If they are not the same, uses `MatMatMatMult()`.
11001: Once the coarse grid problem is constructed, correct for interpolation operators
11002: that are not of full rank, which can legitimately happen in the case of non-nested
11003: geometric multigrid.
11005: Input Parameters:
11006: + restrct - restriction operator
11007: . dA - fine grid matrix
11008: . interpolate - interpolation operator
11009: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11010: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11012: Output Parameter:
11013: . A - the Galerkin coarse matrix
11015: Options Database Key:
11016: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used
11018: Level: developer
11020: Note:
11021: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11023: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11024: @*/
11025: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11026: {
11027: IS zerorows;
11028: Vec diag;
11030: PetscFunctionBegin;
11031: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11032: /* Construct the coarse grid matrix */
11033: if (interpolate == restrct) {
11034: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11035: } else {
11036: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11037: }
11039: /* If the interpolation matrix is not of full rank, A will have zero rows.
11040: This can legitimately happen in the case of non-nested geometric multigrid.
11041: In that event, we set the rows of the matrix to the rows of the identity,
11042: ignoring the equations (as the RHS will also be zero). */
11044: PetscCall(MatFindZeroRows(*A, &zerorows));
11046: if (zerorows != NULL) { /* if there are any zero rows */
11047: PetscCall(MatCreateVecs(*A, &diag, NULL));
11048: PetscCall(MatGetDiagonal(*A, diag));
11049: PetscCall(VecISSet(diag, zerorows, 1.0));
11050: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11051: PetscCall(VecDestroy(&diag));
11052: PetscCall(ISDestroy(&zerorows));
11053: }
11054: PetscFunctionReturn(PETSC_SUCCESS);
11055: }
11057: /*@C
11058: MatSetOperation - Allows user to set a matrix operation for any matrix type
11060: Logically Collective
11062: Input Parameters:
11063: + mat - the matrix
11064: . op - the name of the operation
11065: - f - the function that provides the operation
11067: Level: developer
11069: Example Usage:
11070: .vb
11071: extern PetscErrorCode usermult(Mat, Vec, Vec);
11073: PetscCall(MatCreateXXX(comm, ..., &A));
11074: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
11075: .ve
11077: Notes:
11078: See the file `include/petscmat.h` for a complete list of matrix
11079: operations, which all have the form MATOP_<OPERATION>, where
11080: <OPERATION> is the name (in all capital letters) of the
11081: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11083: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11084: sequence as the usual matrix interface routines, since they
11085: are intended to be accessed via the usual matrix interface
11086: routines, e.g.,
11087: .vb
11088: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11089: .ve
11091: In particular each function MUST return `PETSC_SUCCESS` on success and
11092: nonzero on failure.
11094: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11096: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11097: @*/
11098: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11099: {
11100: PetscFunctionBegin;
11102: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
11103: (((void (**)(void))mat->ops)[op]) = f;
11104: PetscFunctionReturn(PETSC_SUCCESS);
11105: }
11107: /*@C
11108: MatGetOperation - Gets a matrix operation for any matrix type.
11110: Not Collective
11112: Input Parameters:
11113: + mat - the matrix
11114: - op - the name of the operation
11116: Output Parameter:
11117: . f - the function that provides the operation
11119: Level: developer
11121: Example Usage:
11122: .vb
11123: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11125: MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11126: .ve
11128: Notes:
11129: See the file include/petscmat.h for a complete list of matrix
11130: operations, which all have the form MATOP_<OPERATION>, where
11131: <OPERATION> is the name (in all capital letters) of the
11132: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11134: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11136: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11137: @*/
11138: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11139: {
11140: PetscFunctionBegin;
11142: *f = (((void (**)(void))mat->ops)[op]);
11143: PetscFunctionReturn(PETSC_SUCCESS);
11144: }
11146: /*@
11147: MatHasOperation - Determines whether the given matrix supports the particular operation.
11149: Not Collective
11151: Input Parameters:
11152: + mat - the matrix
11153: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11155: Output Parameter:
11156: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11158: Level: advanced
11160: Note:
11161: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11163: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11164: @*/
11165: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11166: {
11167: PetscFunctionBegin;
11169: PetscAssertPointer(has, 3);
11170: if (mat->ops->hasoperation) {
11171: PetscUseTypeMethod(mat, hasoperation, op, has);
11172: } else {
11173: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11174: else {
11175: *has = PETSC_FALSE;
11176: if (op == MATOP_CREATE_SUBMATRIX) {
11177: PetscMPIInt size;
11179: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11180: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11181: }
11182: }
11183: }
11184: PetscFunctionReturn(PETSC_SUCCESS);
11185: }
11187: /*@
11188: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11190: Collective
11192: Input Parameter:
11193: . mat - the matrix
11195: Output Parameter:
11196: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11198: Level: beginner
11200: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11201: @*/
11202: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11203: {
11204: PetscFunctionBegin;
11207: PetscAssertPointer(cong, 2);
11208: if (!mat->rmap || !mat->cmap) {
11209: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11210: PetscFunctionReturn(PETSC_SUCCESS);
11211: }
11212: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11213: PetscCall(PetscLayoutSetUp(mat->rmap));
11214: PetscCall(PetscLayoutSetUp(mat->cmap));
11215: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11216: if (*cong) mat->congruentlayouts = 1;
11217: else mat->congruentlayouts = 0;
11218: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11219: PetscFunctionReturn(PETSC_SUCCESS);
11220: }
11222: PetscErrorCode MatSetInf(Mat A)
11223: {
11224: PetscFunctionBegin;
11225: PetscUseTypeMethod(A, setinf);
11226: PetscFunctionReturn(PETSC_SUCCESS);
11227: }
11229: /*@
11230: 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
11231: and possibly removes small values from the graph structure.
11233: Collective
11235: Input Parameters:
11236: + A - the matrix
11237: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11238: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11239: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11240: . num_idx - size of 'index' array
11241: - index - array of block indices to use for graph strength of connection weight
11243: Output Parameter:
11244: . graph - the resulting graph
11246: Level: advanced
11248: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11249: @*/
11250: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11251: {
11252: PetscFunctionBegin;
11256: PetscAssertPointer(graph, 7);
11257: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11258: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11259: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11260: PetscFunctionReturn(PETSC_SUCCESS);
11261: }
11263: /*@
11264: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11265: meaning the same memory is used for the matrix, and no new memory is allocated.
11267: Collective
11269: Input Parameters:
11270: + A - the matrix
11271: - 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
11273: Level: intermediate
11275: Developer Note:
11276: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11277: of the arrays in the data structure are unneeded.
11279: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11280: @*/
11281: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11282: {
11283: PetscFunctionBegin;
11285: PetscUseTypeMethod(A, eliminatezeros, keep);
11286: PetscFunctionReturn(PETSC_SUCCESS);
11287: }