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: $ -viewer_type {binary, hdf5}
1330: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1331: and src/mat/tutorials/ex10.c with the second approach.
1333: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1334: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1335: Multiple objects, both matrices and vectors, can be stored within the same file.
1336: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1338: Most users should not need to know the details of the binary storage
1339: format, since `MatLoad()` and `MatView()` completely hide these details.
1340: But for anyone who is interested, the standard binary matrix storage
1341: format is
1343: .vb
1344: PetscInt MAT_FILE_CLASSID
1345: PetscInt number of rows
1346: PetscInt number of columns
1347: PetscInt total number of nonzeros
1348: PetscInt *number nonzeros in each row
1349: PetscInt *column indices of all nonzeros (starting index is zero)
1350: PetscScalar *values of all nonzeros
1351: .ve
1352: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1353: 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
1354: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1356: PETSc automatically does the byte swapping for
1357: machines that store the bytes reversed. Thus if you write your own binary
1358: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1359: and `PetscBinaryWrite()` to see how this may be done.
1361: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1362: Each processor's chunk is loaded independently by its owning MPI process.
1363: Multiple objects, both matrices and vectors, can be stored within the same file.
1364: They are looked up by their PetscObject name.
1366: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1367: by default the same structure and naming of the AIJ arrays and column count
1368: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1369: $ save example.mat A b -v7.3
1370: can be directly read by this routine (see Reference 1 for details).
1372: Depending on your MATLAB version, this format might be a default,
1373: otherwise you can set it as default in Preferences.
1375: Unless -nocompression flag is used to save the file in MATLAB,
1376: PETSc must be configured with ZLIB package.
1378: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1380: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1382: Corresponding `MatView()` is not yet implemented.
1384: The loaded matrix is actually a transpose of the original one in MATLAB,
1385: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1386: With this format, matrix is automatically transposed by PETSc,
1387: unless the matrix is marked as SPD or symmetric
1388: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1390: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1392: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1393: @*/
1394: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1395: {
1396: PetscBool flg;
1398: PetscFunctionBegin;
1402: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1404: flg = PETSC_FALSE;
1405: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1406: if (flg) {
1407: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1408: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1409: }
1410: flg = PETSC_FALSE;
1411: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1412: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1414: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1415: PetscUseTypeMethod(mat, load, viewer);
1416: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1417: PetscFunctionReturn(PETSC_SUCCESS);
1418: }
1420: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1421: {
1422: Mat_Redundant *redund = *redundant;
1424: PetscFunctionBegin;
1425: if (redund) {
1426: if (redund->matseq) { /* via MatCreateSubMatrices() */
1427: PetscCall(ISDestroy(&redund->isrow));
1428: PetscCall(ISDestroy(&redund->iscol));
1429: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1430: } else {
1431: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1432: PetscCall(PetscFree(redund->sbuf_j));
1433: PetscCall(PetscFree(redund->sbuf_a));
1434: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1435: PetscCall(PetscFree(redund->rbuf_j[i]));
1436: PetscCall(PetscFree(redund->rbuf_a[i]));
1437: }
1438: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1439: }
1441: if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1442: PetscCall(PetscFree(redund));
1443: }
1444: PetscFunctionReturn(PETSC_SUCCESS);
1445: }
1447: /*@
1448: MatDestroy - Frees space taken by a matrix.
1450: Collective
1452: Input Parameter:
1453: . A - the matrix
1455: Level: beginner
1457: Developer Note:
1458: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1459: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1460: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1461: if changes are needed here.
1463: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1464: @*/
1465: PetscErrorCode MatDestroy(Mat *A)
1466: {
1467: PetscFunctionBegin;
1468: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1470: if (--((PetscObject)*A)->refct > 0) {
1471: *A = NULL;
1472: PetscFunctionReturn(PETSC_SUCCESS);
1473: }
1475: /* if memory was published with SAWs then destroy it */
1476: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1477: PetscTryTypeMethod(*A, destroy);
1479: PetscCall(PetscFree((*A)->factorprefix));
1480: PetscCall(PetscFree((*A)->defaultvectype));
1481: PetscCall(PetscFree((*A)->defaultrandtype));
1482: PetscCall(PetscFree((*A)->bsizes));
1483: PetscCall(PetscFree((*A)->solvertype));
1484: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1485: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1486: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1487: PetscCall(MatProductClear(*A));
1488: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1489: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1490: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1491: PetscCall(MatDestroy(&(*A)->schur));
1492: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1493: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1494: PetscCall(PetscHeaderDestroy(A));
1495: PetscFunctionReturn(PETSC_SUCCESS);
1496: }
1498: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1499: /*@
1500: MatSetValues - Inserts or adds a block of values into a matrix.
1501: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1502: MUST be called after all calls to `MatSetValues()` have been completed.
1504: Not Collective
1506: Input Parameters:
1507: + mat - the matrix
1508: . v - a logically two-dimensional array of values
1509: . m - the number of rows
1510: . idxm - the global indices of the rows
1511: . n - the number of columns
1512: . idxn - the global indices of the columns
1513: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1515: Level: beginner
1517: Notes:
1518: By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.
1520: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1521: options cannot be mixed without intervening calls to the assembly
1522: routines.
1524: `MatSetValues()` uses 0-based row and column numbers in Fortran
1525: as well as in C.
1527: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1528: simply ignored. This allows easily inserting element stiffness matrices
1529: with homogeneous Dirichlet boundary conditions that you don't want represented
1530: in the matrix.
1532: Efficiency Alert:
1533: The routine `MatSetValuesBlocked()` may offer much better efficiency
1534: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1536: Fortran Notes:
1537: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1538: .vb
1539: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1540: .ve
1542: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1544: Developer Note:
1545: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1546: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1548: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1549: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1550: @*/
1551: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1552: {
1553: PetscFunctionBeginHot;
1556: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1557: PetscAssertPointer(idxm, 3);
1558: PetscAssertPointer(idxn, 5);
1559: MatCheckPreallocated(mat, 1);
1561: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1562: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1564: if (PetscDefined(USE_DEBUG)) {
1565: PetscInt i, j;
1567: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1568: if (v) {
1569: for (i = 0; i < m; i++) {
1570: for (j = 0; j < n; j++) {
1571: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1572: #if defined(PETSC_USE_COMPLEX)
1573: 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]);
1574: #else
1575: 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]);
1576: #endif
1577: }
1578: }
1579: }
1580: 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);
1581: 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);
1582: }
1584: if (mat->assembled) {
1585: mat->was_assembled = PETSC_TRUE;
1586: mat->assembled = PETSC_FALSE;
1587: }
1588: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1589: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1590: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1591: PetscFunctionReturn(PETSC_SUCCESS);
1592: }
1594: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1595: /*@
1596: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1597: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1598: MUST be called after all calls to `MatSetValues()` have been completed.
1600: Not Collective
1602: Input Parameters:
1603: + mat - the matrix
1604: . v - a logically two-dimensional array of values
1605: . ism - the rows to provide
1606: . isn - the columns to provide
1607: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1609: Level: beginner
1611: Notes:
1612: By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.
1614: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1615: options cannot be mixed without intervening calls to the assembly
1616: routines.
1618: `MatSetValues()` uses 0-based row and column numbers in Fortran
1619: as well as in C.
1621: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1622: simply ignored. This allows easily inserting element stiffness matrices
1623: with homogeneous Dirichlet boundary conditions that you don't want represented
1624: in the matrix.
1626: Efficiency Alert:
1627: The routine `MatSetValuesBlocked()` may offer much better efficiency
1628: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1630: This is currently not optimized for any particular `ISType`
1632: Developer Note:
1633: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1634: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
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: Developer Note:
2219: This is labelled with C so does not automatically generate Fortran stubs and interfaces
2220: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
2222: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2223: `MatSetValuesLocal()`, `MatGetValues()`
2224: @*/
2225: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2226: {
2227: PetscFunctionBeginHot;
2230: MatCheckPreallocated(mat, 1);
2231: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2232: PetscAssertPointer(irow, 3);
2233: PetscAssertPointer(icol, 5);
2234: if (PetscDefined(USE_DEBUG)) {
2235: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2236: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2237: }
2238: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2239: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2240: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2241: else {
2242: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2243: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2244: irowm = buf;
2245: icolm = buf + nrow;
2246: } else {
2247: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2248: irowm = bufr;
2249: icolm = bufc;
2250: }
2251: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2252: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2253: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2254: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2255: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2256: PetscCall(PetscFree2(bufr, bufc));
2257: }
2258: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2259: PetscFunctionReturn(PETSC_SUCCESS);
2260: }
2262: /*@
2263: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2264: the same size. Currently, this can only be called once and creates the given matrix.
2266: Not Collective
2268: Input Parameters:
2269: + mat - the matrix
2270: . nb - the number of blocks
2271: . bs - the number of rows (and columns) in each block
2272: . rows - a concatenation of the rows for each block
2273: - v - a concatenation of logically two-dimensional arrays of values
2275: Level: advanced
2277: Notes:
2278: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2280: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2282: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2283: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2284: @*/
2285: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2286: {
2287: PetscFunctionBegin;
2290: PetscAssertPointer(rows, 4);
2291: PetscAssertPointer(v, 5);
2292: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2294: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2295: if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2296: else {
2297: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2298: }
2299: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2300: PetscFunctionReturn(PETSC_SUCCESS);
2301: }
2303: /*@
2304: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2305: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2306: using a local (per-processor) numbering.
2308: Not Collective
2310: Input Parameters:
2311: + x - the matrix
2312: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2313: - cmapping - column mapping
2315: Level: intermediate
2317: Note:
2318: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2320: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2321: @*/
2322: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2323: {
2324: PetscFunctionBegin;
2329: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2330: else {
2331: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2332: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2333: }
2334: PetscFunctionReturn(PETSC_SUCCESS);
2335: }
2337: /*@
2338: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2340: Not Collective
2342: Input Parameter:
2343: . A - the matrix
2345: Output Parameters:
2346: + rmapping - row mapping
2347: - cmapping - column mapping
2349: Level: advanced
2351: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2352: @*/
2353: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2354: {
2355: PetscFunctionBegin;
2358: if (rmapping) {
2359: PetscAssertPointer(rmapping, 2);
2360: *rmapping = A->rmap->mapping;
2361: }
2362: if (cmapping) {
2363: PetscAssertPointer(cmapping, 3);
2364: *cmapping = A->cmap->mapping;
2365: }
2366: PetscFunctionReturn(PETSC_SUCCESS);
2367: }
2369: /*@
2370: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2372: Logically Collective
2374: Input Parameters:
2375: + A - the matrix
2376: . rmap - row layout
2377: - cmap - column layout
2379: Level: advanced
2381: Note:
2382: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2384: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2385: @*/
2386: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2387: {
2388: PetscFunctionBegin;
2390: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2391: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2392: PetscFunctionReturn(PETSC_SUCCESS);
2393: }
2395: /*@
2396: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2398: Not Collective
2400: Input Parameter:
2401: . A - the matrix
2403: Output Parameters:
2404: + rmap - row layout
2405: - cmap - column layout
2407: Level: advanced
2409: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2410: @*/
2411: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2412: {
2413: PetscFunctionBegin;
2416: if (rmap) {
2417: PetscAssertPointer(rmap, 2);
2418: *rmap = A->rmap;
2419: }
2420: if (cmap) {
2421: PetscAssertPointer(cmap, 3);
2422: *cmap = A->cmap;
2423: }
2424: PetscFunctionReturn(PETSC_SUCCESS);
2425: }
2427: /*@
2428: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2429: using a local numbering of the rows and columns.
2431: Not Collective
2433: Input Parameters:
2434: + mat - the matrix
2435: . nrow - number of rows
2436: . irow - the row local indices
2437: . ncol - number of columns
2438: . icol - the column local indices
2439: . y - a logically two-dimensional array of values
2440: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2442: Level: intermediate
2444: Notes:
2445: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2447: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2448: options cannot be mixed without intervening calls to the assembly
2449: routines.
2451: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2452: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2454: Fortran Notes:
2455: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2456: .vb
2457: call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2458: .ve
2460: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2462: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2463: `MatGetValuesLocal()`
2464: @*/
2465: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2466: {
2467: PetscFunctionBeginHot;
2470: MatCheckPreallocated(mat, 1);
2471: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2472: PetscAssertPointer(irow, 3);
2473: PetscAssertPointer(icol, 5);
2474: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2475: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2476: if (PetscDefined(USE_DEBUG)) {
2477: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2478: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2479: }
2481: if (mat->assembled) {
2482: mat->was_assembled = PETSC_TRUE;
2483: mat->assembled = PETSC_FALSE;
2484: }
2485: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2486: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2487: else {
2488: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2489: const PetscInt *irowm, *icolm;
2491: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2492: bufr = buf;
2493: bufc = buf + nrow;
2494: irowm = bufr;
2495: icolm = bufc;
2496: } else {
2497: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2498: irowm = bufr;
2499: icolm = bufc;
2500: }
2501: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2502: else irowm = irow;
2503: if (mat->cmap->mapping) {
2504: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2505: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2506: } else icolm = irowm;
2507: } else icolm = icol;
2508: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2509: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2510: }
2511: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2512: PetscFunctionReturn(PETSC_SUCCESS);
2513: }
2515: /*@
2516: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2517: using a local ordering of the nodes a block at a time.
2519: Not Collective
2521: Input Parameters:
2522: + mat - the matrix
2523: . nrow - number of rows
2524: . irow - the row local indices
2525: . ncol - number of columns
2526: . icol - the column local indices
2527: . y - a logically two-dimensional array of values
2528: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2530: Level: intermediate
2532: Notes:
2533: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2534: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2536: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2537: options cannot be mixed without intervening calls to the assembly
2538: routines.
2540: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2541: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2543: Fortran Notes:
2544: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2545: .vb
2546: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2547: .ve
2549: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2551: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2552: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2553: @*/
2554: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2555: {
2556: PetscFunctionBeginHot;
2559: MatCheckPreallocated(mat, 1);
2560: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2561: PetscAssertPointer(irow, 3);
2562: PetscAssertPointer(icol, 5);
2563: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2564: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2565: if (PetscDefined(USE_DEBUG)) {
2566: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2567: 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);
2568: }
2570: if (mat->assembled) {
2571: mat->was_assembled = PETSC_TRUE;
2572: mat->assembled = PETSC_FALSE;
2573: }
2574: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2575: PetscInt irbs, rbs;
2576: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2577: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2578: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2579: }
2580: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2581: PetscInt icbs, cbs;
2582: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2583: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2584: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2585: }
2586: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2587: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2588: else {
2589: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2590: const PetscInt *irowm, *icolm;
2592: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2593: bufr = buf;
2594: bufc = buf + nrow;
2595: irowm = bufr;
2596: icolm = bufc;
2597: } else {
2598: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2599: irowm = bufr;
2600: icolm = bufc;
2601: }
2602: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2603: else irowm = irow;
2604: if (mat->cmap->mapping) {
2605: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2606: PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2607: } else icolm = irowm;
2608: } else icolm = icol;
2609: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2610: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2611: }
2612: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2613: PetscFunctionReturn(PETSC_SUCCESS);
2614: }
2616: /*@
2617: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2619: Collective
2621: Input Parameters:
2622: + mat - the matrix
2623: - x - the vector to be multiplied
2625: Output Parameter:
2626: . y - the result
2628: Level: developer
2630: Note:
2631: The vectors `x` and `y` cannot be the same. I.e., one cannot
2632: call `MatMultDiagonalBlock`(A,y,y).
2634: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2635: @*/
2636: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2637: {
2638: PetscFunctionBegin;
2644: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2645: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2646: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2647: MatCheckPreallocated(mat, 1);
2649: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2650: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2651: PetscFunctionReturn(PETSC_SUCCESS);
2652: }
2654: /*@
2655: MatMult - Computes the matrix-vector product, $y = Ax$.
2657: Neighbor-wise Collective
2659: Input Parameters:
2660: + mat - the matrix
2661: - x - the vector to be multiplied
2663: Output Parameter:
2664: . y - the result
2666: Level: beginner
2668: Note:
2669: The vectors `x` and `y` cannot be the same. I.e., one cannot
2670: call `MatMult`(A,y,y).
2672: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2673: @*/
2674: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2675: {
2676: PetscFunctionBegin;
2680: VecCheckAssembled(x);
2682: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2683: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2684: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2685: 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);
2686: 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);
2687: 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);
2688: 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);
2689: PetscCall(VecSetErrorIfLocked(y, 3));
2690: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2691: MatCheckPreallocated(mat, 1);
2693: PetscCall(VecLockReadPush(x));
2694: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2695: PetscUseTypeMethod(mat, mult, x, y);
2696: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2697: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2698: PetscCall(VecLockReadPop(x));
2699: PetscFunctionReturn(PETSC_SUCCESS);
2700: }
2702: /*@
2703: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2705: Neighbor-wise Collective
2707: Input Parameters:
2708: + mat - the matrix
2709: - x - the vector to be multiplied
2711: Output Parameter:
2712: . y - the result
2714: Level: beginner
2716: Notes:
2717: The vectors `x` and `y` cannot be the same. I.e., one cannot
2718: call `MatMultTranspose`(A,y,y).
2720: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2721: use `MatMultHermitianTranspose()`
2723: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2724: @*/
2725: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2726: {
2727: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2729: PetscFunctionBegin;
2733: VecCheckAssembled(x);
2736: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2737: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2738: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2739: 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);
2740: 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);
2741: 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);
2742: 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);
2743: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2744: MatCheckPreallocated(mat, 1);
2746: if (!mat->ops->multtranspose) {
2747: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2748: 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);
2749: } else op = mat->ops->multtranspose;
2750: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2751: PetscCall(VecLockReadPush(x));
2752: PetscCall((*op)(mat, x, y));
2753: PetscCall(VecLockReadPop(x));
2754: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2755: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2756: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2757: PetscFunctionReturn(PETSC_SUCCESS);
2758: }
2760: /*@
2761: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2763: Neighbor-wise Collective
2765: Input Parameters:
2766: + mat - the matrix
2767: - x - the vector to be multiplied
2769: Output Parameter:
2770: . y - the result
2772: Level: beginner
2774: Notes:
2775: The vectors `x` and `y` cannot be the same. I.e., one cannot
2776: call `MatMultHermitianTranspose`(A,y,y).
2778: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2780: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2782: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2783: @*/
2784: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2785: {
2786: PetscFunctionBegin;
2792: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2793: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2794: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2795: 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);
2796: 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);
2797: 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);
2798: 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);
2799: MatCheckPreallocated(mat, 1);
2801: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2802: #if defined(PETSC_USE_COMPLEX)
2803: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2804: PetscCall(VecLockReadPush(x));
2805: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2806: else PetscUseTypeMethod(mat, mult, x, y);
2807: PetscCall(VecLockReadPop(x));
2808: } else {
2809: Vec w;
2810: PetscCall(VecDuplicate(x, &w));
2811: PetscCall(VecCopy(x, w));
2812: PetscCall(VecConjugate(w));
2813: PetscCall(MatMultTranspose(mat, w, y));
2814: PetscCall(VecDestroy(&w));
2815: PetscCall(VecConjugate(y));
2816: }
2817: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2818: #else
2819: PetscCall(MatMultTranspose(mat, x, y));
2820: #endif
2821: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2822: PetscFunctionReturn(PETSC_SUCCESS);
2823: }
2825: /*@
2826: MatMultAdd - Computes $v3 = v2 + A * v1$.
2828: Neighbor-wise Collective
2830: Input Parameters:
2831: + mat - the matrix
2832: . v1 - the vector to be multiplied by `mat`
2833: - v2 - the vector to be added to the result
2835: Output Parameter:
2836: . v3 - the result
2838: Level: beginner
2840: Note:
2841: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2842: call `MatMultAdd`(A,v1,v2,v1).
2844: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2845: @*/
2846: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2847: {
2848: PetscFunctionBegin;
2855: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2856: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2857: 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);
2858: /* 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);
2859: 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); */
2860: 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);
2861: 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);
2862: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2863: MatCheckPreallocated(mat, 1);
2865: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2866: PetscCall(VecLockReadPush(v1));
2867: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2868: PetscCall(VecLockReadPop(v1));
2869: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2870: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2871: PetscFunctionReturn(PETSC_SUCCESS);
2872: }
2874: /*@
2875: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2877: Neighbor-wise Collective
2879: Input Parameters:
2880: + mat - the matrix
2881: . v1 - the vector to be multiplied by the transpose of the matrix
2882: - v2 - the vector to be added to the result
2884: Output Parameter:
2885: . v3 - the result
2887: Level: beginner
2889: Note:
2890: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2891: call `MatMultTransposeAdd`(A,v1,v2,v1).
2893: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2894: @*/
2895: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2896: {
2897: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2899: PetscFunctionBegin;
2906: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2907: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2908: 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);
2909: 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);
2910: 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);
2911: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2912: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2913: MatCheckPreallocated(mat, 1);
2915: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2916: PetscCall(VecLockReadPush(v1));
2917: PetscCall((*op)(mat, v1, v2, v3));
2918: PetscCall(VecLockReadPop(v1));
2919: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2920: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2921: PetscFunctionReturn(PETSC_SUCCESS);
2922: }
2924: /*@
2925: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2927: Neighbor-wise Collective
2929: Input Parameters:
2930: + mat - the matrix
2931: . v1 - the vector to be multiplied by the Hermitian transpose
2932: - v2 - the vector to be added to the result
2934: Output Parameter:
2935: . v3 - the result
2937: Level: beginner
2939: Note:
2940: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2941: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2943: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2944: @*/
2945: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2946: {
2947: PetscFunctionBegin;
2954: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2955: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2956: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2957: 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);
2958: 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);
2959: 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);
2960: MatCheckPreallocated(mat, 1);
2962: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2963: PetscCall(VecLockReadPush(v1));
2964: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2965: else {
2966: Vec w, z;
2967: PetscCall(VecDuplicate(v1, &w));
2968: PetscCall(VecCopy(v1, w));
2969: PetscCall(VecConjugate(w));
2970: PetscCall(VecDuplicate(v3, &z));
2971: PetscCall(MatMultTranspose(mat, w, z));
2972: PetscCall(VecDestroy(&w));
2973: PetscCall(VecConjugate(z));
2974: if (v2 != v3) {
2975: PetscCall(VecWAXPY(v3, 1.0, v2, z));
2976: } else {
2977: PetscCall(VecAXPY(v3, 1.0, z));
2978: }
2979: PetscCall(VecDestroy(&z));
2980: }
2981: PetscCall(VecLockReadPop(v1));
2982: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2983: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2984: PetscFunctionReturn(PETSC_SUCCESS);
2985: }
2987: /*@
2988: MatGetFactorType - gets the type of factorization a matrix is
2990: Not Collective
2992: Input Parameter:
2993: . mat - the matrix
2995: Output Parameter:
2996: . 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`
2998: Level: intermediate
3000: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3001: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3002: @*/
3003: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3004: {
3005: PetscFunctionBegin;
3008: PetscAssertPointer(t, 2);
3009: *t = mat->factortype;
3010: PetscFunctionReturn(PETSC_SUCCESS);
3011: }
3013: /*@
3014: MatSetFactorType - sets the type of factorization a matrix is
3016: Logically Collective
3018: Input Parameters:
3019: + mat - the matrix
3020: - 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`
3022: Level: intermediate
3024: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3025: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3026: @*/
3027: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3028: {
3029: PetscFunctionBegin;
3032: mat->factortype = t;
3033: PetscFunctionReturn(PETSC_SUCCESS);
3034: }
3036: /*@
3037: MatGetInfo - Returns information about matrix storage (number of
3038: nonzeros, memory, etc.).
3040: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3042: Input Parameters:
3043: + mat - the matrix
3044: - 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)
3046: Output Parameter:
3047: . info - matrix information context
3049: Options Database Key:
3050: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3052: Level: intermediate
3054: Notes:
3055: The `MatInfo` context contains a variety of matrix data, including
3056: number of nonzeros allocated and used, number of mallocs during
3057: matrix assembly, etc. Additional information for factored matrices
3058: is provided (such as the fill ratio, number of mallocs during
3059: factorization, etc.).
3061: Example:
3062: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3063: data within the `MatInfo` context. For example,
3064: .vb
3065: MatInfo info;
3066: Mat A;
3067: double mal, nz_a, nz_u;
3069: MatGetInfo(A, MAT_LOCAL, &info);
3070: mal = info.mallocs;
3071: nz_a = info.nz_allocated;
3072: .ve
3074: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3075: @*/
3076: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3077: {
3078: PetscFunctionBegin;
3081: PetscAssertPointer(info, 3);
3082: MatCheckPreallocated(mat, 1);
3083: PetscUseTypeMethod(mat, getinfo, flag, info);
3084: PetscFunctionReturn(PETSC_SUCCESS);
3085: }
3087: /*
3088: This is used by external packages where it is not easy to get the info from the actual
3089: matrix factorization.
3090: */
3091: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3092: {
3093: PetscFunctionBegin;
3094: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3095: PetscFunctionReturn(PETSC_SUCCESS);
3096: }
3098: /*@
3099: MatLUFactor - Performs in-place LU factorization of matrix.
3101: Collective
3103: Input Parameters:
3104: + mat - the matrix
3105: . row - row permutation
3106: . col - column permutation
3107: - info - options for factorization, includes
3108: .vb
3109: fill - expected fill as ratio of original fill.
3110: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3111: Run with the option -info to determine an optimal value to use
3112: .ve
3114: Level: developer
3116: Notes:
3117: Most users should employ the `KSP` interface for linear solvers
3118: instead of working directly with matrix algebra routines such as this.
3119: See, e.g., `KSPCreate()`.
3121: This changes the state of the matrix to a factored matrix; it cannot be used
3122: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3124: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3125: when not using `KSP`.
3127: Developer Note:
3128: The Fortran interface is not autogenerated as the
3129: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3131: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3132: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3133: @*/
3134: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3135: {
3136: MatFactorInfo tinfo;
3138: PetscFunctionBegin;
3142: if (info) PetscAssertPointer(info, 4);
3144: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3145: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3146: MatCheckPreallocated(mat, 1);
3147: if (!info) {
3148: PetscCall(MatFactorInfoInitialize(&tinfo));
3149: info = &tinfo;
3150: }
3152: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3153: PetscUseTypeMethod(mat, lufactor, row, col, info);
3154: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3155: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3156: PetscFunctionReturn(PETSC_SUCCESS);
3157: }
3159: /*@
3160: MatILUFactor - Performs in-place ILU factorization of matrix.
3162: Collective
3164: Input Parameters:
3165: + mat - the matrix
3166: . row - row permutation
3167: . col - column permutation
3168: - info - structure containing
3169: .vb
3170: levels - number of levels of fill.
3171: expected fill - as ratio of original fill.
3172: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3173: missing diagonal entries)
3174: .ve
3176: Level: developer
3178: Notes:
3179: Most users should employ the `KSP` interface for linear solvers
3180: instead of working directly with matrix algebra routines such as this.
3181: See, e.g., `KSPCreate()`.
3183: Probably really in-place only when level of fill is zero, otherwise allocates
3184: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3185: when not using `KSP`.
3187: Developer Note:
3188: The Fortran interface is not autogenerated as the
3189: interface definition cannot be generated correctly [due to MatFactorInfo]
3191: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3192: @*/
3193: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3194: {
3195: PetscFunctionBegin;
3199: PetscAssertPointer(info, 4);
3201: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3202: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3203: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3204: MatCheckPreallocated(mat, 1);
3206: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3207: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3208: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3209: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3210: PetscFunctionReturn(PETSC_SUCCESS);
3211: }
3213: /*@
3214: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3215: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3217: Collective
3219: Input Parameters:
3220: + fact - the factor matrix obtained with `MatGetFactor()`
3221: . mat - the matrix
3222: . row - the row permutation
3223: . col - the column permutation
3224: - info - options for factorization, includes
3225: .vb
3226: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3227: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3228: .ve
3230: Level: developer
3232: Notes:
3233: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3235: Most users should employ the simplified `KSP` interface for linear solvers
3236: instead of working directly with matrix algebra routines such as this.
3237: See, e.g., `KSPCreate()`.
3239: Developer Note:
3240: The Fortran interface is not autogenerated as the
3241: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3243: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3244: @*/
3245: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3246: {
3247: MatFactorInfo tinfo;
3249: PetscFunctionBegin;
3254: if (info) PetscAssertPointer(info, 5);
3257: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3258: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3259: MatCheckPreallocated(mat, 2);
3260: if (!info) {
3261: PetscCall(MatFactorInfoInitialize(&tinfo));
3262: info = &tinfo;
3263: }
3265: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3266: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3267: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3268: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3269: PetscFunctionReturn(PETSC_SUCCESS);
3270: }
3272: /*@
3273: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3274: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3276: Collective
3278: Input Parameters:
3279: + fact - the factor matrix obtained with `MatGetFactor()`
3280: . mat - the matrix
3281: - info - options for factorization
3283: Level: developer
3285: Notes:
3286: See `MatLUFactor()` for in-place factorization. See
3287: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3289: Most users should employ the `KSP` interface for linear solvers
3290: instead of working directly with matrix algebra routines such as this.
3291: See, e.g., `KSPCreate()`.
3293: Developer Note:
3294: The Fortran interface is not autogenerated as the
3295: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3297: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3298: @*/
3299: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3300: {
3301: MatFactorInfo tinfo;
3303: PetscFunctionBegin;
3308: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3309: 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,
3310: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3312: MatCheckPreallocated(mat, 2);
3313: if (!info) {
3314: PetscCall(MatFactorInfoInitialize(&tinfo));
3315: info = &tinfo;
3316: }
3318: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3319: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3320: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3321: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3322: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3323: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3324: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3325: PetscFunctionReturn(PETSC_SUCCESS);
3326: }
3328: /*@
3329: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3330: symmetric matrix.
3332: Collective
3334: Input Parameters:
3335: + mat - the matrix
3336: . perm - row and column permutations
3337: - info - expected fill as ratio of original fill
3339: Level: developer
3341: Notes:
3342: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3343: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3345: Most users should employ the `KSP` interface for linear solvers
3346: instead of working directly with matrix algebra routines such as this.
3347: See, e.g., `KSPCreate()`.
3349: Developer Note:
3350: The Fortran interface is not autogenerated as the
3351: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3353: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3354: `MatGetOrdering()`
3355: @*/
3356: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3357: {
3358: MatFactorInfo tinfo;
3360: PetscFunctionBegin;
3363: if (info) PetscAssertPointer(info, 3);
3365: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3366: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3367: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3368: MatCheckPreallocated(mat, 1);
3369: if (!info) {
3370: PetscCall(MatFactorInfoInitialize(&tinfo));
3371: info = &tinfo;
3372: }
3374: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3375: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3376: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3377: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3378: PetscFunctionReturn(PETSC_SUCCESS);
3379: }
3381: /*@
3382: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3383: of a symmetric matrix.
3385: Collective
3387: Input Parameters:
3388: + fact - the factor matrix obtained with `MatGetFactor()`
3389: . mat - the matrix
3390: . perm - row and column permutations
3391: - info - options for factorization, includes
3392: .vb
3393: fill - expected fill as ratio of original fill.
3394: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3395: Run with the option -info to determine an optimal value to use
3396: .ve
3398: Level: developer
3400: Notes:
3401: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3402: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3404: Most users should employ the `KSP` interface for linear solvers
3405: instead of working directly with matrix algebra routines such as this.
3406: See, e.g., `KSPCreate()`.
3408: Developer Note:
3409: The Fortran interface is not autogenerated as the
3410: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3412: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3413: `MatGetOrdering()`
3414: @*/
3415: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3416: {
3417: MatFactorInfo tinfo;
3419: PetscFunctionBegin;
3423: if (info) PetscAssertPointer(info, 4);
3426: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3427: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3428: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3429: MatCheckPreallocated(mat, 2);
3430: if (!info) {
3431: PetscCall(MatFactorInfoInitialize(&tinfo));
3432: info = &tinfo;
3433: }
3435: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3436: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3437: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3438: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3439: PetscFunctionReturn(PETSC_SUCCESS);
3440: }
3442: /*@
3443: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3444: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3445: `MatCholeskyFactorSymbolic()`.
3447: Collective
3449: Input Parameters:
3450: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3451: . mat - the initial matrix that is to be factored
3452: - info - options for factorization
3454: Level: developer
3456: Note:
3457: Most users should employ the `KSP` interface for linear solvers
3458: instead of working directly with matrix algebra routines such as this.
3459: See, e.g., `KSPCreate()`.
3461: Developer Note:
3462: The Fortran interface is not autogenerated as the
3463: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3465: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3466: @*/
3467: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3468: {
3469: MatFactorInfo tinfo;
3471: PetscFunctionBegin;
3476: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3477: 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,
3478: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3479: MatCheckPreallocated(mat, 2);
3480: if (!info) {
3481: PetscCall(MatFactorInfoInitialize(&tinfo));
3482: info = &tinfo;
3483: }
3485: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3486: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3487: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3488: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3489: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3490: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3491: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3492: PetscFunctionReturn(PETSC_SUCCESS);
3493: }
3495: /*@
3496: MatQRFactor - Performs in-place QR factorization of matrix.
3498: Collective
3500: Input Parameters:
3501: + mat - the matrix
3502: . col - column permutation
3503: - info - options for factorization, includes
3504: .vb
3505: fill - expected fill as ratio of original fill.
3506: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3507: Run with the option -info to determine an optimal value to use
3508: .ve
3510: Level: developer
3512: Notes:
3513: Most users should employ the `KSP` interface for linear solvers
3514: instead of working directly with matrix algebra routines such as this.
3515: See, e.g., `KSPCreate()`.
3517: This changes the state of the matrix to a factored matrix; it cannot be used
3518: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3520: Developer Note:
3521: The Fortran interface is not autogenerated as the
3522: interface definition cannot be generated correctly [due to MatFactorInfo]
3524: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3525: `MatSetUnfactored()`
3526: @*/
3527: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3528: {
3529: PetscFunctionBegin;
3532: if (info) PetscAssertPointer(info, 3);
3534: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3535: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3536: MatCheckPreallocated(mat, 1);
3537: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3538: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3539: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3540: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3541: PetscFunctionReturn(PETSC_SUCCESS);
3542: }
3544: /*@
3545: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3546: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3548: Collective
3550: Input Parameters:
3551: + fact - the factor matrix obtained with `MatGetFactor()`
3552: . mat - the matrix
3553: . col - column permutation
3554: - info - options for factorization, includes
3555: .vb
3556: fill - expected fill as ratio of original fill.
3557: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3558: Run with the option -info to determine an optimal value to use
3559: .ve
3561: Level: developer
3563: Note:
3564: Most users should employ the `KSP` interface for linear solvers
3565: instead of working directly with matrix algebra routines such as this.
3566: See, e.g., `KSPCreate()`.
3568: Developer Note:
3569: The Fortran interface is not autogenerated as the
3570: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3572: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3573: @*/
3574: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3575: {
3576: MatFactorInfo tinfo;
3578: PetscFunctionBegin;
3582: if (info) PetscAssertPointer(info, 4);
3585: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3586: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3587: MatCheckPreallocated(mat, 2);
3588: if (!info) {
3589: PetscCall(MatFactorInfoInitialize(&tinfo));
3590: info = &tinfo;
3591: }
3593: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3594: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3595: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3596: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3597: PetscFunctionReturn(PETSC_SUCCESS);
3598: }
3600: /*@
3601: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3602: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3604: Collective
3606: Input Parameters:
3607: + fact - the factor matrix obtained with `MatGetFactor()`
3608: . mat - the matrix
3609: - info - options for factorization
3611: Level: developer
3613: Notes:
3614: See `MatQRFactor()` for in-place factorization.
3616: Most users should employ the `KSP` interface for linear solvers
3617: instead of working directly with matrix algebra routines such as this.
3618: See, e.g., `KSPCreate()`.
3620: Developer Note:
3621: The Fortran interface is not autogenerated as the
3622: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3624: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3625: @*/
3626: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3627: {
3628: MatFactorInfo tinfo;
3630: PetscFunctionBegin;
3635: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3636: 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,
3637: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3639: MatCheckPreallocated(mat, 2);
3640: if (!info) {
3641: PetscCall(MatFactorInfoInitialize(&tinfo));
3642: info = &tinfo;
3643: }
3645: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3646: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3647: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3648: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3649: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3650: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3651: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3652: PetscFunctionReturn(PETSC_SUCCESS);
3653: }
3655: /*@
3656: MatSolve - Solves $A x = b$, given a factored matrix.
3658: Neighbor-wise Collective
3660: Input Parameters:
3661: + mat - the factored matrix
3662: - b - the right-hand-side vector
3664: Output Parameter:
3665: . x - the result vector
3667: Level: developer
3669: Notes:
3670: The vectors `b` and `x` cannot be the same. I.e., one cannot
3671: call `MatSolve`(A,x,x).
3673: Most users should employ the `KSP` interface for linear solvers
3674: instead of working directly with matrix algebra routines such as this.
3675: See, e.g., `KSPCreate()`.
3677: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3678: @*/
3679: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3680: {
3681: PetscFunctionBegin;
3686: PetscCheckSameComm(mat, 1, b, 2);
3687: PetscCheckSameComm(mat, 1, x, 3);
3688: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3689: 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);
3690: 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);
3691: 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);
3692: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3693: MatCheckPreallocated(mat, 1);
3695: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3696: PetscCall(VecFlag(x, mat->factorerrortype));
3697: if (mat->factorerrortype) {
3698: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3699: } else PetscUseTypeMethod(mat, solve, b, x);
3700: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3701: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3702: PetscFunctionReturn(PETSC_SUCCESS);
3703: }
3705: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3706: {
3707: Vec b, x;
3708: PetscInt N, i;
3709: PetscErrorCode (*f)(Mat, Vec, Vec);
3710: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3712: PetscFunctionBegin;
3713: if (A->factorerrortype) {
3714: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3715: PetscCall(MatSetInf(X));
3716: PetscFunctionReturn(PETSC_SUCCESS);
3717: }
3718: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3719: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3720: PetscCall(MatBoundToCPU(A, &Abound));
3721: if (!Abound) {
3722: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3723: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3724: }
3725: #if PetscDefined(HAVE_CUDA)
3726: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3727: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3728: #elif PetscDefined(HAVE_HIP)
3729: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3730: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3731: #endif
3732: PetscCall(MatGetSize(B, NULL, &N));
3733: for (i = 0; i < N; i++) {
3734: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3735: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3736: PetscCall((*f)(A, b, x));
3737: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3738: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3739: }
3740: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3741: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3742: PetscFunctionReturn(PETSC_SUCCESS);
3743: }
3745: /*@
3746: MatMatSolve - Solves $A X = B$, given a factored matrix.
3748: Neighbor-wise Collective
3750: Input Parameters:
3751: + A - the factored matrix
3752: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3754: Output Parameter:
3755: . X - the result matrix (dense matrix)
3757: Level: developer
3759: Note:
3760: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3761: otherwise, `B` and `X` cannot be the same.
3763: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3764: @*/
3765: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3766: {
3767: PetscFunctionBegin;
3772: PetscCheckSameComm(A, 1, B, 2);
3773: PetscCheckSameComm(A, 1, X, 3);
3774: 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);
3775: 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);
3776: 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");
3777: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3778: MatCheckPreallocated(A, 1);
3780: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3781: if (!A->ops->matsolve) {
3782: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3783: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3784: } else PetscUseTypeMethod(A, matsolve, B, X);
3785: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3786: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3787: PetscFunctionReturn(PETSC_SUCCESS);
3788: }
3790: /*@
3791: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3793: Neighbor-wise Collective
3795: Input Parameters:
3796: + A - the factored matrix
3797: - B - the right-hand-side matrix (`MATDENSE` matrix)
3799: Output Parameter:
3800: . X - the result matrix (dense matrix)
3802: Level: developer
3804: Note:
3805: The matrices `B` and `X` cannot be the same. I.e., one cannot
3806: call `MatMatSolveTranspose`(A,X,X).
3808: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3809: @*/
3810: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3811: {
3812: PetscFunctionBegin;
3817: PetscCheckSameComm(A, 1, B, 2);
3818: PetscCheckSameComm(A, 1, X, 3);
3819: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3820: 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);
3821: 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);
3822: 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);
3823: 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");
3824: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3825: MatCheckPreallocated(A, 1);
3827: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3828: if (!A->ops->matsolvetranspose) {
3829: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3830: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3831: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3832: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3833: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3834: PetscFunctionReturn(PETSC_SUCCESS);
3835: }
3837: /*@
3838: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3840: Neighbor-wise Collective
3842: Input Parameters:
3843: + A - the factored matrix
3844: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3846: Output Parameter:
3847: . X - the result matrix (dense matrix)
3849: Level: developer
3851: Note:
3852: 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
3853: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3855: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3856: @*/
3857: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3858: {
3859: PetscFunctionBegin;
3864: PetscCheckSameComm(A, 1, Bt, 2);
3865: PetscCheckSameComm(A, 1, X, 3);
3867: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3868: 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);
3869: 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);
3870: 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");
3871: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3872: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3873: MatCheckPreallocated(A, 1);
3875: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3876: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3877: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3878: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3879: PetscFunctionReturn(PETSC_SUCCESS);
3880: }
3882: /*@
3883: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3884: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3886: Neighbor-wise Collective
3888: Input Parameters:
3889: + mat - the factored matrix
3890: - b - the right-hand-side vector
3892: Output Parameter:
3893: . x - the result vector
3895: Level: developer
3897: Notes:
3898: `MatSolve()` should be used for most applications, as it performs
3899: a forward solve followed by a backward solve.
3901: The vectors `b` and `x` cannot be the same, i.e., one cannot
3902: call `MatForwardSolve`(A,x,x).
3904: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3905: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3906: `MatForwardSolve()` solves $U^T*D y = b$, and
3907: `MatBackwardSolve()` solves $U x = y$.
3908: Thus they do not provide a symmetric preconditioner.
3910: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3911: @*/
3912: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3913: {
3914: PetscFunctionBegin;
3919: PetscCheckSameComm(mat, 1, b, 2);
3920: PetscCheckSameComm(mat, 1, x, 3);
3921: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3922: 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);
3923: 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);
3924: 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);
3925: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3926: MatCheckPreallocated(mat, 1);
3928: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3929: PetscUseTypeMethod(mat, forwardsolve, b, x);
3930: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3931: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3932: PetscFunctionReturn(PETSC_SUCCESS);
3933: }
3935: /*@
3936: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3937: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3939: Neighbor-wise Collective
3941: Input Parameters:
3942: + mat - the factored matrix
3943: - b - the right-hand-side vector
3945: Output Parameter:
3946: . x - the result vector
3948: Level: developer
3950: Notes:
3951: `MatSolve()` should be used for most applications, as it performs
3952: a forward solve followed by a backward solve.
3954: The vectors `b` and `x` cannot be the same. I.e., one cannot
3955: call `MatBackwardSolve`(A,x,x).
3957: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3958: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3959: `MatForwardSolve()` solves $U^T*D y = b$, and
3960: `MatBackwardSolve()` solves $U x = y$.
3961: Thus they do not provide a symmetric preconditioner.
3963: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3964: @*/
3965: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3966: {
3967: PetscFunctionBegin;
3972: PetscCheckSameComm(mat, 1, b, 2);
3973: PetscCheckSameComm(mat, 1, x, 3);
3974: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3975: 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);
3976: 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);
3977: 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);
3978: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3979: MatCheckPreallocated(mat, 1);
3981: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3982: PetscUseTypeMethod(mat, backwardsolve, b, x);
3983: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3984: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3985: PetscFunctionReturn(PETSC_SUCCESS);
3986: }
3988: /*@
3989: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
3991: Neighbor-wise Collective
3993: Input Parameters:
3994: + mat - the factored matrix
3995: . b - the right-hand-side vector
3996: - y - the vector to be added to
3998: Output Parameter:
3999: . x - the result vector
4001: Level: developer
4003: Note:
4004: The vectors `b` and `x` cannot be the same. I.e., one cannot
4005: call `MatSolveAdd`(A,x,y,x).
4007: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4008: @*/
4009: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4010: {
4011: PetscScalar one = 1.0;
4012: Vec tmp;
4014: PetscFunctionBegin;
4020: PetscCheckSameComm(mat, 1, b, 2);
4021: PetscCheckSameComm(mat, 1, y, 3);
4022: PetscCheckSameComm(mat, 1, x, 4);
4023: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4024: 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);
4025: 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);
4026: 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);
4027: 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);
4028: 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);
4029: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4030: MatCheckPreallocated(mat, 1);
4032: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4033: PetscCall(VecFlag(x, mat->factorerrortype));
4034: if (mat->factorerrortype) {
4035: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4036: } else if (mat->ops->solveadd) {
4037: PetscUseTypeMethod(mat, solveadd, b, y, x);
4038: } else {
4039: /* do the solve then the add manually */
4040: if (x != y) {
4041: PetscCall(MatSolve(mat, b, x));
4042: PetscCall(VecAXPY(x, one, y));
4043: } else {
4044: PetscCall(VecDuplicate(x, &tmp));
4045: PetscCall(VecCopy(x, tmp));
4046: PetscCall(MatSolve(mat, b, x));
4047: PetscCall(VecAXPY(x, one, tmp));
4048: PetscCall(VecDestroy(&tmp));
4049: }
4050: }
4051: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4052: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4053: PetscFunctionReturn(PETSC_SUCCESS);
4054: }
4056: /*@
4057: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4059: Neighbor-wise Collective
4061: Input Parameters:
4062: + mat - the factored matrix
4063: - b - the right-hand-side vector
4065: Output Parameter:
4066: . x - the result vector
4068: Level: developer
4070: Notes:
4071: The vectors `b` and `x` cannot be the same. I.e., one cannot
4072: call `MatSolveTranspose`(A,x,x).
4074: Most users should employ the `KSP` interface for linear solvers
4075: instead of working directly with matrix algebra routines such as this.
4076: See, e.g., `KSPCreate()`.
4078: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4079: @*/
4080: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4081: {
4082: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4084: PetscFunctionBegin;
4089: PetscCheckSameComm(mat, 1, b, 2);
4090: PetscCheckSameComm(mat, 1, x, 3);
4091: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4092: 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);
4093: 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);
4094: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4095: MatCheckPreallocated(mat, 1);
4096: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4097: PetscCall(VecFlag(x, mat->factorerrortype));
4098: if (mat->factorerrortype) {
4099: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4100: } else {
4101: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4102: PetscCall((*f)(mat, b, x));
4103: }
4104: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4105: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4106: PetscFunctionReturn(PETSC_SUCCESS);
4107: }
4109: /*@
4110: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4111: factored matrix.
4113: Neighbor-wise Collective
4115: Input Parameters:
4116: + mat - the factored matrix
4117: . b - the right-hand-side vector
4118: - y - the vector to be added to
4120: Output Parameter:
4121: . x - the result vector
4123: Level: developer
4125: Note:
4126: The vectors `b` and `x` cannot be the same. I.e., one cannot
4127: call `MatSolveTransposeAdd`(A,x,y,x).
4129: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4130: @*/
4131: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4132: {
4133: PetscScalar one = 1.0;
4134: Vec tmp;
4135: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4137: PetscFunctionBegin;
4143: PetscCheckSameComm(mat, 1, b, 2);
4144: PetscCheckSameComm(mat, 1, y, 3);
4145: PetscCheckSameComm(mat, 1, x, 4);
4146: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4147: 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);
4148: 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);
4149: 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);
4150: 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);
4151: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4152: MatCheckPreallocated(mat, 1);
4154: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4155: PetscCall(VecFlag(x, mat->factorerrortype));
4156: if (mat->factorerrortype) {
4157: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4158: } else if (f) {
4159: PetscCall((*f)(mat, b, y, x));
4160: } else {
4161: /* do the solve then the add manually */
4162: if (x != y) {
4163: PetscCall(MatSolveTranspose(mat, b, x));
4164: PetscCall(VecAXPY(x, one, y));
4165: } else {
4166: PetscCall(VecDuplicate(x, &tmp));
4167: PetscCall(VecCopy(x, tmp));
4168: PetscCall(MatSolveTranspose(mat, b, x));
4169: PetscCall(VecAXPY(x, one, tmp));
4170: PetscCall(VecDestroy(&tmp));
4171: }
4172: }
4173: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4174: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4175: PetscFunctionReturn(PETSC_SUCCESS);
4176: }
4178: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4179: /*@
4180: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4182: Neighbor-wise Collective
4184: Input Parameters:
4185: + mat - the matrix
4186: . b - the right-hand side
4187: . omega - the relaxation factor
4188: . flag - flag indicating the type of SOR (see below)
4189: . shift - diagonal shift
4190: . its - the number of iterations
4191: - lits - the number of local iterations
4193: Output Parameter:
4194: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4196: SOR Flags:
4197: + `SOR_FORWARD_SWEEP` - forward SOR
4198: . `SOR_BACKWARD_SWEEP` - backward SOR
4199: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4200: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4201: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4202: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4203: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4204: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4205: upper/lower triangular part of matrix to
4206: vector (with omega)
4207: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4209: Level: developer
4211: Notes:
4212: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4213: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4214: on each processor.
4216: Application programmers will not generally use `MatSOR()` directly,
4217: but instead will employ the `KSP`/`PC` interface.
4219: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing
4221: Most users should employ the `KSP` interface for linear solvers
4222: instead of working directly with matrix algebra routines such as this.
4223: See, e.g., `KSPCreate()`.
4225: Vectors `x` and `b` CANNOT be the same
4227: The flags are implemented as bitwise inclusive or operations.
4228: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4229: to specify a zero initial guess for SSOR.
4231: Developer Note:
4232: We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes
4234: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4235: @*/
4236: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4237: {
4238: PetscFunctionBegin;
4243: PetscCheckSameComm(mat, 1, b, 2);
4244: PetscCheckSameComm(mat, 1, x, 8);
4245: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4246: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4247: 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);
4248: 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);
4249: 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);
4250: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4251: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4252: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4254: MatCheckPreallocated(mat, 1);
4255: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4256: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4257: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4258: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4259: PetscFunctionReturn(PETSC_SUCCESS);
4260: }
4262: /*
4263: Default matrix copy routine.
4264: */
4265: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4266: {
4267: PetscInt i, rstart = 0, rend = 0, nz;
4268: const PetscInt *cwork;
4269: const PetscScalar *vwork;
4271: PetscFunctionBegin;
4272: if (B->assembled) PetscCall(MatZeroEntries(B));
4273: if (str == SAME_NONZERO_PATTERN) {
4274: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4275: for (i = rstart; i < rend; i++) {
4276: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4277: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4278: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4279: }
4280: } else {
4281: PetscCall(MatAYPX(B, 0.0, A, str));
4282: }
4283: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4284: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4285: PetscFunctionReturn(PETSC_SUCCESS);
4286: }
4288: /*@
4289: MatCopy - Copies a matrix to another matrix.
4291: Collective
4293: Input Parameters:
4294: + A - the matrix
4295: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4297: Output Parameter:
4298: . B - where the copy is put
4300: Level: intermediate
4302: Notes:
4303: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4305: `MatCopy()` copies the matrix entries of a matrix to another existing
4306: matrix (after first zeroing the second matrix). A related routine is
4307: `MatConvert()`, which first creates a new matrix and then copies the data.
4309: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4310: @*/
4311: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4312: {
4313: PetscInt i;
4315: PetscFunctionBegin;
4320: PetscCheckSameComm(A, 1, B, 2);
4321: MatCheckPreallocated(B, 2);
4322: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4323: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4324: 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,
4325: A->cmap->N, B->cmap->N);
4326: MatCheckPreallocated(A, 1);
4327: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4329: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4330: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4331: else PetscCall(MatCopy_Basic(A, B, str));
4333: B->stencil.dim = A->stencil.dim;
4334: B->stencil.noc = A->stencil.noc;
4335: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4336: B->stencil.dims[i] = A->stencil.dims[i];
4337: B->stencil.starts[i] = A->stencil.starts[i];
4338: }
4340: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4341: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4342: PetscFunctionReturn(PETSC_SUCCESS);
4343: }
4345: /*@
4346: MatConvert - Converts a matrix to another matrix, either of the same
4347: or different type.
4349: Collective
4351: Input Parameters:
4352: + mat - the matrix
4353: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4354: same type as the original matrix.
4355: - reuse - denotes if the destination matrix is to be created or reused.
4356: 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
4357: `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).
4359: Output Parameter:
4360: . M - pointer to place new matrix
4362: Level: intermediate
4364: Notes:
4365: `MatConvert()` first creates a new matrix and then copies the data from
4366: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4367: entries of one matrix to another already existing matrix context.
4369: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4370: the MPI communicator of the generated matrix is always the same as the communicator
4371: of the input matrix.
4373: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4374: @*/
4375: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4376: {
4377: PetscBool sametype, issame, flg;
4378: PetscBool3 issymmetric, ishermitian;
4379: char convname[256], mtype[256];
4380: Mat B;
4382: PetscFunctionBegin;
4385: PetscAssertPointer(M, 4);
4386: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4387: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4388: MatCheckPreallocated(mat, 1);
4390: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4391: if (flg) newtype = mtype;
4393: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4394: PetscCall(PetscStrcmp(newtype, "same", &issame));
4395: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4396: if (reuse == MAT_REUSE_MATRIX) {
4398: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4399: }
4401: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4402: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4403: PetscFunctionReturn(PETSC_SUCCESS);
4404: }
4406: /* Cache Mat options because some converters use MatHeaderReplace */
4407: issymmetric = mat->symmetric;
4408: ishermitian = mat->hermitian;
4410: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4411: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4412: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4413: } else {
4414: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4415: const char *prefix[3] = {"seq", "mpi", ""};
4416: PetscInt i;
4417: /*
4418: Order of precedence:
4419: 0) See if newtype is a superclass of the current matrix.
4420: 1) See if a specialized converter is known to the current matrix.
4421: 2) See if a specialized converter is known to the desired matrix class.
4422: 3) See if a good general converter is registered for the desired class
4423: (as of 6/27/03 only MATMPIADJ falls into this category).
4424: 4) See if a good general converter is known for the current matrix.
4425: 5) Use a really basic converter.
4426: */
4428: /* 0) See if newtype is a superclass of the current matrix.
4429: i.e mat is mpiaij and newtype is aij */
4430: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4431: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4432: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4433: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4434: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4435: if (flg) {
4436: if (reuse == MAT_INPLACE_MATRIX) {
4437: PetscCall(PetscInfo(mat, "Early return\n"));
4438: PetscFunctionReturn(PETSC_SUCCESS);
4439: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4440: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4441: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4442: PetscFunctionReturn(PETSC_SUCCESS);
4443: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4444: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4445: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4446: PetscFunctionReturn(PETSC_SUCCESS);
4447: }
4448: }
4449: }
4450: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4451: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4452: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4453: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4454: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4455: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4456: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4457: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4458: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4459: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4460: if (conv) goto foundconv;
4461: }
4463: /* 2) See if a specialized converter is known to the desired matrix class. */
4464: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4465: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4466: PetscCall(MatSetType(B, newtype));
4467: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4468: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4469: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4470: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4471: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4472: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4473: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4474: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4475: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4476: if (conv) {
4477: PetscCall(MatDestroy(&B));
4478: goto foundconv;
4479: }
4480: }
4482: /* 3) See if a good general converter is registered for the desired class */
4483: conv = B->ops->convertfrom;
4484: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4485: PetscCall(MatDestroy(&B));
4486: if (conv) goto foundconv;
4488: /* 4) See if a good general converter is known for the current matrix */
4489: if (mat->ops->convert) conv = mat->ops->convert;
4490: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4491: if (conv) goto foundconv;
4493: /* 5) Use a really basic converter. */
4494: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4495: conv = MatConvert_Basic;
4497: foundconv:
4498: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4499: PetscCall((*conv)(mat, newtype, reuse, M));
4500: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4501: /* the block sizes must be same if the mappings are copied over */
4502: (*M)->rmap->bs = mat->rmap->bs;
4503: (*M)->cmap->bs = mat->cmap->bs;
4504: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4505: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4506: (*M)->rmap->mapping = mat->rmap->mapping;
4507: (*M)->cmap->mapping = mat->cmap->mapping;
4508: }
4509: (*M)->stencil.dim = mat->stencil.dim;
4510: (*M)->stencil.noc = mat->stencil.noc;
4511: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4512: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4513: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4514: }
4515: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4516: }
4517: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4519: /* Copy Mat options */
4520: if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4521: else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4522: if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4523: else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4524: PetscFunctionReturn(PETSC_SUCCESS);
4525: }
4527: /*@
4528: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4530: Not Collective
4532: Input Parameter:
4533: . mat - the matrix, must be a factored matrix
4535: Output Parameter:
4536: . type - the string name of the package (do not free this string)
4538: Level: intermediate
4540: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4541: @*/
4542: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4543: {
4544: PetscErrorCode (*conv)(Mat, MatSolverType *);
4546: PetscFunctionBegin;
4549: PetscAssertPointer(type, 2);
4550: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4551: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4552: if (conv) PetscCall((*conv)(mat, type));
4553: else *type = MATSOLVERPETSC;
4554: PetscFunctionReturn(PETSC_SUCCESS);
4555: }
4557: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4558: struct _MatSolverTypeForSpecifcType {
4559: MatType mtype;
4560: /* no entry for MAT_FACTOR_NONE */
4561: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4562: MatSolverTypeForSpecifcType next;
4563: };
4565: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4566: struct _MatSolverTypeHolder {
4567: char *name;
4568: MatSolverTypeForSpecifcType handlers;
4569: MatSolverTypeHolder next;
4570: };
4572: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4574: /*@C
4575: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4577: Logically Collective, No Fortran Support
4579: Input Parameters:
4580: + package - name of the package, for example `petsc` or `superlu`
4581: . mtype - the matrix type that works with this package
4582: . ftype - the type of factorization supported by the package
4583: - createfactor - routine that will create the factored matrix ready to be used
4585: Level: developer
4587: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4588: `MatGetFactor()`
4589: @*/
4590: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4591: {
4592: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4593: PetscBool flg;
4594: MatSolverTypeForSpecifcType inext, iprev = NULL;
4596: PetscFunctionBegin;
4597: PetscCall(MatInitializePackage());
4598: if (!next) {
4599: PetscCall(PetscNew(&MatSolverTypeHolders));
4600: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4601: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4602: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4603: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4604: PetscFunctionReturn(PETSC_SUCCESS);
4605: }
4606: while (next) {
4607: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4608: if (flg) {
4609: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4610: inext = next->handlers;
4611: while (inext) {
4612: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4613: if (flg) {
4614: inext->createfactor[(int)ftype - 1] = createfactor;
4615: PetscFunctionReturn(PETSC_SUCCESS);
4616: }
4617: iprev = inext;
4618: inext = inext->next;
4619: }
4620: PetscCall(PetscNew(&iprev->next));
4621: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4622: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4623: PetscFunctionReturn(PETSC_SUCCESS);
4624: }
4625: prev = next;
4626: next = next->next;
4627: }
4628: PetscCall(PetscNew(&prev->next));
4629: PetscCall(PetscStrallocpy(package, &prev->next->name));
4630: PetscCall(PetscNew(&prev->next->handlers));
4631: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4632: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4633: PetscFunctionReturn(PETSC_SUCCESS);
4634: }
4636: /*@C
4637: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4639: Input Parameters:
4640: + 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
4641: . ftype - the type of factorization supported by the type
4642: - mtype - the matrix type that works with this type
4644: Output Parameters:
4645: + foundtype - `PETSC_TRUE` if the type was registered
4646: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4647: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4649: Calling sequence of `createfactor`:
4650: + A - the matrix providing the factor matrix
4651: . ftype - the `MatFactorType` of the factor requested
4652: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4654: Level: developer
4656: Note:
4657: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4658: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4659: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4661: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4662: `MatInitializePackage()`
4663: @*/
4664: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4665: {
4666: MatSolverTypeHolder next = MatSolverTypeHolders;
4667: PetscBool flg;
4668: MatSolverTypeForSpecifcType inext;
4670: PetscFunctionBegin;
4671: if (foundtype) *foundtype = PETSC_FALSE;
4672: if (foundmtype) *foundmtype = PETSC_FALSE;
4673: if (createfactor) *createfactor = NULL;
4675: if (type) {
4676: while (next) {
4677: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4678: if (flg) {
4679: if (foundtype) *foundtype = PETSC_TRUE;
4680: inext = next->handlers;
4681: while (inext) {
4682: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4683: if (flg) {
4684: if (foundmtype) *foundmtype = PETSC_TRUE;
4685: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4686: PetscFunctionReturn(PETSC_SUCCESS);
4687: }
4688: inext = inext->next;
4689: }
4690: }
4691: next = next->next;
4692: }
4693: } else {
4694: while (next) {
4695: inext = next->handlers;
4696: while (inext) {
4697: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4698: if (flg && inext->createfactor[(int)ftype - 1]) {
4699: if (foundtype) *foundtype = PETSC_TRUE;
4700: if (foundmtype) *foundmtype = PETSC_TRUE;
4701: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4702: PetscFunctionReturn(PETSC_SUCCESS);
4703: }
4704: inext = inext->next;
4705: }
4706: next = next->next;
4707: }
4708: /* try with base classes inext->mtype */
4709: next = MatSolverTypeHolders;
4710: while (next) {
4711: inext = next->handlers;
4712: while (inext) {
4713: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4714: if (flg && inext->createfactor[(int)ftype - 1]) {
4715: if (foundtype) *foundtype = PETSC_TRUE;
4716: if (foundmtype) *foundmtype = PETSC_TRUE;
4717: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4718: PetscFunctionReturn(PETSC_SUCCESS);
4719: }
4720: inext = inext->next;
4721: }
4722: next = next->next;
4723: }
4724: }
4725: PetscFunctionReturn(PETSC_SUCCESS);
4726: }
4728: PetscErrorCode MatSolverTypeDestroy(void)
4729: {
4730: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4731: MatSolverTypeForSpecifcType inext, iprev;
4733: PetscFunctionBegin;
4734: while (next) {
4735: PetscCall(PetscFree(next->name));
4736: inext = next->handlers;
4737: while (inext) {
4738: PetscCall(PetscFree(inext->mtype));
4739: iprev = inext;
4740: inext = inext->next;
4741: PetscCall(PetscFree(iprev));
4742: }
4743: prev = next;
4744: next = next->next;
4745: PetscCall(PetscFree(prev));
4746: }
4747: MatSolverTypeHolders = NULL;
4748: PetscFunctionReturn(PETSC_SUCCESS);
4749: }
4751: /*@
4752: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4754: Logically Collective
4756: Input Parameter:
4757: . mat - the matrix
4759: Output Parameter:
4760: . flg - `PETSC_TRUE` if uses the ordering
4762: Level: developer
4764: Note:
4765: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4766: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4768: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4769: @*/
4770: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4771: {
4772: PetscFunctionBegin;
4773: *flg = mat->canuseordering;
4774: PetscFunctionReturn(PETSC_SUCCESS);
4775: }
4777: /*@
4778: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4780: Logically Collective
4782: Input Parameters:
4783: + mat - the matrix obtained with `MatGetFactor()`
4784: - ftype - the factorization type to be used
4786: Output Parameter:
4787: . otype - the preferred ordering type
4789: Level: developer
4791: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4792: @*/
4793: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4794: {
4795: PetscFunctionBegin;
4796: *otype = mat->preferredordering[ftype];
4797: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4798: PetscFunctionReturn(PETSC_SUCCESS);
4799: }
4801: /*@
4802: MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()
4804: Collective
4806: Input Parameters:
4807: + mat - the matrix
4808: . 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
4809: the other criteria is returned
4810: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4812: Output Parameter:
4813: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4815: Options Database Keys:
4816: + -pc_factor_mat_solver_type <type> - choose the type at run time. When using `KSP` solvers
4817: . -pc_factor_mat_factor_on_host <bool> - do mat factorization on host (with device matrices). Default is doing it on device
4818: - -pc_factor_mat_solve_on_host <bool> - do mat solve on host (with device matrices). Default is doing it on device
4820: Level: intermediate
4822: Notes:
4823: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4824: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4826: Users usually access the factorization solvers via `KSP`
4828: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4829: 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
4831: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4832: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4833: For example if one configuration had --download-mumps while a different one had --download-superlu_dist.
4835: Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4836: where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4837: call `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4839: Developer Note:
4840: This should actually be called `MatCreateFactor()` since it creates a new factor object
4842: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4843: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4844: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4845: @*/
4846: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4847: {
4848: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4849: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4851: PetscFunctionBegin;
4855: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4856: MatCheckPreallocated(mat, 1);
4858: PetscCall(MatIsShell(mat, &shell));
4859: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4860: if (hasop) {
4861: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4862: PetscFunctionReturn(PETSC_SUCCESS);
4863: }
4865: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4866: if (!foundtype) {
4867: if (type) {
4868: 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],
4869: ((PetscObject)mat)->type_name, type);
4870: } else {
4871: 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);
4872: }
4873: }
4874: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4875: 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);
4877: PetscCall((*conv)(mat, ftype, f));
4878: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4879: PetscFunctionReturn(PETSC_SUCCESS);
4880: }
4882: /*@
4883: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4885: Not Collective
4887: Input Parameters:
4888: + mat - the matrix
4889: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4890: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4892: Output Parameter:
4893: . flg - PETSC_TRUE if the factorization is available
4895: Level: intermediate
4897: Notes:
4898: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4899: such as pastix, superlu, mumps etc.
4901: PETSc must have been ./configure to use the external solver, using the option --download-package
4903: Developer Note:
4904: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
4906: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4907: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4908: @*/
4909: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4910: {
4911: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
4913: PetscFunctionBegin;
4915: PetscAssertPointer(flg, 4);
4917: *flg = PETSC_FALSE;
4918: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
4920: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4921: MatCheckPreallocated(mat, 1);
4923: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4924: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4925: PetscFunctionReturn(PETSC_SUCCESS);
4926: }
4928: /*@
4929: MatDuplicate - Duplicates a matrix including the non-zero structure.
4931: Collective
4933: Input Parameters:
4934: + mat - the matrix
4935: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4936: See the manual page for `MatDuplicateOption()` for an explanation of these options.
4938: Output Parameter:
4939: . M - pointer to place new matrix
4941: Level: intermediate
4943: Notes:
4944: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
4946: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
4948: 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.
4950: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4951: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4952: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
4954: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4955: @*/
4956: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4957: {
4958: Mat B;
4959: VecType vtype;
4960: PetscInt i;
4961: PetscObject dm, container_h, container_d;
4962: void (*viewf)(void);
4964: PetscFunctionBegin;
4967: PetscAssertPointer(M, 3);
4968: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4969: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4970: MatCheckPreallocated(mat, 1);
4972: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4973: PetscUseTypeMethod(mat, duplicate, op, M);
4974: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4975: B = *M;
4977: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4978: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4979: PetscCall(MatGetVecType(mat, &vtype));
4980: PetscCall(MatSetVecType(B, vtype));
4982: B->stencil.dim = mat->stencil.dim;
4983: B->stencil.noc = mat->stencil.noc;
4984: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4985: B->stencil.dims[i] = mat->stencil.dims[i];
4986: B->stencil.starts[i] = mat->stencil.starts[i];
4987: }
4989: B->nooffproczerorows = mat->nooffproczerorows;
4990: B->nooffprocentries = mat->nooffprocentries;
4992: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
4993: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
4994: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
4995: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
4996: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
4997: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
4998: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
4999: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5000: PetscFunctionReturn(PETSC_SUCCESS);
5001: }
5003: /*@
5004: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5006: Logically Collective
5008: Input Parameter:
5009: . mat - the matrix
5011: Output Parameter:
5012: . v - the diagonal of the matrix
5014: Level: intermediate
5016: Note:
5017: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5018: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5019: is larger than `ndiag`, the values of the remaining entries are unspecified.
5021: Currently only correct in parallel for square matrices.
5023: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5024: @*/
5025: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5026: {
5027: PetscFunctionBegin;
5031: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5032: MatCheckPreallocated(mat, 1);
5033: if (PetscDefined(USE_DEBUG)) {
5034: PetscInt nv, row, col, ndiag;
5036: PetscCall(VecGetLocalSize(v, &nv));
5037: PetscCall(MatGetLocalSize(mat, &row, &col));
5038: ndiag = PetscMin(row, col);
5039: 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);
5040: }
5042: PetscUseTypeMethod(mat, getdiagonal, v);
5043: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5044: PetscFunctionReturn(PETSC_SUCCESS);
5045: }
5047: /*@
5048: MatGetRowMin - Gets the minimum value (of the real part) of each
5049: row of the matrix
5051: Logically Collective
5053: Input Parameter:
5054: . mat - the matrix
5056: Output Parameters:
5057: + v - the vector for storing the maximums
5058: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5060: Level: intermediate
5062: Note:
5063: The result of this call are the same as if one converted the matrix to dense format
5064: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5066: This code is only implemented for a couple of matrix formats.
5068: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5069: `MatGetRowMax()`
5070: @*/
5071: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5072: {
5073: PetscFunctionBegin;
5077: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5079: if (!mat->cmap->N) {
5080: PetscCall(VecSet(v, PETSC_MAX_REAL));
5081: if (idx) {
5082: PetscInt i, m = mat->rmap->n;
5083: for (i = 0; i < m; i++) idx[i] = -1;
5084: }
5085: } else {
5086: MatCheckPreallocated(mat, 1);
5087: }
5088: PetscUseTypeMethod(mat, getrowmin, v, idx);
5089: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5090: PetscFunctionReturn(PETSC_SUCCESS);
5091: }
5093: /*@
5094: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5095: row of the matrix
5097: Logically Collective
5099: Input Parameter:
5100: . mat - the matrix
5102: Output Parameters:
5103: + v - the vector for storing the minimums
5104: - idx - the indices of the column found for each row (or `NULL` if not needed)
5106: Level: intermediate
5108: Notes:
5109: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5110: row is 0 (the first column).
5112: This code is only implemented for a couple of matrix formats.
5114: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5115: @*/
5116: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5117: {
5118: PetscFunctionBegin;
5122: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5123: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5125: if (!mat->cmap->N) {
5126: PetscCall(VecSet(v, 0.0));
5127: if (idx) {
5128: PetscInt i, m = mat->rmap->n;
5129: for (i = 0; i < m; i++) idx[i] = -1;
5130: }
5131: } else {
5132: MatCheckPreallocated(mat, 1);
5133: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5134: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5135: }
5136: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5137: PetscFunctionReturn(PETSC_SUCCESS);
5138: }
5140: /*@
5141: MatGetRowMax - Gets the maximum value (of the real part) of each
5142: row of the matrix
5144: Logically Collective
5146: Input Parameter:
5147: . mat - the matrix
5149: Output Parameters:
5150: + v - the vector for storing the maximums
5151: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5153: Level: intermediate
5155: Notes:
5156: The result of this call are the same as if one converted the matrix to dense format
5157: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5159: This code is only implemented for a couple of matrix formats.
5161: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5162: @*/
5163: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5164: {
5165: PetscFunctionBegin;
5169: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5171: if (!mat->cmap->N) {
5172: PetscCall(VecSet(v, PETSC_MIN_REAL));
5173: if (idx) {
5174: PetscInt i, m = mat->rmap->n;
5175: for (i = 0; i < m; i++) idx[i] = -1;
5176: }
5177: } else {
5178: MatCheckPreallocated(mat, 1);
5179: PetscUseTypeMethod(mat, getrowmax, v, idx);
5180: }
5181: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5182: PetscFunctionReturn(PETSC_SUCCESS);
5183: }
5185: /*@
5186: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5187: row of the matrix
5189: Logically Collective
5191: Input Parameter:
5192: . mat - the matrix
5194: Output Parameters:
5195: + v - the vector for storing the maximums
5196: - idx - the indices of the column found for each row (or `NULL` if not needed)
5198: Level: intermediate
5200: Notes:
5201: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5202: row is 0 (the first column).
5204: This code is only implemented for a couple of matrix formats.
5206: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5207: @*/
5208: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5209: {
5210: PetscFunctionBegin;
5214: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5216: if (!mat->cmap->N) {
5217: PetscCall(VecSet(v, 0.0));
5218: if (idx) {
5219: PetscInt i, m = mat->rmap->n;
5220: for (i = 0; i < m; i++) idx[i] = -1;
5221: }
5222: } else {
5223: MatCheckPreallocated(mat, 1);
5224: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5225: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5226: }
5227: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5228: PetscFunctionReturn(PETSC_SUCCESS);
5229: }
5231: /*@
5232: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5234: Logically Collective
5236: Input Parameter:
5237: . mat - the matrix
5239: Output Parameter:
5240: . v - the vector for storing the sum
5242: Level: intermediate
5244: This code is only implemented for a couple of matrix formats.
5246: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5247: @*/
5248: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5249: {
5250: PetscFunctionBegin;
5254: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5256: if (!mat->cmap->N) {
5257: PetscCall(VecSet(v, 0.0));
5258: } else {
5259: MatCheckPreallocated(mat, 1);
5260: PetscUseTypeMethod(mat, getrowsumabs, v);
5261: }
5262: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5263: PetscFunctionReturn(PETSC_SUCCESS);
5264: }
5266: /*@
5267: MatGetRowSum - Gets the sum of each row of the matrix
5269: Logically or Neighborhood Collective
5271: Input Parameter:
5272: . mat - the matrix
5274: Output Parameter:
5275: . v - the vector for storing the sum of rows
5277: Level: intermediate
5279: Note:
5280: This code is slow since it is not currently specialized for different formats
5282: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5283: @*/
5284: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5285: {
5286: Vec ones;
5288: PetscFunctionBegin;
5292: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5293: MatCheckPreallocated(mat, 1);
5294: PetscCall(MatCreateVecs(mat, &ones, NULL));
5295: PetscCall(VecSet(ones, 1.));
5296: PetscCall(MatMult(mat, ones, v));
5297: PetscCall(VecDestroy(&ones));
5298: PetscFunctionReturn(PETSC_SUCCESS);
5299: }
5301: /*@
5302: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5303: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5305: Collective
5307: Input Parameter:
5308: . mat - the matrix to provide the transpose
5310: Output Parameter:
5311: . 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
5313: Level: advanced
5315: Note:
5316: 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
5317: routine allows bypassing that call.
5319: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5320: @*/
5321: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5322: {
5323: MatParentState *rb = NULL;
5325: PetscFunctionBegin;
5326: PetscCall(PetscNew(&rb));
5327: rb->id = ((PetscObject)mat)->id;
5328: rb->state = 0;
5329: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5330: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5331: PetscFunctionReturn(PETSC_SUCCESS);
5332: }
5334: /*@
5335: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5337: Collective
5339: Input Parameters:
5340: + mat - the matrix to transpose
5341: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5343: Output Parameter:
5344: . B - the transpose of the matrix
5346: Level: intermediate
5348: Notes:
5349: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5351: `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
5352: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5354: 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.
5356: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5357: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5359: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5361: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5363: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5364: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5365: @*/
5366: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5367: {
5368: PetscContainer rB = NULL;
5369: MatParentState *rb = NULL;
5371: PetscFunctionBegin;
5374: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5375: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5376: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5377: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5378: MatCheckPreallocated(mat, 1);
5379: if (reuse == MAT_REUSE_MATRIX) {
5380: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5381: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5382: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5383: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5384: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5385: }
5387: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5388: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5389: PetscUseTypeMethod(mat, transpose, reuse, B);
5390: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5391: }
5392: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5394: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5395: if (reuse != MAT_INPLACE_MATRIX) {
5396: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5397: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5398: rb->state = ((PetscObject)mat)->state;
5399: rb->nonzerostate = mat->nonzerostate;
5400: }
5401: PetscFunctionReturn(PETSC_SUCCESS);
5402: }
5404: /*@
5405: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5407: Collective
5409: Input Parameter:
5410: . A - the matrix to transpose
5412: Output Parameter:
5413: . 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
5414: numerical portion.
5416: Level: intermediate
5418: Note:
5419: This is not supported for many matrix types, use `MatTranspose()` in those cases
5421: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5422: @*/
5423: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5424: {
5425: PetscFunctionBegin;
5428: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5429: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5430: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5431: PetscUseTypeMethod(A, transposesymbolic, B);
5432: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5434: PetscCall(MatTransposeSetPrecursor(A, *B));
5435: PetscFunctionReturn(PETSC_SUCCESS);
5436: }
5438: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5439: {
5440: PetscContainer rB;
5441: MatParentState *rb;
5443: PetscFunctionBegin;
5446: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5447: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5448: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5449: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5450: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5451: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5452: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5453: PetscFunctionReturn(PETSC_SUCCESS);
5454: }
5456: /*@
5457: MatIsTranspose - Test whether a matrix is another one's transpose,
5458: or its own, in which case it tests symmetry.
5460: Collective
5462: Input Parameters:
5463: + A - the matrix to test
5464: . B - the matrix to test against, this can equal the first parameter
5465: - tol - tolerance, differences between entries smaller than this are counted as zero
5467: Output Parameter:
5468: . flg - the result
5470: Level: intermediate
5472: Notes:
5473: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5474: test involves parallel copies of the block off-diagonal parts of the matrix.
5476: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5477: @*/
5478: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5479: {
5480: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5482: PetscFunctionBegin;
5485: PetscAssertPointer(flg, 4);
5486: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5487: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5488: *flg = PETSC_FALSE;
5489: if (f && g) {
5490: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5491: PetscCall((*f)(A, B, tol, flg));
5492: } else {
5493: MatType mattype;
5495: PetscCall(MatGetType(f ? B : A, &mattype));
5496: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5497: }
5498: PetscFunctionReturn(PETSC_SUCCESS);
5499: }
5501: /*@
5502: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5504: Collective
5506: Input Parameters:
5507: + mat - the matrix to transpose and complex conjugate
5508: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5510: Output Parameter:
5511: . B - the Hermitian transpose
5513: Level: intermediate
5515: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5516: @*/
5517: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5518: {
5519: PetscFunctionBegin;
5520: PetscCall(MatTranspose(mat, reuse, B));
5521: #if defined(PETSC_USE_COMPLEX)
5522: PetscCall(MatConjugate(*B));
5523: #endif
5524: PetscFunctionReturn(PETSC_SUCCESS);
5525: }
5527: /*@
5528: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5530: Collective
5532: Input Parameters:
5533: + A - the matrix to test
5534: . B - the matrix to test against, this can equal the first parameter
5535: - tol - tolerance, differences between entries smaller than this are counted as zero
5537: Output Parameter:
5538: . flg - the result
5540: Level: intermediate
5542: Notes:
5543: Only available for `MATAIJ` matrices.
5545: The sequential algorithm
5546: has a running time of the order of the number of nonzeros; the parallel
5547: test involves parallel copies of the block off-diagonal parts of the matrix.
5549: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5550: @*/
5551: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5552: {
5553: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5555: PetscFunctionBegin;
5558: PetscAssertPointer(flg, 4);
5559: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5560: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5561: if (f && g) {
5562: PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5563: PetscCall((*f)(A, B, tol, flg));
5564: }
5565: PetscFunctionReturn(PETSC_SUCCESS);
5566: }
5568: /*@
5569: MatPermute - Creates a new matrix with rows and columns permuted from the
5570: original.
5572: Collective
5574: Input Parameters:
5575: + mat - the matrix to permute
5576: . row - row permutation, each processor supplies only the permutation for its rows
5577: - col - column permutation, each processor supplies only the permutation for its columns
5579: Output Parameter:
5580: . B - the permuted matrix
5582: Level: advanced
5584: Note:
5585: The index sets map from row/col of permuted matrix to row/col of original matrix.
5586: The index sets should be on the same communicator as mat and have the same local sizes.
5588: Developer Note:
5589: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5590: exploit the fact that row and col are permutations, consider implementing the
5591: more general `MatCreateSubMatrix()` instead.
5593: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5594: @*/
5595: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5596: {
5597: PetscFunctionBegin;
5602: PetscAssertPointer(B, 4);
5603: PetscCheckSameComm(mat, 1, row, 2);
5604: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5605: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5606: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5607: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5608: MatCheckPreallocated(mat, 1);
5610: if (mat->ops->permute) {
5611: PetscUseTypeMethod(mat, permute, row, col, B);
5612: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5613: } else {
5614: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5615: }
5616: PetscFunctionReturn(PETSC_SUCCESS);
5617: }
5619: /*@
5620: MatEqual - Compares two matrices.
5622: Collective
5624: Input Parameters:
5625: + A - the first matrix
5626: - B - the second matrix
5628: Output Parameter:
5629: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5631: Level: intermediate
5633: Note:
5634: 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
5635: using several randomly created vectors, see `MatMultEqual()`.
5637: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5638: @*/
5639: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5640: {
5641: PetscFunctionBegin;
5646: PetscAssertPointer(flg, 3);
5647: PetscCheckSameComm(A, 1, B, 2);
5648: MatCheckPreallocated(A, 1);
5649: MatCheckPreallocated(B, 2);
5650: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5651: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5652: 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,
5653: B->cmap->N);
5654: if (A->ops->equal && A->ops->equal == B->ops->equal) {
5655: PetscUseTypeMethod(A, equal, B, flg);
5656: } else {
5657: PetscCall(MatMultEqual(A, B, 10, flg));
5658: }
5659: PetscFunctionReturn(PETSC_SUCCESS);
5660: }
5662: /*@
5663: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5664: matrices that are stored as vectors. Either of the two scaling
5665: matrices can be `NULL`.
5667: Collective
5669: Input Parameters:
5670: + mat - the matrix to be scaled
5671: . l - the left scaling vector (or `NULL`)
5672: - r - the right scaling vector (or `NULL`)
5674: Level: intermediate
5676: Note:
5677: `MatDiagonalScale()` computes $A = LAR$, where
5678: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5679: The L scales the rows of the matrix, the R scales the columns of the matrix.
5681: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5682: @*/
5683: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5684: {
5685: PetscFunctionBegin;
5688: if (l) {
5690: PetscCheckSameComm(mat, 1, l, 2);
5691: }
5692: if (r) {
5694: PetscCheckSameComm(mat, 1, r, 3);
5695: }
5696: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5697: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5698: MatCheckPreallocated(mat, 1);
5699: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5701: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5702: PetscUseTypeMethod(mat, diagonalscale, l, r);
5703: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5704: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5705: if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5706: PetscFunctionReturn(PETSC_SUCCESS);
5707: }
5709: /*@
5710: MatScale - Scales all elements of a matrix by a given number.
5712: Logically Collective
5714: Input Parameters:
5715: + mat - the matrix to be scaled
5716: - a - the scaling value
5718: Level: intermediate
5720: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5721: @*/
5722: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5723: {
5724: PetscFunctionBegin;
5727: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5728: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5730: MatCheckPreallocated(mat, 1);
5732: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5733: if (a != (PetscScalar)1.0) {
5734: PetscUseTypeMethod(mat, scale, a);
5735: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5736: }
5737: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5738: PetscFunctionReturn(PETSC_SUCCESS);
5739: }
5741: /*@
5742: MatNorm - Calculates various norms of a matrix.
5744: Collective
5746: Input Parameters:
5747: + mat - the matrix
5748: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5750: Output Parameter:
5751: . nrm - the resulting norm
5753: Level: intermediate
5755: .seealso: [](ch_matrices), `Mat`
5756: @*/
5757: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5758: {
5759: PetscFunctionBegin;
5762: PetscAssertPointer(nrm, 3);
5764: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5765: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5766: MatCheckPreallocated(mat, 1);
5768: PetscUseTypeMethod(mat, norm, type, nrm);
5769: PetscFunctionReturn(PETSC_SUCCESS);
5770: }
5772: /*
5773: This variable is used to prevent counting of MatAssemblyBegin() that
5774: are called from within a MatAssemblyEnd().
5775: */
5776: static PetscInt MatAssemblyEnd_InUse = 0;
5777: /*@
5778: MatAssemblyBegin - Begins assembling the matrix. This routine should
5779: be called after completing all calls to `MatSetValues()`.
5781: Collective
5783: Input Parameters:
5784: + mat - the matrix
5785: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5787: Level: beginner
5789: Notes:
5790: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5791: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5793: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5794: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5795: using the matrix.
5797: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5798: 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
5799: a global collective operation requiring all processes that share the matrix.
5801: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5802: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5803: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5805: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5806: @*/
5807: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5808: {
5809: PetscFunctionBegin;
5812: MatCheckPreallocated(mat, 1);
5813: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5814: if (mat->assembled) {
5815: mat->was_assembled = PETSC_TRUE;
5816: mat->assembled = PETSC_FALSE;
5817: }
5819: if (!MatAssemblyEnd_InUse) {
5820: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5821: PetscTryTypeMethod(mat, assemblybegin, type);
5822: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5823: } else PetscTryTypeMethod(mat, assemblybegin, type);
5824: PetscFunctionReturn(PETSC_SUCCESS);
5825: }
5827: /*@
5828: MatAssembled - Indicates if a matrix has been assembled and is ready for
5829: use; for example, in matrix-vector product.
5831: Not Collective
5833: Input Parameter:
5834: . mat - the matrix
5836: Output Parameter:
5837: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5839: Level: advanced
5841: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5842: @*/
5843: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5844: {
5845: PetscFunctionBegin;
5847: PetscAssertPointer(assembled, 2);
5848: *assembled = mat->assembled;
5849: PetscFunctionReturn(PETSC_SUCCESS);
5850: }
5852: /*@
5853: MatAssemblyEnd - Completes assembling the matrix. This routine should
5854: be called after `MatAssemblyBegin()`.
5856: Collective
5858: Input Parameters:
5859: + mat - the matrix
5860: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5862: Options Database Keys:
5863: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5864: . -mat_view ::ascii_info_detail - Prints more detailed info
5865: . -mat_view - Prints matrix in ASCII format
5866: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
5867: . -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5868: . -display <name> - Sets display name (default is host)
5869: . -draw_pause <sec> - Sets number of seconds to pause after display
5870: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5871: . -viewer_socket_machine <machine> - Machine to use for socket
5872: . -viewer_socket_port <port> - Port number to use for socket
5873: - -mat_view binary:filename[:append] - Save matrix to file in binary format
5875: Level: beginner
5877: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5878: @*/
5879: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5880: {
5881: static PetscInt inassm = 0;
5882: PetscBool flg = PETSC_FALSE;
5884: PetscFunctionBegin;
5888: inassm++;
5889: MatAssemblyEnd_InUse++;
5890: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5891: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5892: PetscTryTypeMethod(mat, assemblyend, type);
5893: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5894: } else PetscTryTypeMethod(mat, assemblyend, type);
5896: /* Flush assembly is not a true assembly */
5897: if (type != MAT_FLUSH_ASSEMBLY) {
5898: if (mat->num_ass) {
5899: if (!mat->symmetry_eternal) {
5900: mat->symmetric = PETSC_BOOL3_UNKNOWN;
5901: mat->hermitian = PETSC_BOOL3_UNKNOWN;
5902: }
5903: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5904: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5905: }
5906: mat->num_ass++;
5907: mat->assembled = PETSC_TRUE;
5908: mat->ass_nonzerostate = mat->nonzerostate;
5909: }
5911: mat->insertmode = NOT_SET_VALUES;
5912: MatAssemblyEnd_InUse--;
5913: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5914: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5915: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
5917: if (mat->checksymmetryonassembly) {
5918: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5919: if (flg) {
5920: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5921: } else {
5922: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5923: }
5924: }
5925: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5926: }
5927: inassm--;
5928: PetscFunctionReturn(PETSC_SUCCESS);
5929: }
5931: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5932: /*@
5933: MatSetOption - Sets a parameter option for a matrix. Some options
5934: may be specific to certain storage formats. Some options
5935: determine how values will be inserted (or added). Sorted,
5936: row-oriented input will generally assemble the fastest. The default
5937: is row-oriented.
5939: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
5941: Input Parameters:
5942: + mat - the matrix
5943: . op - the option, one of those listed below (and possibly others),
5944: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
5946: Options Describing Matrix Structure:
5947: + `MAT_SPD` - symmetric positive definite
5948: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
5949: . `MAT_HERMITIAN` - transpose is the complex conjugation
5950: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
5951: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5952: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5953: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
5955: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5956: do not need to be computed (usually at a high cost)
5958: Options For Use with `MatSetValues()`:
5959: Insert a logically dense subblock, which can be
5960: . `MAT_ROW_ORIENTED` - row-oriented (default)
5962: These options reflect the data you pass in with `MatSetValues()`; it has
5963: nothing to do with how the data is stored internally in the matrix
5964: data structure.
5966: When (re)assembling a matrix, we can restrict the input for
5967: efficiency/debugging purposes. These options include
5968: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
5969: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
5970: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
5971: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
5972: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
5973: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
5974: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5975: performance for very large process counts.
5976: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
5977: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5978: functions, instead sending only neighbor messages.
5980: Level: intermediate
5982: Notes:
5983: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
5985: Some options are relevant only for particular matrix types and
5986: are thus ignored by others. Other options are not supported by
5987: certain matrix types and will generate an error message if set.
5989: If using Fortran to compute a matrix, one may need to
5990: use the column-oriented option (or convert to the row-oriented
5991: format).
5993: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
5994: that would generate a new entry in the nonzero structure is instead
5995: ignored. Thus, if memory has not already been allocated for this particular
5996: data, then the insertion is ignored. For dense matrices, in which
5997: the entire array is allocated, no entries are ever ignored.
5998: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6000: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6001: that would generate a new entry in the nonzero structure instead produces
6002: 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
6004: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6005: that would generate a new entry that has not been preallocated will
6006: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6007: only.) This is a useful flag when debugging matrix memory preallocation.
6008: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6010: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6011: other processors should be dropped, rather than stashed.
6012: This is useful if you know that the "owning" processor is also
6013: always generating the correct matrix entries, so that PETSc need
6014: not transfer duplicate entries generated on another processor.
6016: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6017: searches during matrix assembly. When this flag is set, the hash table
6018: is created during the first matrix assembly. This hash table is
6019: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6020: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6021: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6022: supported by `MATMPIBAIJ` format only.
6024: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6025: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6027: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6028: a zero location in the matrix
6030: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6032: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6033: zero row routines and thus improves performance for very large process counts.
6035: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6036: part of the matrix (since they should match the upper triangular part).
6038: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6039: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6040: with finite difference schemes with non-periodic boundary conditions.
6042: Developer Note:
6043: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6044: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6045: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6046: not changed.
6048: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6049: @*/
6050: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6051: {
6052: PetscFunctionBegin;
6054: if (op > 0) {
6057: }
6059: 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);
6061: switch (op) {
6062: case MAT_FORCE_DIAGONAL_ENTRIES:
6063: mat->force_diagonals = flg;
6064: PetscFunctionReturn(PETSC_SUCCESS);
6065: case MAT_NO_OFF_PROC_ENTRIES:
6066: mat->nooffprocentries = flg;
6067: PetscFunctionReturn(PETSC_SUCCESS);
6068: case MAT_SUBSET_OFF_PROC_ENTRIES:
6069: mat->assembly_subset = flg;
6070: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6071: #if !defined(PETSC_HAVE_MPIUNI)
6072: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6073: #endif
6074: mat->stash.first_assembly_done = PETSC_FALSE;
6075: }
6076: PetscFunctionReturn(PETSC_SUCCESS);
6077: case MAT_NO_OFF_PROC_ZERO_ROWS:
6078: mat->nooffproczerorows = flg;
6079: PetscFunctionReturn(PETSC_SUCCESS);
6080: case MAT_SPD:
6081: if (flg) {
6082: mat->spd = PETSC_BOOL3_TRUE;
6083: mat->symmetric = PETSC_BOOL3_TRUE;
6084: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6085: } else {
6086: mat->spd = PETSC_BOOL3_FALSE;
6087: }
6088: break;
6089: case MAT_SYMMETRIC:
6090: mat->symmetric = PetscBoolToBool3(flg);
6091: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6092: #if !defined(PETSC_USE_COMPLEX)
6093: mat->hermitian = PetscBoolToBool3(flg);
6094: #endif
6095: break;
6096: case MAT_HERMITIAN:
6097: mat->hermitian = PetscBoolToBool3(flg);
6098: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6099: #if !defined(PETSC_USE_COMPLEX)
6100: mat->symmetric = PetscBoolToBool3(flg);
6101: #endif
6102: break;
6103: case MAT_STRUCTURALLY_SYMMETRIC:
6104: mat->structurally_symmetric = PetscBoolToBool3(flg);
6105: break;
6106: case MAT_SYMMETRY_ETERNAL:
6107: 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");
6108: mat->symmetry_eternal = flg;
6109: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6110: break;
6111: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6112: 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");
6113: mat->structural_symmetry_eternal = flg;
6114: break;
6115: case MAT_SPD_ETERNAL:
6116: 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");
6117: mat->spd_eternal = flg;
6118: if (flg) {
6119: mat->structural_symmetry_eternal = PETSC_TRUE;
6120: mat->symmetry_eternal = PETSC_TRUE;
6121: }
6122: break;
6123: case MAT_STRUCTURE_ONLY:
6124: mat->structure_only = flg;
6125: break;
6126: case MAT_SORTED_FULL:
6127: mat->sortedfull = flg;
6128: break;
6129: default:
6130: break;
6131: }
6132: PetscTryTypeMethod(mat, setoption, op, flg);
6133: PetscFunctionReturn(PETSC_SUCCESS);
6134: }
6136: /*@
6137: MatGetOption - Gets a parameter option that has been set for a matrix.
6139: Logically Collective
6141: Input Parameters:
6142: + mat - the matrix
6143: - op - the option, this only responds to certain options, check the code for which ones
6145: Output Parameter:
6146: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6148: Level: intermediate
6150: Notes:
6151: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6153: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6154: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6156: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6157: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6158: @*/
6159: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6160: {
6161: PetscFunctionBegin;
6165: 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);
6166: 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()");
6168: switch (op) {
6169: case MAT_NO_OFF_PROC_ENTRIES:
6170: *flg = mat->nooffprocentries;
6171: break;
6172: case MAT_NO_OFF_PROC_ZERO_ROWS:
6173: *flg = mat->nooffproczerorows;
6174: break;
6175: case MAT_SYMMETRIC:
6176: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6177: break;
6178: case MAT_HERMITIAN:
6179: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6180: break;
6181: case MAT_STRUCTURALLY_SYMMETRIC:
6182: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6183: break;
6184: case MAT_SPD:
6185: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6186: break;
6187: case MAT_SYMMETRY_ETERNAL:
6188: *flg = mat->symmetry_eternal;
6189: break;
6190: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6191: *flg = mat->symmetry_eternal;
6192: break;
6193: default:
6194: break;
6195: }
6196: PetscFunctionReturn(PETSC_SUCCESS);
6197: }
6199: /*@
6200: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6201: this routine retains the old nonzero structure.
6203: Logically Collective
6205: Input Parameter:
6206: . mat - the matrix
6208: Level: intermediate
6210: Note:
6211: 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.
6212: See the Performance chapter of the users manual for information on preallocating matrices.
6214: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6215: @*/
6216: PetscErrorCode MatZeroEntries(Mat mat)
6217: {
6218: PetscFunctionBegin;
6221: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6222: 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");
6223: MatCheckPreallocated(mat, 1);
6225: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6226: PetscUseTypeMethod(mat, zeroentries);
6227: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6228: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6229: PetscFunctionReturn(PETSC_SUCCESS);
6230: }
6232: /*@
6233: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6234: of a set of rows and columns of a matrix.
6236: Collective
6238: Input Parameters:
6239: + mat - the matrix
6240: . numRows - the number of rows/columns to zero
6241: . rows - the global row indices
6242: . diag - value put in the diagonal of the eliminated rows
6243: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6244: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6246: Level: intermediate
6248: Notes:
6249: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6251: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6252: 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
6254: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6255: Krylov method to take advantage of the known solution on the zeroed rows.
6257: For the parallel case, all processes that share the matrix (i.e.,
6258: those in the communicator used for matrix creation) MUST call this
6259: routine, regardless of whether any rows being zeroed are owned by
6260: them.
6262: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6263: 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
6264: missing.
6266: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6267: list only rows local to itself).
6269: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6271: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6272: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6273: @*/
6274: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6275: {
6276: PetscFunctionBegin;
6279: if (numRows) PetscAssertPointer(rows, 3);
6280: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6281: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6282: MatCheckPreallocated(mat, 1);
6284: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6285: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6286: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6287: PetscFunctionReturn(PETSC_SUCCESS);
6288: }
6290: /*@
6291: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6292: of a set of rows and columns of a matrix.
6294: Collective
6296: Input Parameters:
6297: + mat - the matrix
6298: . is - the rows to zero
6299: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6300: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6301: - b - optional vector of right-hand side, that will be adjusted by provided solution
6303: Level: intermediate
6305: Note:
6306: See `MatZeroRowsColumns()` for details on how this routine operates.
6308: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6309: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6310: @*/
6311: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6312: {
6313: PetscInt numRows;
6314: const PetscInt *rows;
6316: PetscFunctionBegin;
6321: PetscCall(ISGetLocalSize(is, &numRows));
6322: PetscCall(ISGetIndices(is, &rows));
6323: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6324: PetscCall(ISRestoreIndices(is, &rows));
6325: PetscFunctionReturn(PETSC_SUCCESS);
6326: }
6328: /*@
6329: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6330: of a set of rows of a matrix.
6332: Collective
6334: Input Parameters:
6335: + mat - the matrix
6336: . numRows - the number of rows to zero
6337: . rows - the global row indices
6338: . diag - value put in the diagonal of the zeroed rows
6339: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6340: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6342: Level: intermediate
6344: Notes:
6345: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6347: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6349: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6350: Krylov method to take advantage of the known solution on the zeroed rows.
6352: 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)
6353: from the matrix.
6355: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6356: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6357: formats this does not alter the nonzero structure.
6359: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6360: of the matrix is not changed the values are
6361: merely zeroed.
6363: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6364: formats can optionally remove the main diagonal entry from the
6365: nonzero structure as well, by passing 0.0 as the final argument).
6367: For the parallel case, all processes that share the matrix (i.e.,
6368: those in the communicator used for matrix creation) MUST call this
6369: routine, regardless of whether any rows being zeroed are owned by
6370: them.
6372: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6373: list only rows local to itself).
6375: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6376: owns that are to be zeroed. This saves a global synchronization in the implementation.
6378: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6379: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6380: @*/
6381: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6382: {
6383: PetscFunctionBegin;
6386: if (numRows) PetscAssertPointer(rows, 3);
6387: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6388: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6389: MatCheckPreallocated(mat, 1);
6391: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6392: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6393: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6394: PetscFunctionReturn(PETSC_SUCCESS);
6395: }
6397: /*@
6398: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6399: of a set of rows of a matrix indicated by an `IS`
6401: Collective
6403: Input Parameters:
6404: + mat - the matrix
6405: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6406: . diag - value put in all diagonals of eliminated rows
6407: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6408: - b - optional vector of right-hand side, that will be adjusted by provided solution
6410: Level: intermediate
6412: Note:
6413: See `MatZeroRows()` for details on how this routine operates.
6415: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6416: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6417: @*/
6418: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6419: {
6420: PetscInt numRows = 0;
6421: const PetscInt *rows = NULL;
6423: PetscFunctionBegin;
6426: if (is) {
6428: PetscCall(ISGetLocalSize(is, &numRows));
6429: PetscCall(ISGetIndices(is, &rows));
6430: }
6431: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6432: if (is) PetscCall(ISRestoreIndices(is, &rows));
6433: PetscFunctionReturn(PETSC_SUCCESS);
6434: }
6436: /*@
6437: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6438: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6440: Collective
6442: Input Parameters:
6443: + mat - the matrix
6444: . numRows - the number of rows to remove
6445: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6446: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6447: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6448: - b - optional vector of right-hand side, that will be adjusted by provided solution
6450: Level: intermediate
6452: Notes:
6453: See `MatZeroRows()` for details on how this routine operates.
6455: The grid coordinates are across the entire grid, not just the local portion
6457: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6458: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6459: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6460: `DM_BOUNDARY_PERIODIC` boundary type.
6462: 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
6463: a single value per point) you can skip filling those indices.
6465: Fortran Note:
6466: `idxm` and `idxn` should be declared as
6467: .vb
6468: MatStencil idxm(4, m)
6469: .ve
6470: and the values inserted using
6471: .vb
6472: idxm(MatStencil_i, 1) = i
6473: idxm(MatStencil_j, 1) = j
6474: idxm(MatStencil_k, 1) = k
6475: idxm(MatStencil_c, 1) = c
6476: etc
6477: .ve
6479: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6480: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6481: @*/
6482: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6483: {
6484: PetscInt dim = mat->stencil.dim;
6485: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6486: PetscInt *dims = mat->stencil.dims + 1;
6487: PetscInt *starts = mat->stencil.starts;
6488: PetscInt *dxm = (PetscInt *)rows;
6489: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6491: PetscFunctionBegin;
6494: if (numRows) PetscAssertPointer(rows, 3);
6496: PetscCall(PetscMalloc1(numRows, &jdxm));
6497: for (i = 0; i < numRows; ++i) {
6498: /* Skip unused dimensions (they are ordered k, j, i, c) */
6499: for (j = 0; j < 3 - sdim; ++j) dxm++;
6500: /* Local index in X dir */
6501: tmp = *dxm++ - starts[0];
6502: /* Loop over remaining dimensions */
6503: for (j = 0; j < dim - 1; ++j) {
6504: /* If nonlocal, set index to be negative */
6505: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6506: /* Update local index */
6507: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6508: }
6509: /* Skip component slot if necessary */
6510: if (mat->stencil.noc) dxm++;
6511: /* Local row number */
6512: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6513: }
6514: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6515: PetscCall(PetscFree(jdxm));
6516: PetscFunctionReturn(PETSC_SUCCESS);
6517: }
6519: /*@
6520: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6521: of a set of rows and columns of a matrix.
6523: Collective
6525: Input Parameters:
6526: + mat - the matrix
6527: . numRows - the number of rows/columns to remove
6528: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6529: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6530: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6531: - b - optional vector of right-hand side, that will be adjusted by provided solution
6533: Level: intermediate
6535: Notes:
6536: See `MatZeroRowsColumns()` for details on how this routine operates.
6538: The grid coordinates are across the entire grid, not just the local portion
6540: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6541: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6542: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6543: `DM_BOUNDARY_PERIODIC` boundary type.
6545: 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
6546: a single value per point) you can skip filling those indices.
6548: Fortran Note:
6549: `idxm` and `idxn` should be declared as
6550: .vb
6551: MatStencil idxm(4, m)
6552: .ve
6553: and the values inserted using
6554: .vb
6555: idxm(MatStencil_i, 1) = i
6556: idxm(MatStencil_j, 1) = j
6557: idxm(MatStencil_k, 1) = k
6558: idxm(MatStencil_c, 1) = c
6559: etc
6560: .ve
6562: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6563: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6564: @*/
6565: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6566: {
6567: PetscInt dim = mat->stencil.dim;
6568: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6569: PetscInt *dims = mat->stencil.dims + 1;
6570: PetscInt *starts = mat->stencil.starts;
6571: PetscInt *dxm = (PetscInt *)rows;
6572: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6574: PetscFunctionBegin;
6577: if (numRows) PetscAssertPointer(rows, 3);
6579: PetscCall(PetscMalloc1(numRows, &jdxm));
6580: for (i = 0; i < numRows; ++i) {
6581: /* Skip unused dimensions (they are ordered k, j, i, c) */
6582: for (j = 0; j < 3 - sdim; ++j) dxm++;
6583: /* Local index in X dir */
6584: tmp = *dxm++ - starts[0];
6585: /* Loop over remaining dimensions */
6586: for (j = 0; j < dim - 1; ++j) {
6587: /* If nonlocal, set index to be negative */
6588: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6589: /* Update local index */
6590: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6591: }
6592: /* Skip component slot if necessary */
6593: if (mat->stencil.noc) dxm++;
6594: /* Local row number */
6595: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6596: }
6597: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6598: PetscCall(PetscFree(jdxm));
6599: PetscFunctionReturn(PETSC_SUCCESS);
6600: }
6602: /*@
6603: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6604: of a set of rows of a matrix; using local numbering of rows.
6606: Collective
6608: Input Parameters:
6609: + mat - the matrix
6610: . numRows - the number of rows to remove
6611: . rows - the local row indices
6612: . diag - value put in all diagonals of eliminated rows
6613: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6614: - b - optional vector of right-hand side, that will be adjusted by provided solution
6616: Level: intermediate
6618: Notes:
6619: Before calling `MatZeroRowsLocal()`, the user must first set the
6620: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6622: See `MatZeroRows()` for details on how this routine operates.
6624: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6625: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6626: @*/
6627: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6628: {
6629: PetscFunctionBegin;
6632: if (numRows) PetscAssertPointer(rows, 3);
6633: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6634: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6635: MatCheckPreallocated(mat, 1);
6637: if (mat->ops->zerorowslocal) {
6638: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6639: } else {
6640: IS is, newis;
6641: const PetscInt *newRows;
6643: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6644: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6645: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6646: PetscCall(ISGetIndices(newis, &newRows));
6647: PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6648: PetscCall(ISRestoreIndices(newis, &newRows));
6649: PetscCall(ISDestroy(&newis));
6650: PetscCall(ISDestroy(&is));
6651: }
6652: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6653: PetscFunctionReturn(PETSC_SUCCESS);
6654: }
6656: /*@
6657: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6658: of a set of rows of a matrix; using local numbering of rows.
6660: Collective
6662: Input Parameters:
6663: + mat - the matrix
6664: . is - index set of rows to remove
6665: . diag - value put in all diagonals of eliminated rows
6666: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6667: - b - optional vector of right-hand side, that will be adjusted by provided solution
6669: Level: intermediate
6671: Notes:
6672: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6673: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6675: See `MatZeroRows()` for details on how this routine operates.
6677: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6678: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6679: @*/
6680: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6681: {
6682: PetscInt numRows;
6683: const PetscInt *rows;
6685: PetscFunctionBegin;
6689: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6690: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6691: MatCheckPreallocated(mat, 1);
6693: PetscCall(ISGetLocalSize(is, &numRows));
6694: PetscCall(ISGetIndices(is, &rows));
6695: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6696: PetscCall(ISRestoreIndices(is, &rows));
6697: PetscFunctionReturn(PETSC_SUCCESS);
6698: }
6700: /*@
6701: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6702: of a set of rows and columns of a matrix; using local numbering of rows.
6704: Collective
6706: Input Parameters:
6707: + mat - the matrix
6708: . numRows - the number of rows to remove
6709: . rows - the global row indices
6710: . diag - value put in all diagonals of eliminated rows
6711: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6712: - b - optional vector of right-hand side, that will be adjusted by provided solution
6714: Level: intermediate
6716: Notes:
6717: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6718: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6720: See `MatZeroRowsColumns()` for details on how this routine operates.
6722: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6723: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6724: @*/
6725: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6726: {
6727: IS is, newis;
6728: const PetscInt *newRows;
6730: PetscFunctionBegin;
6733: if (numRows) PetscAssertPointer(rows, 3);
6734: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6735: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6736: MatCheckPreallocated(mat, 1);
6738: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6739: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6740: PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6741: PetscCall(ISGetIndices(newis, &newRows));
6742: PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6743: PetscCall(ISRestoreIndices(newis, &newRows));
6744: PetscCall(ISDestroy(&newis));
6745: PetscCall(ISDestroy(&is));
6746: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6747: PetscFunctionReturn(PETSC_SUCCESS);
6748: }
6750: /*@
6751: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6752: of a set of rows and columns of a matrix; using local numbering of rows.
6754: Collective
6756: Input Parameters:
6757: + mat - the matrix
6758: . is - index set of rows to remove
6759: . diag - value put in all diagonals of eliminated rows
6760: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6761: - b - optional vector of right-hand side, that will be adjusted by provided solution
6763: Level: intermediate
6765: Notes:
6766: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6767: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6769: See `MatZeroRowsColumns()` for details on how this routine operates.
6771: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6772: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6773: @*/
6774: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6775: {
6776: PetscInt numRows;
6777: const PetscInt *rows;
6779: PetscFunctionBegin;
6783: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6784: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6785: MatCheckPreallocated(mat, 1);
6787: PetscCall(ISGetLocalSize(is, &numRows));
6788: PetscCall(ISGetIndices(is, &rows));
6789: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6790: PetscCall(ISRestoreIndices(is, &rows));
6791: PetscFunctionReturn(PETSC_SUCCESS);
6792: }
6794: /*@
6795: MatGetSize - Returns the numbers of rows and columns in a matrix.
6797: Not Collective
6799: Input Parameter:
6800: . mat - the matrix
6802: Output Parameters:
6803: + m - the number of global rows
6804: - n - the number of global columns
6806: Level: beginner
6808: Note:
6809: Both output parameters can be `NULL` on input.
6811: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6812: @*/
6813: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6814: {
6815: PetscFunctionBegin;
6817: if (m) *m = mat->rmap->N;
6818: if (n) *n = mat->cmap->N;
6819: PetscFunctionReturn(PETSC_SUCCESS);
6820: }
6822: /*@
6823: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6824: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6826: Not Collective
6828: Input Parameter:
6829: . mat - the matrix
6831: Output Parameters:
6832: + m - the number of local rows, use `NULL` to not obtain this value
6833: - n - the number of local columns, use `NULL` to not obtain this value
6835: Level: beginner
6837: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6838: @*/
6839: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6840: {
6841: PetscFunctionBegin;
6843: if (m) PetscAssertPointer(m, 2);
6844: if (n) PetscAssertPointer(n, 3);
6845: if (m) *m = mat->rmap->n;
6846: if (n) *n = mat->cmap->n;
6847: PetscFunctionReturn(PETSC_SUCCESS);
6848: }
6850: /*@
6851: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6852: vector one multiplies this matrix by that are owned by this processor.
6854: Not Collective, unless matrix has not been allocated, then collective
6856: Input Parameter:
6857: . mat - the matrix
6859: Output Parameters:
6860: + m - the global index of the first local column, use `NULL` to not obtain this value
6861: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
6863: Level: developer
6865: Notes:
6866: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6868: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6869: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6871: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6872: the local values in the matrix.
6874: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6875: Layouts](sec_matlayout) for details on matrix layouts.
6877: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6878: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6879: @*/
6880: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6881: {
6882: PetscFunctionBegin;
6885: if (m) PetscAssertPointer(m, 2);
6886: if (n) PetscAssertPointer(n, 3);
6887: MatCheckPreallocated(mat, 1);
6888: if (m) *m = mat->cmap->rstart;
6889: if (n) *n = mat->cmap->rend;
6890: PetscFunctionReturn(PETSC_SUCCESS);
6891: }
6893: /*@
6894: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6895: this MPI process.
6897: Not Collective
6899: Input Parameter:
6900: . mat - the matrix
6902: Output Parameters:
6903: + m - the global index of the first local row, use `NULL` to not obtain this value
6904: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
6906: Level: beginner
6908: Notes:
6909: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6911: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6912: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6914: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6915: the local values in the matrix.
6917: The high argument is one more than the last element stored locally.
6919: For all matrices it returns the range of matrix rows associated with rows of a vector that
6920: would contain the result of a matrix vector product with this matrix. See [Matrix
6921: Layouts](sec_matlayout) for details on matrix layouts.
6923: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6924: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6925: @*/
6926: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6927: {
6928: PetscFunctionBegin;
6931: if (m) PetscAssertPointer(m, 2);
6932: if (n) PetscAssertPointer(n, 3);
6933: MatCheckPreallocated(mat, 1);
6934: if (m) *m = mat->rmap->rstart;
6935: if (n) *n = mat->rmap->rend;
6936: PetscFunctionReturn(PETSC_SUCCESS);
6937: }
6939: /*@C
6940: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6941: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
6943: Not Collective, unless matrix has not been allocated
6945: Input Parameter:
6946: . mat - the matrix
6948: Output Parameter:
6949: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6950: where `size` is the number of MPI processes used by `mat`
6952: Level: beginner
6954: Notes:
6955: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6957: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6958: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6960: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6961: the local values in the matrix.
6963: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
6964: would contain the result of a matrix vector product with this matrix. See [Matrix
6965: Layouts](sec_matlayout) for details on matrix layouts.
6967: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6968: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6969: `DMDAGetGhostCorners()`, `DM`
6970: @*/
6971: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6972: {
6973: PetscFunctionBegin;
6976: MatCheckPreallocated(mat, 1);
6977: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6978: PetscFunctionReturn(PETSC_SUCCESS);
6979: }
6981: /*@C
6982: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6983: vector one multiplies this vector by that are owned by each processor.
6985: Not Collective, unless matrix has not been allocated
6987: Input Parameter:
6988: . mat - the matrix
6990: Output Parameter:
6991: . ranges - start of each processors portion plus one more than the total length at the end
6993: Level: beginner
6995: Notes:
6996: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6998: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6999: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7001: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7002: the local values in the matrix.
7004: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7005: Layouts](sec_matlayout) for details on matrix layouts.
7007: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7008: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7009: `DMDAGetGhostCorners()`, `DM`
7010: @*/
7011: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7012: {
7013: PetscFunctionBegin;
7016: MatCheckPreallocated(mat, 1);
7017: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7018: PetscFunctionReturn(PETSC_SUCCESS);
7019: }
7021: /*@
7022: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7024: Not Collective
7026: Input Parameter:
7027: . A - matrix
7029: Output Parameters:
7030: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7031: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7033: Level: intermediate
7035: Note:
7036: You should call `ISDestroy()` on the returned `IS`
7038: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7039: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7040: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7041: details on matrix layouts.
7043: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7044: @*/
7045: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7046: {
7047: PetscErrorCode (*f)(Mat, IS *, IS *);
7049: PetscFunctionBegin;
7052: MatCheckPreallocated(A, 1);
7053: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7054: if (f) {
7055: PetscCall((*f)(A, rows, cols));
7056: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7057: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7058: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7059: }
7060: PetscFunctionReturn(PETSC_SUCCESS);
7061: }
7063: /*@
7064: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7065: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7066: to complete the factorization.
7068: Collective
7070: Input Parameters:
7071: + fact - the factorized matrix obtained with `MatGetFactor()`
7072: . mat - the matrix
7073: . row - row permutation
7074: . col - column permutation
7075: - info - structure containing
7076: .vb
7077: levels - number of levels of fill.
7078: expected fill - as ratio of original fill.
7079: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7080: missing diagonal entries)
7081: .ve
7083: Level: developer
7085: Notes:
7086: See [Matrix Factorization](sec_matfactor) for additional information.
7088: Most users should employ the `KSP` interface for linear solvers
7089: instead of working directly with matrix algebra routines such as this.
7090: See, e.g., `KSPCreate()`.
7092: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7094: Developer Note:
7095: The Fortran interface is not autogenerated as the
7096: interface definition cannot be generated correctly [due to `MatFactorInfo`]
7098: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7099: `MatGetOrdering()`, `MatFactorInfo`
7100: @*/
7101: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7102: {
7103: PetscFunctionBegin;
7108: PetscAssertPointer(info, 5);
7109: PetscAssertPointer(fact, 1);
7110: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7111: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7112: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7113: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7114: MatCheckPreallocated(mat, 2);
7116: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7117: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7118: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7119: PetscFunctionReturn(PETSC_SUCCESS);
7120: }
7122: /*@
7123: MatICCFactorSymbolic - Performs symbolic incomplete
7124: Cholesky factorization for a symmetric matrix. Use
7125: `MatCholeskyFactorNumeric()` to complete the factorization.
7127: Collective
7129: Input Parameters:
7130: + fact - the factorized matrix obtained with `MatGetFactor()`
7131: . mat - the matrix to be factored
7132: . perm - row and column permutation
7133: - info - structure containing
7134: .vb
7135: levels - number of levels of fill.
7136: expected fill - as ratio of original fill.
7137: .ve
7139: Level: developer
7141: Notes:
7142: Most users should employ the `KSP` interface for linear solvers
7143: instead of working directly with matrix algebra routines such as this.
7144: See, e.g., `KSPCreate()`.
7146: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7148: Developer Note:
7149: The Fortran interface is not autogenerated as the
7150: interface definition cannot be generated correctly [due to `MatFactorInfo`]
7152: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7153: @*/
7154: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7155: {
7156: PetscFunctionBegin;
7160: PetscAssertPointer(info, 4);
7161: PetscAssertPointer(fact, 1);
7162: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7163: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7164: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7165: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7166: MatCheckPreallocated(mat, 2);
7168: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7169: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7170: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7171: PetscFunctionReturn(PETSC_SUCCESS);
7172: }
7174: /*@C
7175: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7176: points to an array of valid matrices, they may be reused to store the new
7177: submatrices.
7179: Collective
7181: Input Parameters:
7182: + mat - the matrix
7183: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7184: . irow - index set of rows to extract
7185: . icol - index set of columns to extract
7186: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7188: Output Parameter:
7189: . submat - the array of submatrices
7191: Level: advanced
7193: Notes:
7194: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7195: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7196: to extract a parallel submatrix.
7198: Some matrix types place restrictions on the row and column
7199: indices, such as that they be sorted or that they be equal to each other.
7201: The index sets may not have duplicate entries.
7203: When extracting submatrices from a parallel matrix, each processor can
7204: form a different submatrix by setting the rows and columns of its
7205: individual index sets according to the local submatrix desired.
7207: When finished using the submatrices, the user should destroy
7208: them with `MatDestroySubMatrices()`.
7210: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7211: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7213: This routine creates the matrices in submat; you should NOT create them before
7214: calling it. It also allocates the array of matrix pointers submat.
7216: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7217: request one row/column in a block, they must request all rows/columns that are in
7218: that block. For example, if the block size is 2 you cannot request just row 0 and
7219: column 0.
7221: Fortran Note:
7222: .vb
7223: Mat, pointer :: submat(:)
7224: .ve
7226: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7227: @*/
7228: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7229: {
7230: PetscInt i;
7231: PetscBool eq;
7233: PetscFunctionBegin;
7236: if (n) {
7237: PetscAssertPointer(irow, 3);
7239: PetscAssertPointer(icol, 4);
7241: }
7242: PetscAssertPointer(submat, 6);
7243: if (n && scall == MAT_REUSE_MATRIX) {
7244: PetscAssertPointer(*submat, 6);
7246: }
7247: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7248: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7249: MatCheckPreallocated(mat, 1);
7250: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7251: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7252: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7253: for (i = 0; i < n; i++) {
7254: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7255: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7256: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7257: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7258: if (mat->boundtocpu && mat->bindingpropagates) {
7259: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7260: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7261: }
7262: #endif
7263: }
7264: PetscFunctionReturn(PETSC_SUCCESS);
7265: }
7267: /*@C
7268: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7270: Collective
7272: Input Parameters:
7273: + mat - the matrix
7274: . n - the number of submatrixes to be extracted
7275: . irow - index set of rows to extract
7276: . icol - index set of columns to extract
7277: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7279: Output Parameter:
7280: . submat - the array of submatrices
7282: Level: advanced
7284: Note:
7285: This is used by `PCGASM`
7287: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7288: @*/
7289: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7290: {
7291: PetscInt i;
7292: PetscBool eq;
7294: PetscFunctionBegin;
7297: if (n) {
7298: PetscAssertPointer(irow, 3);
7300: PetscAssertPointer(icol, 4);
7302: }
7303: PetscAssertPointer(submat, 6);
7304: if (n && scall == MAT_REUSE_MATRIX) {
7305: PetscAssertPointer(*submat, 6);
7307: }
7308: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7309: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7310: MatCheckPreallocated(mat, 1);
7312: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7313: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7314: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7315: for (i = 0; i < n; i++) {
7316: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7317: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7318: }
7319: PetscFunctionReturn(PETSC_SUCCESS);
7320: }
7322: /*@C
7323: MatDestroyMatrices - Destroys an array of matrices
7325: Collective
7327: Input Parameters:
7328: + n - the number of local matrices
7329: - mat - the matrices (this is a pointer to the array of matrices)
7331: Level: advanced
7333: Notes:
7334: Frees not only the matrices, but also the array that contains the matrices
7336: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7338: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7339: @*/
7340: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7341: {
7342: PetscInt i;
7344: PetscFunctionBegin;
7345: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7346: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7347: PetscAssertPointer(mat, 2);
7349: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7351: /* memory is allocated even if n = 0 */
7352: PetscCall(PetscFree(*mat));
7353: PetscFunctionReturn(PETSC_SUCCESS);
7354: }
7356: /*@C
7357: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7359: Collective
7361: Input Parameters:
7362: + n - the number of local matrices
7363: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7365: Level: advanced
7367: Note:
7368: Frees not only the matrices, but also the array that contains the matrices
7370: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7371: @*/
7372: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7373: {
7374: Mat mat0;
7376: PetscFunctionBegin;
7377: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7378: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7379: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7380: PetscAssertPointer(mat, 2);
7382: mat0 = (*mat)[0];
7383: if (mat0 && mat0->ops->destroysubmatrices) {
7384: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7385: } else {
7386: PetscCall(MatDestroyMatrices(n, mat));
7387: }
7388: PetscFunctionReturn(PETSC_SUCCESS);
7389: }
7391: /*@
7392: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7394: Collective
7396: Input Parameter:
7397: . mat - the matrix
7399: Output Parameter:
7400: . matstruct - the sequential matrix with the nonzero structure of `mat`
7402: Level: developer
7404: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7405: @*/
7406: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7407: {
7408: PetscFunctionBegin;
7410: PetscAssertPointer(matstruct, 2);
7413: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7414: MatCheckPreallocated(mat, 1);
7416: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7417: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7418: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7419: PetscFunctionReturn(PETSC_SUCCESS);
7420: }
7422: /*@C
7423: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7425: Collective
7427: Input Parameter:
7428: . mat - the matrix
7430: Level: advanced
7432: Note:
7433: This is not needed, one can just call `MatDestroy()`
7435: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7436: @*/
7437: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7438: {
7439: PetscFunctionBegin;
7440: PetscAssertPointer(mat, 1);
7441: PetscCall(MatDestroy(mat));
7442: PetscFunctionReturn(PETSC_SUCCESS);
7443: }
7445: /*@
7446: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7447: replaces the index sets by larger ones that represent submatrices with
7448: additional overlap.
7450: Collective
7452: Input Parameters:
7453: + mat - the matrix
7454: . n - the number of index sets
7455: . is - the array of index sets (these index sets will changed during the call)
7456: - ov - the additional overlap requested
7458: Options Database Key:
7459: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7461: Level: developer
7463: Note:
7464: The computed overlap preserves the matrix block sizes when the blocks are square.
7465: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7466: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7468: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7469: @*/
7470: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7471: {
7472: PetscInt i, bs, cbs;
7474: PetscFunctionBegin;
7478: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7479: if (n) {
7480: PetscAssertPointer(is, 3);
7482: }
7483: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7484: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7485: MatCheckPreallocated(mat, 1);
7487: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7488: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7489: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7490: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7491: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7492: if (bs == cbs) {
7493: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7494: }
7495: PetscFunctionReturn(PETSC_SUCCESS);
7496: }
7498: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7500: /*@
7501: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7502: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7503: additional overlap.
7505: Collective
7507: Input Parameters:
7508: + mat - the matrix
7509: . n - the number of index sets
7510: . is - the array of index sets (these index sets will changed during the call)
7511: - ov - the additional overlap requested
7513: ` Options Database Key:
7514: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7516: Level: developer
7518: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7519: @*/
7520: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7521: {
7522: PetscInt i;
7524: PetscFunctionBegin;
7527: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7528: if (n) {
7529: PetscAssertPointer(is, 3);
7531: }
7532: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7533: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7534: MatCheckPreallocated(mat, 1);
7535: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7536: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7537: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7538: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7539: PetscFunctionReturn(PETSC_SUCCESS);
7540: }
7542: /*@
7543: MatGetBlockSize - Returns the matrix block size.
7545: Not Collective
7547: Input Parameter:
7548: . mat - the matrix
7550: Output Parameter:
7551: . bs - block size
7553: Level: intermediate
7555: Notes:
7556: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7558: If the block size has not been set yet this routine returns 1.
7560: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7561: @*/
7562: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7563: {
7564: PetscFunctionBegin;
7566: PetscAssertPointer(bs, 2);
7567: *bs = mat->rmap->bs;
7568: PetscFunctionReturn(PETSC_SUCCESS);
7569: }
7571: /*@
7572: MatGetBlockSizes - Returns the matrix block row and column sizes.
7574: Not Collective
7576: Input Parameter:
7577: . mat - the matrix
7579: Output Parameters:
7580: + rbs - row block size
7581: - cbs - column block size
7583: Level: intermediate
7585: Notes:
7586: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7587: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7589: If a block size has not been set yet this routine returns 1.
7591: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7592: @*/
7593: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7594: {
7595: PetscFunctionBegin;
7597: if (rbs) PetscAssertPointer(rbs, 2);
7598: if (cbs) PetscAssertPointer(cbs, 3);
7599: if (rbs) *rbs = mat->rmap->bs;
7600: if (cbs) *cbs = mat->cmap->bs;
7601: PetscFunctionReturn(PETSC_SUCCESS);
7602: }
7604: /*@
7605: MatSetBlockSize - Sets the matrix block size.
7607: Logically Collective
7609: Input Parameters:
7610: + mat - the matrix
7611: - bs - block size
7613: Level: intermediate
7615: Notes:
7616: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7617: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7619: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7620: is compatible with the matrix local sizes.
7622: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7623: @*/
7624: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7625: {
7626: PetscFunctionBegin;
7629: PetscCall(MatSetBlockSizes(mat, bs, bs));
7630: PetscFunctionReturn(PETSC_SUCCESS);
7631: }
7633: typedef struct {
7634: PetscInt n;
7635: IS *is;
7636: Mat *mat;
7637: PetscObjectState nonzerostate;
7638: Mat C;
7639: } EnvelopeData;
7641: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7642: {
7643: EnvelopeData *edata = (EnvelopeData *)*ptr;
7645: PetscFunctionBegin;
7646: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7647: PetscCall(PetscFree(edata->is));
7648: PetscCall(PetscFree(edata));
7649: PetscFunctionReturn(PETSC_SUCCESS);
7650: }
7652: /*@
7653: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7654: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7656: Collective
7658: Input Parameter:
7659: . mat - the matrix
7661: Level: intermediate
7663: Notes:
7664: There can be zeros within the blocks
7666: The blocks can overlap between processes, including laying on more than two processes
7668: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7669: @*/
7670: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7671: {
7672: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7673: PetscInt *diag, *odiag, sc;
7674: VecScatter scatter;
7675: PetscScalar *seqv;
7676: const PetscScalar *parv;
7677: const PetscInt *ia, *ja;
7678: PetscBool set, flag, done;
7679: Mat AA = mat, A;
7680: MPI_Comm comm;
7681: PetscMPIInt rank, size, tag;
7682: MPI_Status status;
7683: PetscContainer container;
7684: EnvelopeData *edata;
7685: Vec seq, par;
7686: IS isglobal;
7688: PetscFunctionBegin;
7690: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7691: if (!set || !flag) {
7692: /* TODO: only needs nonzero structure of transpose */
7693: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7694: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7695: }
7696: PetscCall(MatAIJGetLocalMat(AA, &A));
7697: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7698: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7700: PetscCall(MatGetLocalSize(mat, &n, NULL));
7701: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7702: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7703: PetscCallMPI(MPI_Comm_size(comm, &size));
7704: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7706: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7708: if (rank > 0) {
7709: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7710: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7711: }
7712: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7713: for (i = 0; i < n; i++) {
7714: env = PetscMax(env, ja[ia[i + 1] - 1]);
7715: II = rstart + i;
7716: if (env == II) {
7717: starts[lblocks] = tbs;
7718: sizes[lblocks++] = 1 + II - tbs;
7719: tbs = 1 + II;
7720: }
7721: }
7722: if (rank < size - 1) {
7723: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7724: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7725: }
7727: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7728: if (!set || !flag) PetscCall(MatDestroy(&AA));
7729: PetscCall(MatDestroy(&A));
7731: PetscCall(PetscNew(&edata));
7732: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7733: edata->n = lblocks;
7734: /* create IS needed for extracting blocks from the original matrix */
7735: PetscCall(PetscMalloc1(lblocks, &edata->is));
7736: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7738: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7739: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7740: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7741: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7742: PetscCall(MatSetType(edata->C, MATAIJ));
7744: /* Communicate the start and end of each row, from each block to the correct rank */
7745: /* TODO: Use PetscSF instead of VecScatter */
7746: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7747: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7748: PetscCall(VecGetArrayWrite(seq, &seqv));
7749: for (PetscInt i = 0; i < lblocks; i++) {
7750: for (PetscInt j = 0; j < sizes[i]; j++) {
7751: seqv[cnt] = starts[i];
7752: seqv[cnt + 1] = starts[i] + sizes[i];
7753: cnt += 2;
7754: }
7755: }
7756: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7757: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7758: sc -= cnt;
7759: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7760: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7761: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7762: PetscCall(ISDestroy(&isglobal));
7763: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7764: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7765: PetscCall(VecScatterDestroy(&scatter));
7766: PetscCall(VecDestroy(&seq));
7767: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7768: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7769: PetscCall(VecGetArrayRead(par, &parv));
7770: cnt = 0;
7771: PetscCall(MatGetSize(mat, NULL, &n));
7772: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7773: PetscInt start, end, d = 0, od = 0;
7775: start = (PetscInt)PetscRealPart(parv[cnt]);
7776: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7777: cnt += 2;
7779: if (start < cstart) {
7780: od += cstart - start + n - cend;
7781: d += cend - cstart;
7782: } else if (start < cend) {
7783: od += n - cend;
7784: d += cend - start;
7785: } else od += n - start;
7786: if (end <= cstart) {
7787: od -= cstart - end + n - cend;
7788: d -= cend - cstart;
7789: } else if (end < cend) {
7790: od -= n - cend;
7791: d -= cend - end;
7792: } else od -= n - end;
7794: odiag[i] = od;
7795: diag[i] = d;
7796: }
7797: PetscCall(VecRestoreArrayRead(par, &parv));
7798: PetscCall(VecDestroy(&par));
7799: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7800: PetscCall(PetscFree2(diag, odiag));
7801: PetscCall(PetscFree2(sizes, starts));
7803: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7804: PetscCall(PetscContainerSetPointer(container, edata));
7805: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7806: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7807: PetscCall(PetscObjectDereference((PetscObject)container));
7808: PetscFunctionReturn(PETSC_SUCCESS);
7809: }
7811: /*@
7812: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7814: Collective
7816: Input Parameters:
7817: + A - the matrix
7818: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7820: Output Parameter:
7821: . C - matrix with inverted block diagonal of `A`
7823: Level: advanced
7825: Note:
7826: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7828: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7829: @*/
7830: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7831: {
7832: PetscContainer container;
7833: EnvelopeData *edata;
7834: PetscObjectState nonzerostate;
7836: PetscFunctionBegin;
7837: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7838: if (!container) {
7839: PetscCall(MatComputeVariableBlockEnvelope(A));
7840: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7841: }
7842: PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7843: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7844: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7845: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7847: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7848: *C = edata->C;
7850: for (PetscInt i = 0; i < edata->n; i++) {
7851: Mat D;
7852: PetscScalar *dvalues;
7854: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7855: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7856: PetscCall(MatSeqDenseInvert(D));
7857: PetscCall(MatDenseGetArray(D, &dvalues));
7858: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7859: PetscCall(MatDestroy(&D));
7860: }
7861: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7862: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7863: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7864: PetscFunctionReturn(PETSC_SUCCESS);
7865: }
7867: /*@
7868: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
7870: Not Collective
7872: Input Parameters:
7873: + mat - the matrix
7874: . nblocks - the number of blocks on this process, each block can only exist on a single process
7875: - bsizes - the block sizes
7877: Level: intermediate
7879: Notes:
7880: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
7882: 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.
7884: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7885: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7886: @*/
7887: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7888: {
7889: PetscInt ncnt = 0, nlocal;
7891: PetscFunctionBegin;
7893: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7894: 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);
7895: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7896: 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);
7897: PetscCall(PetscFree(mat->bsizes));
7898: mat->nblocks = nblocks;
7899: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7900: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7901: PetscFunctionReturn(PETSC_SUCCESS);
7902: }
7904: /*@C
7905: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
7907: Not Collective; No Fortran Support
7909: Input Parameter:
7910: . mat - the matrix
7912: Output Parameters:
7913: + nblocks - the number of blocks on this process
7914: - bsizes - the block sizes
7916: Level: intermediate
7918: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7919: @*/
7920: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7921: {
7922: PetscFunctionBegin;
7924: if (nblocks) *nblocks = mat->nblocks;
7925: if (bsizes) *bsizes = mat->bsizes;
7926: PetscFunctionReturn(PETSC_SUCCESS);
7927: }
7929: /*@
7930: MatSetBlockSizes - Sets the matrix block row and column sizes.
7932: Logically Collective
7934: Input Parameters:
7935: + mat - the matrix
7936: . rbs - row block size
7937: - cbs - column block size
7939: Level: intermediate
7941: Notes:
7942: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7943: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7944: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7946: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7947: are compatible with the matrix local sizes.
7949: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
7951: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7952: @*/
7953: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7954: {
7955: PetscFunctionBegin;
7959: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7960: if (mat->rmap->refcnt) {
7961: ISLocalToGlobalMapping l2g = NULL;
7962: PetscLayout nmap = NULL;
7964: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7965: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7966: PetscCall(PetscLayoutDestroy(&mat->rmap));
7967: mat->rmap = nmap;
7968: mat->rmap->mapping = l2g;
7969: }
7970: if (mat->cmap->refcnt) {
7971: ISLocalToGlobalMapping l2g = NULL;
7972: PetscLayout nmap = NULL;
7974: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7975: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7976: PetscCall(PetscLayoutDestroy(&mat->cmap));
7977: mat->cmap = nmap;
7978: mat->cmap->mapping = l2g;
7979: }
7980: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7981: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7982: PetscFunctionReturn(PETSC_SUCCESS);
7983: }
7985: /*@
7986: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
7988: Logically Collective
7990: Input Parameters:
7991: + mat - the matrix
7992: . fromRow - matrix from which to copy row block size
7993: - fromCol - matrix from which to copy column block size (can be same as fromRow)
7995: Level: developer
7997: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
7998: @*/
7999: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8000: {
8001: PetscFunctionBegin;
8005: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8006: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8007: PetscFunctionReturn(PETSC_SUCCESS);
8008: }
8010: /*@
8011: MatResidual - Default routine to calculate the residual r = b - Ax
8013: Collective
8015: Input Parameters:
8016: + mat - the matrix
8017: . b - the right-hand-side
8018: - x - the approximate solution
8020: Output Parameter:
8021: . r - location to store the residual
8023: Level: developer
8025: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8026: @*/
8027: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8028: {
8029: PetscFunctionBegin;
8035: MatCheckPreallocated(mat, 1);
8036: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8037: if (!mat->ops->residual) {
8038: PetscCall(MatMult(mat, x, r));
8039: PetscCall(VecAYPX(r, -1.0, b));
8040: } else {
8041: PetscUseTypeMethod(mat, residual, b, x, r);
8042: }
8043: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8044: PetscFunctionReturn(PETSC_SUCCESS);
8045: }
8047: /*@C
8048: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8050: Collective
8052: Input Parameters:
8053: + mat - the matrix
8054: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8055: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8056: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8057: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8058: always used.
8060: Output Parameters:
8061: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8062: . 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
8063: . ja - the column indices, use `NULL` if not needed
8064: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8065: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8067: Level: developer
8069: Notes:
8070: You CANNOT change any of the ia[] or ja[] values.
8072: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8074: Fortran Notes:
8075: Use
8076: .vb
8077: PetscInt, pointer :: ia(:),ja(:)
8078: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8079: ! Access the ith and jth entries via ia(i) and ja(j)
8080: .ve
8082: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8083: @*/
8084: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8085: {
8086: PetscFunctionBegin;
8089: if (n) PetscAssertPointer(n, 5);
8090: if (ia) PetscAssertPointer(ia, 6);
8091: if (ja) PetscAssertPointer(ja, 7);
8092: if (done) PetscAssertPointer(done, 8);
8093: MatCheckPreallocated(mat, 1);
8094: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8095: else {
8096: if (done) *done = PETSC_TRUE;
8097: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8098: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8099: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8100: }
8101: PetscFunctionReturn(PETSC_SUCCESS);
8102: }
8104: /*@C
8105: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8107: Collective
8109: Input Parameters:
8110: + mat - the matrix
8111: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8112: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8113: symmetrized
8114: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8115: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8116: always used.
8117: . n - number of columns in the (possibly compressed) matrix
8118: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8119: - ja - the row indices
8121: Output Parameter:
8122: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8124: Level: developer
8126: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8127: @*/
8128: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8129: {
8130: PetscFunctionBegin;
8133: PetscAssertPointer(n, 5);
8134: if (ia) PetscAssertPointer(ia, 6);
8135: if (ja) PetscAssertPointer(ja, 7);
8136: PetscAssertPointer(done, 8);
8137: MatCheckPreallocated(mat, 1);
8138: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8139: else {
8140: *done = PETSC_TRUE;
8141: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8142: }
8143: PetscFunctionReturn(PETSC_SUCCESS);
8144: }
8146: /*@C
8147: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8149: Collective
8151: Input Parameters:
8152: + mat - the matrix
8153: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8154: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8155: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8156: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8157: always used.
8158: . n - size of (possibly compressed) matrix
8159: . ia - the row pointers
8160: - ja - the column indices
8162: Output Parameter:
8163: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8165: Level: developer
8167: Note:
8168: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8169: us of the array after it has been restored. If you pass `NULL`, it will
8170: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8172: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8173: @*/
8174: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8175: {
8176: PetscFunctionBegin;
8179: if (ia) PetscAssertPointer(ia, 6);
8180: if (ja) PetscAssertPointer(ja, 7);
8181: if (done) PetscAssertPointer(done, 8);
8182: MatCheckPreallocated(mat, 1);
8184: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8185: else {
8186: if (done) *done = PETSC_TRUE;
8187: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8188: if (n) *n = 0;
8189: if (ia) *ia = NULL;
8190: if (ja) *ja = NULL;
8191: }
8192: PetscFunctionReturn(PETSC_SUCCESS);
8193: }
8195: /*@C
8196: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8198: Collective
8200: Input Parameters:
8201: + mat - the matrix
8202: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8203: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8204: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8205: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8206: always used.
8208: Output Parameters:
8209: + n - size of (possibly compressed) matrix
8210: . ia - the column pointers
8211: . ja - the row indices
8212: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8214: Level: developer
8216: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8217: @*/
8218: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8219: {
8220: PetscFunctionBegin;
8223: if (ia) PetscAssertPointer(ia, 6);
8224: if (ja) PetscAssertPointer(ja, 7);
8225: PetscAssertPointer(done, 8);
8226: MatCheckPreallocated(mat, 1);
8228: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8229: else {
8230: *done = PETSC_TRUE;
8231: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8232: if (n) *n = 0;
8233: if (ia) *ia = NULL;
8234: if (ja) *ja = NULL;
8235: }
8236: PetscFunctionReturn(PETSC_SUCCESS);
8237: }
8239: /*@
8240: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8241: `MatGetColumnIJ()`.
8243: Collective
8245: Input Parameters:
8246: + mat - the matrix
8247: . ncolors - maximum color value
8248: . n - number of entries in colorarray
8249: - colorarray - array indicating color for each column
8251: Output Parameter:
8252: . iscoloring - coloring generated using colorarray information
8254: Level: developer
8256: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8257: @*/
8258: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8259: {
8260: PetscFunctionBegin;
8263: PetscAssertPointer(colorarray, 4);
8264: PetscAssertPointer(iscoloring, 5);
8265: MatCheckPreallocated(mat, 1);
8267: if (!mat->ops->coloringpatch) {
8268: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8269: } else {
8270: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8271: }
8272: PetscFunctionReturn(PETSC_SUCCESS);
8273: }
8275: /*@
8276: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8278: Logically Collective
8280: Input Parameter:
8281: . mat - the factored matrix to be reset
8283: Level: developer
8285: Notes:
8286: This routine should be used only with factored matrices formed by in-place
8287: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8288: format). This option can save memory, for example, when solving nonlinear
8289: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8290: ILU(0) preconditioner.
8292: One can specify in-place ILU(0) factorization by calling
8293: .vb
8294: PCType(pc,PCILU);
8295: PCFactorSeUseInPlace(pc);
8296: .ve
8297: or by using the options -pc_type ilu -pc_factor_in_place
8299: In-place factorization ILU(0) can also be used as a local
8300: solver for the blocks within the block Jacobi or additive Schwarz
8301: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8302: for details on setting local solver options.
8304: Most users should employ the `KSP` interface for linear solvers
8305: instead of working directly with matrix algebra routines such as this.
8306: See, e.g., `KSPCreate()`.
8308: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8309: @*/
8310: PetscErrorCode MatSetUnfactored(Mat mat)
8311: {
8312: PetscFunctionBegin;
8315: MatCheckPreallocated(mat, 1);
8316: mat->factortype = MAT_FACTOR_NONE;
8317: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8318: PetscUseTypeMethod(mat, setunfactored);
8319: PetscFunctionReturn(PETSC_SUCCESS);
8320: }
8322: /*@
8323: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8324: as the original matrix.
8326: Collective
8328: Input Parameters:
8329: + mat - the original matrix
8330: . isrow - parallel `IS` containing the rows this processor should obtain
8331: . 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.
8332: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8334: Output Parameter:
8335: . newmat - the new submatrix, of the same type as the original matrix
8337: Level: advanced
8339: Notes:
8340: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8342: Some matrix types place restrictions on the row and column indices, such
8343: 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;
8344: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8346: The index sets may not have duplicate entries.
8348: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8349: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8350: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8351: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8352: you are finished using it.
8354: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8355: the input matrix.
8357: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8359: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8360: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8362: Example usage:
8363: Consider the following 8x8 matrix with 34 non-zero values, that is
8364: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8365: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8366: as follows
8367: .vb
8368: 1 2 0 | 0 3 0 | 0 4
8369: Proc0 0 5 6 | 7 0 0 | 8 0
8370: 9 0 10 | 11 0 0 | 12 0
8371: -------------------------------------
8372: 13 0 14 | 15 16 17 | 0 0
8373: Proc1 0 18 0 | 19 20 21 | 0 0
8374: 0 0 0 | 22 23 0 | 24 0
8375: -------------------------------------
8376: Proc2 25 26 27 | 0 0 28 | 29 0
8377: 30 0 0 | 31 32 33 | 0 34
8378: .ve
8380: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8382: .vb
8383: 2 0 | 0 3 0 | 0
8384: Proc0 5 6 | 7 0 0 | 8
8385: -------------------------------
8386: Proc1 18 0 | 19 20 21 | 0
8387: -------------------------------
8388: Proc2 26 27 | 0 0 28 | 29
8389: 0 0 | 31 32 33 | 0
8390: .ve
8392: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8393: @*/
8394: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8395: {
8396: PetscMPIInt size;
8397: Mat *local;
8398: IS iscoltmp;
8399: PetscBool flg;
8401: PetscFunctionBegin;
8405: PetscAssertPointer(newmat, 5);
8408: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8409: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8411: MatCheckPreallocated(mat, 1);
8412: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8414: if (!iscol || isrow == iscol) {
8415: PetscBool stride;
8416: PetscMPIInt grabentirematrix = 0, grab;
8417: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8418: if (stride) {
8419: PetscInt first, step, n, rstart, rend;
8420: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8421: if (step == 1) {
8422: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8423: if (rstart == first) {
8424: PetscCall(ISGetLocalSize(isrow, &n));
8425: if (n == rend - rstart) grabentirematrix = 1;
8426: }
8427: }
8428: }
8429: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8430: if (grab) {
8431: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8432: if (cll == MAT_INITIAL_MATRIX) {
8433: *newmat = mat;
8434: PetscCall(PetscObjectReference((PetscObject)mat));
8435: }
8436: PetscFunctionReturn(PETSC_SUCCESS);
8437: }
8438: }
8440: if (!iscol) {
8441: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8442: } else {
8443: iscoltmp = iscol;
8444: }
8446: /* if original matrix is on just one processor then use submatrix generated */
8447: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8448: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8449: goto setproperties;
8450: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8451: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8452: *newmat = *local;
8453: PetscCall(PetscFree(local));
8454: goto setproperties;
8455: } else if (!mat->ops->createsubmatrix) {
8456: /* Create a new matrix type that implements the operation using the full matrix */
8457: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8458: switch (cll) {
8459: case MAT_INITIAL_MATRIX:
8460: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8461: break;
8462: case MAT_REUSE_MATRIX:
8463: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8464: break;
8465: default:
8466: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8467: }
8468: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8469: goto setproperties;
8470: }
8472: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8473: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8474: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8476: setproperties:
8477: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8478: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8479: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8480: }
8481: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8482: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8483: PetscFunctionReturn(PETSC_SUCCESS);
8484: }
8486: /*@
8487: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8489: Not Collective
8491: Input Parameters:
8492: + A - the matrix we wish to propagate options from
8493: - B - the matrix we wish to propagate options to
8495: Level: beginner
8497: Note:
8498: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8500: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8501: @*/
8502: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8503: {
8504: PetscFunctionBegin;
8507: B->symmetry_eternal = A->symmetry_eternal;
8508: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8509: B->symmetric = A->symmetric;
8510: B->structurally_symmetric = A->structurally_symmetric;
8511: B->spd = A->spd;
8512: B->hermitian = A->hermitian;
8513: PetscFunctionReturn(PETSC_SUCCESS);
8514: }
8516: /*@
8517: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8518: used during the assembly process to store values that belong to
8519: other processors.
8521: Not Collective
8523: Input Parameters:
8524: + mat - the matrix
8525: . size - the initial size of the stash.
8526: - bsize - the initial size of the block-stash(if used).
8528: Options Database Keys:
8529: + -matstash_initial_size <size> or <size0,size1,...sizep-1> - set initial size
8530: - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> - set initial block size
8532: Level: intermediate
8534: Notes:
8535: The block-stash is used for values set with `MatSetValuesBlocked()` while
8536: the stash is used for values set with `MatSetValues()`
8538: Run with the option -info and look for output of the form
8539: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8540: to determine the appropriate value, MM, to use for size and
8541: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8542: to determine the value, BMM to use for bsize
8544: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8545: @*/
8546: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8547: {
8548: PetscFunctionBegin;
8551: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8552: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8553: PetscFunctionReturn(PETSC_SUCCESS);
8554: }
8556: /*@
8557: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8558: the matrix
8560: Neighbor-wise Collective
8562: Input Parameters:
8563: + A - the matrix
8564: . x - the vector to be multiplied by the interpolation operator
8565: - y - the vector to be added to the result
8567: Output Parameter:
8568: . w - the resulting vector
8570: Level: intermediate
8572: Notes:
8573: `w` may be the same vector as `y`.
8575: This allows one to use either the restriction or interpolation (its transpose)
8576: matrix to do the interpolation
8578: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8579: @*/
8580: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8581: {
8582: PetscInt M, N, Ny;
8584: PetscFunctionBegin;
8589: PetscCall(MatGetSize(A, &M, &N));
8590: PetscCall(VecGetSize(y, &Ny));
8591: if (M == Ny) {
8592: PetscCall(MatMultAdd(A, x, y, w));
8593: } else {
8594: PetscCall(MatMultTransposeAdd(A, x, y, w));
8595: }
8596: PetscFunctionReturn(PETSC_SUCCESS);
8597: }
8599: /*@
8600: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8601: the matrix
8603: Neighbor-wise Collective
8605: Input Parameters:
8606: + A - the matrix
8607: - x - the vector to be interpolated
8609: Output Parameter:
8610: . y - the resulting vector
8612: Level: intermediate
8614: Note:
8615: This allows one to use either the restriction or interpolation (its transpose)
8616: matrix to do the interpolation
8618: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8619: @*/
8620: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8621: {
8622: PetscInt M, N, Ny;
8624: PetscFunctionBegin;
8628: PetscCall(MatGetSize(A, &M, &N));
8629: PetscCall(VecGetSize(y, &Ny));
8630: if (M == Ny) {
8631: PetscCall(MatMult(A, x, y));
8632: } else {
8633: PetscCall(MatMultTranspose(A, x, y));
8634: }
8635: PetscFunctionReturn(PETSC_SUCCESS);
8636: }
8638: /*@
8639: MatRestrict - $y = A*x$ or $A^T*x$
8641: Neighbor-wise Collective
8643: Input Parameters:
8644: + A - the matrix
8645: - x - the vector to be restricted
8647: Output Parameter:
8648: . y - the resulting vector
8650: Level: intermediate
8652: Note:
8653: This allows one to use either the restriction or interpolation (its transpose)
8654: matrix to do the restriction
8656: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8657: @*/
8658: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8659: {
8660: PetscInt M, N, Nx;
8662: PetscFunctionBegin;
8666: PetscCall(MatGetSize(A, &M, &N));
8667: PetscCall(VecGetSize(x, &Nx));
8668: if (M == Nx) {
8669: PetscCall(MatMultTranspose(A, x, y));
8670: } else {
8671: PetscCall(MatMult(A, x, y));
8672: }
8673: PetscFunctionReturn(PETSC_SUCCESS);
8674: }
8676: /*@
8677: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8679: Neighbor-wise Collective
8681: Input Parameters:
8682: + A - the matrix
8683: . x - the input dense matrix to be multiplied
8684: - w - the input dense matrix to be added to the result
8686: Output Parameter:
8687: . y - the output dense matrix
8689: Level: intermediate
8691: Note:
8692: This allows one to use either the restriction or interpolation (its transpose)
8693: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8694: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8696: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8697: @*/
8698: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8699: {
8700: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8701: PetscBool trans = PETSC_TRUE;
8702: MatReuse reuse = MAT_INITIAL_MATRIX;
8704: PetscFunctionBegin;
8710: PetscCall(MatGetSize(A, &M, &N));
8711: PetscCall(MatGetSize(x, &Mx, &Nx));
8712: if (N == Mx) trans = PETSC_FALSE;
8713: 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);
8714: Mo = trans ? N : M;
8715: if (*y) {
8716: PetscCall(MatGetSize(*y, &My, &Ny));
8717: if (Mo == My && Nx == Ny) {
8718: reuse = MAT_REUSE_MATRIX;
8719: } else {
8720: 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);
8721: PetscCall(MatDestroy(y));
8722: }
8723: }
8725: if (w && *y == w) { /* this is to minimize changes in PCMG */
8726: PetscBool flg;
8728: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8729: if (w) {
8730: PetscInt My, Ny, Mw, Nw;
8732: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8733: PetscCall(MatGetSize(*y, &My, &Ny));
8734: PetscCall(MatGetSize(w, &Mw, &Nw));
8735: if (!flg || My != Mw || Ny != Nw) w = NULL;
8736: }
8737: if (!w) {
8738: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8739: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8740: PetscCall(PetscObjectDereference((PetscObject)w));
8741: } else {
8742: PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8743: }
8744: }
8745: if (!trans) {
8746: PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8747: } else {
8748: PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8749: }
8750: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8751: PetscFunctionReturn(PETSC_SUCCESS);
8752: }
8754: /*@
8755: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8757: Neighbor-wise Collective
8759: Input Parameters:
8760: + A - the matrix
8761: - x - the input dense matrix
8763: Output Parameter:
8764: . y - the output dense matrix
8766: Level: intermediate
8768: Note:
8769: This allows one to use either the restriction or interpolation (its transpose)
8770: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8771: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8773: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8774: @*/
8775: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8776: {
8777: PetscFunctionBegin;
8778: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8779: PetscFunctionReturn(PETSC_SUCCESS);
8780: }
8782: /*@
8783: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8785: Neighbor-wise Collective
8787: Input Parameters:
8788: + A - the matrix
8789: - x - the input dense matrix
8791: Output Parameter:
8792: . y - the output dense matrix
8794: Level: intermediate
8796: Note:
8797: This allows one to use either the restriction or interpolation (its transpose)
8798: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8799: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8801: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8802: @*/
8803: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8804: {
8805: PetscFunctionBegin;
8806: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8807: PetscFunctionReturn(PETSC_SUCCESS);
8808: }
8810: /*@
8811: MatGetNullSpace - retrieves the null space of a matrix.
8813: Logically Collective
8815: Input Parameters:
8816: + mat - the matrix
8817: - nullsp - the null space object
8819: Level: developer
8821: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8822: @*/
8823: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8824: {
8825: PetscFunctionBegin;
8827: PetscAssertPointer(nullsp, 2);
8828: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8829: PetscFunctionReturn(PETSC_SUCCESS);
8830: }
8832: /*@C
8833: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
8835: Logically Collective
8837: Input Parameters:
8838: + n - the number of matrices
8839: - mat - the array of matrices
8841: Output Parameters:
8842: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
8844: Level: developer
8846: Note:
8847: Call `MatRestoreNullspaces()` to provide these to another array of matrices
8849: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8850: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8851: @*/
8852: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8853: {
8854: PetscFunctionBegin;
8855: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8856: PetscAssertPointer(mat, 2);
8857: PetscAssertPointer(nullsp, 3);
8859: PetscCall(PetscCalloc1(3 * n, nullsp));
8860: for (PetscInt i = 0; i < n; i++) {
8862: (*nullsp)[i] = mat[i]->nullsp;
8863: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8864: (*nullsp)[n + i] = mat[i]->nearnullsp;
8865: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8866: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8867: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8868: }
8869: PetscFunctionReturn(PETSC_SUCCESS);
8870: }
8872: /*@C
8873: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
8875: Logically Collective
8877: Input Parameters:
8878: + n - the number of matrices
8879: . mat - the array of matrices
8880: - nullsp - an array of null spaces
8882: Level: developer
8884: Note:
8885: Call `MatGetNullSpaces()` to create `nullsp`
8887: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8888: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8889: @*/
8890: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8891: {
8892: PetscFunctionBegin;
8893: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8894: PetscAssertPointer(mat, 2);
8895: PetscAssertPointer(nullsp, 3);
8896: PetscAssertPointer(*nullsp, 3);
8898: for (PetscInt i = 0; i < n; i++) {
8900: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8901: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
8902: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
8903: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
8904: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
8905: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
8906: }
8907: PetscCall(PetscFree(*nullsp));
8908: PetscFunctionReturn(PETSC_SUCCESS);
8909: }
8911: /*@
8912: MatSetNullSpace - attaches a null space to a matrix.
8914: Logically Collective
8916: Input Parameters:
8917: + mat - the matrix
8918: - nullsp - the null space object
8920: Level: advanced
8922: Notes:
8923: This null space is used by the `KSP` linear solvers to solve singular systems.
8925: 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`
8927: 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
8928: to zero but the linear system will still be solved in a least squares sense.
8930: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8931: the domain of a matrix A (from $R^n$ to $R^m$ (m rows, n columns) $R^n$ = the direct sum of the null space of A, n(A), + the range of $A^T$, $R(A^T)$.
8932: 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
8933: 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
8934: 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($A^T$).
8935: This \hat{b} can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
8937: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one as called
8938: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
8939: routine also automatically calls `MatSetTransposeNullSpace()`.
8941: The user should call `MatNullSpaceDestroy()`.
8943: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
8944: `KSPSetPCSide()`
8945: @*/
8946: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
8947: {
8948: PetscFunctionBegin;
8951: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8952: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
8953: mat->nullsp = nullsp;
8954: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
8955: PetscFunctionReturn(PETSC_SUCCESS);
8956: }
8958: /*@
8959: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
8961: Logically Collective
8963: Input Parameters:
8964: + mat - the matrix
8965: - nullsp - the null space object
8967: Level: developer
8969: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
8970: @*/
8971: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8972: {
8973: PetscFunctionBegin;
8976: PetscAssertPointer(nullsp, 2);
8977: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
8978: PetscFunctionReturn(PETSC_SUCCESS);
8979: }
8981: /*@
8982: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
8984: Logically Collective
8986: Input Parameters:
8987: + mat - the matrix
8988: - nullsp - the null space object
8990: Level: advanced
8992: Notes:
8993: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
8995: See `MatSetNullSpace()`
8997: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
8998: @*/
8999: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9000: {
9001: PetscFunctionBegin;
9004: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9005: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9006: mat->transnullsp = nullsp;
9007: PetscFunctionReturn(PETSC_SUCCESS);
9008: }
9010: /*@
9011: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9012: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9014: Logically Collective
9016: Input Parameters:
9017: + mat - the matrix
9018: - nullsp - the null space object
9020: Level: advanced
9022: Notes:
9023: Overwrites any previous near null space that may have been attached
9025: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9027: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9028: @*/
9029: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9030: {
9031: PetscFunctionBegin;
9035: MatCheckPreallocated(mat, 1);
9036: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9037: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9038: mat->nearnullsp = nullsp;
9039: PetscFunctionReturn(PETSC_SUCCESS);
9040: }
9042: /*@
9043: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9045: Not Collective
9047: Input Parameter:
9048: . mat - the matrix
9050: Output Parameter:
9051: . nullsp - the null space object, `NULL` if not set
9053: Level: advanced
9055: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9056: @*/
9057: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9058: {
9059: PetscFunctionBegin;
9062: PetscAssertPointer(nullsp, 2);
9063: MatCheckPreallocated(mat, 1);
9064: *nullsp = mat->nearnullsp;
9065: PetscFunctionReturn(PETSC_SUCCESS);
9066: }
9068: /*@
9069: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9071: Collective
9073: Input Parameters:
9074: + mat - the matrix
9075: . row - row/column permutation
9076: - info - information on desired factorization process
9078: Level: developer
9080: Notes:
9081: Probably really in-place only when level of fill is zero, otherwise allocates
9082: new space to store factored matrix and deletes previous memory.
9084: Most users should employ the `KSP` interface for linear solvers
9085: instead of working directly with matrix algebra routines such as this.
9086: See, e.g., `KSPCreate()`.
9088: Developer Note:
9089: The Fortran interface is not autogenerated as the
9090: interface definition cannot be generated correctly [due to `MatFactorInfo`]
9092: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9093: @*/
9094: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9095: {
9096: PetscFunctionBegin;
9100: PetscAssertPointer(info, 3);
9101: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9102: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9103: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9104: MatCheckPreallocated(mat, 1);
9105: PetscUseTypeMethod(mat, iccfactor, row, info);
9106: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9107: PetscFunctionReturn(PETSC_SUCCESS);
9108: }
9110: /*@
9111: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9112: ghosted ones.
9114: Not Collective
9116: Input Parameters:
9117: + mat - the matrix
9118: - diag - the diagonal values, including ghost ones
9120: Level: developer
9122: Notes:
9123: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9125: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9127: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9128: @*/
9129: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9130: {
9131: PetscMPIInt size;
9133: PetscFunctionBegin;
9138: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9139: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9140: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9141: if (size == 1) {
9142: PetscInt n, m;
9143: PetscCall(VecGetSize(diag, &n));
9144: PetscCall(MatGetSize(mat, NULL, &m));
9145: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9146: PetscCall(MatDiagonalScale(mat, NULL, diag));
9147: } else {
9148: PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9149: }
9150: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9151: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9152: PetscFunctionReturn(PETSC_SUCCESS);
9153: }
9155: /*@
9156: MatGetInertia - Gets the inertia from a factored matrix
9158: Collective
9160: Input Parameter:
9161: . mat - the matrix
9163: Output Parameters:
9164: + nneg - number of negative eigenvalues
9165: . nzero - number of zero eigenvalues
9166: - npos - number of positive eigenvalues
9168: Level: advanced
9170: Note:
9171: Matrix must have been factored by `MatCholeskyFactor()`
9173: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9174: @*/
9175: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9176: {
9177: PetscFunctionBegin;
9180: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9181: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9182: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9183: PetscFunctionReturn(PETSC_SUCCESS);
9184: }
9186: /*@C
9187: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9189: Neighbor-wise Collective
9191: Input Parameters:
9192: + mat - the factored matrix obtained with `MatGetFactor()`
9193: - b - the right-hand-side vectors
9195: Output Parameter:
9196: . x - the result vectors
9198: Level: developer
9200: Note:
9201: The vectors `b` and `x` cannot be the same. I.e., one cannot
9202: call `MatSolves`(A,x,x).
9204: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9205: @*/
9206: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9207: {
9208: PetscFunctionBegin;
9211: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9212: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9213: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9215: MatCheckPreallocated(mat, 1);
9216: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9217: PetscUseTypeMethod(mat, solves, b, x);
9218: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9219: PetscFunctionReturn(PETSC_SUCCESS);
9220: }
9222: /*@
9223: MatIsSymmetric - Test whether a matrix is symmetric
9225: Collective
9227: Input Parameters:
9228: + A - the matrix to test
9229: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9231: Output Parameter:
9232: . flg - the result
9234: Level: intermediate
9236: Notes:
9237: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9239: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9241: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9242: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9244: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9245: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9246: @*/
9247: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9248: {
9249: PetscFunctionBegin;
9251: PetscAssertPointer(flg, 3);
9252: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9253: else {
9254: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9255: else PetscCall(MatIsTranspose(A, A, tol, flg));
9256: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9257: }
9258: PetscFunctionReturn(PETSC_SUCCESS);
9259: }
9261: /*@
9262: MatIsHermitian - Test whether a matrix is Hermitian
9264: Collective
9266: Input Parameters:
9267: + A - the matrix to test
9268: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9270: Output Parameter:
9271: . flg - the result
9273: Level: intermediate
9275: Notes:
9276: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9278: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9280: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9281: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9283: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9284: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9285: @*/
9286: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9287: {
9288: PetscFunctionBegin;
9290: PetscAssertPointer(flg, 3);
9291: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9292: else {
9293: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9294: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9295: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9296: }
9297: PetscFunctionReturn(PETSC_SUCCESS);
9298: }
9300: /*@
9301: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9303: Not Collective
9305: Input Parameter:
9306: . A - the matrix to check
9308: Output Parameters:
9309: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9310: - flg - the result (only valid if set is `PETSC_TRUE`)
9312: Level: advanced
9314: Notes:
9315: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9316: if you want it explicitly checked
9318: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9319: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9321: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9322: @*/
9323: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9324: {
9325: PetscFunctionBegin;
9327: PetscAssertPointer(set, 2);
9328: PetscAssertPointer(flg, 3);
9329: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9330: *set = PETSC_TRUE;
9331: *flg = PetscBool3ToBool(A->symmetric);
9332: } else {
9333: *set = PETSC_FALSE;
9334: }
9335: PetscFunctionReturn(PETSC_SUCCESS);
9336: }
9338: /*@
9339: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9341: Not Collective
9343: Input Parameter:
9344: . A - the matrix to check
9346: Output Parameters:
9347: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9348: - flg - the result (only valid if set is `PETSC_TRUE`)
9350: Level: advanced
9352: Notes:
9353: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9355: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9356: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9358: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9359: @*/
9360: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9361: {
9362: PetscFunctionBegin;
9364: PetscAssertPointer(set, 2);
9365: PetscAssertPointer(flg, 3);
9366: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9367: *set = PETSC_TRUE;
9368: *flg = PetscBool3ToBool(A->spd);
9369: } else {
9370: *set = PETSC_FALSE;
9371: }
9372: PetscFunctionReturn(PETSC_SUCCESS);
9373: }
9375: /*@
9376: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9378: Not Collective
9380: Input Parameter:
9381: . A - the matrix to check
9383: Output Parameters:
9384: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9385: - flg - the result (only valid if set is `PETSC_TRUE`)
9387: Level: advanced
9389: Notes:
9390: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9391: if you want it explicitly checked
9393: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9394: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9396: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9397: @*/
9398: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9399: {
9400: PetscFunctionBegin;
9402: PetscAssertPointer(set, 2);
9403: PetscAssertPointer(flg, 3);
9404: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9405: *set = PETSC_TRUE;
9406: *flg = PetscBool3ToBool(A->hermitian);
9407: } else {
9408: *set = PETSC_FALSE;
9409: }
9410: PetscFunctionReturn(PETSC_SUCCESS);
9411: }
9413: /*@
9414: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9416: Collective
9418: Input Parameter:
9419: . A - the matrix to test
9421: Output Parameter:
9422: . flg - the result
9424: Level: intermediate
9426: Notes:
9427: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9429: 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
9430: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9432: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9433: @*/
9434: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9435: {
9436: PetscFunctionBegin;
9438: PetscAssertPointer(flg, 2);
9439: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9440: *flg = PetscBool3ToBool(A->structurally_symmetric);
9441: } else {
9442: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9443: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9444: }
9445: PetscFunctionReturn(PETSC_SUCCESS);
9446: }
9448: /*@
9449: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9451: Not Collective
9453: Input Parameter:
9454: . A - the matrix to check
9456: Output Parameters:
9457: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9458: - flg - the result (only valid if set is PETSC_TRUE)
9460: Level: advanced
9462: Notes:
9463: 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
9464: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9466: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9468: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9469: @*/
9470: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9471: {
9472: PetscFunctionBegin;
9474: PetscAssertPointer(set, 2);
9475: PetscAssertPointer(flg, 3);
9476: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9477: *set = PETSC_TRUE;
9478: *flg = PetscBool3ToBool(A->structurally_symmetric);
9479: } else {
9480: *set = PETSC_FALSE;
9481: }
9482: PetscFunctionReturn(PETSC_SUCCESS);
9483: }
9485: /*@
9486: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9487: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9489: Not Collective
9491: Input Parameter:
9492: . mat - the matrix
9494: Output Parameters:
9495: + nstash - the size of the stash
9496: . reallocs - the number of additional mallocs incurred.
9497: . bnstash - the size of the block stash
9498: - breallocs - the number of additional mallocs incurred.in the block stash
9500: Level: advanced
9502: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9503: @*/
9504: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9505: {
9506: PetscFunctionBegin;
9507: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9508: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9509: PetscFunctionReturn(PETSC_SUCCESS);
9510: }
9512: /*@
9513: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9514: parallel layout, `PetscLayout` for rows and columns
9516: Collective
9518: Input Parameter:
9519: . mat - the matrix
9521: Output Parameters:
9522: + right - (optional) vector that the matrix can be multiplied against
9523: - left - (optional) vector that the matrix vector product can be stored in
9525: Level: advanced
9527: Notes:
9528: 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()`.
9530: These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed
9532: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9533: @*/
9534: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9535: {
9536: PetscFunctionBegin;
9539: if (mat->ops->getvecs) {
9540: PetscUseTypeMethod(mat, getvecs, right, left);
9541: } else {
9542: if (right) {
9543: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9544: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9545: PetscCall(VecSetType(*right, mat->defaultvectype));
9546: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9547: if (mat->boundtocpu && mat->bindingpropagates) {
9548: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9549: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9550: }
9551: #endif
9552: }
9553: if (left) {
9554: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9555: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9556: PetscCall(VecSetType(*left, mat->defaultvectype));
9557: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9558: if (mat->boundtocpu && mat->bindingpropagates) {
9559: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9560: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9561: }
9562: #endif
9563: }
9564: }
9565: PetscFunctionReturn(PETSC_SUCCESS);
9566: }
9568: /*@
9569: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9570: with default values.
9572: Not Collective
9574: Input Parameter:
9575: . info - the `MatFactorInfo` data structure
9577: Level: developer
9579: Notes:
9580: The solvers are generally used through the `KSP` and `PC` objects, for example
9581: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9583: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9585: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9586: @*/
9587: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9588: {
9589: PetscFunctionBegin;
9590: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9591: PetscFunctionReturn(PETSC_SUCCESS);
9592: }
9594: /*@
9595: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9597: Collective
9599: Input Parameters:
9600: + mat - the factored matrix
9601: - is - the index set defining the Schur indices (0-based)
9603: Level: advanced
9605: Notes:
9606: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9608: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9610: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9612: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9613: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9614: @*/
9615: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9616: {
9617: PetscErrorCode (*f)(Mat, IS);
9619: PetscFunctionBegin;
9624: PetscCheckSameComm(mat, 1, is, 2);
9625: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9626: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9627: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9628: PetscCall(MatDestroy(&mat->schur));
9629: PetscCall((*f)(mat, is));
9630: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9631: PetscFunctionReturn(PETSC_SUCCESS);
9632: }
9634: /*@
9635: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9637: Logically Collective
9639: Input Parameters:
9640: + F - the factored matrix obtained by calling `MatGetFactor()`
9641: . S - location where to return the Schur complement, can be `NULL`
9642: - status - the status of the Schur complement matrix, can be `NULL`
9644: Level: advanced
9646: Notes:
9647: You must call `MatFactorSetSchurIS()` before calling this routine.
9649: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9651: The routine provides a copy of the Schur matrix stored within the solver data structures.
9652: The caller must destroy the object when it is no longer needed.
9653: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9655: 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)
9657: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9659: Developer Note:
9660: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9661: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9663: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9664: @*/
9665: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9666: {
9667: PetscFunctionBegin;
9669: if (S) PetscAssertPointer(S, 2);
9670: if (status) PetscAssertPointer(status, 3);
9671: if (S) {
9672: PetscErrorCode (*f)(Mat, Mat *);
9674: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9675: if (f) {
9676: PetscCall((*f)(F, S));
9677: } else {
9678: PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9679: }
9680: }
9681: if (status) *status = F->schur_status;
9682: PetscFunctionReturn(PETSC_SUCCESS);
9683: }
9685: /*@
9686: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9688: Logically Collective
9690: Input Parameters:
9691: + F - the factored matrix obtained by calling `MatGetFactor()`
9692: . S - location where to return the Schur complement, can be `NULL`
9693: - status - the status of the Schur complement matrix, can be `NULL`
9695: Level: advanced
9697: Notes:
9698: You must call `MatFactorSetSchurIS()` before calling this routine.
9700: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9702: The routine returns a the Schur Complement stored within the data structures of the solver.
9704: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9706: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9708: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9710: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9712: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9713: @*/
9714: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9715: {
9716: PetscFunctionBegin;
9718: if (S) {
9719: PetscAssertPointer(S, 2);
9720: *S = F->schur;
9721: }
9722: if (status) {
9723: PetscAssertPointer(status, 3);
9724: *status = F->schur_status;
9725: }
9726: PetscFunctionReturn(PETSC_SUCCESS);
9727: }
9729: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9730: {
9731: Mat S = F->schur;
9733: PetscFunctionBegin;
9734: switch (F->schur_status) {
9735: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9736: case MAT_FACTOR_SCHUR_INVERTED:
9737: if (S) {
9738: S->ops->solve = NULL;
9739: S->ops->matsolve = NULL;
9740: S->ops->solvetranspose = NULL;
9741: S->ops->matsolvetranspose = NULL;
9742: S->ops->solveadd = NULL;
9743: S->ops->solvetransposeadd = NULL;
9744: S->factortype = MAT_FACTOR_NONE;
9745: PetscCall(PetscFree(S->solvertype));
9746: }
9747: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9748: break;
9749: default:
9750: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9751: }
9752: PetscFunctionReturn(PETSC_SUCCESS);
9753: }
9755: /*@
9756: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9758: Logically Collective
9760: Input Parameters:
9761: + F - the factored matrix obtained by calling `MatGetFactor()`
9762: . S - location where the Schur complement is stored
9763: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9765: Level: advanced
9767: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9768: @*/
9769: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9770: {
9771: PetscFunctionBegin;
9773: if (S) {
9775: *S = NULL;
9776: }
9777: F->schur_status = status;
9778: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9779: PetscFunctionReturn(PETSC_SUCCESS);
9780: }
9782: /*@
9783: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9785: Logically Collective
9787: Input Parameters:
9788: + F - the factored matrix obtained by calling `MatGetFactor()`
9789: . rhs - location where the right-hand side of the Schur complement system is stored
9790: - sol - location where the solution of the Schur complement system has to be returned
9792: Level: advanced
9794: Notes:
9795: The sizes of the vectors should match the size of the Schur complement
9797: Must be called after `MatFactorSetSchurIS()`
9799: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9800: @*/
9801: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9802: {
9803: PetscFunctionBegin;
9810: PetscCheckSameComm(F, 1, rhs, 2);
9811: PetscCheckSameComm(F, 1, sol, 3);
9812: PetscCall(MatFactorFactorizeSchurComplement(F));
9813: switch (F->schur_status) {
9814: case MAT_FACTOR_SCHUR_FACTORED:
9815: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9816: break;
9817: case MAT_FACTOR_SCHUR_INVERTED:
9818: PetscCall(MatMultTranspose(F->schur, rhs, sol));
9819: break;
9820: default:
9821: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9822: }
9823: PetscFunctionReturn(PETSC_SUCCESS);
9824: }
9826: /*@
9827: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
9829: Logically Collective
9831: Input Parameters:
9832: + F - the factored matrix obtained by calling `MatGetFactor()`
9833: . rhs - location where the right-hand side of the Schur complement system is stored
9834: - sol - location where the solution of the Schur complement system has to be returned
9836: Level: advanced
9838: Notes:
9839: The sizes of the vectors should match the size of the Schur complement
9841: Must be called after `MatFactorSetSchurIS()`
9843: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9844: @*/
9845: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9846: {
9847: PetscFunctionBegin;
9854: PetscCheckSameComm(F, 1, rhs, 2);
9855: PetscCheckSameComm(F, 1, sol, 3);
9856: PetscCall(MatFactorFactorizeSchurComplement(F));
9857: switch (F->schur_status) {
9858: case MAT_FACTOR_SCHUR_FACTORED:
9859: PetscCall(MatSolve(F->schur, rhs, sol));
9860: break;
9861: case MAT_FACTOR_SCHUR_INVERTED:
9862: PetscCall(MatMult(F->schur, rhs, sol));
9863: break;
9864: default:
9865: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9866: }
9867: PetscFunctionReturn(PETSC_SUCCESS);
9868: }
9870: PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9871: #if PetscDefined(HAVE_CUDA)
9872: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9873: #endif
9875: /* Schur status updated in the interface */
9876: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9877: {
9878: Mat S = F->schur;
9880: PetscFunctionBegin;
9881: if (S) {
9882: PetscMPIInt size;
9883: PetscBool isdense, isdensecuda;
9885: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9886: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9887: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9888: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9889: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9890: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9891: if (isdense) {
9892: PetscCall(MatSeqDenseInvertFactors_Private(S));
9893: } else if (isdensecuda) {
9894: #if defined(PETSC_HAVE_CUDA)
9895: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9896: #endif
9897: }
9898: // HIP??????????????
9899: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9900: }
9901: PetscFunctionReturn(PETSC_SUCCESS);
9902: }
9904: /*@
9905: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
9907: Logically Collective
9909: Input Parameter:
9910: . F - the factored matrix obtained by calling `MatGetFactor()`
9912: Level: advanced
9914: Notes:
9915: Must be called after `MatFactorSetSchurIS()`.
9917: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
9919: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9920: @*/
9921: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9922: {
9923: PetscFunctionBegin;
9926: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9927: PetscCall(MatFactorFactorizeSchurComplement(F));
9928: PetscCall(MatFactorInvertSchurComplement_Private(F));
9929: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9930: PetscFunctionReturn(PETSC_SUCCESS);
9931: }
9933: /*@
9934: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
9936: Logically Collective
9938: Input Parameter:
9939: . F - the factored matrix obtained by calling `MatGetFactor()`
9941: Level: advanced
9943: Note:
9944: Must be called after `MatFactorSetSchurIS()`
9946: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
9947: @*/
9948: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9949: {
9950: MatFactorInfo info;
9952: PetscFunctionBegin;
9955: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
9956: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
9957: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
9958: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
9959: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
9960: } else {
9961: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
9962: }
9963: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
9964: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9965: PetscFunctionReturn(PETSC_SUCCESS);
9966: }
9968: /*@
9969: MatPtAP - Creates the matrix product $C = P^T * A * P$
9971: Neighbor-wise Collective
9973: Input Parameters:
9974: + A - the matrix
9975: . P - the projection matrix
9976: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
9977: - 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
9978: if the result is a dense matrix this is irrelevant
9980: Output Parameter:
9981: . C - the product matrix
9983: Level: intermediate
9985: Notes:
9986: C will be created and must be destroyed by the user with `MatDestroy()`.
9988: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
9990: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
9992: Developer Note:
9993: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
9995: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
9996: @*/
9997: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
9998: {
9999: PetscFunctionBegin;
10000: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10001: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10003: if (scall == MAT_INITIAL_MATRIX) {
10004: PetscCall(MatProductCreate(A, P, NULL, C));
10005: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10006: PetscCall(MatProductSetAlgorithm(*C, "default"));
10007: PetscCall(MatProductSetFill(*C, fill));
10009: (*C)->product->api_user = PETSC_TRUE;
10010: PetscCall(MatProductSetFromOptions(*C));
10011: 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);
10012: PetscCall(MatProductSymbolic(*C));
10013: } else { /* scall == MAT_REUSE_MATRIX */
10014: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10015: }
10017: PetscCall(MatProductNumeric(*C));
10018: (*C)->symmetric = A->symmetric;
10019: (*C)->spd = A->spd;
10020: PetscFunctionReturn(PETSC_SUCCESS);
10021: }
10023: /*@
10024: MatRARt - Creates the matrix product $C = R * A * R^T$
10026: Neighbor-wise Collective
10028: Input Parameters:
10029: + A - the matrix
10030: . R - the projection matrix
10031: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10032: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10033: if the result is a dense matrix this is irrelevant
10035: Output Parameter:
10036: . C - the product matrix
10038: Level: intermediate
10040: Notes:
10041: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10043: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
10045: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10046: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10047: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10048: We recommend using `MatPtAP()` when possible.
10050: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10052: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10053: @*/
10054: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10055: {
10056: PetscFunctionBegin;
10057: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10058: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10060: if (scall == MAT_INITIAL_MATRIX) {
10061: PetscCall(MatProductCreate(A, R, NULL, C));
10062: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10063: PetscCall(MatProductSetAlgorithm(*C, "default"));
10064: PetscCall(MatProductSetFill(*C, fill));
10066: (*C)->product->api_user = PETSC_TRUE;
10067: PetscCall(MatProductSetFromOptions(*C));
10068: 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);
10069: PetscCall(MatProductSymbolic(*C));
10070: } else { /* scall == MAT_REUSE_MATRIX */
10071: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10072: }
10074: PetscCall(MatProductNumeric(*C));
10075: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10076: PetscFunctionReturn(PETSC_SUCCESS);
10077: }
10079: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10080: {
10081: PetscBool flg = PETSC_TRUE;
10083: PetscFunctionBegin;
10084: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10085: if (scall == MAT_INITIAL_MATRIX) {
10086: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10087: PetscCall(MatProductCreate(A, B, NULL, C));
10088: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10089: PetscCall(MatProductSetFill(*C, fill));
10090: } else { /* scall == MAT_REUSE_MATRIX */
10091: Mat_Product *product = (*C)->product;
10093: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10094: if (flg && product && product->type != ptype) {
10095: PetscCall(MatProductClear(*C));
10096: product = NULL;
10097: }
10098: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10099: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10100: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10101: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10102: product = (*C)->product;
10103: product->fill = fill;
10104: product->clear = PETSC_TRUE;
10105: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10106: flg = PETSC_FALSE;
10107: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10108: }
10109: }
10110: if (flg) {
10111: (*C)->product->api_user = PETSC_TRUE;
10112: PetscCall(MatProductSetType(*C, ptype));
10113: PetscCall(MatProductSetFromOptions(*C));
10114: PetscCall(MatProductSymbolic(*C));
10115: }
10116: PetscCall(MatProductNumeric(*C));
10117: PetscFunctionReturn(PETSC_SUCCESS);
10118: }
10120: /*@
10121: MatMatMult - Performs matrix-matrix multiplication C=A*B.
10123: Neighbor-wise Collective
10125: Input Parameters:
10126: + A - the left matrix
10127: . B - the right matrix
10128: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10129: - 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
10130: if the result is a dense matrix this is irrelevant
10132: Output Parameter:
10133: . C - the product matrix
10135: Notes:
10136: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10138: `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
10139: call to this function with `MAT_INITIAL_MATRIX`.
10141: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10143: 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`,
10144: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10146: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10148: Example of Usage:
10149: .vb
10150: MatProductCreate(A,B,NULL,&C);
10151: MatProductSetType(C,MATPRODUCT_AB);
10152: MatProductSymbolic(C);
10153: MatProductNumeric(C); // compute C=A * B
10154: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10155: MatProductNumeric(C);
10156: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10157: MatProductNumeric(C);
10158: .ve
10160: Level: intermediate
10162: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10163: @*/
10164: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10165: {
10166: PetscFunctionBegin;
10167: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10168: PetscFunctionReturn(PETSC_SUCCESS);
10169: }
10171: /*@
10172: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10174: Neighbor-wise Collective
10176: Input Parameters:
10177: + A - the left matrix
10178: . B - the right matrix
10179: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10180: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10182: Output Parameter:
10183: . C - the product matrix
10185: Options Database Key:
10186: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10187: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10188: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10190: Level: intermediate
10192: Notes:
10193: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10195: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10197: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10198: actually needed.
10200: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10201: and for pairs of `MATMPIDENSE` matrices.
10203: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`
10205: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10207: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10208: @*/
10209: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10210: {
10211: PetscFunctionBegin;
10212: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10213: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10214: PetscFunctionReturn(PETSC_SUCCESS);
10215: }
10217: /*@
10218: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10220: Neighbor-wise Collective
10222: Input Parameters:
10223: + A - the left matrix
10224: . B - the right matrix
10225: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10226: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10228: Output Parameter:
10229: . C - the product matrix
10231: Level: intermediate
10233: Notes:
10234: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10236: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.
10238: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`
10240: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10241: actually needed.
10243: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10244: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10246: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10248: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10249: @*/
10250: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10251: {
10252: PetscFunctionBegin;
10253: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10254: PetscFunctionReturn(PETSC_SUCCESS);
10255: }
10257: /*@
10258: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10260: Neighbor-wise Collective
10262: Input Parameters:
10263: + A - the left matrix
10264: . B - the middle matrix
10265: . C - the right matrix
10266: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10267: - 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
10268: if the result is a dense matrix this is irrelevant
10270: Output Parameter:
10271: . D - the product matrix
10273: Level: intermediate
10275: Notes:
10276: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10278: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10280: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`
10282: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10283: actually needed.
10285: If you have many matrices with the same non-zero structure to multiply, you
10286: should use `MAT_REUSE_MATRIX` in all calls but the first
10288: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10290: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10291: @*/
10292: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10293: {
10294: PetscFunctionBegin;
10295: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10296: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10298: if (scall == MAT_INITIAL_MATRIX) {
10299: PetscCall(MatProductCreate(A, B, C, D));
10300: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10301: PetscCall(MatProductSetAlgorithm(*D, "default"));
10302: PetscCall(MatProductSetFill(*D, fill));
10304: (*D)->product->api_user = PETSC_TRUE;
10305: PetscCall(MatProductSetFromOptions(*D));
10306: 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,
10307: ((PetscObject)C)->type_name);
10308: PetscCall(MatProductSymbolic(*D));
10309: } else { /* user may change input matrices when REUSE */
10310: PetscCall(MatProductReplaceMats(A, B, C, *D));
10311: }
10312: PetscCall(MatProductNumeric(*D));
10313: PetscFunctionReturn(PETSC_SUCCESS);
10314: }
10316: /*@
10317: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10319: Collective
10321: Input Parameters:
10322: + mat - the matrix
10323: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10324: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10325: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10327: Output Parameter:
10328: . matredundant - redundant matrix
10330: Level: advanced
10332: Notes:
10333: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10334: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10336: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10337: calling it.
10339: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10341: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10342: @*/
10343: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10344: {
10345: MPI_Comm comm;
10346: PetscMPIInt size;
10347: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10348: Mat_Redundant *redund = NULL;
10349: PetscSubcomm psubcomm = NULL;
10350: MPI_Comm subcomm_in = subcomm;
10351: Mat *matseq;
10352: IS isrow, iscol;
10353: PetscBool newsubcomm = PETSC_FALSE;
10355: PetscFunctionBegin;
10357: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10358: PetscAssertPointer(*matredundant, 5);
10360: }
10362: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10363: if (size == 1 || nsubcomm == 1) {
10364: if (reuse == MAT_INITIAL_MATRIX) {
10365: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10366: } else {
10367: 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");
10368: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10369: }
10370: PetscFunctionReturn(PETSC_SUCCESS);
10371: }
10373: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10374: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10375: MatCheckPreallocated(mat, 1);
10377: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10378: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10379: /* create psubcomm, then get subcomm */
10380: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10381: PetscCallMPI(MPI_Comm_size(comm, &size));
10382: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10384: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10385: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10386: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10387: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10388: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10389: newsubcomm = PETSC_TRUE;
10390: PetscCall(PetscSubcommDestroy(&psubcomm));
10391: }
10393: /* get isrow, iscol and a local sequential matrix matseq[0] */
10394: if (reuse == MAT_INITIAL_MATRIX) {
10395: mloc_sub = PETSC_DECIDE;
10396: nloc_sub = PETSC_DECIDE;
10397: if (bs < 1) {
10398: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10399: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10400: } else {
10401: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10402: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10403: }
10404: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10405: rstart = rend - mloc_sub;
10406: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10407: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10408: PetscCall(ISSetIdentity(iscol));
10409: } else { /* reuse == MAT_REUSE_MATRIX */
10410: 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");
10411: /* retrieve subcomm */
10412: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10413: redund = (*matredundant)->redundant;
10414: isrow = redund->isrow;
10415: iscol = redund->iscol;
10416: matseq = redund->matseq;
10417: }
10418: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10420: /* get matredundant over subcomm */
10421: if (reuse == MAT_INITIAL_MATRIX) {
10422: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10424: /* create a supporting struct and attach it to C for reuse */
10425: PetscCall(PetscNew(&redund));
10426: (*matredundant)->redundant = redund;
10427: redund->isrow = isrow;
10428: redund->iscol = iscol;
10429: redund->matseq = matseq;
10430: if (newsubcomm) {
10431: redund->subcomm = subcomm;
10432: } else {
10433: redund->subcomm = MPI_COMM_NULL;
10434: }
10435: } else {
10436: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10437: }
10438: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10439: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10440: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10441: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10442: }
10443: #endif
10444: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10445: PetscFunctionReturn(PETSC_SUCCESS);
10446: }
10448: /*@C
10449: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10450: a given `Mat`. Each submatrix can span multiple procs.
10452: Collective
10454: Input Parameters:
10455: + mat - the matrix
10456: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10457: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10459: Output Parameter:
10460: . subMat - parallel sub-matrices each spanning a given `subcomm`
10462: Level: advanced
10464: Notes:
10465: The submatrix partition across processors is dictated by `subComm` a
10466: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10467: is not restricted to be grouped with consecutive original MPI processes.
10469: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10470: map directly to the layout of the original matrix [wrt the local
10471: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10472: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10473: the `subMat`. However the offDiagMat looses some columns - and this is
10474: reconstructed with `MatSetValues()`
10476: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10478: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10479: @*/
10480: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10481: {
10482: PetscMPIInt commsize, subCommSize;
10484: PetscFunctionBegin;
10485: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10486: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10487: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10489: 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");
10490: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10491: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10492: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10493: PetscFunctionReturn(PETSC_SUCCESS);
10494: }
10496: /*@
10497: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10499: Not Collective
10501: Input Parameters:
10502: + mat - matrix to extract local submatrix from
10503: . isrow - local row indices for submatrix
10504: - iscol - local column indices for submatrix
10506: Output Parameter:
10507: . submat - the submatrix
10509: Level: intermediate
10511: Notes:
10512: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10514: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10515: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10517: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10518: `MatSetValuesBlockedLocal()` will also be implemented.
10520: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10521: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10523: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10524: @*/
10525: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10526: {
10527: PetscFunctionBegin;
10531: PetscCheckSameComm(isrow, 2, iscol, 3);
10532: PetscAssertPointer(submat, 4);
10533: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10535: if (mat->ops->getlocalsubmatrix) {
10536: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10537: } else {
10538: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10539: }
10540: PetscFunctionReturn(PETSC_SUCCESS);
10541: }
10543: /*@
10544: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10546: Not Collective
10548: Input Parameters:
10549: + mat - matrix to extract local submatrix from
10550: . isrow - local row indices for submatrix
10551: . iscol - local column indices for submatrix
10552: - submat - the submatrix
10554: Level: intermediate
10556: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10557: @*/
10558: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10559: {
10560: PetscFunctionBegin;
10564: PetscCheckSameComm(isrow, 2, iscol, 3);
10565: PetscAssertPointer(submat, 4);
10568: if (mat->ops->restorelocalsubmatrix) {
10569: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10570: } else {
10571: PetscCall(MatDestroy(submat));
10572: }
10573: *submat = NULL;
10574: PetscFunctionReturn(PETSC_SUCCESS);
10575: }
10577: /*@
10578: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10580: Collective
10582: Input Parameter:
10583: . mat - the matrix
10585: Output Parameter:
10586: . is - if any rows have zero diagonals this contains the list of them
10588: Level: developer
10590: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10591: @*/
10592: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10593: {
10594: PetscFunctionBegin;
10597: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10598: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10600: if (!mat->ops->findzerodiagonals) {
10601: Vec diag;
10602: const PetscScalar *a;
10603: PetscInt *rows;
10604: PetscInt rStart, rEnd, r, nrow = 0;
10606: PetscCall(MatCreateVecs(mat, &diag, NULL));
10607: PetscCall(MatGetDiagonal(mat, diag));
10608: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10609: PetscCall(VecGetArrayRead(diag, &a));
10610: for (r = 0; r < rEnd - rStart; ++r)
10611: if (a[r] == 0.0) ++nrow;
10612: PetscCall(PetscMalloc1(nrow, &rows));
10613: nrow = 0;
10614: for (r = 0; r < rEnd - rStart; ++r)
10615: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10616: PetscCall(VecRestoreArrayRead(diag, &a));
10617: PetscCall(VecDestroy(&diag));
10618: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10619: } else {
10620: PetscUseTypeMethod(mat, findzerodiagonals, is);
10621: }
10622: PetscFunctionReturn(PETSC_SUCCESS);
10623: }
10625: /*@
10626: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10628: Collective
10630: Input Parameter:
10631: . mat - the matrix
10633: Output Parameter:
10634: . is - contains the list of rows with off block diagonal entries
10636: Level: developer
10638: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10639: @*/
10640: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10641: {
10642: PetscFunctionBegin;
10645: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10646: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10648: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10649: PetscFunctionReturn(PETSC_SUCCESS);
10650: }
10652: /*@C
10653: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10655: Collective; No Fortran Support
10657: Input Parameter:
10658: . mat - the matrix
10660: Output Parameter:
10661: . values - the block inverses in column major order (FORTRAN-like)
10663: Level: advanced
10665: Notes:
10666: The size of the blocks is determined by the block size of the matrix.
10668: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10670: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10672: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10673: @*/
10674: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10675: {
10676: PetscFunctionBegin;
10678: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10679: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10680: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10681: PetscFunctionReturn(PETSC_SUCCESS);
10682: }
10684: /*@
10685: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10687: Collective; No Fortran Support
10689: Input Parameters:
10690: + mat - the matrix
10691: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10692: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10694: Output Parameter:
10695: . values - the block inverses in column major order (FORTRAN-like)
10697: Level: advanced
10699: Notes:
10700: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10702: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10704: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10705: @*/
10706: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10707: {
10708: PetscFunctionBegin;
10710: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10711: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10712: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10713: PetscFunctionReturn(PETSC_SUCCESS);
10714: }
10716: /*@
10717: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10719: Collective
10721: Input Parameters:
10722: + A - the matrix
10723: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10725: Level: advanced
10727: Note:
10728: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10730: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10731: @*/
10732: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10733: {
10734: const PetscScalar *vals;
10735: PetscInt *dnnz;
10736: PetscInt m, rstart, rend, bs, i, j;
10738: PetscFunctionBegin;
10739: PetscCall(MatInvertBlockDiagonal(A, &vals));
10740: PetscCall(MatGetBlockSize(A, &bs));
10741: PetscCall(MatGetLocalSize(A, &m, NULL));
10742: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10743: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10744: PetscCall(PetscMalloc1(m / bs, &dnnz));
10745: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10746: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10747: PetscCall(PetscFree(dnnz));
10748: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10749: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10750: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10751: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10752: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10753: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10754: PetscFunctionReturn(PETSC_SUCCESS);
10755: }
10757: /*@
10758: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10759: via `MatTransposeColoringCreate()`.
10761: Collective
10763: Input Parameter:
10764: . c - coloring context
10766: Level: intermediate
10768: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10769: @*/
10770: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10771: {
10772: MatTransposeColoring matcolor = *c;
10774: PetscFunctionBegin;
10775: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10776: if (--((PetscObject)matcolor)->refct > 0) {
10777: matcolor = NULL;
10778: PetscFunctionReturn(PETSC_SUCCESS);
10779: }
10781: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10782: PetscCall(PetscFree(matcolor->rows));
10783: PetscCall(PetscFree(matcolor->den2sp));
10784: PetscCall(PetscFree(matcolor->colorforcol));
10785: PetscCall(PetscFree(matcolor->columns));
10786: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10787: PetscCall(PetscHeaderDestroy(c));
10788: PetscFunctionReturn(PETSC_SUCCESS);
10789: }
10791: /*@
10792: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10793: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10794: `MatTransposeColoring` to sparse `B`.
10796: Collective
10798: Input Parameters:
10799: + coloring - coloring context created with `MatTransposeColoringCreate()`
10800: - B - sparse matrix
10802: Output Parameter:
10803: . Btdense - dense matrix $B^T$
10805: Level: developer
10807: Note:
10808: These are used internally for some implementations of `MatRARt()`
10810: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10811: @*/
10812: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10813: {
10814: PetscFunctionBegin;
10819: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10820: PetscFunctionReturn(PETSC_SUCCESS);
10821: }
10823: /*@
10824: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10825: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10826: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10827: $C_{sp}$ from $C_{den}$.
10829: Collective
10831: Input Parameters:
10832: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10833: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
10835: Output Parameter:
10836: . Csp - sparse matrix
10838: Level: developer
10840: Note:
10841: These are used internally for some implementations of `MatRARt()`
10843: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10844: @*/
10845: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10846: {
10847: PetscFunctionBegin;
10852: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10853: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10854: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10855: PetscFunctionReturn(PETSC_SUCCESS);
10856: }
10858: /*@
10859: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
10861: Collective
10863: Input Parameters:
10864: + mat - the matrix product C
10865: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
10867: Output Parameter:
10868: . color - the new coloring context
10870: Level: intermediate
10872: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10873: `MatTransColoringApplyDenToSp()`
10874: @*/
10875: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10876: {
10877: MatTransposeColoring c;
10878: MPI_Comm comm;
10880: PetscFunctionBegin;
10881: PetscAssertPointer(color, 3);
10883: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10884: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10885: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10886: c->ctype = iscoloring->ctype;
10887: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10888: *color = c;
10889: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10890: PetscFunctionReturn(PETSC_SUCCESS);
10891: }
10893: /*@
10894: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10895: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
10897: Not Collective
10899: Input Parameter:
10900: . mat - the matrix
10902: Output Parameter:
10903: . state - the current state
10905: Level: intermediate
10907: Notes:
10908: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10909: different matrices
10911: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
10913: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
10915: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10916: @*/
10917: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10918: {
10919: PetscFunctionBegin;
10921: *state = mat->nonzerostate;
10922: PetscFunctionReturn(PETSC_SUCCESS);
10923: }
10925: /*@
10926: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10927: matrices from each processor
10929: Collective
10931: Input Parameters:
10932: + comm - the communicators the parallel matrix will live on
10933: . seqmat - the input sequential matrices
10934: . n - number of local columns (or `PETSC_DECIDE`)
10935: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10937: Output Parameter:
10938: . mpimat - the parallel matrix generated
10940: Level: developer
10942: Note:
10943: The number of columns of the matrix in EACH processor MUST be the same.
10945: .seealso: [](ch_matrices), `Mat`
10946: @*/
10947: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
10948: {
10949: PetscMPIInt size;
10951: PetscFunctionBegin;
10952: PetscCallMPI(MPI_Comm_size(comm, &size));
10953: if (size == 1) {
10954: if (reuse == MAT_INITIAL_MATRIX) {
10955: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
10956: } else {
10957: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
10958: }
10959: PetscFunctionReturn(PETSC_SUCCESS);
10960: }
10962: 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");
10964: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
10965: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
10966: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
10967: PetscFunctionReturn(PETSC_SUCCESS);
10968: }
10970: /*@
10971: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
10973: Collective
10975: Input Parameters:
10976: + A - the matrix to create subdomains from
10977: - N - requested number of subdomains
10979: Output Parameters:
10980: + n - number of subdomains resulting on this MPI process
10981: - iss - `IS` list with indices of subdomains on this MPI process
10983: Level: advanced
10985: Note:
10986: The number of subdomains must be smaller than the communicator size
10988: .seealso: [](ch_matrices), `Mat`, `IS`
10989: @*/
10990: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
10991: {
10992: MPI_Comm comm, subcomm;
10993: PetscMPIInt size, rank, color;
10994: PetscInt rstart, rend, k;
10996: PetscFunctionBegin;
10997: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
10998: PetscCallMPI(MPI_Comm_size(comm, &size));
10999: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11000: 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);
11001: *n = 1;
11002: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11003: color = rank / k;
11004: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11005: PetscCall(PetscMalloc1(1, iss));
11006: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11007: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11008: PetscCallMPI(MPI_Comm_free(&subcomm));
11009: PetscFunctionReturn(PETSC_SUCCESS);
11010: }
11012: /*@
11013: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11015: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11016: If they are not the same, uses `MatMatMatMult()`.
11018: Once the coarse grid problem is constructed, correct for interpolation operators
11019: that are not of full rank, which can legitimately happen in the case of non-nested
11020: geometric multigrid.
11022: Input Parameters:
11023: + restrct - restriction operator
11024: . dA - fine grid matrix
11025: . interpolate - interpolation operator
11026: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11027: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11029: Output Parameter:
11030: . A - the Galerkin coarse matrix
11032: Options Database Key:
11033: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used
11035: Level: developer
11037: Note:
11038: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11040: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11041: @*/
11042: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11043: {
11044: IS zerorows;
11045: Vec diag;
11047: PetscFunctionBegin;
11048: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11049: /* Construct the coarse grid matrix */
11050: if (interpolate == restrct) {
11051: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11052: } else {
11053: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11054: }
11056: /* If the interpolation matrix is not of full rank, A will have zero rows.
11057: This can legitimately happen in the case of non-nested geometric multigrid.
11058: In that event, we set the rows of the matrix to the rows of the identity,
11059: ignoring the equations (as the RHS will also be zero). */
11061: PetscCall(MatFindZeroRows(*A, &zerorows));
11063: if (zerorows != NULL) { /* if there are any zero rows */
11064: PetscCall(MatCreateVecs(*A, &diag, NULL));
11065: PetscCall(MatGetDiagonal(*A, diag));
11066: PetscCall(VecISSet(diag, zerorows, 1.0));
11067: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11068: PetscCall(VecDestroy(&diag));
11069: PetscCall(ISDestroy(&zerorows));
11070: }
11071: PetscFunctionReturn(PETSC_SUCCESS);
11072: }
11074: /*@C
11075: MatSetOperation - Allows user to set a matrix operation for any matrix type
11077: Logically Collective
11079: Input Parameters:
11080: + mat - the matrix
11081: . op - the name of the operation
11082: - f - the function that provides the operation
11084: Level: developer
11086: Example Usage:
11087: .vb
11088: extern PetscErrorCode usermult(Mat, Vec, Vec);
11090: PetscCall(MatCreateXXX(comm, ..., &A));
11091: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
11092: .ve
11094: Notes:
11095: See the file `include/petscmat.h` for a complete list of matrix
11096: operations, which all have the form MATOP_<OPERATION>, where
11097: <OPERATION> is the name (in all capital letters) of the
11098: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11100: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11101: sequence as the usual matrix interface routines, since they
11102: are intended to be accessed via the usual matrix interface
11103: routines, e.g.,
11104: .vb
11105: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11106: .ve
11108: In particular each function MUST return `PETSC_SUCCESS` on success and
11109: nonzero on failure.
11111: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11113: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11114: @*/
11115: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11116: {
11117: PetscFunctionBegin;
11119: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
11120: (((void (**)(void))mat->ops)[op]) = f;
11121: PetscFunctionReturn(PETSC_SUCCESS);
11122: }
11124: /*@C
11125: MatGetOperation - Gets a matrix operation for any matrix type.
11127: Not Collective
11129: Input Parameters:
11130: + mat - the matrix
11131: - op - the name of the operation
11133: Output Parameter:
11134: . f - the function that provides the operation
11136: Level: developer
11138: Example Usage:
11139: .vb
11140: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11142: MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11143: .ve
11145: Notes:
11146: See the file include/petscmat.h for a complete list of matrix
11147: operations, which all have the form MATOP_<OPERATION>, where
11148: <OPERATION> is the name (in all capital letters) of the
11149: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11151: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11153: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11154: @*/
11155: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11156: {
11157: PetscFunctionBegin;
11159: *f = (((void (**)(void))mat->ops)[op]);
11160: PetscFunctionReturn(PETSC_SUCCESS);
11161: }
11163: /*@
11164: MatHasOperation - Determines whether the given matrix supports the particular operation.
11166: Not Collective
11168: Input Parameters:
11169: + mat - the matrix
11170: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11172: Output Parameter:
11173: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11175: Level: advanced
11177: Note:
11178: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11180: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11181: @*/
11182: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11183: {
11184: PetscFunctionBegin;
11186: PetscAssertPointer(has, 3);
11187: if (mat->ops->hasoperation) {
11188: PetscUseTypeMethod(mat, hasoperation, op, has);
11189: } else {
11190: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11191: else {
11192: *has = PETSC_FALSE;
11193: if (op == MATOP_CREATE_SUBMATRIX) {
11194: PetscMPIInt size;
11196: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11197: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11198: }
11199: }
11200: }
11201: PetscFunctionReturn(PETSC_SUCCESS);
11202: }
11204: /*@
11205: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11207: Collective
11209: Input Parameter:
11210: . mat - the matrix
11212: Output Parameter:
11213: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11215: Level: beginner
11217: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11218: @*/
11219: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11220: {
11221: PetscFunctionBegin;
11224: PetscAssertPointer(cong, 2);
11225: if (!mat->rmap || !mat->cmap) {
11226: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11227: PetscFunctionReturn(PETSC_SUCCESS);
11228: }
11229: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11230: PetscCall(PetscLayoutSetUp(mat->rmap));
11231: PetscCall(PetscLayoutSetUp(mat->cmap));
11232: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11233: if (*cong) mat->congruentlayouts = 1;
11234: else mat->congruentlayouts = 0;
11235: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11236: PetscFunctionReturn(PETSC_SUCCESS);
11237: }
11239: PetscErrorCode MatSetInf(Mat A)
11240: {
11241: PetscFunctionBegin;
11242: PetscUseTypeMethod(A, setinf);
11243: PetscFunctionReturn(PETSC_SUCCESS);
11244: }
11246: /*@
11247: 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
11248: and possibly removes small values from the graph structure.
11250: Collective
11252: Input Parameters:
11253: + A - the matrix
11254: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11255: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11256: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11257: . num_idx - size of 'index' array
11258: - index - array of block indices to use for graph strength of connection weight
11260: Output Parameter:
11261: . graph - the resulting graph
11263: Level: advanced
11265: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11266: @*/
11267: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11268: {
11269: PetscFunctionBegin;
11273: PetscAssertPointer(graph, 7);
11274: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11275: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11276: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11277: PetscFunctionReturn(PETSC_SUCCESS);
11278: }
11280: /*@
11281: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11282: meaning the same memory is used for the matrix, and no new memory is allocated.
11284: Collective
11286: Input Parameters:
11287: + A - the matrix
11288: - 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
11290: Level: intermediate
11292: Developer Note:
11293: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11294: of the arrays in the data structure are unneeded.
11296: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11297: @*/
11298: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11299: {
11300: PetscFunctionBegin;
11302: PetscUseTypeMethod(A, eliminatezeros, keep);
11303: PetscFunctionReturn(PETSC_SUCCESS);
11304: }