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_ADot, MAT_ANorm;
19: PetscLogEvent MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve, MAT_MatTrSolve;
20: PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
21: PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
22: PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
23: PetscLogEvent MAT_QRFactorNumeric, MAT_QRFactorSymbolic, MAT_QRFactor;
24: PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
25: PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_PartitioningND, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
26: PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply, MAT_Transpose, MAT_FDColoringFunction, MAT_CreateSubMat;
27: PetscLogEvent MAT_TransposeColoringCreate;
28: PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
29: PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric, MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
30: PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
31: PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
32: PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
33: PetscLogEvent MAT_MultHermitianTranspose, MAT_MultHermitianTransposeAdd;
34: PetscLogEvent MAT_Getsymtransreduced, MAT_GetBrowsOfAcols;
35: PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
36: PetscLogEvent MAT_GetMultiProcBlock;
37: PetscLogEvent MAT_CUSPARSECopyToGPU, MAT_CUSPARSECopyFromGPU, MAT_CUSPARSEGenerateTranspose, MAT_CUSPARSESolveAnalysis;
38: PetscLogEvent MAT_HIPSPARSECopyToGPU, MAT_HIPSPARSECopyFromGPU, MAT_HIPSPARSEGenerateTranspose, MAT_HIPSPARSESolveAnalysis;
39: PetscLogEvent MAT_PreallCOO, MAT_SetVCOO;
40: PetscLogEvent MAT_CreateGraph;
41: PetscLogEvent MAT_SetValuesBatch;
42: PetscLogEvent MAT_ViennaCLCopyToGPU;
43: PetscLogEvent MAT_CUDACopyToGPU, MAT_HIPCopyToGPU;
44: PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
45: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
46: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
47: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
48: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;
50: const char *const MatFactorTypes[] = {"NONE", "LU", "CHOLESKY", "ILU", "ICC", "ILUDT", "QR", "MatFactorType", "MAT_FACTOR_", NULL};
52: /*@
53: MatSetRandom - Sets all components of a matrix to random numbers.
55: Logically Collective
57: Input Parameters:
58: + x - the matrix
59: - rctx - the `PetscRandom` object, formed by `PetscRandomCreate()`, or `NULL` and
60: it will create one internally.
62: Example:
63: .vb
64: PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
65: MatSetRandom(x,rctx);
66: PetscRandomDestroy(rctx);
67: .ve
69: Level: intermediate
71: Notes:
72: For sparse matrices that have been preallocated but not been assembled, it randomly selects appropriate locations,
74: for sparse matrices that already have nonzero locations, it fills the locations with random numbers.
76: It generates an error if used on unassembled sparse matrices that have not been preallocated.
78: .seealso: [](ch_matrices), `Mat`, `PetscRandom`, `PetscRandomCreate()`, `MatZeroEntries()`, `MatSetValues()`, `PetscRandomDestroy()`
79: @*/
80: PetscErrorCode MatSetRandom(Mat x, PetscRandom rctx)
81: {
82: PetscRandom randObj = NULL;
84: PetscFunctionBegin;
88: MatCheckPreallocated(x, 1);
90: if (!rctx) {
91: MPI_Comm comm;
92: PetscCall(PetscObjectGetComm((PetscObject)x, &comm));
93: PetscCall(PetscRandomCreate(comm, &randObj));
94: PetscCall(PetscRandomSetType(randObj, x->defaultrandtype));
95: PetscCall(PetscRandomSetFromOptions(randObj));
96: rctx = randObj;
97: }
98: PetscCall(PetscLogEventBegin(MAT_SetRandom, x, rctx, 0, 0));
99: PetscUseTypeMethod(x, setrandom, rctx);
100: PetscCall(PetscLogEventEnd(MAT_SetRandom, x, rctx, 0, 0));
102: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
103: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
104: PetscCall(PetscRandomDestroy(&randObj));
105: PetscFunctionReturn(PETSC_SUCCESS);
106: }
108: /*@
109: MatCopyHashToXAIJ - copy hash table entries into an XAIJ matrix type
111: Logically Collective
113: Input Parameter:
114: . A - A matrix in unassembled, hash table form
116: Output Parameter:
117: . B - The XAIJ matrix. This can either be `A` or some matrix of equivalent size, e.g. obtained from `A` via `MatDuplicate()`
119: Example:
120: .vb
121: PetscCall(MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, &B));
122: PetscCall(MatCopyHashToXAIJ(A, B));
123: .ve
125: Level: advanced
127: Notes:
128: If `B` is `A`, then the hash table data structure will be destroyed. `B` is assembled
130: .seealso: [](ch_matrices), `Mat`, `MAT_USE_HASH_TABLE`
131: @*/
132: PetscErrorCode MatCopyHashToXAIJ(Mat A, Mat B)
133: {
134: PetscFunctionBegin;
136: PetscUseTypeMethod(A, copyhashtoxaij, B);
137: PetscFunctionReturn(PETSC_SUCCESS);
138: }
140: /*@
141: MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in
143: Logically Collective
145: Input Parameter:
146: . mat - the factored matrix
148: Output Parameters:
149: + pivot - the pivot value computed
150: - row - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
151: the share the matrix
153: Level: advanced
155: Notes:
156: This routine does not work for factorizations done with external packages.
158: This routine should only be called if `MatGetFactorError()` returns a value of `MAT_FACTOR_NUMERIC_ZEROPIVOT`
160: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
162: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
163: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
164: `MAT_FACTOR_NUMERIC_ZEROPIVOT`
165: @*/
166: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
167: {
168: PetscFunctionBegin;
170: PetscAssertPointer(pivot, 2);
171: PetscAssertPointer(row, 3);
172: *pivot = mat->factorerror_zeropivot_value;
173: *row = mat->factorerror_zeropivot_row;
174: PetscFunctionReturn(PETSC_SUCCESS);
175: }
177: /*@
178: MatFactorGetError - gets the error code from a factorization
180: Logically Collective
182: Input Parameter:
183: . mat - the factored matrix
185: Output Parameter:
186: . err - the error code
188: Level: advanced
190: Note:
191: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
193: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
194: `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
195: @*/
196: PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
197: {
198: PetscFunctionBegin;
200: PetscAssertPointer(err, 2);
201: *err = mat->factorerrortype;
202: PetscFunctionReturn(PETSC_SUCCESS);
203: }
205: /*@
206: MatFactorClearError - clears the error code in a factorization
208: Logically Collective
210: Input Parameter:
211: . mat - the factored matrix
213: Level: developer
215: Note:
216: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
218: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
219: `MatGetErrorCode()`, `MatFactorError`
220: @*/
221: PetscErrorCode MatFactorClearError(Mat mat)
222: {
223: PetscFunctionBegin;
225: mat->factorerrortype = MAT_FACTOR_NOERROR;
226: mat->factorerror_zeropivot_value = 0.0;
227: mat->factorerror_zeropivot_row = 0;
228: PetscFunctionReturn(PETSC_SUCCESS);
229: }
231: PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
232: {
233: Vec r, l;
234: const PetscScalar *al;
235: PetscInt i, nz, gnz, N, n, st;
237: PetscFunctionBegin;
238: PetscCall(MatCreateVecs(mat, &r, &l));
239: if (!cols) { /* nonzero rows */
240: PetscCall(MatGetOwnershipRange(mat, &st, NULL));
241: PetscCall(MatGetSize(mat, &N, NULL));
242: PetscCall(MatGetLocalSize(mat, &n, NULL));
243: PetscCall(VecSetRandom(r, NULL));
244: PetscCall(MatMult(mat, r, l));
245: PetscCall(VecGetArrayRead(l, &al));
246: } else { /* nonzero columns */
247: PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
248: PetscCall(MatGetSize(mat, NULL, &N));
249: PetscCall(MatGetLocalSize(mat, NULL, &n));
250: PetscCall(VecSet(r, 0.0));
251: PetscCall(VecSetRandom(l, NULL));
252: PetscCall(MatMultTranspose(mat, l, r));
253: PetscCall(VecGetArrayRead(r, &al));
254: }
255: if (tol <= 0.0) {
256: for (i = 0, nz = 0; i < n; i++)
257: if (al[i] != 0.0) nz++;
258: } else {
259: for (i = 0, nz = 0; i < n; i++)
260: if (PetscAbsScalar(al[i]) > tol) nz++;
261: }
262: PetscCallMPI(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
263: if (gnz != N) {
264: PetscInt *nzr;
265: PetscCall(PetscMalloc1(nz, &nzr));
266: if (nz) {
267: if (tol < 0) {
268: for (i = 0, nz = 0; i < n; i++)
269: if (al[i] != 0.0) nzr[nz++] = i + st;
270: } else {
271: for (i = 0, nz = 0; i < n; i++)
272: if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
273: }
274: }
275: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
276: } else *nonzero = NULL;
277: if (!cols) { /* nonzero rows */
278: PetscCall(VecRestoreArrayRead(l, &al));
279: } else {
280: PetscCall(VecRestoreArrayRead(r, &al));
281: }
282: PetscCall(VecDestroy(&l));
283: PetscCall(VecDestroy(&r));
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: /*@
288: MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix
290: Input Parameter:
291: . mat - the matrix
293: Output Parameter:
294: . keptrows - the rows that are not completely zero
296: Level: intermediate
298: Note:
299: `keptrows` is set to `NULL` if all rows are nonzero.
301: Developer Note:
302: If `keptrows` is not `NULL`, it must be sorted.
304: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
305: @*/
306: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
307: {
308: PetscFunctionBegin;
311: PetscAssertPointer(keptrows, 2);
312: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
313: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
314: if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
315: else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
316: if (keptrows && *keptrows) PetscCall(ISSetInfo(*keptrows, IS_SORTED, IS_GLOBAL, PETSC_FALSE, PETSC_TRUE));
317: PetscFunctionReturn(PETSC_SUCCESS);
318: }
320: /*@
321: MatFindZeroRows - Locate all rows that are completely zero in the matrix
323: Input Parameter:
324: . mat - the matrix
326: Output Parameter:
327: . zerorows - the rows that are completely zero
329: Level: intermediate
331: Note:
332: `zerorows` is set to `NULL` if no rows are zero.
334: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
335: @*/
336: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
337: {
338: IS keptrows;
339: PetscInt m, n;
341: PetscFunctionBegin;
344: PetscAssertPointer(zerorows, 2);
345: PetscCall(MatFindNonzeroRows(mat, &keptrows));
346: /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
347: In keeping with this convention, we set zerorows to NULL if there are no zero
348: rows. */
349: if (keptrows == NULL) {
350: *zerorows = NULL;
351: } else {
352: PetscCall(MatGetOwnershipRange(mat, &m, &n));
353: PetscCall(ISComplement(keptrows, m, n, zerorows));
354: PetscCall(ISDestroy(&keptrows));
355: }
356: PetscFunctionReturn(PETSC_SUCCESS);
357: }
359: /*@
360: MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling
362: Not Collective
364: Input Parameter:
365: . A - the matrix
367: Output Parameter:
368: . a - the diagonal part (which is a SEQUENTIAL matrix)
370: Level: advanced
372: Notes:
373: See `MatCreateAIJ()` for more information on the "diagonal part" of the matrix.
375: Use caution, as the reference count on the returned matrix is not incremented and it is used as part of `A`'s normal operation.
377: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
378: @*/
379: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
380: {
381: PetscFunctionBegin;
384: PetscAssertPointer(a, 2);
385: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
386: if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
387: else {
388: PetscMPIInt size;
390: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
391: PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
392: *a = A;
393: }
394: PetscFunctionReturn(PETSC_SUCCESS);
395: }
397: /*@
398: MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.
400: Collective
402: Input Parameter:
403: . mat - the matrix
405: Output Parameter:
406: . trace - the sum of the diagonal entries
408: Level: advanced
410: .seealso: [](ch_matrices), `Mat`
411: @*/
412: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
413: {
414: Vec diag;
416: PetscFunctionBegin;
418: PetscAssertPointer(trace, 2);
419: PetscCall(MatCreateVecs(mat, &diag, NULL));
420: PetscCall(MatGetDiagonal(mat, diag));
421: PetscCall(VecSum(diag, trace));
422: PetscCall(VecDestroy(&diag));
423: PetscFunctionReturn(PETSC_SUCCESS);
424: }
426: /*@
427: MatRealPart - Zeros out the imaginary part of the matrix
429: Logically Collective
431: Input Parameter:
432: . mat - the matrix
434: Level: advanced
436: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
437: @*/
438: PetscErrorCode MatRealPart(Mat mat)
439: {
440: PetscFunctionBegin;
443: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
444: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
445: MatCheckPreallocated(mat, 1);
446: PetscUseTypeMethod(mat, realpart);
447: PetscFunctionReturn(PETSC_SUCCESS);
448: }
450: /*@C
451: MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix
453: Collective
455: Input Parameter:
456: . mat - the matrix
458: Output Parameters:
459: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
460: - ghosts - the global indices of the ghost points
462: Level: advanced
464: Note:
465: `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`
467: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
468: @*/
469: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
470: {
471: PetscFunctionBegin;
474: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
475: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
476: if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
477: else {
478: if (nghosts) *nghosts = 0;
479: if (ghosts) *ghosts = NULL;
480: }
481: PetscFunctionReturn(PETSC_SUCCESS);
482: }
484: /*@
485: MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part
487: Logically Collective
489: Input Parameter:
490: . mat - the matrix
492: Level: advanced
494: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
495: @*/
496: PetscErrorCode MatImaginaryPart(Mat mat)
497: {
498: PetscFunctionBegin;
501: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
502: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
503: MatCheckPreallocated(mat, 1);
504: PetscUseTypeMethod(mat, imaginarypart);
505: PetscFunctionReturn(PETSC_SUCCESS);
506: }
508: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
509: /*@C
510: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
511: for each row that you get to ensure that your application does
512: not bleed memory.
514: Not Collective
516: Input Parameters:
517: + mat - the matrix
518: - row - the row to get
520: Output Parameters:
521: + ncols - if not `NULL`, the number of nonzeros in `row`
522: . cols - if not `NULL`, the column numbers
523: - vals - if not `NULL`, the numerical values
525: Level: advanced
527: Notes:
528: This routine is provided for people who need to have direct access
529: to the structure of a matrix. We hope that we provide enough
530: high-level matrix routines that few users will need it.
532: `MatGetRow()` always returns 0-based column indices, regardless of
533: whether the internal representation is 0-based (default) or 1-based.
535: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
536: not wish to extract these quantities.
538: The user can only examine the values extracted with `MatGetRow()`;
539: the values CANNOT be altered. To change the matrix entries, one
540: must use `MatSetValues()`.
542: You can only have one call to `MatGetRow()` outstanding for a particular
543: matrix at a time, per processor. `MatGetRow()` can only obtain rows
544: associated with the given processor, it cannot get rows from the
545: other processors; for that we suggest using `MatCreateSubMatrices()`, then
546: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
547: is in the global number of rows.
549: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
551: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
553: Fortran Note:
554: .vb
555: PetscInt, pointer :: cols(:)
556: PetscScalar, pointer :: vals(:)
557: .ve
559: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
560: @*/
561: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
562: {
563: PetscInt incols;
565: PetscFunctionBegin;
568: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
569: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
570: MatCheckPreallocated(mat, 1);
571: PetscCheck(row >= mat->rmap->rstart && row < mat->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Only for local rows, %" PetscInt_FMT " not in [%" PetscInt_FMT ",%" PetscInt_FMT ")", row, mat->rmap->rstart, mat->rmap->rend);
572: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
573: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
574: if (ncols) *ncols = incols;
575: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
576: PetscFunctionReturn(PETSC_SUCCESS);
577: }
579: /*@
580: MatConjugate - replaces the matrix values with their complex conjugates
582: Logically Collective
584: Input Parameter:
585: . mat - the matrix
587: Level: advanced
589: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
590: @*/
591: PetscErrorCode MatConjugate(Mat mat)
592: {
593: PetscFunctionBegin;
595: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
596: if (PetscDefined(USE_COMPLEX) && !(mat->symmetric == PETSC_BOOL3_TRUE && mat->hermitian == PETSC_BOOL3_TRUE)) {
597: PetscUseTypeMethod(mat, conjugate);
598: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
599: }
600: PetscFunctionReturn(PETSC_SUCCESS);
601: }
603: /*@C
604: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
606: Not Collective
608: Input Parameters:
609: + mat - the matrix
610: . row - the row to get
611: . ncols - the number of nonzeros
612: . cols - the columns of the nonzeros
613: - vals - if nonzero the column values
615: Level: advanced
617: Notes:
618: This routine should be called after you have finished examining the entries.
620: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
621: us of the array after it has been restored. If you pass `NULL`, it will
622: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
624: Fortran Note:
625: .vb
626: PetscInt, pointer :: cols(:)
627: PetscScalar, pointer :: vals(:)
628: .ve
630: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
631: @*/
632: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
633: {
634: PetscFunctionBegin;
636: if (ncols) PetscAssertPointer(ncols, 3);
637: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
638: PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
639: if (ncols) *ncols = 0;
640: if (cols) *cols = NULL;
641: if (vals) *vals = NULL;
642: PetscFunctionReturn(PETSC_SUCCESS);
643: }
645: /*@
646: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
647: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
649: Not Collective
651: Input Parameter:
652: . mat - the matrix
654: Level: advanced
656: Note:
657: The flag is to ensure that users are aware that `MatGetRow()` only provides the upper triangular part of the row for the matrices in `MATSBAIJ` format.
659: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
660: @*/
661: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
662: {
663: PetscFunctionBegin;
666: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
667: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
668: MatCheckPreallocated(mat, 1);
669: PetscTryTypeMethod(mat, getrowuppertriangular);
670: PetscFunctionReturn(PETSC_SUCCESS);
671: }
673: /*@
674: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
676: Not Collective
678: Input Parameter:
679: . mat - the matrix
681: Level: advanced
683: Note:
684: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
686: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
687: @*/
688: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
689: {
690: PetscFunctionBegin;
693: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
694: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
695: MatCheckPreallocated(mat, 1);
696: PetscTryTypeMethod(mat, restorerowuppertriangular);
697: PetscFunctionReturn(PETSC_SUCCESS);
698: }
700: /*@
701: MatSetOptionsPrefix - Sets the prefix used for searching for all
702: `Mat` options in the database.
704: Logically Collective
706: Input Parameters:
707: + A - the matrix
708: - prefix - the prefix to prepend to all option names
710: Level: advanced
712: Notes:
713: A hyphen (-) must NOT be given at the beginning of the prefix name.
714: The first character of all runtime options is AUTOMATICALLY the hyphen.
716: This is NOT used for options for the factorization of the matrix. Normally the
717: prefix is automatically passed in from the PC calling the factorization. To set
718: it directly use `MatSetOptionsPrefixFactor()`
720: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
721: @*/
722: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
723: {
724: PetscFunctionBegin;
726: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
727: PetscTryMethod(A, "MatSetOptionsPrefix_C", (Mat, const char[]), (A, prefix));
728: PetscFunctionReturn(PETSC_SUCCESS);
729: }
731: /*@
732: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
733: for matrices created with `MatGetFactor()`
735: Logically Collective
737: Input Parameters:
738: + A - the matrix
739: - prefix - the prefix to prepend to all option names for the factored matrix
741: Level: developer
743: Notes:
744: A hyphen (-) must NOT be given at the beginning of the prefix name.
745: The first character of all runtime options is AUTOMATICALLY the hyphen.
747: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
748: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
750: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
751: @*/
752: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
753: {
754: PetscFunctionBegin;
756: if (prefix) {
757: PetscAssertPointer(prefix, 2);
758: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
759: if (prefix != A->factorprefix) {
760: PetscCall(PetscFree(A->factorprefix));
761: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
762: }
763: } else PetscCall(PetscFree(A->factorprefix));
764: PetscFunctionReturn(PETSC_SUCCESS);
765: }
767: /*@
768: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
769: for matrices created with `MatGetFactor()`
771: Logically Collective
773: Input Parameters:
774: + A - the matrix
775: - prefix - the prefix to prepend to all option names for the factored matrix
777: Level: developer
779: Notes:
780: A hyphen (-) must NOT be given at the beginning of the prefix name.
781: The first character of all runtime options is AUTOMATICALLY the hyphen.
783: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
784: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
786: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
787: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
788: `MatSetOptionsPrefix()`
789: @*/
790: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
791: {
792: size_t len1, len2, new_len;
794: PetscFunctionBegin;
796: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
797: if (!A->factorprefix) {
798: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
799: PetscFunctionReturn(PETSC_SUCCESS);
800: }
801: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
803: PetscCall(PetscStrlen(A->factorprefix, &len1));
804: PetscCall(PetscStrlen(prefix, &len2));
805: new_len = len1 + len2 + 1;
806: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
807: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
808: PetscFunctionReturn(PETSC_SUCCESS);
809: }
811: /*@
812: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
813: matrix options in the database.
815: Logically Collective
817: Input Parameters:
818: + A - the matrix
819: - prefix - the prefix to prepend to all option names
821: Level: advanced
823: Note:
824: A hyphen (-) must NOT be given at the beginning of the prefix name.
825: The first character of all runtime options is AUTOMATICALLY the hyphen.
827: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
828: @*/
829: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
830: {
831: PetscFunctionBegin;
833: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
834: PetscTryMethod(A, "MatAppendOptionsPrefix_C", (Mat, const char[]), (A, prefix));
835: PetscFunctionReturn(PETSC_SUCCESS);
836: }
838: /*@
839: MatGetOptionsPrefix - Gets the prefix used for searching for all
840: matrix options in the database.
842: Not Collective
844: Input Parameter:
845: . A - the matrix
847: Output Parameter:
848: . prefix - pointer to the prefix string used
850: Level: advanced
852: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
853: @*/
854: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
855: {
856: PetscFunctionBegin;
858: PetscAssertPointer(prefix, 2);
859: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
860: PetscFunctionReturn(PETSC_SUCCESS);
861: }
863: /*@
864: MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`
866: Not Collective
868: Input Parameter:
869: . A - the matrix
871: Output Parameter:
872: . state - the object state
874: Level: advanced
876: Note:
877: Object state is an integer which gets increased every time
878: the object is changed. By saving and later querying the object state
879: one can determine whether information about the object is still current.
881: See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.
883: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
884: @*/
885: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
886: {
887: PetscFunctionBegin;
889: PetscAssertPointer(state, 2);
890: PetscCall(PetscObjectStateGet((PetscObject)A, state));
891: PetscFunctionReturn(PETSC_SUCCESS);
892: }
894: /*@
895: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
897: Collective
899: Input Parameter:
900: . A - the matrix
902: Level: beginner
904: Notes:
905: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
906: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
907: makes all of the preallocation space available
909: Current values in the matrix are lost in this call
911: Currently only supported for `MATAIJ` matrices.
913: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
914: @*/
915: PetscErrorCode MatResetPreallocation(Mat A)
916: {
917: PetscFunctionBegin;
920: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
921: PetscFunctionReturn(PETSC_SUCCESS);
922: }
924: /*@
925: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
927: Collective
929: Input Parameter:
930: . A - the matrix
932: Level: intermediate
934: Notes:
935: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
937: Currently only supported for `MATAIJ` matrices.
939: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
940: @*/
941: PetscErrorCode MatResetHash(Mat A)
942: {
943: PetscFunctionBegin;
946: PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset to hash state after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
947: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
948: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
949: /* These flags are used to determine whether certain setups occur */
950: A->was_assembled = PETSC_FALSE;
951: A->assembled = PETSC_FALSE;
952: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
953: PetscCall(PetscObjectStateIncrease((PetscObject)A));
954: PetscFunctionReturn(PETSC_SUCCESS);
955: }
957: /*@
958: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
960: Collective
962: Input Parameter:
963: . A - the matrix
965: Level: advanced
967: Notes:
968: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
969: setting values in the matrix.
971: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
973: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
974: @*/
975: PetscErrorCode MatSetUp(Mat A)
976: {
977: PetscFunctionBegin;
979: if (!((PetscObject)A)->type_name) {
980: PetscMPIInt size;
982: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
983: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
984: }
985: if (!A->preallocated) PetscTryTypeMethod(A, setup);
986: PetscCall(PetscLayoutSetUp(A->rmap));
987: PetscCall(PetscLayoutSetUp(A->cmap));
988: A->preallocated = PETSC_TRUE;
989: PetscFunctionReturn(PETSC_SUCCESS);
990: }
992: #if defined(PETSC_HAVE_SAWS)
993: #include <petscviewersaws.h>
994: #endif
996: /*
997: If threadsafety is on extraneous matrices may be printed
999: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1000: */
1001: #if !defined(PETSC_HAVE_THREADSAFETY)
1002: static PetscInt insidematview = 0;
1003: #endif
1005: /*@
1006: MatViewFromOptions - View properties of the matrix based on options set in the options database
1008: Collective
1010: Input Parameters:
1011: + A - the matrix
1012: . obj - optional additional object that provides the options prefix to use
1013: - name - command line option
1015: Options Database Key:
1016: . -name [viewertype][:...] - option name and values. See `PetscObjectViewFromOptions()` for the possible arguments
1018: Level: intermediate
1020: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1021: @*/
1022: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1023: {
1024: PetscFunctionBegin;
1026: #if !defined(PETSC_HAVE_THREADSAFETY)
1027: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1028: #endif
1029: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1030: PetscFunctionReturn(PETSC_SUCCESS);
1031: }
1033: /*@
1034: MatView - display information about a matrix in a variety ways
1036: Collective on viewer
1038: Input Parameters:
1039: + mat - the matrix
1040: - viewer - visualization context
1042: Options Database Keys:
1043: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1044: . -mat_view ::ascii_info_detail - Prints more detailed info
1045: . -mat_view - Prints matrix in ASCII format
1046: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1047: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1048: . -display name - Sets display name (default is host)
1049: . -draw_pause sec - Sets number of seconds to pause after display
1050: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1051: . -viewer_socket_machine machine - -
1052: . -viewer_socket_port port - -
1053: . -mat_view binary - save matrix to file in binary format
1054: - -viewer_binary_filename name - -
1056: Level: beginner
1058: Notes:
1059: The available visualization contexts include
1060: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1061: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1062: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1063: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1065: The user can open alternative visualization contexts with
1066: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1067: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1068: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1069: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1071: The user can call `PetscViewerPushFormat()` to specify the output
1072: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1073: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1074: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1075: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1076: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1077: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1078: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1079: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1080: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1082: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1083: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1085: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1087: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1088: viewer is used.
1090: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1091: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1093: One can use `-mat_view draw -draw_pause -1` to pause the graphical display of matrix nonzero structure,
1094: and then use the following mouse functions.
1095: .vb
1096: left mouse: zoom in
1097: middle mouse: zoom out
1098: right mouse: continue with the simulation
1099: .ve
1101: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1102: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1103: @*/
1104: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1105: {
1106: PetscInt rows, cols, rbs, cbs;
1107: PetscBool isascii, isstring, issaws;
1108: PetscViewerFormat format;
1109: PetscMPIInt size;
1111: PetscFunctionBegin;
1114: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1117: PetscCall(PetscViewerGetFormat(viewer, &format));
1118: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1119: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1121: #if !defined(PETSC_HAVE_THREADSAFETY)
1122: insidematview++;
1123: #endif
1124: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1125: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1126: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1127: 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");
1129: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1130: if (isascii) {
1131: if (!mat->preallocated) {
1132: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1133: #if !defined(PETSC_HAVE_THREADSAFETY)
1134: insidematview--;
1135: #endif
1136: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1137: PetscFunctionReturn(PETSC_SUCCESS);
1138: }
1139: if (!mat->assembled) {
1140: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1141: #if !defined(PETSC_HAVE_THREADSAFETY)
1142: insidematview--;
1143: #endif
1144: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1145: PetscFunctionReturn(PETSC_SUCCESS);
1146: }
1147: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1148: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1149: MatNullSpace nullsp, transnullsp;
1151: PetscCall(PetscViewerASCIIPushTab(viewer));
1152: PetscCall(MatGetSize(mat, &rows, &cols));
1153: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1154: if (rbs != 1 || cbs != 1) {
1155: 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" : ""));
1156: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1157: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1158: if (mat->factortype) {
1159: MatSolverType solver;
1160: PetscCall(MatFactorGetSolverType(mat, &solver));
1161: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1162: }
1163: if (mat->ops->getinfo) {
1164: PetscBool is_constant_or_diagonal;
1166: // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1167: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1168: if (!is_constant_or_diagonal) {
1169: MatInfo info;
1171: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1172: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1173: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1174: }
1175: }
1176: PetscCall(MatGetNullSpace(mat, &nullsp));
1177: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1178: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1179: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1180: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1181: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1182: PetscCall(PetscViewerASCIIPushTab(viewer));
1183: PetscCall(MatProductView(mat, viewer));
1184: PetscCall(PetscViewerASCIIPopTab(viewer));
1185: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1186: IS tmp;
1188: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1189: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1190: PetscCall(PetscViewerASCIIPushTab(viewer));
1191: PetscCall(ISView(tmp, viewer));
1192: PetscCall(PetscViewerASCIIPopTab(viewer));
1193: PetscCall(ISDestroy(&tmp));
1194: }
1195: }
1196: } else if (issaws) {
1197: #if defined(PETSC_HAVE_SAWS)
1198: PetscMPIInt rank;
1200: PetscCall(PetscObjectName((PetscObject)mat));
1201: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1202: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1203: #endif
1204: } else if (isstring) {
1205: const char *type;
1206: PetscCall(MatGetType(mat, &type));
1207: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1208: PetscTryTypeMethod(mat, view, viewer);
1209: }
1210: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1211: PetscCall(PetscViewerASCIIPushTab(viewer));
1212: PetscUseTypeMethod(mat, viewnative, viewer);
1213: PetscCall(PetscViewerASCIIPopTab(viewer));
1214: } else if (mat->ops->view) {
1215: PetscCall(PetscViewerASCIIPushTab(viewer));
1216: PetscUseTypeMethod(mat, view, viewer);
1217: PetscCall(PetscViewerASCIIPopTab(viewer));
1218: }
1219: if (isascii) {
1220: PetscCall(PetscViewerGetFormat(viewer, &format));
1221: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1222: }
1223: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1224: #if !defined(PETSC_HAVE_THREADSAFETY)
1225: insidematview--;
1226: #endif
1227: PetscFunctionReturn(PETSC_SUCCESS);
1228: }
1230: #if defined(PETSC_USE_DEBUG)
1231: #include <../src/sys/totalview/tv_data_display.h>
1232: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1233: {
1234: TV_add_row("Local rows", "int", &mat->rmap->n);
1235: TV_add_row("Local columns", "int", &mat->cmap->n);
1236: TV_add_row("Global rows", "int", &mat->rmap->N);
1237: TV_add_row("Global columns", "int", &mat->cmap->N);
1238: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1239: return TV_format_OK;
1240: }
1241: #endif
1243: /*@
1244: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1245: with `MatView()`. The matrix format is determined from the options database.
1246: Generates a parallel MPI matrix if the communicator has more than one
1247: processor. The default matrix type is `MATAIJ`.
1249: Collective
1251: Input Parameters:
1252: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1253: or some related function before a call to `MatLoad()`
1254: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1256: Options Database Key:
1257: . -matload_block_size bs - set block size
1259: Level: beginner
1261: Notes:
1262: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1263: `Mat` before calling this routine if you wish to set it from the options database.
1265: `MatLoad()` automatically loads into the options database any options
1266: given in the file filename.info where filename is the name of the file
1267: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1268: file will be ignored if you use the -viewer_binary_skip_info option.
1270: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1271: sets the default matrix type AIJ and sets the local and global sizes.
1272: If type and/or size is already set, then the same are used.
1274: In parallel, each processor can load a subset of rows (or the
1275: entire matrix). This routine is especially useful when a large
1276: matrix is stored on disk and only part of it is desired on each
1277: processor. For example, a parallel solver may access only some of
1278: the rows from each processor. The algorithm used here reads
1279: relatively small blocks of data rather than reading the entire
1280: matrix and then subsetting it.
1282: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1283: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1284: or the sequence like
1285: .vb
1286: `PetscViewer` v;
1287: `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1288: `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1289: `PetscViewerSetFromOptions`(v);
1290: `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1291: `PetscViewerFileSetName`(v,"datafile");
1292: .ve
1293: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1294: .vb
1295: -viewer_type {binary, hdf5}
1296: .ve
1298: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1299: and src/mat/tutorials/ex10.c with the second approach.
1301: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1302: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1303: Multiple objects, both matrices and vectors, can be stored within the same file.
1304: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1306: Most users should not need to know the details of the binary storage
1307: format, since `MatLoad()` and `MatView()` completely hide these details.
1308: But for anyone who is interested, the standard binary matrix storage
1309: format is
1311: .vb
1312: PetscInt MAT_FILE_CLASSID
1313: PetscInt number of rows
1314: PetscInt number of columns
1315: PetscInt total number of nonzeros
1316: PetscInt *number nonzeros in each row
1317: PetscInt *column indices of all nonzeros (starting index is zero)
1318: PetscScalar *values of all nonzeros
1319: .ve
1320: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1321: 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
1322: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1324: PETSc automatically does the byte swapping for
1325: machines that store the bytes reversed. Thus if you write your own binary
1326: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1327: and `PetscBinaryWrite()` to see how this may be done.
1329: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1330: Each processor's chunk is loaded independently by its owning MPI process.
1331: Multiple objects, both matrices and vectors, can be stored within the same file.
1332: They are looked up by their PetscObject name.
1334: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1335: by default the same structure and naming of the AIJ arrays and column count
1336: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1337: .vb
1338: save example.mat A b -v7.3
1339: .ve
1340: can be directly read by this routine (see Reference 1 for details).
1342: Depending on your MATLAB version, this format might be a default,
1343: otherwise you can set it as default in Preferences.
1345: Unless -nocompression flag is used to save the file in MATLAB,
1346: PETSc must be configured with ZLIB package.
1348: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1350: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1352: Corresponding `MatView()` is not yet implemented.
1354: The loaded matrix is actually a transpose of the original one in MATLAB,
1355: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1356: With this format, matrix is automatically transposed by PETSc,
1357: unless the matrix is marked as SPD or symmetric
1358: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1360: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1362: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1363: @*/
1364: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1365: {
1366: PetscBool flg;
1368: PetscFunctionBegin;
1372: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1374: flg = PETSC_FALSE;
1375: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1376: if (flg) {
1377: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1378: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1379: }
1380: flg = PETSC_FALSE;
1381: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1382: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1384: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1385: PetscUseTypeMethod(mat, load, viewer);
1386: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1387: PetscFunctionReturn(PETSC_SUCCESS);
1388: }
1390: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1391: {
1392: Mat_Redundant *redund = *redundant;
1394: PetscFunctionBegin;
1395: if (redund) {
1396: if (redund->matseq) { /* via MatCreateSubMatrices() */
1397: PetscCall(ISDestroy(&redund->isrow));
1398: PetscCall(ISDestroy(&redund->iscol));
1399: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1400: } else {
1401: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1402: PetscCall(PetscFree(redund->sbuf_j));
1403: PetscCall(PetscFree(redund->sbuf_a));
1404: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1405: PetscCall(PetscFree(redund->rbuf_j[i]));
1406: PetscCall(PetscFree(redund->rbuf_a[i]));
1407: }
1408: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1409: }
1411: PetscCall(PetscCommDestroy(&redund->subcomm));
1412: PetscCall(PetscFree(redund));
1413: }
1414: PetscFunctionReturn(PETSC_SUCCESS);
1415: }
1417: /*@
1418: MatDestroy - Frees space taken by a matrix.
1420: Collective
1422: Input Parameter:
1423: . A - the matrix
1425: Level: beginner
1427: Developer Note:
1428: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1429: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1430: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1431: if changes are needed here.
1433: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1434: @*/
1435: PetscErrorCode MatDestroy(Mat *A)
1436: {
1437: PetscFunctionBegin;
1438: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1440: if (--((PetscObject)*A)->refct > 0) {
1441: *A = NULL;
1442: PetscFunctionReturn(PETSC_SUCCESS);
1443: }
1445: /* if memory was published with SAWs then destroy it */
1446: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1447: PetscTryTypeMethod(*A, destroy);
1449: PetscCall(PetscFree((*A)->factorprefix));
1450: PetscCall(PetscFree((*A)->defaultvectype));
1451: PetscCall(PetscFree((*A)->defaultrandtype));
1452: PetscCall(PetscFree((*A)->bsizes));
1453: PetscCall(PetscFree((*A)->solvertype));
1454: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1455: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1456: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1457: PetscCall(MatProductClear(*A));
1458: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1459: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1460: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1461: PetscCall(MatDestroy(&(*A)->schur));
1462: PetscCall(VecDestroy(&(*A)->dot_vec));
1463: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1464: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1465: PetscCall(PetscHeaderDestroy(A));
1466: PetscFunctionReturn(PETSC_SUCCESS);
1467: }
1469: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1470: /*@
1471: MatSetValues - Inserts or adds a block of values into a matrix.
1472: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1473: MUST be called after all calls to `MatSetValues()` have been completed.
1475: Not Collective
1477: Input Parameters:
1478: + mat - the matrix
1479: . m - the number of rows
1480: . idxm - the global indices of the rows
1481: . n - the number of columns
1482: . idxn - the global indices of the columns
1483: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1484: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1485: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1487: Level: beginner
1489: Notes:
1490: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1491: options cannot be mixed without intervening calls to the assembly
1492: routines.
1494: `MatSetValues()` uses 0-based row and column numbers in Fortran
1495: as well as in C.
1497: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1498: simply ignored. This allows easily inserting element stiffness matrices
1499: with homogeneous Dirichlet boundary conditions that you don't want represented
1500: in the matrix.
1502: Efficiency Alert:
1503: The routine `MatSetValuesBlocked()` may offer much better efficiency
1504: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1506: Fortran Notes:
1507: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1508: .vb
1509: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1510: .ve
1512: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1514: Developer Note:
1515: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1516: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1518: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1519: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1520: @*/
1521: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1522: {
1523: PetscFunctionBeginHot;
1526: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1527: PetscAssertPointer(idxm, 3);
1528: PetscAssertPointer(idxn, 5);
1529: MatCheckPreallocated(mat, 1);
1531: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1532: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1534: if (PetscDefined(USE_DEBUG)) {
1535: PetscInt i, j;
1537: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1538: if (v) {
1539: for (i = 0; i < m; i++) {
1540: for (j = 0; j < n; j++) {
1541: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1542: #if defined(PETSC_USE_COMPLEX)
1543: 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]);
1544: #else
1545: 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]);
1546: #endif
1547: }
1548: }
1549: }
1550: 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);
1551: 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);
1552: }
1554: if (mat->assembled) {
1555: mat->was_assembled = PETSC_TRUE;
1556: mat->assembled = PETSC_FALSE;
1557: }
1558: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1559: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1560: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1561: PetscFunctionReturn(PETSC_SUCCESS);
1562: }
1564: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1565: /*@
1566: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1567: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1568: MUST be called after all calls to `MatSetValues()` have been completed.
1570: Not Collective
1572: Input Parameters:
1573: + mat - the matrix
1574: . ism - the rows to provide
1575: . isn - the columns to provide
1576: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1577: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1578: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1580: Level: beginner
1582: Notes:
1583: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1585: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1586: options cannot be mixed without intervening calls to the assembly
1587: routines.
1589: `MatSetValues()` uses 0-based row and column numbers in Fortran
1590: as well as in C.
1592: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1593: simply ignored. This allows easily inserting element stiffness matrices
1594: with homogeneous Dirichlet boundary conditions that you don't want represented
1595: in the matrix.
1597: Fortran Note:
1598: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1600: Efficiency Alert:
1601: The routine `MatSetValuesBlocked()` may offer much better efficiency
1602: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1604: This is currently not optimized for any particular `ISType`
1606: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1607: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1608: @*/
1609: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1610: {
1611: PetscInt m, n;
1612: const PetscInt *rows, *cols;
1614: PetscFunctionBeginHot;
1616: PetscCall(ISGetIndices(ism, &rows));
1617: PetscCall(ISGetIndices(isn, &cols));
1618: PetscCall(ISGetLocalSize(ism, &m));
1619: PetscCall(ISGetLocalSize(isn, &n));
1620: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1621: PetscCall(ISRestoreIndices(ism, &rows));
1622: PetscCall(ISRestoreIndices(isn, &cols));
1623: PetscFunctionReturn(PETSC_SUCCESS);
1624: }
1626: /*@
1627: MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1628: values into a matrix
1630: Not Collective
1632: Input Parameters:
1633: + mat - the matrix
1634: . row - the (block) row to set
1635: - v - a one-dimensional array that contains the values. For `MATBAIJ` they are implicitly stored as a two-dimensional array, by default in row-major order.
1636: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1638: Level: intermediate
1640: Notes:
1641: The values, `v`, are column-oriented (for the block version) and sorted
1643: All the nonzero values in `row` must be provided
1645: The matrix must have previously had its column indices set, likely by having been assembled.
1647: `row` must belong to this MPI process
1649: Fortran Note:
1650: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1652: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1653: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1654: @*/
1655: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1656: {
1657: PetscInt globalrow;
1659: PetscFunctionBegin;
1662: PetscAssertPointer(v, 3);
1663: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1664: PetscCall(MatSetValuesRow(mat, globalrow, v));
1665: PetscFunctionReturn(PETSC_SUCCESS);
1666: }
1668: /*@
1669: MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1670: values into a matrix
1672: Not Collective
1674: Input Parameters:
1675: + mat - the matrix
1676: . row - the (block) row to set
1677: - v - a logically two-dimensional (column major) array of values for block matrices with blocksize larger than one, otherwise a one dimensional array of values
1679: Level: advanced
1681: Notes:
1682: The values, `v`, are column-oriented for the block version.
1684: All the nonzeros in `row` must be provided
1686: THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.
1688: `row` must belong to this process
1690: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1691: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1692: @*/
1693: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1694: {
1695: PetscFunctionBeginHot;
1698: MatCheckPreallocated(mat, 1);
1699: PetscAssertPointer(v, 3);
1700: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1701: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1702: mat->insertmode = INSERT_VALUES;
1704: if (mat->assembled) {
1705: mat->was_assembled = PETSC_TRUE;
1706: mat->assembled = PETSC_FALSE;
1707: }
1708: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1709: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1710: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1711: PetscFunctionReturn(PETSC_SUCCESS);
1712: }
1714: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1715: /*@
1716: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1717: Using structured grid indexing
1719: Not Collective
1721: Input Parameters:
1722: + mat - the matrix
1723: . m - number of rows being entered
1724: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1725: . n - number of columns being entered
1726: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1727: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1728: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1729: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1731: Level: beginner
1733: Notes:
1734: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1736: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1737: options cannot be mixed without intervening calls to the assembly
1738: routines.
1740: The grid coordinates are across the entire grid, not just the local portion
1742: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1743: as well as in C.
1745: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1747: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1748: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1750: The columns and rows in the stencil passed in MUST be contained within the
1751: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1752: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1753: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1754: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1756: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1757: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1758: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1759: `DM_BOUNDARY_PERIODIC` boundary type.
1761: 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
1762: a single value per point) you can skip filling those indices.
1764: Inspired by the structured grid interface to the HYPRE package
1765: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1767: Fortran Note:
1768: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1770: Efficiency Alert:
1771: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1772: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1774: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1775: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1776: @*/
1777: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1778: {
1779: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1780: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1781: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1783: PetscFunctionBegin;
1784: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1787: PetscAssertPointer(idxm, 3);
1788: PetscAssertPointer(idxn, 5);
1790: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1791: jdxm = buf;
1792: jdxn = buf + m;
1793: } else {
1794: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1795: jdxm = bufm;
1796: jdxn = bufn;
1797: }
1798: for (i = 0; i < m; i++) {
1799: for (j = 0; j < 3 - sdim; j++) dxm++;
1800: tmp = *dxm++ - starts[0];
1801: for (j = 0; j < dim - 1; j++) {
1802: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1803: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1804: }
1805: if (mat->stencil.noc) dxm++;
1806: jdxm[i] = tmp;
1807: }
1808: for (i = 0; i < n; i++) {
1809: for (j = 0; j < 3 - sdim; j++) dxn++;
1810: tmp = *dxn++ - starts[0];
1811: for (j = 0; j < dim - 1; j++) {
1812: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1813: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1814: }
1815: if (mat->stencil.noc) dxn++;
1816: jdxn[i] = tmp;
1817: }
1818: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1819: PetscCall(PetscFree2(bufm, bufn));
1820: PetscFunctionReturn(PETSC_SUCCESS);
1821: }
1823: /*@
1824: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1825: Using structured grid indexing
1827: Not Collective
1829: Input Parameters:
1830: + mat - the matrix
1831: . m - number of rows being entered
1832: . idxm - grid coordinates for matrix rows being entered
1833: . n - number of columns being entered
1834: . idxn - grid coordinates for matrix columns being entered
1835: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1836: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1837: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1839: Level: beginner
1841: Notes:
1842: By default the values, `v`, are row-oriented and unsorted.
1843: See `MatSetOption()` for other options.
1845: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1846: options cannot be mixed without intervening calls to the assembly
1847: routines.
1849: The grid coordinates are across the entire grid, not just the local portion
1851: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1852: as well as in C.
1854: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1856: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1857: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1859: The columns and rows in the stencil passed in MUST be contained within the
1860: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1861: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1862: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1863: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1865: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1866: simply ignored. This allows easily inserting element stiffness matrices
1867: with homogeneous Dirichlet boundary conditions that you don't want represented
1868: in the matrix.
1870: Inspired by the structured grid interface to the HYPRE package
1871: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1873: Fortran Notes:
1874: `idxm` and `idxn` should be declared as
1875: .vb
1876: MatStencil idxm(4,m),idxn(4,n)
1877: .ve
1878: and the values inserted using
1879: .vb
1880: idxm(MatStencil_i,1) = i
1881: idxm(MatStencil_j,1) = j
1882: idxm(MatStencil_k,1) = k
1883: etc
1884: .ve
1886: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1888: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1889: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1890: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1891: @*/
1892: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1893: {
1894: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1895: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1896: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1898: PetscFunctionBegin;
1899: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1902: PetscAssertPointer(idxm, 3);
1903: PetscAssertPointer(idxn, 5);
1904: PetscAssertPointer(v, 6);
1906: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1907: jdxm = buf;
1908: jdxn = buf + m;
1909: } else {
1910: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1911: jdxm = bufm;
1912: jdxn = bufn;
1913: }
1914: for (i = 0; i < m; i++) {
1915: for (j = 0; j < 3 - sdim; j++) dxm++;
1916: tmp = *dxm++ - starts[0];
1917: for (j = 0; j < sdim - 1; j++) {
1918: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1919: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1920: }
1921: dxm++;
1922: jdxm[i] = tmp;
1923: }
1924: for (i = 0; i < n; i++) {
1925: for (j = 0; j < 3 - sdim; j++) dxn++;
1926: tmp = *dxn++ - starts[0];
1927: for (j = 0; j < sdim - 1; j++) {
1928: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1929: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1930: }
1931: dxn++;
1932: jdxn[i] = tmp;
1933: }
1934: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1935: PetscCall(PetscFree2(bufm, bufn));
1936: PetscFunctionReturn(PETSC_SUCCESS);
1937: }
1939: /*@
1940: MatSetStencil - Sets the grid information for setting values into a matrix via
1941: `MatSetValuesStencil()`
1943: Not Collective
1945: Input Parameters:
1946: + mat - the matrix
1947: . dim - dimension of the grid 1, 2, or 3
1948: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1949: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1950: - dof - number of degrees of freedom per node
1952: Level: beginner
1954: Notes:
1955: Inspired by the structured grid interface to the HYPRE package
1956: (www.llnl.gov/CASC/hyper)
1958: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1959: user.
1961: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1962: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1963: @*/
1964: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1965: {
1966: PetscFunctionBegin;
1968: PetscAssertPointer(dims, 3);
1969: PetscAssertPointer(starts, 4);
1971: mat->stencil.dim = dim + (dof > 1);
1972: for (PetscInt i = 0; i < dim; i++) {
1973: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
1974: mat->stencil.starts[i] = starts[dim - i - 1];
1975: }
1976: mat->stencil.dims[dim] = dof;
1977: mat->stencil.starts[dim] = 0;
1978: mat->stencil.noc = (PetscBool)(dof == 1);
1979: PetscFunctionReturn(PETSC_SUCCESS);
1980: }
1982: /*@
1983: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
1985: Not Collective
1987: Input Parameters:
1988: + mat - the matrix
1989: . m - the number of block rows
1990: . idxm - the global block indices
1991: . n - the number of block columns
1992: . idxn - the global block indices
1993: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1994: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1995: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
1997: Level: intermediate
1999: Notes:
2000: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2001: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2003: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2004: NOT the total number of rows/columns; for example, if the block size is 2 and
2005: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2006: The values in `idxm` would be 1 2; that is the first index for each block divided by
2007: the block size.
2009: You must call `MatSetBlockSize()` when constructing this matrix (before
2010: preallocating it).
2012: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2014: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2015: options cannot be mixed without intervening calls to the assembly
2016: routines.
2018: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2019: as well as in C.
2021: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2022: simply ignored. This allows easily inserting element stiffness matrices
2023: with homogeneous Dirichlet boundary conditions that you don't want represented
2024: in the matrix.
2026: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2027: internal searching must be done to determine where to place the
2028: data in the matrix storage space. By instead inserting blocks of
2029: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2030: reduced.
2032: Example:
2033: .vb
2034: Suppose m=n=2 and block size(bs) = 2 The array is
2036: 1 2 | 3 4
2037: 5 6 | 7 8
2038: - - - | - - -
2039: 9 10 | 11 12
2040: 13 14 | 15 16
2042: v[] should be passed in like
2043: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2045: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2046: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2047: .ve
2049: Fortran Notes:
2050: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2051: .vb
2052: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2053: .ve
2055: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2057: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2058: @*/
2059: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2060: {
2061: PetscFunctionBeginHot;
2064: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2065: PetscAssertPointer(idxm, 3);
2066: PetscAssertPointer(idxn, 5);
2067: MatCheckPreallocated(mat, 1);
2068: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2069: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2070: if (PetscDefined(USE_DEBUG)) {
2071: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2072: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2073: }
2074: if (PetscDefined(USE_DEBUG)) {
2075: PetscInt rbs, cbs, M, N, i;
2076: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2077: PetscCall(MatGetSize(mat, &M, &N));
2078: 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);
2079: for (i = 0; i < n; i++)
2080: 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);
2081: }
2082: if (mat->assembled) {
2083: mat->was_assembled = PETSC_TRUE;
2084: mat->assembled = PETSC_FALSE;
2085: }
2086: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2087: if (mat->ops->setvaluesblocked) PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2088: else {
2089: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2090: PetscInt i, j, bs, cbs;
2092: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2093: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2094: iidxm = buf;
2095: iidxn = buf + m * bs;
2096: } else {
2097: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2098: iidxm = bufr;
2099: iidxn = bufc;
2100: }
2101: for (i = 0; i < m; i++) {
2102: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2103: }
2104: if (m != n || bs != cbs || idxm != idxn) {
2105: for (i = 0; i < n; i++) {
2106: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2107: }
2108: } else iidxn = iidxm;
2109: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2110: PetscCall(PetscFree2(bufr, bufc));
2111: }
2112: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2113: PetscFunctionReturn(PETSC_SUCCESS);
2114: }
2116: /*@
2117: MatGetValues - Gets a block of local values from a matrix.
2119: Not Collective; can only return values that are owned by the give process
2121: Input Parameters:
2122: + mat - the matrix
2123: . v - a logically two-dimensional array for storing the values
2124: . m - the number of rows
2125: . idxm - the global indices of the rows
2126: . n - the number of columns
2127: - idxn - the global indices of the columns
2129: Level: advanced
2131: Notes:
2132: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2133: The values, `v`, are then returned in a row-oriented format,
2134: analogous to that used by default in `MatSetValues()`.
2136: `MatGetValues()` uses 0-based row and column numbers in
2137: Fortran as well as in C.
2139: `MatGetValues()` requires that the matrix has been assembled
2140: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2141: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2142: without intermediate matrix assembly.
2144: Negative row or column indices will be ignored and those locations in `v` will be
2145: left unchanged.
2147: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2148: That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2149: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2151: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2152: @*/
2153: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2154: {
2155: PetscFunctionBegin;
2158: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2159: PetscAssertPointer(idxm, 3);
2160: PetscAssertPointer(idxn, 5);
2161: PetscAssertPointer(v, 6);
2162: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2163: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2164: MatCheckPreallocated(mat, 1);
2166: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2167: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2168: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2169: PetscFunctionReturn(PETSC_SUCCESS);
2170: }
2172: /*@
2173: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2174: defined previously by `MatSetLocalToGlobalMapping()`
2176: Not Collective
2178: Input Parameters:
2179: + mat - the matrix
2180: . nrow - number of rows
2181: . irow - the row local indices
2182: . ncol - number of columns
2183: - icol - the column local indices
2185: Output Parameter:
2186: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2187: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2189: Level: advanced
2191: Notes:
2192: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2194: 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,
2195: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2196: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2197: with `MatSetLocalToGlobalMapping()`.
2199: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2200: `MatSetValuesLocal()`, `MatGetValues()`
2201: @*/
2202: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2203: {
2204: PetscFunctionBeginHot;
2207: MatCheckPreallocated(mat, 1);
2208: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2209: PetscAssertPointer(irow, 3);
2210: PetscAssertPointer(icol, 5);
2211: if (PetscDefined(USE_DEBUG)) {
2212: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2213: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2214: }
2215: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2216: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2217: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2218: else {
2219: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2220: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2221: irowm = buf;
2222: icolm = buf + nrow;
2223: } else {
2224: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2225: irowm = bufr;
2226: icolm = bufc;
2227: }
2228: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2229: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2230: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2231: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2232: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2233: PetscCall(PetscFree2(bufr, bufc));
2234: }
2235: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2236: PetscFunctionReturn(PETSC_SUCCESS);
2237: }
2239: /*@
2240: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2241: the same size. Currently, this can only be called once and creates the given matrix.
2243: Not Collective
2245: Input Parameters:
2246: + mat - the matrix
2247: . nb - the number of blocks
2248: . bs - the number of rows (and columns) in each block
2249: . rows - a concatenation of the rows for each block
2250: - v - a concatenation of logically two-dimensional arrays of values
2252: Level: advanced
2254: Notes:
2255: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2257: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2259: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2260: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2261: @*/
2262: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2263: {
2264: PetscFunctionBegin;
2267: PetscAssertPointer(rows, 4);
2268: PetscAssertPointer(v, 5);
2269: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2271: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2272: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2273: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2274: PetscFunctionReturn(PETSC_SUCCESS);
2275: }
2277: /*@
2278: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2279: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2280: using a local (per-processor) numbering.
2282: Not Collective
2284: Input Parameters:
2285: + x - the matrix
2286: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2287: - cmapping - column mapping
2289: Level: intermediate
2291: Note:
2292: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2294: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2295: @*/
2296: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2297: {
2298: PetscFunctionBegin;
2303: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2304: else {
2305: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2306: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2307: }
2308: PetscFunctionReturn(PETSC_SUCCESS);
2309: }
2311: /*@
2312: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2314: Not Collective
2316: Input Parameter:
2317: . A - the matrix
2319: Output Parameters:
2320: + rmapping - row mapping
2321: - cmapping - column mapping
2323: Level: advanced
2325: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2326: @*/
2327: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2328: {
2329: PetscFunctionBegin;
2332: if (rmapping) {
2333: PetscAssertPointer(rmapping, 2);
2334: *rmapping = A->rmap->mapping;
2335: }
2336: if (cmapping) {
2337: PetscAssertPointer(cmapping, 3);
2338: *cmapping = A->cmap->mapping;
2339: }
2340: PetscFunctionReturn(PETSC_SUCCESS);
2341: }
2343: /*@
2344: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2346: Logically Collective
2348: Input Parameters:
2349: + A - the matrix
2350: . rmap - row layout
2351: - cmap - column layout
2353: Level: advanced
2355: Note:
2356: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2358: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2359: @*/
2360: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2361: {
2362: PetscFunctionBegin;
2364: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2365: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2366: PetscFunctionReturn(PETSC_SUCCESS);
2367: }
2369: /*@
2370: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2372: Not Collective
2374: Input Parameter:
2375: . A - the matrix
2377: Output Parameters:
2378: + rmap - row layout
2379: - cmap - column layout
2381: Level: advanced
2383: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2384: @*/
2385: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2386: {
2387: PetscFunctionBegin;
2390: if (rmap) {
2391: PetscAssertPointer(rmap, 2);
2392: *rmap = A->rmap;
2393: }
2394: if (cmap) {
2395: PetscAssertPointer(cmap, 3);
2396: *cmap = A->cmap;
2397: }
2398: PetscFunctionReturn(PETSC_SUCCESS);
2399: }
2401: /*@
2402: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2403: using a local numbering of the rows and columns.
2405: Not Collective
2407: Input Parameters:
2408: + mat - the matrix
2409: . nrow - number of rows
2410: . irow - the row local indices
2411: . ncol - number of columns
2412: . icol - the column local indices
2413: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2414: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2415: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2417: Level: intermediate
2419: Notes:
2420: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2422: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2423: options cannot be mixed without intervening calls to the assembly
2424: routines.
2426: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2427: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2429: Fortran Notes:
2430: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2431: .vb
2432: call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2433: .ve
2435: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2437: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2438: `MatGetValuesLocal()`
2439: @*/
2440: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2441: {
2442: PetscFunctionBeginHot;
2445: MatCheckPreallocated(mat, 1);
2446: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2447: PetscAssertPointer(irow, 3);
2448: PetscAssertPointer(icol, 5);
2449: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2450: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2451: if (PetscDefined(USE_DEBUG)) {
2452: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2453: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2454: }
2456: if (mat->assembled) {
2457: mat->was_assembled = PETSC_TRUE;
2458: mat->assembled = PETSC_FALSE;
2459: }
2460: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2461: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2462: else {
2463: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2464: const PetscInt *irowm, *icolm;
2466: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2467: bufr = buf;
2468: bufc = buf + nrow;
2469: irowm = bufr;
2470: icolm = bufc;
2471: } else {
2472: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2473: irowm = bufr;
2474: icolm = bufc;
2475: }
2476: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2477: else irowm = irow;
2478: if (mat->cmap->mapping) {
2479: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2480: else icolm = irowm;
2481: } else icolm = icol;
2482: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2483: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2484: }
2485: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2486: PetscFunctionReturn(PETSC_SUCCESS);
2487: }
2489: /*@
2490: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2491: using a local ordering of the nodes a block at a time.
2493: Not Collective
2495: Input Parameters:
2496: + mat - the matrix
2497: . nrow - number of rows
2498: . irow - the row local indices
2499: . ncol - number of columns
2500: . icol - the column local indices
2501: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2502: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2503: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2505: Level: intermediate
2507: Notes:
2508: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2509: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2511: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2512: options cannot be mixed without intervening calls to the assembly
2513: routines.
2515: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2516: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2518: Fortran Notes:
2519: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2520: .vb
2521: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2522: .ve
2524: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2526: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2527: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2528: @*/
2529: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2530: {
2531: PetscFunctionBeginHot;
2534: MatCheckPreallocated(mat, 1);
2535: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2536: PetscAssertPointer(irow, 3);
2537: PetscAssertPointer(icol, 5);
2538: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2539: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2540: if (PetscDefined(USE_DEBUG)) {
2541: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2542: 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);
2543: }
2545: if (mat->assembled) {
2546: mat->was_assembled = PETSC_TRUE;
2547: mat->assembled = PETSC_FALSE;
2548: }
2549: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2550: PetscInt irbs, rbs;
2551: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2552: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2553: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2554: }
2555: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2556: PetscInt icbs, cbs;
2557: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2558: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2559: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2560: }
2561: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2562: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2563: else {
2564: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2565: const PetscInt *irowm, *icolm;
2567: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2568: bufr = buf;
2569: bufc = buf + nrow;
2570: irowm = bufr;
2571: icolm = bufc;
2572: } else {
2573: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2574: irowm = bufr;
2575: icolm = bufc;
2576: }
2577: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2578: else irowm = irow;
2579: if (mat->cmap->mapping) {
2580: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2581: else icolm = irowm;
2582: } else icolm = icol;
2583: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2584: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2585: }
2586: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2587: PetscFunctionReturn(PETSC_SUCCESS);
2588: }
2590: /*@
2591: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2593: Collective
2595: Input Parameters:
2596: + mat - the matrix
2597: - x - the vector to be multiplied
2599: Output Parameter:
2600: . y - the result
2602: Level: developer
2604: Note:
2605: The vectors `x` and `y` cannot be the same. I.e., one cannot
2606: call `MatMultDiagonalBlock`(A,y,y).
2608: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2609: @*/
2610: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2611: {
2612: PetscFunctionBegin;
2618: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2619: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2620: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2621: MatCheckPreallocated(mat, 1);
2623: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2624: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2625: PetscFunctionReturn(PETSC_SUCCESS);
2626: }
2628: /*@
2629: MatMult - Computes the matrix-vector product, $y = Ax$.
2631: Neighbor-wise Collective
2633: Input Parameters:
2634: + mat - the matrix
2635: - x - the vector to be multiplied
2637: Output Parameter:
2638: . y - the result
2640: Level: beginner
2642: Note:
2643: The vectors `x` and `y` cannot be the same. I.e., one cannot
2644: call `MatMult`(A,y,y).
2646: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2647: @*/
2648: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2649: {
2650: PetscFunctionBegin;
2654: VecCheckAssembled(x);
2656: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2657: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2658: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2659: 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);
2660: 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);
2661: 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);
2662: 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);
2663: PetscCall(VecSetErrorIfLocked(y, 3));
2664: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2665: MatCheckPreallocated(mat, 1);
2667: PetscCall(VecLockReadPush(x));
2668: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2669: PetscUseTypeMethod(mat, mult, x, y);
2670: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2671: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2672: PetscCall(VecLockReadPop(x));
2673: PetscFunctionReturn(PETSC_SUCCESS);
2674: }
2676: /*@
2677: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2679: Neighbor-wise Collective
2681: Input Parameters:
2682: + mat - the matrix
2683: - x - the vector to be multiplied
2685: Output Parameter:
2686: . y - the result
2688: Level: beginner
2690: Notes:
2691: The vectors `x` and `y` cannot be the same. I.e., one cannot
2692: call `MatMultTranspose`(A,y,y).
2694: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2695: use `MatMultHermitianTranspose()`
2697: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2698: @*/
2699: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2700: {
2701: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2703: PetscFunctionBegin;
2707: VecCheckAssembled(x);
2710: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2711: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2712: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2713: 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);
2714: 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);
2715: 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);
2716: 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);
2717: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2718: MatCheckPreallocated(mat, 1);
2720: if (!mat->ops->multtranspose) {
2721: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2722: 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);
2723: } else op = mat->ops->multtranspose;
2724: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2725: PetscCall(VecLockReadPush(x));
2726: PetscCall((*op)(mat, x, y));
2727: PetscCall(VecLockReadPop(x));
2728: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2729: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2730: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2731: PetscFunctionReturn(PETSC_SUCCESS);
2732: }
2734: /*@
2735: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2737: Neighbor-wise Collective
2739: Input Parameters:
2740: + mat - the matrix
2741: - x - the vector to be multiplied
2743: Output Parameter:
2744: . y - the result
2746: Level: beginner
2748: Notes:
2749: The vectors `x` and `y` cannot be the same. I.e., one cannot
2750: call `MatMultHermitianTranspose`(A,y,y).
2752: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2754: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2756: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2757: @*/
2758: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2759: {
2760: PetscFunctionBegin;
2766: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2767: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2768: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2769: 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);
2770: 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);
2771: 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);
2772: 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);
2773: MatCheckPreallocated(mat, 1);
2775: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2776: #if defined(PETSC_USE_COMPLEX)
2777: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2778: PetscCall(VecLockReadPush(x));
2779: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2780: else PetscUseTypeMethod(mat, mult, x, y);
2781: PetscCall(VecLockReadPop(x));
2782: } else {
2783: Vec w;
2784: PetscCall(VecDuplicate(x, &w));
2785: PetscCall(VecCopy(x, w));
2786: PetscCall(VecConjugate(w));
2787: PetscCall(MatMultTranspose(mat, w, y));
2788: PetscCall(VecDestroy(&w));
2789: PetscCall(VecConjugate(y));
2790: }
2791: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2792: #else
2793: PetscCall(MatMultTranspose(mat, x, y));
2794: #endif
2795: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2796: PetscFunctionReturn(PETSC_SUCCESS);
2797: }
2799: /*@
2800: MatMultAdd - Computes $v3 = v2 + A * v1$.
2802: Neighbor-wise Collective
2804: Input Parameters:
2805: + mat - the matrix
2806: . v1 - the vector to be multiplied by `mat`
2807: - v2 - the vector to be added to the result
2809: Output Parameter:
2810: . v3 - the result
2812: Level: beginner
2814: Note:
2815: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2816: call `MatMultAdd`(A,v1,v2,v1).
2818: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2819: @*/
2820: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2821: {
2822: PetscFunctionBegin;
2829: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2830: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2831: 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);
2832: /* 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);
2833: 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); */
2834: 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);
2835: 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);
2836: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2837: MatCheckPreallocated(mat, 1);
2839: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2840: PetscCall(VecLockReadPush(v1));
2841: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2842: PetscCall(VecLockReadPop(v1));
2843: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2844: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2845: PetscFunctionReturn(PETSC_SUCCESS);
2846: }
2848: /*@
2849: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2851: Neighbor-wise Collective
2853: Input Parameters:
2854: + mat - the matrix
2855: . v1 - the vector to be multiplied by the transpose of the matrix
2856: - v2 - the vector to be added to the result
2858: Output Parameter:
2859: . v3 - the result
2861: Level: beginner
2863: Note:
2864: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2865: call `MatMultTransposeAdd`(A,v1,v2,v1).
2867: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2868: @*/
2869: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2870: {
2871: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2873: PetscFunctionBegin;
2880: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2881: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2882: 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);
2883: 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);
2884: 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);
2885: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2886: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2887: MatCheckPreallocated(mat, 1);
2889: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2890: PetscCall(VecLockReadPush(v1));
2891: PetscCall((*op)(mat, v1, v2, v3));
2892: PetscCall(VecLockReadPop(v1));
2893: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2894: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2895: PetscFunctionReturn(PETSC_SUCCESS);
2896: }
2898: /*@
2899: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2901: Neighbor-wise Collective
2903: Input Parameters:
2904: + mat - the matrix
2905: . v1 - the vector to be multiplied by the Hermitian transpose
2906: - v2 - the vector to be added to the result
2908: Output Parameter:
2909: . v3 - the result
2911: Level: beginner
2913: Note:
2914: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2915: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2917: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2918: @*/
2919: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2920: {
2921: PetscFunctionBegin;
2928: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2929: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2930: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2931: 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);
2932: 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);
2933: 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);
2934: MatCheckPreallocated(mat, 1);
2936: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2937: PetscCall(VecLockReadPush(v1));
2938: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2939: else {
2940: Vec w, z;
2941: PetscCall(VecDuplicate(v1, &w));
2942: PetscCall(VecCopy(v1, w));
2943: PetscCall(VecConjugate(w));
2944: PetscCall(VecDuplicate(v3, &z));
2945: PetscCall(MatMultTranspose(mat, w, z));
2946: PetscCall(VecDestroy(&w));
2947: PetscCall(VecConjugate(z));
2948: if (v2 != v3) PetscCall(VecWAXPY(v3, 1.0, v2, z));
2949: else PetscCall(VecAXPY(v3, 1.0, z));
2950: PetscCall(VecDestroy(&z));
2951: }
2952: PetscCall(VecLockReadPop(v1));
2953: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2954: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2955: PetscFunctionReturn(PETSC_SUCCESS);
2956: }
2958: PetscErrorCode MatADot_Default(Mat mat, Vec x, Vec y, PetscScalar *val)
2959: {
2960: PetscFunctionBegin;
2961: if (!mat->dot_vec) PetscCall(MatCreateVecs(mat, &mat->dot_vec, NULL));
2962: PetscCall(MatMult(mat, x, mat->dot_vec));
2963: PetscCall(VecDot(mat->dot_vec, y, val));
2964: PetscFunctionReturn(PETSC_SUCCESS);
2965: }
2967: PetscErrorCode MatANorm_Default(Mat mat, Vec x, PetscReal *val)
2968: {
2969: PetscScalar sval;
2971: PetscFunctionBegin;
2972: PetscCall(MatADot_Default(mat, x, x, &sval));
2973: PetscCheck(PetscRealPart(sval) >= 0.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not positive definite");
2974: PetscCheck(PetscAbsReal(PetscImaginaryPart(sval)) < 100 * PETSC_MACHINE_EPSILON, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not Hermitian");
2975: *val = PetscSqrtReal(PetscRealPart(sval));
2976: PetscFunctionReturn(PETSC_SUCCESS);
2977: }
2979: /*@
2980: MatADot - Computes the inner product with respect to a matrix, i.e., $(x, y)_A = y^H A x$ where $A$ is symmetric (Hermitian when using complex)
2981: positive definite.
2983: Collective
2985: Input Parameters:
2986: + mat - matrix used to define the inner product
2987: . x - first vector
2988: - y - second vector
2990: Output Parameter:
2991: . val - the dot product with respect to `A`
2993: Level: intermediate
2995: Note:
2996: For complex vectors, `MatADot()` computes
2997: $$
2998: val = (x,y)_A = y^H A x,
2999: $$
3000: where $y^H$ denotes the conjugate transpose of `y`. Note that this corresponds to the "mathematicians" complex
3001: inner product where the SECOND argument gets the complex conjugate.
3003: .seealso: [](ch_matrices), `Mat`, `MatANorm()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3004: @*/
3005: PetscErrorCode MatADot(Mat mat, Vec x, Vec y, PetscScalar *val)
3006: {
3007: PetscFunctionBegin;
3011: VecCheckAssembled(x);
3013: VecCheckAssembled(y);
3016: PetscAssertPointer(val, 4);
3017: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3018: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3019: 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);
3020: 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);
3021: 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);
3022: 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);
3023: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3024: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_TRUE));
3025: MatCheckPreallocated(mat, 1);
3027: PetscCall(VecLockReadPush(x));
3028: PetscCall(VecLockReadPush(y));
3029: PetscCall(PetscLogEventBegin(MAT_ADot, mat, x, y, 0));
3030: PetscUseTypeMethod(mat, adot, x, y, val);
3031: PetscCall(PetscLogEventEnd(MAT_ADot, mat, x, y, 0));
3032: PetscCall(VecLockReadPop(y));
3033: PetscCall(VecLockReadPop(x));
3034: PetscFunctionReturn(PETSC_SUCCESS);
3035: }
3037: /*@
3038: MatANorm - Computes the norm with respect to a matrix, i.e., $(x, x)_A^{1/2} = (x^H A x)^{1/2}$ where $A$ is symmetric (Hermitian when using complex)
3039: positive definite.
3041: Collective
3043: Input Parameters:
3044: + mat - matrix used to define norm
3045: - x - the vector to compute the norm of
3047: Output Parameter:
3048: . val - the norm with respect to `A`
3050: Level: intermediate
3052: Note:
3053: For complex vectors, `MatANorm()` computes
3054: $$
3055: val = (x,x)_A^{1/2} = (x^H A x)^{1/2},
3056: $$
3057: where $x^H$ denotes the conjugate transpose of `x`.
3059: .seealso: [](ch_matrices), `Mat`, `MatADot()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3060: @*/
3061: PetscErrorCode MatANorm(Mat mat, Vec x, PetscReal *val)
3062: {
3063: PetscFunctionBegin;
3067: VecCheckAssembled(x);
3069: PetscAssertPointer(val, 3);
3070: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3071: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3072: 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);
3073: 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);
3074: 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);
3075: 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);
3076: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3077: MatCheckPreallocated(mat, 1);
3079: PetscCall(VecLockReadPush(x));
3080: PetscCall(PetscLogEventBegin(MAT_ANorm, mat, x, 0, 0));
3081: PetscUseTypeMethod(mat, anorm, x, val);
3082: PetscCall(PetscLogEventEnd(MAT_ANorm, mat, x, 0, 0));
3083: PetscCall(VecLockReadPop(x));
3084: PetscFunctionReturn(PETSC_SUCCESS);
3085: }
3087: /*@
3088: MatGetFactorType - gets the type of factorization a matrix is
3090: Not Collective
3092: Input Parameter:
3093: . mat - the matrix
3095: Output Parameter:
3096: . 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`
3098: Level: intermediate
3100: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3101: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3102: @*/
3103: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3104: {
3105: PetscFunctionBegin;
3108: PetscAssertPointer(t, 2);
3109: *t = mat->factortype;
3110: PetscFunctionReturn(PETSC_SUCCESS);
3111: }
3113: /*@
3114: MatSetFactorType - sets the type of factorization a matrix is
3116: Logically Collective
3118: Input Parameters:
3119: + mat - the matrix
3120: - 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`
3122: Level: intermediate
3124: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3125: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3126: @*/
3127: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3128: {
3129: PetscFunctionBegin;
3132: mat->factortype = t;
3133: PetscFunctionReturn(PETSC_SUCCESS);
3134: }
3136: /*@
3137: MatGetInfo - Returns information about matrix storage (number of
3138: nonzeros, memory, etc.).
3140: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3142: Input Parameters:
3143: + mat - the matrix
3144: - 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)
3146: Output Parameter:
3147: . info - matrix information context
3149: Options Database Key:
3150: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3152: Level: intermediate
3154: Notes:
3155: The `MatInfo` context contains a variety of matrix data, including
3156: number of nonzeros allocated and used, number of mallocs during
3157: matrix assembly, etc. Additional information for factored matrices
3158: is provided (such as the fill ratio, number of mallocs during
3159: factorization, etc.).
3161: Example:
3162: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3163: data within the `MatInfo` context. For example,
3164: .vb
3165: MatInfo info;
3166: Mat A;
3167: double mal, nz_a, nz_u;
3169: MatGetInfo(A, MAT_LOCAL, &info);
3170: mal = info.mallocs;
3171: nz_a = info.nz_allocated;
3172: .ve
3174: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3175: @*/
3176: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3177: {
3178: PetscFunctionBegin;
3181: PetscAssertPointer(info, 3);
3182: MatCheckPreallocated(mat, 1);
3183: PetscUseTypeMethod(mat, getinfo, flag, info);
3184: PetscFunctionReturn(PETSC_SUCCESS);
3185: }
3187: /*
3188: This is used by external packages where it is not easy to get the info from the actual
3189: matrix factorization.
3190: */
3191: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3192: {
3193: PetscFunctionBegin;
3194: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3195: PetscFunctionReturn(PETSC_SUCCESS);
3196: }
3198: /*@
3199: MatLUFactor - Performs in-place LU factorization of matrix.
3201: Collective
3203: Input Parameters:
3204: + mat - the matrix
3205: . row - row permutation
3206: . col - column permutation
3207: - info - options for factorization, includes
3208: .vb
3209: fill - expected fill as ratio of original fill.
3210: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3211: Run with the option -info to determine an optimal value to use
3212: .ve
3214: Level: developer
3216: Notes:
3217: Most users should employ the `KSP` interface for linear solvers
3218: instead of working directly with matrix algebra routines such as this.
3219: See, e.g., `KSPCreate()`.
3221: This changes the state of the matrix to a factored matrix; it cannot be used
3222: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3224: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3225: when not using `KSP`.
3227: Fortran Note:
3228: A valid (non-null) `info` argument must be provided
3230: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3231: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3232: @*/
3233: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3234: {
3235: MatFactorInfo tinfo;
3237: PetscFunctionBegin;
3241: if (info) PetscAssertPointer(info, 4);
3243: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3244: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3245: MatCheckPreallocated(mat, 1);
3246: if (!info) {
3247: PetscCall(MatFactorInfoInitialize(&tinfo));
3248: info = &tinfo;
3249: }
3251: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3252: PetscUseTypeMethod(mat, lufactor, row, col, info);
3253: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3254: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3255: PetscFunctionReturn(PETSC_SUCCESS);
3256: }
3258: /*@
3259: MatILUFactor - Performs in-place ILU factorization of matrix.
3261: Collective
3263: Input Parameters:
3264: + mat - the matrix
3265: . row - row permutation
3266: . col - column permutation
3267: - info - structure containing
3268: .vb
3269: levels - number of levels of fill.
3270: expected fill - as ratio of original fill.
3271: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3272: missing diagonal entries)
3273: .ve
3275: Level: developer
3277: Notes:
3278: Most users should employ the `KSP` interface for linear solvers
3279: instead of working directly with matrix algebra routines such as this.
3280: See, e.g., `KSPCreate()`.
3282: Probably really in-place only when level of fill is zero, otherwise allocates
3283: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatLUFactorNumeric()`
3284: when not using `KSP`.
3286: Fortran Note:
3287: A valid (non-null) `info` argument must be provided
3289: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3290: @*/
3291: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3292: {
3293: PetscFunctionBegin;
3297: PetscAssertPointer(info, 4);
3299: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3300: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3301: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3302: MatCheckPreallocated(mat, 1);
3304: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3305: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3306: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3307: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3308: PetscFunctionReturn(PETSC_SUCCESS);
3309: }
3311: /*@
3312: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3313: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3315: Collective
3317: Input Parameters:
3318: + fact - the factor matrix obtained with `MatGetFactor()`
3319: . mat - the matrix
3320: . row - the row permutation
3321: . col - the column permutation
3322: - info - options for factorization, includes
3323: .vb
3324: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3325: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3326: .ve
3328: Level: developer
3330: Notes:
3331: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3333: Most users should employ the simplified `KSP` interface for linear solvers
3334: instead of working directly with matrix algebra routines such as this.
3335: See, e.g., `KSPCreate()`.
3337: Fortran Note:
3338: A valid (non-null) `info` argument must be provided
3340: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3341: @*/
3342: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3343: {
3344: MatFactorInfo tinfo;
3346: PetscFunctionBegin;
3351: if (info) PetscAssertPointer(info, 5);
3354: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3355: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3356: MatCheckPreallocated(mat, 2);
3357: if (!info) {
3358: PetscCall(MatFactorInfoInitialize(&tinfo));
3359: info = &tinfo;
3360: }
3362: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3363: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3364: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3365: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3366: PetscFunctionReturn(PETSC_SUCCESS);
3367: }
3369: /*@
3370: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3371: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3373: Collective
3375: Input Parameters:
3376: + fact - the factor matrix obtained with `MatGetFactor()`
3377: . mat - the matrix
3378: - info - options for factorization
3380: Level: developer
3382: Notes:
3383: See `MatLUFactor()` for in-place factorization. See
3384: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3386: Most users should employ the `KSP` interface for linear solvers
3387: instead of working directly with matrix algebra routines such as this.
3388: See, e.g., `KSPCreate()`.
3390: Fortran Note:
3391: A valid (non-null) `info` argument must be provided
3393: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3394: @*/
3395: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3396: {
3397: MatFactorInfo tinfo;
3399: PetscFunctionBegin;
3404: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3405: 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,
3406: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3408: MatCheckPreallocated(mat, 2);
3409: if (!info) {
3410: PetscCall(MatFactorInfoInitialize(&tinfo));
3411: info = &tinfo;
3412: }
3414: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3415: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3416: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3417: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3418: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3419: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3420: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3421: PetscFunctionReturn(PETSC_SUCCESS);
3422: }
3424: /*@
3425: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3426: symmetric matrix.
3428: Collective
3430: Input Parameters:
3431: + mat - the matrix
3432: . perm - row and column permutations
3433: - info - expected fill as ratio of original fill
3435: Level: developer
3437: Notes:
3438: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3439: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3441: Most users should employ the `KSP` interface for linear solvers
3442: instead of working directly with matrix algebra routines such as this.
3443: See, e.g., `KSPCreate()`.
3445: Fortran Note:
3446: A valid (non-null) `info` argument must be provided
3448: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`,
3449: `MatGetOrdering()`
3450: @*/
3451: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3452: {
3453: MatFactorInfo tinfo;
3455: PetscFunctionBegin;
3458: if (info) PetscAssertPointer(info, 3);
3460: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3461: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3462: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3463: MatCheckPreallocated(mat, 1);
3464: if (!info) {
3465: PetscCall(MatFactorInfoInitialize(&tinfo));
3466: info = &tinfo;
3467: }
3469: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3470: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3471: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3472: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3473: PetscFunctionReturn(PETSC_SUCCESS);
3474: }
3476: /*@
3477: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3478: of a symmetric matrix.
3480: Collective
3482: Input Parameters:
3483: + fact - the factor matrix obtained with `MatGetFactor()`
3484: . mat - the matrix
3485: . perm - row and column permutations
3486: - info - options for factorization, includes
3487: .vb
3488: fill - expected fill as ratio of original fill.
3489: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3490: Run with the option -info to determine an optimal value to use
3491: .ve
3493: Level: developer
3495: Notes:
3496: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3497: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3499: Most users should employ the `KSP` interface for linear solvers
3500: instead of working directly with matrix algebra routines such as this.
3501: See, e.g., `KSPCreate()`.
3503: Fortran Note:
3504: A valid (non-null) `info` argument must be provided
3506: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`,
3507: `MatGetOrdering()`
3508: @*/
3509: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3510: {
3511: MatFactorInfo tinfo;
3513: PetscFunctionBegin;
3517: if (info) PetscAssertPointer(info, 4);
3520: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3521: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3522: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3523: MatCheckPreallocated(mat, 2);
3524: if (!info) {
3525: PetscCall(MatFactorInfoInitialize(&tinfo));
3526: info = &tinfo;
3527: }
3529: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3530: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3531: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3532: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3533: PetscFunctionReturn(PETSC_SUCCESS);
3534: }
3536: /*@
3537: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3538: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3539: `MatCholeskyFactorSymbolic()`.
3541: Collective
3543: Input Parameters:
3544: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3545: . mat - the initial matrix that is to be factored
3546: - info - options for factorization
3548: Level: developer
3550: Note:
3551: Most users should employ the `KSP` interface for linear solvers
3552: instead of working directly with matrix algebra routines such as this.
3553: See, e.g., `KSPCreate()`.
3555: Fortran Note:
3556: A valid (non-null) `info` argument must be provided
3558: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3559: @*/
3560: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3561: {
3562: MatFactorInfo tinfo;
3564: PetscFunctionBegin;
3569: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3570: 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,
3571: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3572: MatCheckPreallocated(mat, 2);
3573: if (!info) {
3574: PetscCall(MatFactorInfoInitialize(&tinfo));
3575: info = &tinfo;
3576: }
3578: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3579: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3580: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3581: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3582: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3583: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3584: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3585: PetscFunctionReturn(PETSC_SUCCESS);
3586: }
3588: /*@
3589: MatQRFactor - Performs in-place QR factorization of matrix.
3591: Collective
3593: Input Parameters:
3594: + mat - the matrix
3595: . col - column permutation
3596: - info - options for factorization, includes
3597: .vb
3598: fill - expected fill as ratio of original fill.
3599: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3600: Run with the option -info to determine an optimal value to use
3601: .ve
3603: Level: developer
3605: Notes:
3606: Most users should employ the `KSP` interface for linear solvers
3607: instead of working directly with matrix algebra routines such as this.
3608: See, e.g., `KSPCreate()`.
3610: This changes the state of the matrix to a factored matrix; it cannot be used
3611: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3613: Fortran Note:
3614: A valid (non-null) `info` argument must be provided
3616: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3617: `MatSetUnfactored()`
3618: @*/
3619: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3620: {
3621: PetscFunctionBegin;
3624: if (info) PetscAssertPointer(info, 3);
3626: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3627: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3628: MatCheckPreallocated(mat, 1);
3629: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3630: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3631: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3632: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3633: PetscFunctionReturn(PETSC_SUCCESS);
3634: }
3636: /*@
3637: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3638: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3640: Collective
3642: Input Parameters:
3643: + fact - the factor matrix obtained with `MatGetFactor()`
3644: . mat - the matrix
3645: . col - column permutation
3646: - info - options for factorization, includes
3647: .vb
3648: fill - expected fill as ratio of original fill.
3649: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3650: Run with the option -info to determine an optimal value to use
3651: .ve
3653: Level: developer
3655: Note:
3656: Most users should employ the `KSP` interface for linear solvers
3657: instead of working directly with matrix algebra routines such as this.
3658: See, e.g., `KSPCreate()`.
3660: Fortran Note:
3661: A valid (non-null) `info` argument must be provided
3663: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3664: @*/
3665: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3666: {
3667: MatFactorInfo tinfo;
3669: PetscFunctionBegin;
3673: if (info) PetscAssertPointer(info, 4);
3676: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3677: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3678: MatCheckPreallocated(mat, 2);
3679: if (!info) {
3680: PetscCall(MatFactorInfoInitialize(&tinfo));
3681: info = &tinfo;
3682: }
3684: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3685: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3686: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3687: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3688: PetscFunctionReturn(PETSC_SUCCESS);
3689: }
3691: /*@
3692: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3693: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3695: Collective
3697: Input Parameters:
3698: + fact - the factor matrix obtained with `MatGetFactor()`
3699: . mat - the matrix
3700: - info - options for factorization
3702: Level: developer
3704: Notes:
3705: See `MatQRFactor()` for in-place factorization.
3707: Most users should employ the `KSP` interface for linear solvers
3708: instead of working directly with matrix algebra routines such as this.
3709: See, e.g., `KSPCreate()`.
3711: Fortran Note:
3712: A valid (non-null) `info` argument must be provided
3714: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3715: @*/
3716: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3717: {
3718: MatFactorInfo tinfo;
3720: PetscFunctionBegin;
3725: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3726: 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,
3727: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3729: MatCheckPreallocated(mat, 2);
3730: if (!info) {
3731: PetscCall(MatFactorInfoInitialize(&tinfo));
3732: info = &tinfo;
3733: }
3735: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3736: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3737: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3738: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3739: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3740: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3741: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3742: PetscFunctionReturn(PETSC_SUCCESS);
3743: }
3745: /*@
3746: MatSolve - Solves $A x = b$, given a factored matrix.
3748: Neighbor-wise Collective
3750: Input Parameters:
3751: + mat - the factored matrix
3752: - b - the right-hand-side vector
3754: Output Parameter:
3755: . x - the result vector
3757: Level: developer
3759: Notes:
3760: The vectors `b` and `x` cannot be the same. I.e., one cannot
3761: call `MatSolve`(A,x,x).
3763: Most users should employ the `KSP` interface for linear solvers
3764: instead of working directly with matrix algebra routines such as this.
3765: See, e.g., `KSPCreate()`.
3767: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3768: @*/
3769: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3770: {
3771: PetscFunctionBegin;
3776: PetscCheckSameComm(mat, 1, b, 2);
3777: PetscCheckSameComm(mat, 1, x, 3);
3778: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3779: 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);
3780: 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);
3781: 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);
3782: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3783: MatCheckPreallocated(mat, 1);
3785: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3786: PetscCall(VecFlag(x, mat->factorerrortype));
3787: if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3788: else PetscUseTypeMethod(mat, solve, b, x);
3789: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3790: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3791: PetscFunctionReturn(PETSC_SUCCESS);
3792: }
3794: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3795: {
3796: Vec b, x;
3797: PetscInt N;
3798: PetscErrorCode (*f)(Mat, Vec, Vec);
3799: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3801: PetscFunctionBegin;
3802: if (A->factorerrortype) {
3803: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3804: PetscCall(MatSetInf(X));
3805: PetscFunctionReturn(PETSC_SUCCESS);
3806: }
3807: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3808: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3809: PetscCall(MatBoundToCPU(A, &Abound));
3810: if (!Abound) {
3811: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3812: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3813: }
3814: #if PetscDefined(HAVE_CUDA)
3815: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3816: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3817: #elif PetscDefined(HAVE_HIP)
3818: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3819: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3820: #endif
3821: PetscCall(MatGetSize(B, NULL, &N));
3822: for (PetscInt i = 0; i < N; i++) {
3823: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3824: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3825: PetscCall((*f)(A, b, x));
3826: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3827: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3828: }
3829: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3830: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3831: PetscFunctionReturn(PETSC_SUCCESS);
3832: }
3834: /*@
3835: MatMatSolve - Solves $A X = B$, given a factored matrix.
3837: Neighbor-wise Collective
3839: Input Parameters:
3840: + A - the factored matrix
3841: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3843: Output Parameter:
3844: . X - the result matrix (dense matrix)
3846: Level: developer
3848: Note:
3849: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3850: otherwise, `B` and `X` cannot be the same.
3852: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3853: @*/
3854: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3855: {
3856: PetscFunctionBegin;
3861: PetscCheckSameComm(A, 1, B, 2);
3862: PetscCheckSameComm(A, 1, X, 3);
3863: 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);
3864: 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);
3865: 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");
3866: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3867: MatCheckPreallocated(A, 1);
3869: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3870: if (!A->ops->matsolve) {
3871: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3872: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3873: } else PetscUseTypeMethod(A, matsolve, B, X);
3874: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3875: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3876: PetscFunctionReturn(PETSC_SUCCESS);
3877: }
3879: /*@
3880: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3882: Neighbor-wise Collective
3884: Input Parameters:
3885: + A - the factored matrix
3886: - B - the right-hand-side matrix (`MATDENSE` matrix)
3888: Output Parameter:
3889: . X - the result matrix (dense matrix)
3891: Level: developer
3893: Note:
3894: The matrices `B` and `X` cannot be the same. I.e., one cannot
3895: call `MatMatSolveTranspose`(A,X,X).
3897: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3898: @*/
3899: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3900: {
3901: PetscFunctionBegin;
3906: PetscCheckSameComm(A, 1, B, 2);
3907: PetscCheckSameComm(A, 1, X, 3);
3908: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3909: 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);
3910: 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);
3911: 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);
3912: 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");
3913: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3914: MatCheckPreallocated(A, 1);
3916: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3917: if (!A->ops->matsolvetranspose) {
3918: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3919: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3920: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3921: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3922: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3923: PetscFunctionReturn(PETSC_SUCCESS);
3924: }
3926: /*@
3927: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3929: Neighbor-wise Collective
3931: Input Parameters:
3932: + A - the factored matrix
3933: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3935: Output Parameter:
3936: . X - the result matrix (dense matrix)
3938: Level: developer
3940: Note:
3941: 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
3942: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3944: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3945: @*/
3946: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3947: {
3948: PetscFunctionBegin;
3953: PetscCheckSameComm(A, 1, Bt, 2);
3954: PetscCheckSameComm(A, 1, X, 3);
3956: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3957: 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);
3958: 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);
3959: 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");
3960: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3961: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3962: MatCheckPreallocated(A, 1);
3964: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3965: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3966: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3967: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3968: PetscFunctionReturn(PETSC_SUCCESS);
3969: }
3971: /*@
3972: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3973: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3975: Neighbor-wise Collective
3977: Input Parameters:
3978: + mat - the factored matrix
3979: - b - the right-hand-side vector
3981: Output Parameter:
3982: . x - the result vector
3984: Level: developer
3986: Notes:
3987: `MatSolve()` should be used for most applications, as it performs
3988: a forward solve followed by a backward solve.
3990: The vectors `b` and `x` cannot be the same, i.e., one cannot
3991: call `MatForwardSolve`(A,x,x).
3993: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3994: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3995: `MatForwardSolve()` solves $U^T*D y = b$, and
3996: `MatBackwardSolve()` solves $U x = y$.
3997: Thus they do not provide a symmetric preconditioner.
3999: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
4000: @*/
4001: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
4002: {
4003: PetscFunctionBegin;
4008: PetscCheckSameComm(mat, 1, b, 2);
4009: PetscCheckSameComm(mat, 1, x, 3);
4010: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4011: 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);
4012: 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);
4013: PetscCheck(mat->rmap->n == b->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, b->map->n);
4014: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4015: MatCheckPreallocated(mat, 1);
4017: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
4018: PetscUseTypeMethod(mat, forwardsolve, b, x);
4019: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
4020: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4021: PetscFunctionReturn(PETSC_SUCCESS);
4022: }
4024: /*@
4025: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
4026: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4028: Neighbor-wise Collective
4030: Input Parameters:
4031: + mat - the factored matrix
4032: - b - the right-hand-side vector
4034: Output Parameter:
4035: . x - the result vector
4037: Level: developer
4039: Notes:
4040: `MatSolve()` should be used for most applications, as it performs
4041: a forward solve followed by a backward solve.
4043: The vectors `b` and `x` cannot be the same. I.e., one cannot
4044: call `MatBackwardSolve`(A,x,x).
4046: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4047: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4048: `MatForwardSolve()` solves $U^T*D y = b$, and
4049: `MatBackwardSolve()` solves $U x = y$.
4050: Thus they do not provide a symmetric preconditioner.
4052: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
4053: @*/
4054: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
4055: {
4056: PetscFunctionBegin;
4061: PetscCheckSameComm(mat, 1, b, 2);
4062: PetscCheckSameComm(mat, 1, x, 3);
4063: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4064: 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);
4065: 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);
4066: 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);
4067: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4068: MatCheckPreallocated(mat, 1);
4070: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
4071: PetscUseTypeMethod(mat, backwardsolve, b, x);
4072: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
4073: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4074: PetscFunctionReturn(PETSC_SUCCESS);
4075: }
4077: /*@
4078: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
4080: Neighbor-wise Collective
4082: Input Parameters:
4083: + mat - the factored matrix
4084: . b - the right-hand-side vector
4085: - y - the vector to be added to
4087: Output Parameter:
4088: . x - the result vector
4090: Level: developer
4092: Note:
4093: The vectors `b` and `x` cannot be the same. I.e., one cannot
4094: call `MatSolveAdd`(A,x,y,x).
4096: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4097: @*/
4098: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4099: {
4100: PetscScalar one = 1.0;
4101: Vec tmp;
4103: PetscFunctionBegin;
4109: PetscCheckSameComm(mat, 1, b, 2);
4110: PetscCheckSameComm(mat, 1, y, 3);
4111: PetscCheckSameComm(mat, 1, x, 4);
4112: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4113: 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);
4114: 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);
4115: 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);
4116: 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);
4117: 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);
4118: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4119: MatCheckPreallocated(mat, 1);
4121: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4122: PetscCall(VecFlag(x, mat->factorerrortype));
4123: if (mat->factorerrortype) {
4124: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4125: } else if (mat->ops->solveadd) {
4126: PetscUseTypeMethod(mat, solveadd, b, y, x);
4127: } else {
4128: /* do the solve then the add manually */
4129: if (x != y) {
4130: PetscCall(MatSolve(mat, b, x));
4131: PetscCall(VecAXPY(x, one, y));
4132: } else {
4133: PetscCall(VecDuplicate(x, &tmp));
4134: PetscCall(VecCopy(x, tmp));
4135: PetscCall(MatSolve(mat, b, x));
4136: PetscCall(VecAXPY(x, one, tmp));
4137: PetscCall(VecDestroy(&tmp));
4138: }
4139: }
4140: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4141: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4142: PetscFunctionReturn(PETSC_SUCCESS);
4143: }
4145: /*@
4146: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4148: Neighbor-wise Collective
4150: Input Parameters:
4151: + mat - the factored matrix
4152: - b - the right-hand-side vector
4154: Output Parameter:
4155: . x - the result vector
4157: Level: developer
4159: Notes:
4160: The vectors `b` and `x` cannot be the same. I.e., one cannot
4161: call `MatSolveTranspose`(A,x,x).
4163: Most users should employ the `KSP` interface for linear solvers
4164: instead of working directly with matrix algebra routines such as this.
4165: See, e.g., `KSPCreate()`.
4167: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4168: @*/
4169: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4170: {
4171: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4173: PetscFunctionBegin;
4178: PetscCheckSameComm(mat, 1, b, 2);
4179: PetscCheckSameComm(mat, 1, x, 3);
4180: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4181: 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);
4182: 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);
4183: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4184: MatCheckPreallocated(mat, 1);
4185: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4186: PetscCall(VecFlag(x, mat->factorerrortype));
4187: if (mat->factorerrortype) {
4188: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4189: } else {
4190: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4191: PetscCall((*f)(mat, b, x));
4192: }
4193: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4194: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4195: PetscFunctionReturn(PETSC_SUCCESS);
4196: }
4198: /*@
4199: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4200: factored matrix.
4202: Neighbor-wise Collective
4204: Input Parameters:
4205: + mat - the factored matrix
4206: . b - the right-hand-side vector
4207: - y - the vector to be added to
4209: Output Parameter:
4210: . x - the result vector
4212: Level: developer
4214: Note:
4215: The vectors `b` and `x` cannot be the same. I.e., one cannot
4216: call `MatSolveTransposeAdd`(A,x,y,x).
4218: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4219: @*/
4220: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4221: {
4222: PetscScalar one = 1.0;
4223: Vec tmp;
4224: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4226: PetscFunctionBegin;
4232: PetscCheckSameComm(mat, 1, b, 2);
4233: PetscCheckSameComm(mat, 1, y, 3);
4234: PetscCheckSameComm(mat, 1, x, 4);
4235: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4236: 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);
4237: 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);
4238: 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);
4239: 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);
4240: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4241: MatCheckPreallocated(mat, 1);
4243: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4244: PetscCall(VecFlag(x, mat->factorerrortype));
4245: if (mat->factorerrortype) {
4246: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4247: } else if (f) {
4248: PetscCall((*f)(mat, b, y, x));
4249: } else {
4250: /* do the solve then the add manually */
4251: if (x != y) {
4252: PetscCall(MatSolveTranspose(mat, b, x));
4253: PetscCall(VecAXPY(x, one, y));
4254: } else {
4255: PetscCall(VecDuplicate(x, &tmp));
4256: PetscCall(VecCopy(x, tmp));
4257: PetscCall(MatSolveTranspose(mat, b, x));
4258: PetscCall(VecAXPY(x, one, tmp));
4259: PetscCall(VecDestroy(&tmp));
4260: }
4261: }
4262: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4263: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4264: PetscFunctionReturn(PETSC_SUCCESS);
4265: }
4267: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4268: /*@
4269: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4271: Neighbor-wise Collective
4273: Input Parameters:
4274: + mat - the matrix
4275: . b - the right-hand side
4276: . omega - the relaxation factor
4277: . flag - flag indicating the type of SOR (see below)
4278: . shift - diagonal shift
4279: . its - the number of iterations
4280: - lits - the number of local iterations
4282: Output Parameter:
4283: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4285: SOR Flags:
4286: + `SOR_FORWARD_SWEEP` - forward SOR
4287: . `SOR_BACKWARD_SWEEP` - backward SOR
4288: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4289: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4290: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4291: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4292: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4293: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies upper/lower triangular part of matrix to vector (with `omega`)
4294: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4296: Level: developer
4298: Notes:
4299: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4300: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4301: on each processor.
4303: Application programmers will not generally use `MatSOR()` directly,
4304: but instead will employ `PCSOR` or `PCEISENSTAT`
4306: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with inodes, this does a block SOR smoothing, otherwise it does a pointwise smoothing.
4307: For `MATAIJ` matrices with inodes, the block sizes are determined by the inode sizes, not the block size set with `MatSetBlockSize()`
4309: Vectors `x` and `b` CANNOT be the same
4311: The flags are implemented as bitwise inclusive or operations.
4312: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4313: to specify a zero initial guess for SSOR.
4315: Developer Note:
4316: We should add block SOR support for `MATAIJ` matrices with block size set to greater than one and no inodes
4318: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4319: @*/
4320: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4321: {
4322: PetscFunctionBegin;
4327: PetscCheckSameComm(mat, 1, b, 2);
4328: PetscCheckSameComm(mat, 1, x, 8);
4329: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4330: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4331: 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);
4332: 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);
4333: 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);
4334: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4335: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4336: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4338: MatCheckPreallocated(mat, 1);
4339: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4340: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4341: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4342: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4343: PetscFunctionReturn(PETSC_SUCCESS);
4344: }
4346: /*
4347: Default matrix copy routine.
4348: */
4349: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4350: {
4351: PetscInt i, rstart = 0, rend = 0, nz;
4352: const PetscInt *cwork;
4353: const PetscScalar *vwork;
4355: PetscFunctionBegin;
4356: if (B->assembled) PetscCall(MatZeroEntries(B));
4357: if (str == SAME_NONZERO_PATTERN) {
4358: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4359: for (i = rstart; i < rend; i++) {
4360: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4361: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4362: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4363: }
4364: } else {
4365: PetscCall(MatAYPX(B, 0.0, A, str));
4366: }
4367: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4368: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4369: PetscFunctionReturn(PETSC_SUCCESS);
4370: }
4372: /*@
4373: MatCopy - Copies a matrix to another matrix.
4375: Collective
4377: Input Parameters:
4378: + A - the matrix
4379: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4381: Output Parameter:
4382: . B - where the copy is put
4384: Level: intermediate
4386: Notes:
4387: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4389: `MatCopy()` copies the matrix entries of a matrix to another existing
4390: matrix (after first zeroing the second matrix). A related routine is
4391: `MatConvert()`, which first creates a new matrix and then copies the data.
4393: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4394: @*/
4395: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4396: {
4397: PetscInt i;
4399: PetscFunctionBegin;
4404: PetscCheckSameComm(A, 1, B, 2);
4405: MatCheckPreallocated(B, 2);
4406: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4407: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4408: 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,
4409: A->cmap->N, B->cmap->N);
4410: MatCheckPreallocated(A, 1);
4411: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4413: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4414: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4415: else PetscCall(MatCopy_Basic(A, B, str));
4417: B->stencil.dim = A->stencil.dim;
4418: B->stencil.noc = A->stencil.noc;
4419: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4420: B->stencil.dims[i] = A->stencil.dims[i];
4421: B->stencil.starts[i] = A->stencil.starts[i];
4422: }
4424: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4425: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4426: PetscFunctionReturn(PETSC_SUCCESS);
4427: }
4429: /*@
4430: MatConvert - Converts a matrix to another matrix, either of the same
4431: or different type.
4433: Collective
4435: Input Parameters:
4436: + mat - the matrix
4437: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4438: same type as the original matrix.
4439: - reuse - denotes if the destination matrix is to be created or reused.
4440: 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
4441: `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).
4443: Output Parameter:
4444: . M - pointer to place new matrix
4446: Level: intermediate
4448: Notes:
4449: `MatConvert()` first creates a new matrix and then copies the data from
4450: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4451: entries of one matrix to another already existing matrix context.
4453: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4454: the MPI communicator of the generated matrix is always the same as the communicator
4455: of the input matrix.
4457: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4458: @*/
4459: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4460: {
4461: PetscBool sametype, issame, flg;
4462: PetscBool3 issymmetric, ishermitian, isspd;
4463: char convname[256], mtype[256];
4464: Mat B;
4466: PetscFunctionBegin;
4469: PetscAssertPointer(M, 4);
4470: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4471: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4472: MatCheckPreallocated(mat, 1);
4474: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4475: if (flg) newtype = mtype;
4477: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4478: PetscCall(PetscStrcmp(newtype, "same", &issame));
4479: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4480: if (reuse == MAT_REUSE_MATRIX) {
4482: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4483: }
4485: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4486: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4487: PetscFunctionReturn(PETSC_SUCCESS);
4488: }
4490: /* Cache Mat options because some converters use MatHeaderReplace() */
4491: issymmetric = mat->symmetric;
4492: ishermitian = mat->hermitian;
4493: isspd = mat->spd;
4495: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4496: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4497: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4498: } else {
4499: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4500: const char *prefix[3] = {"seq", "mpi", ""};
4501: PetscInt i;
4502: /*
4503: Order of precedence:
4504: 0) See if newtype is a superclass of the current matrix.
4505: 1) See if a specialized converter is known to the current matrix.
4506: 2) See if a specialized converter is known to the desired matrix class.
4507: 3) See if a good general converter is registered for the desired class
4508: (as of 6/27/03 only MATMPIADJ falls into this category).
4509: 4) See if a good general converter is known for the current matrix.
4510: 5) Use a really basic converter.
4511: */
4513: /* 0) See if newtype is a superclass of the current matrix.
4514: i.e mat is mpiaij and newtype is aij */
4515: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4516: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4517: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4518: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4519: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4520: if (flg) {
4521: if (reuse == MAT_INPLACE_MATRIX) {
4522: PetscCall(PetscInfo(mat, "Early return\n"));
4523: PetscFunctionReturn(PETSC_SUCCESS);
4524: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4525: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4526: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4527: PetscFunctionReturn(PETSC_SUCCESS);
4528: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4529: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4530: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4531: PetscFunctionReturn(PETSC_SUCCESS);
4532: }
4533: }
4534: }
4535: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4536: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4537: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4538: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4539: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4540: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4541: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4542: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4543: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4544: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4545: if (conv) goto foundconv;
4546: }
4548: /* 2) See if a specialized converter is known to the desired matrix class. */
4549: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4550: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4551: PetscCall(MatSetType(B, newtype));
4552: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4553: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4554: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4555: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4556: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4557: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4558: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4559: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4560: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4561: if (conv) {
4562: PetscCall(MatDestroy(&B));
4563: goto foundconv;
4564: }
4565: }
4567: /* 3) See if a good general converter is registered for the desired class */
4568: conv = B->ops->convertfrom;
4569: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4570: PetscCall(MatDestroy(&B));
4571: if (conv) goto foundconv;
4573: /* 4) See if a good general converter is known for the current matrix */
4574: if (mat->ops->convert) conv = mat->ops->convert;
4575: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4576: if (conv) goto foundconv;
4578: /* 5) Use a really basic converter. */
4579: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4580: conv = MatConvert_Basic;
4582: foundconv:
4583: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4584: PetscCall((*conv)(mat, newtype, reuse, M));
4585: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4586: /* the block sizes must be same if the mappings are copied over */
4587: (*M)->rmap->bs = mat->rmap->bs;
4588: (*M)->cmap->bs = mat->cmap->bs;
4589: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4590: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4591: (*M)->rmap->mapping = mat->rmap->mapping;
4592: (*M)->cmap->mapping = mat->cmap->mapping;
4593: }
4594: (*M)->stencil.dim = mat->stencil.dim;
4595: (*M)->stencil.noc = mat->stencil.noc;
4596: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4597: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4598: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4599: }
4600: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4601: }
4602: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4604: /* Reset Mat options */
4605: if (issymmetric != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PetscBool3ToBool(issymmetric)));
4606: if (ishermitian != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PetscBool3ToBool(ishermitian)));
4607: if (isspd != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SPD, PetscBool3ToBool(isspd)));
4608: PetscFunctionReturn(PETSC_SUCCESS);
4609: }
4611: /*@
4612: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4614: Not Collective
4616: Input Parameter:
4617: . mat - the matrix, must be a factored matrix
4619: Output Parameter:
4620: . type - the string name of the package (do not free this string)
4622: Level: intermediate
4624: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4625: @*/
4626: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4627: {
4628: PetscErrorCode (*conv)(Mat, MatSolverType *);
4630: PetscFunctionBegin;
4633: PetscAssertPointer(type, 2);
4634: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4635: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4636: if (conv) PetscCall((*conv)(mat, type));
4637: else *type = MATSOLVERPETSC;
4638: PetscFunctionReturn(PETSC_SUCCESS);
4639: }
4641: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4642: struct _MatSolverTypeForSpecifcType {
4643: MatType mtype;
4644: /* no entry for MAT_FACTOR_NONE */
4645: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4646: MatSolverTypeForSpecifcType next;
4647: };
4649: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4650: struct _MatSolverTypeHolder {
4651: char *name;
4652: MatSolverTypeForSpecifcType handlers;
4653: MatSolverTypeHolder next;
4654: };
4656: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4658: /*@C
4659: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4661: Logically Collective, No Fortran Support
4663: Input Parameters:
4664: + package - name of the package, for example `petsc` or `superlu`
4665: . mtype - the matrix type that works with this package
4666: . ftype - the type of factorization supported by the package
4667: - createfactor - routine that will create the factored matrix ready to be used
4669: Level: developer
4671: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4672: `MatGetFactor()`
4673: @*/
4674: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4675: {
4676: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4677: PetscBool flg;
4678: MatSolverTypeForSpecifcType inext, iprev = NULL;
4680: PetscFunctionBegin;
4681: PetscCall(MatInitializePackage());
4682: if (!next) {
4683: PetscCall(PetscNew(&MatSolverTypeHolders));
4684: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4685: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4686: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4687: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4688: PetscFunctionReturn(PETSC_SUCCESS);
4689: }
4690: while (next) {
4691: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4692: if (flg) {
4693: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4694: inext = next->handlers;
4695: while (inext) {
4696: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4697: if (flg) {
4698: inext->createfactor[(int)ftype - 1] = createfactor;
4699: PetscFunctionReturn(PETSC_SUCCESS);
4700: }
4701: iprev = inext;
4702: inext = inext->next;
4703: }
4704: PetscCall(PetscNew(&iprev->next));
4705: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4706: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4707: PetscFunctionReturn(PETSC_SUCCESS);
4708: }
4709: prev = next;
4710: next = next->next;
4711: }
4712: PetscCall(PetscNew(&prev->next));
4713: PetscCall(PetscStrallocpy(package, &prev->next->name));
4714: PetscCall(PetscNew(&prev->next->handlers));
4715: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4716: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4717: PetscFunctionReturn(PETSC_SUCCESS);
4718: }
4720: /*@C
4721: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4723: Input Parameters:
4724: + 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
4725: . ftype - the type of factorization supported by the type
4726: - mtype - the matrix type that works with this type
4728: Output Parameters:
4729: + foundtype - `PETSC_TRUE` if the type was registered
4730: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4731: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4733: Calling sequence of `createfactor`:
4734: + A - the matrix providing the factor matrix
4735: . ftype - the `MatFactorType` of the factor requested
4736: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4738: Level: developer
4740: Note:
4741: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4742: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4743: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4745: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4746: `MatInitializePackage()`
4747: @*/
4748: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4749: {
4750: MatSolverTypeHolder next = MatSolverTypeHolders;
4751: PetscBool flg;
4752: MatSolverTypeForSpecifcType inext;
4754: PetscFunctionBegin;
4755: if (foundtype) *foundtype = PETSC_FALSE;
4756: if (foundmtype) *foundmtype = PETSC_FALSE;
4757: if (createfactor) *createfactor = NULL;
4759: if (type) {
4760: while (next) {
4761: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4762: if (flg) {
4763: if (foundtype) *foundtype = PETSC_TRUE;
4764: inext = next->handlers;
4765: while (inext) {
4766: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4767: if (flg) {
4768: if (foundmtype) *foundmtype = PETSC_TRUE;
4769: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4770: PetscFunctionReturn(PETSC_SUCCESS);
4771: }
4772: inext = inext->next;
4773: }
4774: }
4775: next = next->next;
4776: }
4777: } else {
4778: while (next) {
4779: inext = next->handlers;
4780: while (inext) {
4781: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4782: if (flg && inext->createfactor[(int)ftype - 1]) {
4783: if (foundtype) *foundtype = PETSC_TRUE;
4784: if (foundmtype) *foundmtype = PETSC_TRUE;
4785: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4786: PetscFunctionReturn(PETSC_SUCCESS);
4787: }
4788: inext = inext->next;
4789: }
4790: next = next->next;
4791: }
4792: /* try with base classes inext->mtype */
4793: next = MatSolverTypeHolders;
4794: while (next) {
4795: inext = next->handlers;
4796: while (inext) {
4797: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4798: if (flg && inext->createfactor[(int)ftype - 1]) {
4799: if (foundtype) *foundtype = PETSC_TRUE;
4800: if (foundmtype) *foundmtype = PETSC_TRUE;
4801: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4802: PetscFunctionReturn(PETSC_SUCCESS);
4803: }
4804: inext = inext->next;
4805: }
4806: next = next->next;
4807: }
4808: }
4809: PetscFunctionReturn(PETSC_SUCCESS);
4810: }
4812: PetscErrorCode MatSolverTypeDestroy(void)
4813: {
4814: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4815: MatSolverTypeForSpecifcType inext, iprev;
4817: PetscFunctionBegin;
4818: while (next) {
4819: PetscCall(PetscFree(next->name));
4820: inext = next->handlers;
4821: while (inext) {
4822: PetscCall(PetscFree(inext->mtype));
4823: iprev = inext;
4824: inext = inext->next;
4825: PetscCall(PetscFree(iprev));
4826: }
4827: prev = next;
4828: next = next->next;
4829: PetscCall(PetscFree(prev));
4830: }
4831: MatSolverTypeHolders = NULL;
4832: PetscFunctionReturn(PETSC_SUCCESS);
4833: }
4835: /*@
4836: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4838: Logically Collective
4840: Input Parameter:
4841: . mat - the matrix
4843: Output Parameter:
4844: . flg - `PETSC_TRUE` if uses the ordering
4846: Level: developer
4848: Note:
4849: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4850: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4852: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4853: @*/
4854: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4855: {
4856: PetscFunctionBegin;
4857: *flg = mat->canuseordering;
4858: PetscFunctionReturn(PETSC_SUCCESS);
4859: }
4861: /*@
4862: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4864: Logically Collective
4866: Input Parameters:
4867: + mat - the matrix obtained with `MatGetFactor()`
4868: - ftype - the factorization type to be used
4870: Output Parameter:
4871: . otype - the preferred ordering type
4873: Level: developer
4875: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4876: @*/
4877: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4878: {
4879: PetscFunctionBegin;
4880: *otype = mat->preferredordering[ftype];
4881: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4882: PetscFunctionReturn(PETSC_SUCCESS);
4883: }
4885: /*@
4886: MatGetFactor - Returns a matrix suitable to calls to routines such as `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
4887: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, and `MatCholeskyFactorNumeric()`
4889: Collective
4891: Input Parameters:
4892: + mat - the matrix
4893: . type - name of solver type, for example, `superlu_dist`, `petsc` (to use PETSc's solver if it is available), if this is `NULL`, then the first result that satisfies
4894: the other criteria is returned
4895: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4897: Output Parameter:
4898: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4900: Options Database Keys:
4901: + -pc_factor_mat_solver_type type - choose the type at run time. When using `KSP` solvers
4902: . -pc_factor_mat_factor_on_host (true|false) - do matrix factorization on host (with device matrices). Default is doing it on device
4903: - -pc_factor_mat_solve_on_host (true|false) - do matrix solve on host (with device matrices). Default is doing it on device
4905: Level: intermediate
4907: Notes:
4908: Some of the packages, such as MUMPS, have options for controlling the factorization, these are in the form `-prefix_mat_packagename_packageoption`
4909: (for example, `-mat_mumps_icntl_6 1`) where `prefix` is normally set automatically from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly,
4910: without using a `PC`, one can set the prefix by
4911: calling `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4913: Some PETSc matrix formats have alternative solvers available that are provided by alternative packages
4914: such as PaStiX, SuperLU_DIST, MUMPS etc. PETSc must have been configured to use the external solver,
4915: using the corresponding `./configure` option such as `--download-package` or `--with-package-dir`.
4917: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4918: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4919: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4921: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4922: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4924: Developer Note:
4925: This should actually be called `MatCreateFactor()` since it creates a new factor object
4927: The `MatGetFactor()` implementations should not be accessing the PETSc options database or making other decisions about solver options,
4928: that should be delayed until the later operations. This is to ensure the correct options prefix has been set in the factor matrix.
4930: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4931: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`,
4932: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`,
4933: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
4934: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactorNumeric()`
4935: @*/
4936: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4937: {
4938: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4939: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4941: PetscFunctionBegin;
4945: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4946: MatCheckPreallocated(mat, 1);
4948: PetscCall(MatIsShell(mat, &shell));
4949: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4950: if (hasop) {
4951: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4952: PetscFunctionReturn(PETSC_SUCCESS);
4953: }
4955: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4956: if (!foundtype) {
4957: if (type) {
4958: 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],
4959: ((PetscObject)mat)->type_name, type);
4960: } else {
4961: 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);
4962: }
4963: }
4964: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4965: 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);
4967: PetscCall((*conv)(mat, ftype, f));
4968: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4969: PetscFunctionReturn(PETSC_SUCCESS);
4970: }
4972: /*@
4973: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4975: Not Collective
4977: Input Parameters:
4978: + mat - the matrix
4979: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4980: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4982: Output Parameter:
4983: . flg - PETSC_TRUE if the factorization is available
4985: Level: intermediate
4987: Notes:
4988: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4989: such as pastix, superlu, mumps etc.
4991: PETSc must have been ./configure to use the external solver, using the option --download-package
4993: Developer Note:
4994: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
4996: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4997: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4998: @*/
4999: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
5000: {
5001: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
5003: PetscFunctionBegin;
5005: PetscAssertPointer(flg, 4);
5007: *flg = PETSC_FALSE;
5008: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
5010: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5011: MatCheckPreallocated(mat, 1);
5013: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
5014: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
5015: PetscFunctionReturn(PETSC_SUCCESS);
5016: }
5018: /*@
5019: MatDuplicate - Duplicates a matrix including the non-zero structure.
5021: Collective
5023: Input Parameters:
5024: + mat - the matrix
5025: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
5026: See the manual page for `MatDuplicateOption()` for an explanation of these options.
5028: Output Parameter:
5029: . M - pointer to place new matrix
5031: Level: intermediate
5033: Notes:
5034: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
5036: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
5038: 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.
5040: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
5041: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
5042: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
5044: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
5045: @*/
5046: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
5047: {
5048: Mat B;
5049: VecType vtype;
5050: PetscInt i;
5051: PetscObject dm, container_h, container_d;
5052: PetscErrorCodeFn *viewf;
5054: PetscFunctionBegin;
5057: PetscAssertPointer(M, 3);
5058: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
5059: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5060: MatCheckPreallocated(mat, 1);
5062: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
5063: PetscUseTypeMethod(mat, duplicate, op, M);
5064: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
5065: B = *M;
5067: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
5068: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
5069: PetscCall(MatGetVecType(mat, &vtype));
5070: PetscCall(MatSetVecType(B, vtype));
5072: B->stencil.dim = mat->stencil.dim;
5073: B->stencil.noc = mat->stencil.noc;
5074: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
5075: B->stencil.dims[i] = mat->stencil.dims[i];
5076: B->stencil.starts[i] = mat->stencil.starts[i];
5077: }
5079: B->nooffproczerorows = mat->nooffproczerorows;
5080: B->nooffprocentries = mat->nooffprocentries;
5082: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5083: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5084: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5085: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5086: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5087: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5088: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5089: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5090: PetscFunctionReturn(PETSC_SUCCESS);
5091: }
5093: /*@
5094: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5096: Logically Collective
5098: Input Parameter:
5099: . mat - the matrix
5101: Output Parameter:
5102: . v - the diagonal of the matrix
5104: Level: intermediate
5106: Note:
5107: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5108: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5109: is larger than `ndiag`, the values of the remaining entries are unspecified.
5111: Currently only correct in parallel for square matrices.
5113: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5114: @*/
5115: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5116: {
5117: PetscFunctionBegin;
5121: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5122: MatCheckPreallocated(mat, 1);
5123: if (PetscDefined(USE_DEBUG)) {
5124: PetscInt nv, row, col, ndiag;
5126: PetscCall(VecGetLocalSize(v, &nv));
5127: PetscCall(MatGetLocalSize(mat, &row, &col));
5128: ndiag = PetscMin(row, col);
5129: 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);
5130: }
5132: PetscUseTypeMethod(mat, getdiagonal, v);
5133: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5134: PetscFunctionReturn(PETSC_SUCCESS);
5135: }
5137: /*@
5138: MatGetRowMin - Gets the minimum value (of the real part) of each
5139: row of the matrix
5141: Logically Collective
5143: Input Parameter:
5144: . mat - the matrix
5146: Output Parameters:
5147: + v - the vector for storing the maximums
5148: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5150: Level: intermediate
5152: Note:
5153: The result of this call are the same as if one converted the matrix to dense format
5154: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5156: This code is only implemented for a couple of matrix formats.
5158: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5159: `MatGetRowMax()`
5160: @*/
5161: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5162: {
5163: PetscFunctionBegin;
5167: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5169: if (!mat->cmap->N) {
5170: PetscCall(VecSet(v, PETSC_MAX_REAL));
5171: if (idx) {
5172: PetscInt i, m = mat->rmap->n;
5173: for (i = 0; i < m; i++) idx[i] = -1;
5174: }
5175: } else {
5176: MatCheckPreallocated(mat, 1);
5177: }
5178: PetscUseTypeMethod(mat, getrowmin, v, idx);
5179: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5180: PetscFunctionReturn(PETSC_SUCCESS);
5181: }
5183: /*@
5184: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5185: row of the matrix
5187: Logically Collective
5189: Input Parameter:
5190: . mat - the matrix
5192: Output Parameters:
5193: + v - the vector for storing the minimums
5194: - idx - the indices of the column found for each row (or `NULL` if not needed)
5196: Level: intermediate
5198: Notes:
5199: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5200: row is 0 (the first column).
5202: This code is only implemented for a couple of matrix formats.
5204: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5205: @*/
5206: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5207: {
5208: PetscFunctionBegin;
5212: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5213: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5215: if (!mat->cmap->N) {
5216: PetscCall(VecSet(v, 0.0));
5217: if (idx) {
5218: PetscInt i, m = mat->rmap->n;
5219: for (i = 0; i < m; i++) idx[i] = -1;
5220: }
5221: } else {
5222: MatCheckPreallocated(mat, 1);
5223: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5224: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5225: }
5226: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5227: PetscFunctionReturn(PETSC_SUCCESS);
5228: }
5230: /*@
5231: MatGetRowMax - Gets the maximum value (of the real part) of each
5232: row of the matrix
5234: Logically Collective
5236: Input Parameter:
5237: . mat - the matrix
5239: Output Parameters:
5240: + v - the vector for storing the maximums
5241: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5243: Level: intermediate
5245: Notes:
5246: The result of this call are the same as if one converted the matrix to dense format
5247: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5249: This code is only implemented for a couple of matrix formats.
5251: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5252: @*/
5253: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5254: {
5255: PetscFunctionBegin;
5259: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5261: if (!mat->cmap->N) {
5262: PetscCall(VecSet(v, PETSC_MIN_REAL));
5263: if (idx) {
5264: PetscInt i, m = mat->rmap->n;
5265: for (i = 0; i < m; i++) idx[i] = -1;
5266: }
5267: } else {
5268: MatCheckPreallocated(mat, 1);
5269: PetscUseTypeMethod(mat, getrowmax, v, idx);
5270: }
5271: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5272: PetscFunctionReturn(PETSC_SUCCESS);
5273: }
5275: /*@
5276: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5277: row of the matrix
5279: Logically Collective
5281: Input Parameter:
5282: . mat - the matrix
5284: Output Parameters:
5285: + v - the vector for storing the maximums
5286: - idx - the indices of the column found for each row (or `NULL` if not needed)
5288: Level: intermediate
5290: Notes:
5291: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5292: row is 0 (the first column).
5294: This code is only implemented for a couple of matrix formats.
5296: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5297: @*/
5298: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5299: {
5300: PetscFunctionBegin;
5304: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5306: if (!mat->cmap->N) {
5307: PetscCall(VecSet(v, 0.0));
5308: if (idx) {
5309: PetscInt i, m = mat->rmap->n;
5310: for (i = 0; i < m; i++) idx[i] = -1;
5311: }
5312: } else {
5313: MatCheckPreallocated(mat, 1);
5314: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5315: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5316: }
5317: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5318: PetscFunctionReturn(PETSC_SUCCESS);
5319: }
5321: /*@
5322: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5324: Logically Collective
5326: Input Parameter:
5327: . mat - the matrix
5329: Output Parameter:
5330: . v - the vector for storing the sum
5332: Level: intermediate
5334: This code is only implemented for a couple of matrix formats.
5336: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5337: @*/
5338: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5339: {
5340: PetscFunctionBegin;
5344: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5346: if (!mat->cmap->N) PetscCall(VecSet(v, 0.0));
5347: else {
5348: MatCheckPreallocated(mat, 1);
5349: PetscUseTypeMethod(mat, getrowsumabs, v);
5350: }
5351: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5352: PetscFunctionReturn(PETSC_SUCCESS);
5353: }
5355: /*@
5356: MatGetRowSum - Gets the sum of each row of the matrix
5358: Logically or Neighborhood Collective
5360: Input Parameter:
5361: . mat - the matrix
5363: Output Parameter:
5364: . v - the vector for storing the sum of rows
5366: Level: intermediate
5368: Note:
5369: This code is slow since it is not currently specialized for different formats
5371: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5372: @*/
5373: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5374: {
5375: Vec ones;
5377: PetscFunctionBegin;
5381: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5382: MatCheckPreallocated(mat, 1);
5383: PetscCall(MatCreateVecs(mat, &ones, NULL));
5384: PetscCall(VecSet(ones, 1.));
5385: PetscCall(MatMult(mat, ones, v));
5386: PetscCall(VecDestroy(&ones));
5387: PetscFunctionReturn(PETSC_SUCCESS);
5388: }
5390: /*@
5391: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5392: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5394: Collective
5396: Input Parameter:
5397: . mat - the matrix to provide the transpose
5399: Output Parameter:
5400: . 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
5402: Level: advanced
5404: Note:
5405: 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
5406: routine allows bypassing that call.
5408: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5409: @*/
5410: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5411: {
5412: MatParentState *rb = NULL;
5414: PetscFunctionBegin;
5415: PetscCall(PetscNew(&rb));
5416: rb->id = ((PetscObject)mat)->id;
5417: rb->state = 0;
5418: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5419: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5420: PetscFunctionReturn(PETSC_SUCCESS);
5421: }
5423: static PetscErrorCode MatTranspose_Private(Mat mat, MatReuse reuse, Mat *B, PetscBool conjugate)
5424: {
5425: PetscContainer rB = NULL;
5426: MatParentState *rb = NULL;
5427: PetscErrorCode (*f)(Mat, MatReuse, Mat *) = NULL;
5429: PetscFunctionBegin;
5432: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5433: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5434: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5435: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5436: MatCheckPreallocated(mat, 1);
5437: if (reuse == MAT_REUSE_MATRIX) {
5438: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5439: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5440: PetscCall(PetscContainerGetPointer(rB, &rb));
5441: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5442: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5443: }
5445: if (conjugate) {
5446: f = mat->ops->hermitiantranspose;
5447: if (f) PetscCall((*f)(mat, reuse, B));
5448: }
5449: if (!f && !(reuse == MAT_INPLACE_MATRIX && mat->hermitian == PETSC_BOOL3_TRUE && conjugate)) {
5450: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5451: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5452: PetscUseTypeMethod(mat, transpose, reuse, B);
5453: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5454: }
5455: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5456: if (conjugate) PetscCall(MatConjugate(*B));
5457: }
5459: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5460: if (reuse != MAT_INPLACE_MATRIX) {
5461: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5462: PetscCall(PetscContainerGetPointer(rB, &rb));
5463: rb->state = ((PetscObject)mat)->state;
5464: rb->nonzerostate = mat->nonzerostate;
5465: }
5466: PetscFunctionReturn(PETSC_SUCCESS);
5467: }
5469: /*@
5470: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5472: Collective
5474: Input Parameters:
5475: + mat - the matrix to transpose
5476: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5478: Output Parameter:
5479: . B - the transpose of the matrix
5481: Level: intermediate
5483: Notes:
5484: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5486: `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
5487: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5489: 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.
5491: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5492: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5494: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5496: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5498: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5499: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5500: @*/
5501: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5502: {
5503: PetscFunctionBegin;
5504: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_FALSE));
5505: PetscFunctionReturn(PETSC_SUCCESS);
5506: }
5508: /*@
5509: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5511: Collective
5513: Input Parameter:
5514: . A - the matrix to transpose
5516: Output Parameter:
5517: . 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
5518: numerical portion.
5520: Level: intermediate
5522: Note:
5523: This is not supported for many matrix types, use `MatTranspose()` in those cases
5525: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5526: @*/
5527: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5528: {
5529: PetscFunctionBegin;
5532: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5533: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5534: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5535: PetscUseTypeMethod(A, transposesymbolic, B);
5536: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5538: PetscCall(MatTransposeSetPrecursor(A, *B));
5539: PetscFunctionReturn(PETSC_SUCCESS);
5540: }
5542: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5543: {
5544: PetscContainer rB;
5545: MatParentState *rb;
5547: PetscFunctionBegin;
5550: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5551: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5552: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5553: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5554: PetscCall(PetscContainerGetPointer(rB, &rb));
5555: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5556: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5557: PetscFunctionReturn(PETSC_SUCCESS);
5558: }
5560: /*@
5561: MatIsTranspose - Test whether a matrix is another one's transpose,
5562: or its own, in which case it tests symmetry.
5564: Collective
5566: Input Parameters:
5567: + A - the matrix to test
5568: . B - the matrix to test against, this can equal the first parameter
5569: - tol - tolerance, differences between entries smaller than this are counted as zero
5571: Output Parameter:
5572: . flg - the result
5574: Level: intermediate
5576: Notes:
5577: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5578: test involves parallel copies of the block off-diagonal parts of the matrix.
5580: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5581: @*/
5582: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5583: {
5584: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5586: PetscFunctionBegin;
5589: PetscAssertPointer(flg, 4);
5590: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5591: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5592: *flg = PETSC_FALSE;
5593: if (f && g) {
5594: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5595: PetscCall((*f)(A, B, tol, flg));
5596: } else {
5597: MatType mattype;
5599: PetscCall(MatGetType(f ? B : A, &mattype));
5600: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5601: }
5602: PetscFunctionReturn(PETSC_SUCCESS);
5603: }
5605: /*@
5606: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5608: Collective
5610: Input Parameters:
5611: + mat - the matrix to transpose and complex conjugate
5612: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5614: Output Parameter:
5615: . B - the Hermitian transpose
5617: Level: intermediate
5619: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5620: @*/
5621: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5622: {
5623: PetscFunctionBegin;
5624: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_TRUE));
5625: PetscFunctionReturn(PETSC_SUCCESS);
5626: }
5628: /*@
5629: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5631: Collective
5633: Input Parameters:
5634: + A - the matrix to test
5635: . B - the matrix to test against, this can equal the first parameter
5636: - tol - tolerance, differences between entries smaller than this are counted as zero
5638: Output Parameter:
5639: . flg - the result
5641: Level: intermediate
5643: Notes:
5644: Only available for `MATAIJ` matrices.
5646: The sequential algorithm
5647: has a running time of the order of the number of nonzeros; the parallel
5648: test involves parallel copies of the block off-diagonal parts of the matrix.
5650: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5651: @*/
5652: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5653: {
5654: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5656: PetscFunctionBegin;
5659: PetscAssertPointer(flg, 4);
5660: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5661: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5662: if (f && g) {
5663: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5664: PetscCall((*f)(A, B, tol, flg));
5665: } else {
5666: MatType mattype;
5668: PetscCall(MatGetType(f ? B : A, &mattype));
5669: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for Hermitian transpose", mattype);
5670: }
5671: PetscFunctionReturn(PETSC_SUCCESS);
5672: }
5674: /*@
5675: MatPermute - Creates a new matrix with rows and columns permuted from the
5676: original.
5678: Collective
5680: Input Parameters:
5681: + mat - the matrix to permute
5682: . row - row permutation, each processor supplies only the permutation for its rows
5683: - col - column permutation, each processor supplies only the permutation for its columns
5685: Output Parameter:
5686: . B - the permuted matrix
5688: Level: advanced
5690: Note:
5691: The index sets map from row/col of permuted matrix to row/col of original matrix.
5692: The index sets should be on the same communicator as mat and have the same local sizes.
5694: Developer Note:
5695: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5696: exploit the fact that row and col are permutations, consider implementing the
5697: more general `MatCreateSubMatrix()` instead.
5699: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5700: @*/
5701: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5702: {
5703: PetscFunctionBegin;
5708: PetscAssertPointer(B, 4);
5709: PetscCheckSameComm(mat, 1, row, 2);
5710: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5711: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5712: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5713: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5714: MatCheckPreallocated(mat, 1);
5716: if (mat->ops->permute) {
5717: PetscUseTypeMethod(mat, permute, row, col, B);
5718: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5719: } else {
5720: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5721: }
5722: PetscFunctionReturn(PETSC_SUCCESS);
5723: }
5725: /*@
5726: MatEqual - Compares two matrices.
5728: Collective
5730: Input Parameters:
5731: + A - the first matrix
5732: - B - the second matrix
5734: Output Parameter:
5735: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5737: Level: intermediate
5739: Note:
5740: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5741: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5743: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5744: @*/
5745: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5746: {
5747: PetscFunctionBegin;
5752: PetscAssertPointer(flg, 3);
5753: PetscCheckSameComm(A, 1, B, 2);
5754: MatCheckPreallocated(A, 1);
5755: MatCheckPreallocated(B, 2);
5756: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5757: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5758: 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,
5759: B->cmap->N);
5760: if (A->ops->equal && A->ops->equal == B->ops->equal) PetscUseTypeMethod(A, equal, B, flg);
5761: else PetscCall(MatMultEqual(A, B, 10, flg));
5762: PetscFunctionReturn(PETSC_SUCCESS);
5763: }
5765: /*@
5766: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5767: matrices that are stored as vectors. Either of the two scaling
5768: matrices can be `NULL`.
5770: Collective
5772: Input Parameters:
5773: + mat - the matrix to be scaled
5774: . l - the left scaling vector (or `NULL`)
5775: - r - the right scaling vector (or `NULL`)
5777: Level: intermediate
5779: Note:
5780: `MatDiagonalScale()` computes $A = LAR$, where
5781: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5782: The L scales the rows of the matrix, the R scales the columns of the matrix.
5784: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5785: @*/
5786: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5787: {
5788: PetscBool flg = PETSC_FALSE;
5790: PetscFunctionBegin;
5793: if (l) {
5795: PetscCheckSameComm(mat, 1, l, 2);
5796: }
5797: if (r) {
5799: PetscCheckSameComm(mat, 1, r, 3);
5800: }
5801: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5802: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5803: MatCheckPreallocated(mat, 1);
5804: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5806: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5807: PetscUseTypeMethod(mat, diagonalscale, l, r);
5808: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5809: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5810: if (l != r && (PetscBool3ToBool(mat->symmetric) || PetscBool3ToBool(mat->hermitian))) {
5811: if (!PetscDefined(USE_COMPLEX) || PetscBool3ToBool(mat->symmetric)) {
5812: if (l && r) PetscCall(VecEqual(l, r, &flg));
5813: if (!flg) {
5814: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5815: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format, left and right scaling vectors must be the same");
5816: mat->symmetric = mat->spd = PETSC_BOOL3_FALSE;
5817: if (!PetscDefined(USE_COMPLEX)) mat->hermitian = PETSC_BOOL3_FALSE;
5818: else mat->hermitian = PETSC_BOOL3_UNKNOWN;
5819: }
5820: }
5821: if (PetscDefined(USE_COMPLEX) && PetscBool3ToBool(mat->hermitian)) {
5822: flg = PETSC_FALSE;
5823: if (l && r) {
5824: Vec conjugate;
5826: PetscCall(VecDuplicate(l, &conjugate));
5827: PetscCall(VecCopy(l, conjugate));
5828: PetscCall(VecConjugate(conjugate));
5829: PetscCall(VecEqual(conjugate, r, &flg));
5830: PetscCall(VecDestroy(&conjugate));
5831: }
5832: if (!flg) {
5833: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5834: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format and Hermitian matrix, left and right scaling vectors must be conjugate one of the other");
5835: mat->hermitian = PETSC_BOOL3_FALSE;
5836: mat->symmetric = mat->spd = PETSC_BOOL3_UNKNOWN;
5837: }
5838: }
5839: }
5840: PetscFunctionReturn(PETSC_SUCCESS);
5841: }
5843: /*@
5844: MatScale - Scales all elements of a matrix by a given number.
5846: Logically Collective
5848: Input Parameters:
5849: + mat - the matrix to be scaled
5850: - a - the scaling value
5852: Level: intermediate
5854: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5855: @*/
5856: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5857: {
5858: PetscFunctionBegin;
5861: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5862: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5864: MatCheckPreallocated(mat, 1);
5866: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5867: if (a != (PetscScalar)1.0) {
5868: PetscUseTypeMethod(mat, scale, a);
5869: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5870: }
5871: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5872: PetscFunctionReturn(PETSC_SUCCESS);
5873: }
5875: /*@
5876: MatNorm - Calculates various norms of a matrix.
5878: Collective
5880: Input Parameters:
5881: + mat - the matrix
5882: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5884: Output Parameter:
5885: . nrm - the resulting norm
5887: Level: intermediate
5889: .seealso: [](ch_matrices), `Mat`
5890: @*/
5891: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5892: {
5893: PetscFunctionBegin;
5896: PetscAssertPointer(nrm, 3);
5898: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5899: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5900: MatCheckPreallocated(mat, 1);
5902: PetscUseTypeMethod(mat, norm, type, nrm);
5903: PetscFunctionReturn(PETSC_SUCCESS);
5904: }
5906: /*
5907: This variable is used to prevent counting of MatAssemblyBegin() that
5908: are called from within a MatAssemblyEnd().
5909: */
5910: static PetscInt MatAssemblyEnd_InUse = 0;
5911: /*@
5912: MatAssemblyBegin - Begins assembling the matrix. This routine should
5913: be called after completing all calls to `MatSetValues()`.
5915: Collective
5917: Input Parameters:
5918: + mat - the matrix
5919: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5921: Level: beginner
5923: Notes:
5924: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5925: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5927: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5928: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5929: using the matrix.
5931: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5932: 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
5933: a global collective operation requiring all processes that share the matrix.
5935: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5936: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5937: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5939: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5940: @*/
5941: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5942: {
5943: PetscFunctionBegin;
5946: MatCheckPreallocated(mat, 1);
5947: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5948: if (mat->assembled) {
5949: mat->was_assembled = PETSC_TRUE;
5950: mat->assembled = PETSC_FALSE;
5951: }
5953: if (!MatAssemblyEnd_InUse) {
5954: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5955: PetscTryTypeMethod(mat, assemblybegin, type);
5956: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5957: } else PetscTryTypeMethod(mat, assemblybegin, type);
5958: PetscFunctionReturn(PETSC_SUCCESS);
5959: }
5961: /*@
5962: MatAssembled - Indicates if a matrix has been assembled and is ready for
5963: use; for example, in matrix-vector product.
5965: Not Collective
5967: Input Parameter:
5968: . mat - the matrix
5970: Output Parameter:
5971: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5973: Level: advanced
5975: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5976: @*/
5977: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5978: {
5979: PetscFunctionBegin;
5981: PetscAssertPointer(assembled, 2);
5982: *assembled = mat->assembled;
5983: PetscFunctionReturn(PETSC_SUCCESS);
5984: }
5986: /*@
5987: MatAssemblyEnd - Completes assembling the matrix. This routine should
5988: be called after `MatAssemblyBegin()`.
5990: Collective
5992: Input Parameters:
5993: + mat - the matrix
5994: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5996: Options Database Key:
5997: . -mat_view [viewertype][:...] - option name and values. See `MatViewFromOptions()`/`PetscObjectViewFromOptions()` for the possible arguments
5999: Level: beginner
6001: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`,
6002: `MatViewFromOptions()`, `PetscObjectViewFromOptions()`
6003: @*/
6004: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
6005: {
6006: static PetscInt inassm = 0;
6007: PetscBool flg = PETSC_FALSE;
6009: PetscFunctionBegin;
6013: inassm++;
6014: MatAssemblyEnd_InUse++;
6015: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
6016: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
6017: PetscTryTypeMethod(mat, assemblyend, type);
6018: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
6019: } else PetscTryTypeMethod(mat, assemblyend, type);
6021: /* Flush assembly is not a true assembly */
6022: if (type != MAT_FLUSH_ASSEMBLY) {
6023: if (mat->num_ass) {
6024: if (!mat->symmetry_eternal) {
6025: mat->symmetric = PETSC_BOOL3_UNKNOWN;
6026: mat->hermitian = PETSC_BOOL3_UNKNOWN;
6027: }
6028: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
6029: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
6030: }
6031: mat->num_ass++;
6032: mat->assembled = PETSC_TRUE;
6033: mat->ass_nonzerostate = mat->nonzerostate;
6034: }
6036: mat->insertmode = NOT_SET_VALUES;
6037: MatAssemblyEnd_InUse--;
6038: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6039: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
6040: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6042: if (mat->checksymmetryonassembly) {
6043: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
6044: if (flg) {
6045: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6046: } else {
6047: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6048: }
6049: }
6050: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
6051: }
6052: inassm--;
6053: PetscFunctionReturn(PETSC_SUCCESS);
6054: }
6056: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
6057: /*@
6058: MatSetOption - Sets a parameter option for a matrix. Some options
6059: may be specific to certain storage formats. Some options
6060: determine how values will be inserted (or added). Sorted,
6061: row-oriented input will generally assemble the fastest. The default
6062: is row-oriented.
6064: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
6066: Input Parameters:
6067: + mat - the matrix
6068: . op - the option, one of those listed below (and possibly others),
6069: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6071: Options Describing Matrix Structure:
6072: + `MAT_SPD` - symmetric positive definite
6073: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
6074: . `MAT_HERMITIAN` - transpose is the complex conjugation
6075: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
6076: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
6077: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
6078: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
6080: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
6081: do not need to be computed (usually at a high cost)
6083: Options For Use with `MatSetValues()`:
6084: Insert a logically dense subblock, which can be
6085: . `MAT_ROW_ORIENTED` - row-oriented (default)
6087: These options reflect the data you pass in with `MatSetValues()`; it has
6088: nothing to do with how the data is stored internally in the matrix
6089: data structure.
6091: When (re)assembling a matrix, we can restrict the input for
6092: efficiency/debugging purposes. These options include
6093: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
6094: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
6095: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
6096: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
6097: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
6098: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
6099: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
6100: performance for very large process counts.
6101: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
6102: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
6103: functions, instead sending only neighbor messages.
6105: Level: intermediate
6107: Notes:
6108: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
6110: Some options are relevant only for particular matrix types and
6111: are thus ignored by others. Other options are not supported by
6112: certain matrix types and will generate an error message if set.
6114: If using Fortran to compute a matrix, one may need to
6115: use the column-oriented option (or convert to the row-oriented
6116: format).
6118: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6119: that would generate a new entry in the nonzero structure is instead
6120: ignored. Thus, if memory has not already been allocated for this particular
6121: data, then the insertion is ignored. For dense matrices, in which
6122: the entire array is allocated, no entries are ever ignored.
6123: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6125: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6126: that would generate a new entry in the nonzero structure instead produces
6127: 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
6129: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6130: that would generate a new entry that has not been preallocated will
6131: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6132: only.) This is a useful flag when debugging matrix memory preallocation.
6133: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6135: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6136: other processors should be dropped, rather than stashed.
6137: This is useful if you know that the "owning" processor is also
6138: always generating the correct matrix entries, so that PETSc need
6139: not transfer duplicate entries generated on another processor.
6141: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6142: searches during matrix assembly. When this flag is set, the hash table
6143: is created during the first matrix assembly. This hash table is
6144: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6145: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6146: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6147: supported by `MATMPIBAIJ` format only.
6149: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6150: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6152: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6153: a zero location in the matrix
6155: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6157: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6158: zero row routines and thus improves performance for very large process counts.
6160: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6161: part of the matrix (since they should match the upper triangular part).
6163: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6164: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6165: with finite difference schemes with non-periodic boundary conditions.
6167: Developer Note:
6168: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6169: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6170: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6171: not changed.
6173: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6174: @*/
6175: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6176: {
6177: PetscFunctionBegin;
6179: if (op > 0) {
6182: }
6184: 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);
6186: switch (op) {
6187: case MAT_FORCE_DIAGONAL_ENTRIES:
6188: mat->force_diagonals = flg;
6189: PetscFunctionReturn(PETSC_SUCCESS);
6190: case MAT_NO_OFF_PROC_ENTRIES:
6191: mat->nooffprocentries = flg;
6192: PetscFunctionReturn(PETSC_SUCCESS);
6193: case MAT_SUBSET_OFF_PROC_ENTRIES:
6194: mat->assembly_subset = flg;
6195: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6196: #if !defined(PETSC_HAVE_MPIUNI)
6197: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6198: #endif
6199: mat->stash.first_assembly_done = PETSC_FALSE;
6200: }
6201: PetscFunctionReturn(PETSC_SUCCESS);
6202: case MAT_NO_OFF_PROC_ZERO_ROWS:
6203: mat->nooffproczerorows = flg;
6204: PetscFunctionReturn(PETSC_SUCCESS);
6205: case MAT_SPD:
6206: if (flg) {
6207: mat->spd = PETSC_BOOL3_TRUE;
6208: mat->symmetric = PETSC_BOOL3_TRUE;
6209: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6210: #if !defined(PETSC_USE_COMPLEX)
6211: mat->hermitian = PETSC_BOOL3_TRUE;
6212: #endif
6213: } else {
6214: mat->spd = PETSC_BOOL3_FALSE;
6215: }
6216: break;
6217: case MAT_SYMMETRIC:
6218: mat->symmetric = PetscBoolToBool3(flg);
6219: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6220: #if !defined(PETSC_USE_COMPLEX)
6221: mat->hermitian = PetscBoolToBool3(flg);
6222: #endif
6223: break;
6224: case MAT_HERMITIAN:
6225: mat->hermitian = PetscBoolToBool3(flg);
6226: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6227: #if !defined(PETSC_USE_COMPLEX)
6228: mat->symmetric = PetscBoolToBool3(flg);
6229: #endif
6230: break;
6231: case MAT_STRUCTURALLY_SYMMETRIC:
6232: mat->structurally_symmetric = PetscBoolToBool3(flg);
6233: break;
6234: case MAT_SYMMETRY_ETERNAL:
6235: 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");
6236: mat->symmetry_eternal = flg;
6237: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6238: break;
6239: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6240: 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");
6241: mat->structural_symmetry_eternal = flg;
6242: break;
6243: case MAT_SPD_ETERNAL:
6244: 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");
6245: mat->spd_eternal = flg;
6246: if (flg) {
6247: mat->structural_symmetry_eternal = PETSC_TRUE;
6248: mat->symmetry_eternal = PETSC_TRUE;
6249: }
6250: break;
6251: case MAT_STRUCTURE_ONLY:
6252: mat->structure_only = flg;
6253: break;
6254: case MAT_SORTED_FULL:
6255: mat->sortedfull = flg;
6256: break;
6257: default:
6258: break;
6259: }
6260: PetscTryTypeMethod(mat, setoption, op, flg);
6261: PetscFunctionReturn(PETSC_SUCCESS);
6262: }
6264: /*@
6265: MatGetOption - Gets a parameter option that has been set for a matrix.
6267: Logically Collective
6269: Input Parameters:
6270: + mat - the matrix
6271: - op - the option, this only responds to certain options, check the code for which ones
6273: Output Parameter:
6274: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6276: Level: intermediate
6278: Notes:
6279: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6281: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6282: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6284: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6285: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6286: @*/
6287: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6288: {
6289: PetscFunctionBegin;
6293: 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);
6294: 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()");
6296: switch (op) {
6297: case MAT_NO_OFF_PROC_ENTRIES:
6298: *flg = mat->nooffprocentries;
6299: break;
6300: case MAT_NO_OFF_PROC_ZERO_ROWS:
6301: *flg = mat->nooffproczerorows;
6302: break;
6303: case MAT_SYMMETRIC:
6304: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6305: break;
6306: case MAT_HERMITIAN:
6307: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6308: break;
6309: case MAT_STRUCTURALLY_SYMMETRIC:
6310: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6311: break;
6312: case MAT_SPD:
6313: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6314: break;
6315: case MAT_SYMMETRY_ETERNAL:
6316: *flg = mat->symmetry_eternal;
6317: break;
6318: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6319: *flg = mat->symmetry_eternal;
6320: break;
6321: default:
6322: break;
6323: }
6324: PetscFunctionReturn(PETSC_SUCCESS);
6325: }
6327: /*@
6328: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6329: this routine retains the old nonzero structure.
6331: Logically Collective
6333: Input Parameter:
6334: . mat - the matrix
6336: Level: intermediate
6338: Note:
6339: 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.
6340: See the Performance chapter of the users manual for information on preallocating matrices.
6342: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6343: @*/
6344: PetscErrorCode MatZeroEntries(Mat mat)
6345: {
6346: PetscFunctionBegin;
6349: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6350: 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");
6351: MatCheckPreallocated(mat, 1);
6353: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6354: PetscUseTypeMethod(mat, zeroentries);
6355: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6356: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6357: PetscFunctionReturn(PETSC_SUCCESS);
6358: }
6360: /*@
6361: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6362: of a set of rows and columns of a matrix.
6364: Collective
6366: Input Parameters:
6367: + mat - the matrix
6368: . numRows - the number of rows/columns to zero
6369: . rows - the global row indices
6370: . diag - value put in the diagonal of the eliminated rows
6371: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6372: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6374: Level: intermediate
6376: Notes:
6377: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6379: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6380: 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
6382: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6383: Krylov method to take advantage of the known solution on the zeroed rows.
6385: For the parallel case, all processes that share the matrix (i.e.,
6386: those in the communicator used for matrix creation) MUST call this
6387: routine, regardless of whether any rows being zeroed are owned by
6388: them.
6390: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6391: 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
6392: missing.
6394: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6395: list only rows local to itself).
6397: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6399: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6400: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6401: @*/
6402: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6403: {
6404: PetscFunctionBegin;
6407: if (numRows) PetscAssertPointer(rows, 3);
6408: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6409: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6410: MatCheckPreallocated(mat, 1);
6412: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6413: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6414: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6415: PetscFunctionReturn(PETSC_SUCCESS);
6416: }
6418: /*@
6419: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6420: of a set of rows and columns of a matrix.
6422: Collective
6424: Input Parameters:
6425: + mat - the matrix
6426: . is - the rows to zero
6427: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6428: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6429: - b - optional vector of right-hand side, that will be adjusted by provided solution
6431: Level: intermediate
6433: Note:
6434: See `MatZeroRowsColumns()` for details on how this routine operates.
6436: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6437: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6438: @*/
6439: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6440: {
6441: PetscInt numRows;
6442: const PetscInt *rows;
6444: PetscFunctionBegin;
6449: PetscCall(ISGetLocalSize(is, &numRows));
6450: PetscCall(ISGetIndices(is, &rows));
6451: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6452: PetscCall(ISRestoreIndices(is, &rows));
6453: PetscFunctionReturn(PETSC_SUCCESS);
6454: }
6456: /*@
6457: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6458: of a set of rows of a matrix.
6460: Collective
6462: Input Parameters:
6463: + mat - the matrix
6464: . numRows - the number of rows to zero
6465: . rows - the global row indices
6466: . diag - value put in the diagonal of the zeroed rows
6467: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6468: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6470: Level: intermediate
6472: Notes:
6473: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6475: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6477: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6478: Krylov method to take advantage of the known solution on the zeroed rows.
6480: 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)
6481: from the matrix.
6483: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6484: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6485: formats this does not alter the nonzero structure.
6487: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6488: of the matrix is not changed the values are
6489: merely zeroed.
6491: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6492: formats can optionally remove the main diagonal entry from the
6493: nonzero structure as well, by passing 0.0 as the final argument).
6495: For the parallel case, all processes that share the matrix (i.e.,
6496: those in the communicator used for matrix creation) MUST call this
6497: routine, regardless of whether any rows being zeroed are owned by
6498: them.
6500: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6501: list only rows local to itself).
6503: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6504: owns that are to be zeroed. This saves a global synchronization in the implementation.
6506: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6507: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6508: @*/
6509: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6510: {
6511: PetscFunctionBegin;
6514: if (numRows) PetscAssertPointer(rows, 3);
6515: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6516: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6517: MatCheckPreallocated(mat, 1);
6519: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6520: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6521: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6522: PetscFunctionReturn(PETSC_SUCCESS);
6523: }
6525: /*@
6526: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6527: of a set of rows of a matrix indicated by an `IS`
6529: Collective
6531: Input Parameters:
6532: + mat - the matrix
6533: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6534: . diag - value put in all diagonals of eliminated rows
6535: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6536: - b - optional vector of right-hand side, that will be adjusted by provided solution
6538: Level: intermediate
6540: Note:
6541: See `MatZeroRows()` for details on how this routine operates.
6543: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6544: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6545: @*/
6546: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6547: {
6548: PetscInt numRows = 0;
6549: const PetscInt *rows = NULL;
6551: PetscFunctionBegin;
6554: if (is) {
6556: PetscCall(ISGetLocalSize(is, &numRows));
6557: PetscCall(ISGetIndices(is, &rows));
6558: }
6559: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6560: if (is) PetscCall(ISRestoreIndices(is, &rows));
6561: PetscFunctionReturn(PETSC_SUCCESS);
6562: }
6564: /*@
6565: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6566: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6568: Collective
6570: Input Parameters:
6571: + mat - the matrix
6572: . numRows - the number of rows to remove
6573: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6574: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6575: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6576: - b - optional vector of right-hand side, that will be adjusted by provided solution
6578: Level: intermediate
6580: Notes:
6581: See `MatZeroRows()` for details on how this routine operates.
6583: The grid coordinates are across the entire grid, not just the local portion
6585: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6586: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6587: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6588: `DM_BOUNDARY_PERIODIC` boundary type.
6590: 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
6591: a single value per point) you can skip filling those indices.
6593: Fortran Note:
6594: `idxm` and `idxn` should be declared as
6595: .vb
6596: MatStencil idxm(4, m)
6597: .ve
6598: and the values inserted using
6599: .vb
6600: idxm(MatStencil_i, 1) = i
6601: idxm(MatStencil_j, 1) = j
6602: idxm(MatStencil_k, 1) = k
6603: idxm(MatStencil_c, 1) = c
6604: etc
6605: .ve
6607: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6608: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6609: @*/
6610: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6611: {
6612: PetscInt dim = mat->stencil.dim;
6613: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6614: PetscInt *dims = mat->stencil.dims + 1;
6615: PetscInt *starts = mat->stencil.starts;
6616: PetscInt *dxm = (PetscInt *)rows;
6617: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6619: PetscFunctionBegin;
6622: if (numRows) PetscAssertPointer(rows, 3);
6624: PetscCall(PetscMalloc1(numRows, &jdxm));
6625: for (i = 0; i < numRows; ++i) {
6626: /* Skip unused dimensions (they are ordered k, j, i, c) */
6627: for (j = 0; j < 3 - sdim; ++j) dxm++;
6628: /* Local index in X dir */
6629: tmp = *dxm++ - starts[0];
6630: /* Loop over remaining dimensions */
6631: for (j = 0; j < dim - 1; ++j) {
6632: /* If nonlocal, set index to be negative */
6633: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6634: /* Update local index */
6635: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6636: }
6637: /* Skip component slot if necessary */
6638: if (mat->stencil.noc) dxm++;
6639: /* Local row number */
6640: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6641: }
6642: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6643: PetscCall(PetscFree(jdxm));
6644: PetscFunctionReturn(PETSC_SUCCESS);
6645: }
6647: /*@
6648: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6649: of a set of rows and columns of a matrix.
6651: Collective
6653: Input Parameters:
6654: + mat - the matrix
6655: . numRows - the number of rows/columns to remove
6656: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6657: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6658: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6659: - b - optional vector of right-hand side, that will be adjusted by provided solution
6661: Level: intermediate
6663: Notes:
6664: See `MatZeroRowsColumns()` for details on how this routine operates.
6666: The grid coordinates are across the entire grid, not just the local portion
6668: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6669: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6670: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6671: `DM_BOUNDARY_PERIODIC` boundary type.
6673: 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
6674: a single value per point) you can skip filling those indices.
6676: Fortran Note:
6677: `idxm` and `idxn` should be declared as
6678: .vb
6679: MatStencil idxm(4, m)
6680: .ve
6681: and the values inserted using
6682: .vb
6683: idxm(MatStencil_i, 1) = i
6684: idxm(MatStencil_j, 1) = j
6685: idxm(MatStencil_k, 1) = k
6686: idxm(MatStencil_c, 1) = c
6687: etc
6688: .ve
6690: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6691: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6692: @*/
6693: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6694: {
6695: PetscInt dim = mat->stencil.dim;
6696: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6697: PetscInt *dims = mat->stencil.dims + 1;
6698: PetscInt *starts = mat->stencil.starts;
6699: PetscInt *dxm = (PetscInt *)rows;
6700: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6702: PetscFunctionBegin;
6705: if (numRows) PetscAssertPointer(rows, 3);
6707: PetscCall(PetscMalloc1(numRows, &jdxm));
6708: for (i = 0; i < numRows; ++i) {
6709: /* Skip unused dimensions (they are ordered k, j, i, c) */
6710: for (j = 0; j < 3 - sdim; ++j) dxm++;
6711: /* Local index in X dir */
6712: tmp = *dxm++ - starts[0];
6713: /* Loop over remaining dimensions */
6714: for (j = 0; j < dim - 1; ++j) {
6715: /* If nonlocal, set index to be negative */
6716: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6717: /* Update local index */
6718: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6719: }
6720: /* Skip component slot if necessary */
6721: if (mat->stencil.noc) dxm++;
6722: /* Local row number */
6723: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6724: }
6725: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6726: PetscCall(PetscFree(jdxm));
6727: PetscFunctionReturn(PETSC_SUCCESS);
6728: }
6730: /*@
6731: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6732: of a set of rows of a matrix; using local numbering of rows.
6734: Collective
6736: Input Parameters:
6737: + mat - the matrix
6738: . numRows - the number of rows to remove
6739: . rows - the local row indices
6740: . diag - value put in all diagonals of eliminated rows
6741: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6742: - b - optional vector of right-hand side, that will be adjusted by provided solution
6744: Level: intermediate
6746: Notes:
6747: Before calling `MatZeroRowsLocal()`, the user must first set the
6748: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6750: See `MatZeroRows()` for details on how this routine operates.
6752: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6753: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6754: @*/
6755: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6756: {
6757: PetscFunctionBegin;
6760: if (numRows) PetscAssertPointer(rows, 3);
6761: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6762: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6763: MatCheckPreallocated(mat, 1);
6765: if (mat->ops->zerorowslocal) {
6766: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6767: } else {
6768: IS is, newis;
6769: PetscInt *newRows, nl = 0;
6771: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6772: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6773: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6774: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6775: for (PetscInt i = 0; i < numRows; i++)
6776: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6777: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
6778: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6779: PetscCall(ISDestroy(&newis));
6780: PetscCall(ISDestroy(&is));
6781: }
6782: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6783: PetscFunctionReturn(PETSC_SUCCESS);
6784: }
6786: /*@
6787: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6788: of a set of rows of a matrix; using local numbering of rows.
6790: Collective
6792: Input Parameters:
6793: + mat - the matrix
6794: . is - index set of rows to remove
6795: . diag - value put in all diagonals of eliminated rows
6796: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6797: - b - optional vector of right-hand side, that will be adjusted by provided solution
6799: Level: intermediate
6801: Notes:
6802: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6803: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6805: See `MatZeroRows()` for details on how this routine operates.
6807: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6808: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6809: @*/
6810: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6811: {
6812: PetscInt numRows;
6813: const PetscInt *rows;
6815: PetscFunctionBegin;
6819: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6820: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6821: MatCheckPreallocated(mat, 1);
6823: PetscCall(ISGetLocalSize(is, &numRows));
6824: PetscCall(ISGetIndices(is, &rows));
6825: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6826: PetscCall(ISRestoreIndices(is, &rows));
6827: PetscFunctionReturn(PETSC_SUCCESS);
6828: }
6830: /*@
6831: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6832: of a set of rows and columns of a matrix; using local numbering of rows.
6834: Collective
6836: Input Parameters:
6837: + mat - the matrix
6838: . numRows - the number of rows to remove
6839: . rows - the global row indices
6840: . diag - value put in all diagonals of eliminated rows
6841: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6842: - b - optional vector of right-hand side, that will be adjusted by provided solution
6844: Level: intermediate
6846: Notes:
6847: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6848: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6850: See `MatZeroRowsColumns()` for details on how this routine operates.
6852: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6853: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6854: @*/
6855: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6856: {
6857: PetscFunctionBegin;
6860: if (numRows) PetscAssertPointer(rows, 3);
6861: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6862: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6863: MatCheckPreallocated(mat, 1);
6865: if (mat->ops->zerorowscolumnslocal) {
6866: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
6867: } else {
6868: IS is, newis;
6869: PetscInt *newRows, nl = 0;
6871: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6872: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6873: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6874: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6875: for (PetscInt i = 0; i < numRows; i++)
6876: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6877: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
6878: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6879: PetscCall(ISDestroy(&newis));
6880: PetscCall(ISDestroy(&is));
6881: }
6882: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6883: PetscFunctionReturn(PETSC_SUCCESS);
6884: }
6886: /*@
6887: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6888: of a set of rows and columns of a matrix; using local numbering of rows.
6890: Collective
6892: Input Parameters:
6893: + mat - the matrix
6894: . is - index set of rows to remove
6895: . diag - value put in all diagonals of eliminated rows
6896: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6897: - b - optional vector of right-hand side, that will be adjusted by provided solution
6899: Level: intermediate
6901: Notes:
6902: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6903: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6905: See `MatZeroRowsColumns()` for details on how this routine operates.
6907: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6908: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6909: @*/
6910: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6911: {
6912: PetscInt numRows;
6913: const PetscInt *rows;
6915: PetscFunctionBegin;
6919: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6920: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6921: MatCheckPreallocated(mat, 1);
6923: PetscCall(ISGetLocalSize(is, &numRows));
6924: PetscCall(ISGetIndices(is, &rows));
6925: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6926: PetscCall(ISRestoreIndices(is, &rows));
6927: PetscFunctionReturn(PETSC_SUCCESS);
6928: }
6930: /*@
6931: MatGetSize - Returns the numbers of rows and columns in a matrix.
6933: Not Collective
6935: Input Parameter:
6936: . mat - the matrix
6938: Output Parameters:
6939: + m - the number of global rows
6940: - n - the number of global columns
6942: Level: beginner
6944: Note:
6945: Both output parameters can be `NULL` on input.
6947: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6948: @*/
6949: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6950: {
6951: PetscFunctionBegin;
6953: if (m) *m = mat->rmap->N;
6954: if (n) *n = mat->cmap->N;
6955: PetscFunctionReturn(PETSC_SUCCESS);
6956: }
6958: /*@
6959: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6960: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6962: Not Collective
6964: Input Parameter:
6965: . mat - the matrix
6967: Output Parameters:
6968: + m - the number of local rows, use `NULL` to not obtain this value
6969: - n - the number of local columns, use `NULL` to not obtain this value
6971: Level: beginner
6973: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6974: @*/
6975: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6976: {
6977: PetscFunctionBegin;
6979: if (m) PetscAssertPointer(m, 2);
6980: if (n) PetscAssertPointer(n, 3);
6981: if (m) *m = mat->rmap->n;
6982: if (n) *n = mat->cmap->n;
6983: PetscFunctionReturn(PETSC_SUCCESS);
6984: }
6986: /*@
6987: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6988: vector one multiplies this matrix by that are owned by this processor.
6990: Not Collective, unless matrix has not been allocated, then collective
6992: Input Parameter:
6993: . mat - the matrix
6995: Output Parameters:
6996: + m - the global index of the first local column, use `NULL` to not obtain this value
6997: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
6999: Level: developer
7001: Notes:
7002: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7004: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7005: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7007: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7008: the local values in the matrix.
7010: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
7011: Layouts](sec_matlayout) for details on matrix layouts.
7013: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7014: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7015: @*/
7016: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
7017: {
7018: PetscFunctionBegin;
7021: if (m) PetscAssertPointer(m, 2);
7022: if (n) PetscAssertPointer(n, 3);
7023: MatCheckPreallocated(mat, 1);
7024: if (m) *m = mat->cmap->rstart;
7025: if (n) *n = mat->cmap->rend;
7026: PetscFunctionReturn(PETSC_SUCCESS);
7027: }
7029: /*@
7030: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
7031: this MPI process.
7033: Not Collective
7035: Input Parameter:
7036: . mat - the matrix
7038: Output Parameters:
7039: + m - the global index of the first local row, use `NULL` to not obtain this value
7040: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
7042: Level: beginner
7044: Notes:
7045: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7047: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7048: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7050: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7051: the local values in the matrix.
7053: The high argument is one more than the last element stored locally.
7055: For all matrices it returns the range of matrix rows associated with rows of a vector that
7056: would contain the result of a matrix vector product with this matrix. See [Matrix
7057: Layouts](sec_matlayout) for details on matrix layouts.
7059: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
7060: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7061: @*/
7062: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
7063: {
7064: PetscFunctionBegin;
7067: if (m) PetscAssertPointer(m, 2);
7068: if (n) PetscAssertPointer(n, 3);
7069: MatCheckPreallocated(mat, 1);
7070: if (m) *m = mat->rmap->rstart;
7071: if (n) *n = mat->rmap->rend;
7072: PetscFunctionReturn(PETSC_SUCCESS);
7073: }
7075: /*@C
7076: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
7077: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
7079: Not Collective, unless matrix has not been allocated
7081: Input Parameter:
7082: . mat - the matrix
7084: Output Parameter:
7085: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
7086: where `size` is the number of MPI processes used by `mat`
7088: Level: beginner
7090: Notes:
7091: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7093: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7094: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7096: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7097: the local values in the matrix.
7099: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
7100: would contain the result of a matrix vector product with this matrix. See [Matrix
7101: Layouts](sec_matlayout) for details on matrix layouts.
7103: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7104: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
7105: `DMDAGetGhostCorners()`, `DM`
7106: @*/
7107: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
7108: {
7109: PetscFunctionBegin;
7112: MatCheckPreallocated(mat, 1);
7113: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
7114: PetscFunctionReturn(PETSC_SUCCESS);
7115: }
7117: /*@C
7118: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
7119: vector one multiplies this vector by that are owned by each processor.
7121: Not Collective, unless matrix has not been allocated
7123: Input Parameter:
7124: . mat - the matrix
7126: Output Parameter:
7127: . ranges - start of each processors portion plus one more than the total length at the end
7129: Level: beginner
7131: Notes:
7132: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7134: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7135: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7137: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7138: the local values in the matrix.
7140: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7141: Layouts](sec_matlayout) for details on matrix layouts.
7143: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7144: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7145: `DMDAGetGhostCorners()`, `DM`
7146: @*/
7147: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7148: {
7149: PetscFunctionBegin;
7152: MatCheckPreallocated(mat, 1);
7153: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7154: PetscFunctionReturn(PETSC_SUCCESS);
7155: }
7157: /*@
7158: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7160: Not Collective
7162: Input Parameter:
7163: . A - matrix
7165: Output Parameters:
7166: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7167: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7169: Level: intermediate
7171: Note:
7172: You should call `ISDestroy()` on the returned `IS`
7174: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7175: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7176: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7177: details on matrix layouts.
7179: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7180: @*/
7181: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7182: {
7183: PetscErrorCode (*f)(Mat, IS *, IS *);
7185: PetscFunctionBegin;
7188: MatCheckPreallocated(A, 1);
7189: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7190: if (f) {
7191: PetscCall((*f)(A, rows, cols));
7192: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7193: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7194: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7195: }
7196: PetscFunctionReturn(PETSC_SUCCESS);
7197: }
7199: /*@
7200: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7201: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7202: to complete the factorization.
7204: Collective
7206: Input Parameters:
7207: + fact - the factorized matrix obtained with `MatGetFactor()`
7208: . mat - the matrix
7209: . row - row permutation
7210: . col - column permutation
7211: - info - structure containing
7212: .vb
7213: levels - number of levels of fill.
7214: expected fill - as ratio of original fill.
7215: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7216: missing diagonal entries)
7217: .ve
7219: Level: developer
7221: Notes:
7222: See [Matrix Factorization](sec_matfactor) for additional information.
7224: Most users should employ the `KSP` interface for linear solvers
7225: instead of working directly with matrix algebra routines such as this.
7226: See, e.g., `KSPCreate()`.
7228: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7230: Fortran Note:
7231: A valid (non-null) `info` argument must be provided
7233: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
7234: `MatGetOrdering()`, `MatFactorInfo`
7235: @*/
7236: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7237: {
7238: PetscFunctionBegin;
7243: PetscAssertPointer(info, 5);
7244: PetscAssertPointer(fact, 1);
7245: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7246: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
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, 2);
7251: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7252: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7253: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7254: PetscFunctionReturn(PETSC_SUCCESS);
7255: }
7257: /*@
7258: MatICCFactorSymbolic - Performs symbolic incomplete
7259: Cholesky factorization for a symmetric matrix. Use
7260: `MatCholeskyFactorNumeric()` to complete the factorization.
7262: Collective
7264: Input Parameters:
7265: + fact - the factorized matrix obtained with `MatGetFactor()`
7266: . mat - the matrix to be factored
7267: . perm - row and column permutation
7268: - info - structure containing
7269: .vb
7270: levels - number of levels of fill.
7271: expected fill - as ratio of original fill.
7272: .ve
7274: Level: developer
7276: Notes:
7277: Most users should employ the `KSP` interface for linear solvers
7278: instead of working directly with matrix algebra routines such as this.
7279: See, e.g., `KSPCreate()`.
7281: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7283: Fortran Note:
7284: A valid (non-null) `info` argument must be provided
7286: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7287: @*/
7288: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7289: {
7290: PetscFunctionBegin;
7294: PetscAssertPointer(info, 4);
7295: PetscAssertPointer(fact, 1);
7296: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7297: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7298: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7299: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7300: MatCheckPreallocated(mat, 2);
7302: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7303: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7304: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7305: PetscFunctionReturn(PETSC_SUCCESS);
7306: }
7308: /*@C
7309: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7310: points to an array of valid matrices, they may be reused to store the new
7311: submatrices.
7313: Collective
7315: Input Parameters:
7316: + mat - the matrix
7317: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7318: . irow - index set of rows to extract
7319: . icol - index set of columns to extract
7320: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7322: Output Parameter:
7323: . submat - the array of submatrices
7325: Level: advanced
7327: Notes:
7328: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7329: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7330: to extract a parallel submatrix.
7332: Some matrix types place restrictions on the row and column
7333: indices, such as that they be sorted or that they be equal to each other.
7335: The index sets may not have duplicate entries.
7337: When extracting submatrices from a parallel matrix, each processor can
7338: form a different submatrix by setting the rows and columns of its
7339: individual index sets according to the local submatrix desired.
7341: When finished using the submatrices, the user should destroy
7342: them with `MatDestroySubMatrices()`.
7344: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7345: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7347: This routine creates the matrices in submat; you should NOT create them before
7348: calling it. It also allocates the array of matrix pointers submat.
7350: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7351: request one row/column in a block, they must request all rows/columns that are in
7352: that block. For example, if the block size is 2 you cannot request just row 0 and
7353: column 0.
7355: Fortran Note:
7356: .vb
7357: Mat, pointer :: submat(:)
7358: .ve
7360: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7361: @*/
7362: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7363: {
7364: PetscInt i;
7365: PetscBool eq;
7367: PetscFunctionBegin;
7370: if (n) {
7371: PetscAssertPointer(irow, 3);
7373: PetscAssertPointer(icol, 4);
7375: }
7376: PetscAssertPointer(submat, 6);
7377: if (n && scall == MAT_REUSE_MATRIX) {
7378: PetscAssertPointer(*submat, 6);
7380: }
7381: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7382: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7383: MatCheckPreallocated(mat, 1);
7384: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7385: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7386: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7387: for (i = 0; i < n; i++) {
7388: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7389: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7390: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7391: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7392: if (mat->boundtocpu && mat->bindingpropagates) {
7393: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7394: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7395: }
7396: #endif
7397: }
7398: PetscFunctionReturn(PETSC_SUCCESS);
7399: }
7401: /*@C
7402: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7404: Collective
7406: Input Parameters:
7407: + mat - the matrix
7408: . n - the number of submatrixes to be extracted
7409: . irow - index set of rows to extract
7410: . icol - index set of columns to extract
7411: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7413: Output Parameter:
7414: . submat - the array of submatrices
7416: Level: advanced
7418: Note:
7419: This is used by `PCGASM`
7421: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7422: @*/
7423: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7424: {
7425: PetscInt i;
7426: PetscBool eq;
7428: PetscFunctionBegin;
7431: if (n) {
7432: PetscAssertPointer(irow, 3);
7434: PetscAssertPointer(icol, 4);
7436: }
7437: PetscAssertPointer(submat, 6);
7438: if (n && scall == MAT_REUSE_MATRIX) {
7439: PetscAssertPointer(*submat, 6);
7441: }
7442: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7443: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7444: MatCheckPreallocated(mat, 1);
7446: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7447: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7448: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7449: for (i = 0; i < n; i++) {
7450: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7451: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7452: }
7453: PetscFunctionReturn(PETSC_SUCCESS);
7454: }
7456: /*@C
7457: MatDestroyMatrices - Destroys an array of matrices
7459: Collective
7461: Input Parameters:
7462: + n - the number of local matrices
7463: - mat - the matrices (this is a pointer to the array of matrices)
7465: Level: advanced
7467: Notes:
7468: Frees not only the matrices, but also the array that contains the matrices
7470: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7472: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7473: @*/
7474: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7475: {
7476: PetscInt i;
7478: PetscFunctionBegin;
7479: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7480: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7481: PetscAssertPointer(mat, 2);
7483: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7485: /* memory is allocated even if n = 0 */
7486: PetscCall(PetscFree(*mat));
7487: PetscFunctionReturn(PETSC_SUCCESS);
7488: }
7490: /*@C
7491: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7493: Collective
7495: Input Parameters:
7496: + n - the number of local matrices
7497: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7499: Level: advanced
7501: Note:
7502: Frees not only the matrices, but also the array that contains the matrices
7504: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7505: @*/
7506: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7507: {
7508: Mat mat0;
7510: PetscFunctionBegin;
7511: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7512: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7513: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7514: PetscAssertPointer(mat, 2);
7516: mat0 = (*mat)[0];
7517: if (mat0 && mat0->ops->destroysubmatrices) {
7518: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7519: } else {
7520: PetscCall(MatDestroyMatrices(n, mat));
7521: }
7522: PetscFunctionReturn(PETSC_SUCCESS);
7523: }
7525: /*@
7526: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7528: Collective
7530: Input Parameter:
7531: . mat - the matrix
7533: Output Parameter:
7534: . matstruct - the sequential matrix with the nonzero structure of `mat`
7536: Level: developer
7538: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7539: @*/
7540: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7541: {
7542: PetscFunctionBegin;
7544: PetscAssertPointer(matstruct, 2);
7547: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7548: MatCheckPreallocated(mat, 1);
7550: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7551: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7552: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7553: PetscFunctionReturn(PETSC_SUCCESS);
7554: }
7556: /*@C
7557: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7559: Collective
7561: Input Parameter:
7562: . mat - the matrix
7564: Level: advanced
7566: Note:
7567: This is not needed, one can just call `MatDestroy()`
7569: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7570: @*/
7571: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7572: {
7573: PetscFunctionBegin;
7574: PetscAssertPointer(mat, 1);
7575: PetscCall(MatDestroy(mat));
7576: PetscFunctionReturn(PETSC_SUCCESS);
7577: }
7579: /*@
7580: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7581: replaces the index sets by larger ones that represent submatrices with
7582: additional overlap.
7584: Collective
7586: Input Parameters:
7587: + mat - the matrix
7588: . n - the number of index sets
7589: . is - the array of index sets (these index sets will changed during the call)
7590: - ov - the additional overlap requested
7592: Options Database Key:
7593: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7595: Level: developer
7597: Note:
7598: The computed overlap preserves the matrix block sizes when the blocks are square.
7599: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7600: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7602: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7603: @*/
7604: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7605: {
7606: PetscInt i, bs, cbs;
7608: PetscFunctionBegin;
7612: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7613: if (n) {
7614: PetscAssertPointer(is, 3);
7616: }
7617: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7618: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7619: MatCheckPreallocated(mat, 1);
7621: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7622: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7623: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7624: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7625: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7626: if (bs == cbs) {
7627: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7628: }
7629: PetscFunctionReturn(PETSC_SUCCESS);
7630: }
7632: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7634: /*@
7635: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7636: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7637: additional overlap.
7639: Collective
7641: Input Parameters:
7642: + mat - the matrix
7643: . n - the number of index sets
7644: . is - the array of index sets (these index sets will changed during the call)
7645: - ov - the additional overlap requested
7647: ` Options Database Key:
7648: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7650: Level: developer
7652: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7653: @*/
7654: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7655: {
7656: PetscInt i;
7658: PetscFunctionBegin;
7661: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7662: if (n) {
7663: PetscAssertPointer(is, 3);
7665: }
7666: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7667: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7668: MatCheckPreallocated(mat, 1);
7669: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7670: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7671: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7672: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7673: PetscFunctionReturn(PETSC_SUCCESS);
7674: }
7676: /*@
7677: MatGetBlockSize - Returns the matrix block size.
7679: Not Collective
7681: Input Parameter:
7682: . mat - the matrix
7684: Output Parameter:
7685: . bs - block size
7687: Level: intermediate
7689: Notes:
7690: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7692: If the block size has not been set yet this routine returns 1.
7694: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7695: @*/
7696: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7697: {
7698: PetscFunctionBegin;
7700: PetscAssertPointer(bs, 2);
7701: *bs = mat->rmap->bs;
7702: PetscFunctionReturn(PETSC_SUCCESS);
7703: }
7705: /*@
7706: MatGetBlockSizes - Returns the matrix block row and column sizes.
7708: Not Collective
7710: Input Parameter:
7711: . mat - the matrix
7713: Output Parameters:
7714: + rbs - row block size
7715: - cbs - column block size
7717: Level: intermediate
7719: Notes:
7720: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7721: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7723: If a block size has not been set yet this routine returns 1.
7725: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7726: @*/
7727: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7728: {
7729: PetscFunctionBegin;
7731: if (rbs) PetscAssertPointer(rbs, 2);
7732: if (cbs) PetscAssertPointer(cbs, 3);
7733: if (rbs) *rbs = mat->rmap->bs;
7734: if (cbs) *cbs = mat->cmap->bs;
7735: PetscFunctionReturn(PETSC_SUCCESS);
7736: }
7738: /*@
7739: MatSetBlockSize - Sets the matrix block size.
7741: Logically Collective
7743: Input Parameters:
7744: + mat - the matrix
7745: - bs - block size
7747: Level: intermediate
7749: Notes:
7750: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7751: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7753: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7754: is compatible with the matrix local sizes.
7756: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7757: @*/
7758: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7759: {
7760: PetscFunctionBegin;
7763: PetscCall(MatSetBlockSizes(mat, bs, bs));
7764: PetscFunctionReturn(PETSC_SUCCESS);
7765: }
7767: typedef struct {
7768: PetscInt n;
7769: IS *is;
7770: Mat *mat;
7771: PetscObjectState nonzerostate;
7772: Mat C;
7773: } EnvelopeData;
7775: static PetscErrorCode EnvelopeDataDestroy(PetscCtxRt ptr)
7776: {
7777: EnvelopeData *edata = *(EnvelopeData **)ptr;
7779: PetscFunctionBegin;
7780: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7781: PetscCall(PetscFree(edata->is));
7782: PetscCall(PetscFree(edata));
7783: PetscFunctionReturn(PETSC_SUCCESS);
7784: }
7786: /*@
7787: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7788: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7790: Collective
7792: Input Parameter:
7793: . mat - the matrix
7795: Level: intermediate
7797: Notes:
7798: There can be zeros within the blocks
7800: The blocks can overlap between processes, including laying on more than two processes
7802: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7803: @*/
7804: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7805: {
7806: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7807: PetscInt *diag, *odiag, sc;
7808: VecScatter scatter;
7809: PetscScalar *seqv;
7810: const PetscScalar *parv;
7811: const PetscInt *ia, *ja;
7812: PetscBool set, flag, done;
7813: Mat AA = mat, A;
7814: MPI_Comm comm;
7815: PetscMPIInt rank, size, tag;
7816: MPI_Status status;
7817: PetscContainer container;
7818: EnvelopeData *edata;
7819: Vec seq, par;
7820: IS isglobal;
7822: PetscFunctionBegin;
7824: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7825: if (!set || !flag) {
7826: /* TODO: only needs nonzero structure of transpose */
7827: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7828: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7829: }
7830: PetscCall(MatAIJGetLocalMat(AA, &A));
7831: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7832: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7834: PetscCall(MatGetLocalSize(mat, &n, NULL));
7835: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7836: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7837: PetscCallMPI(MPI_Comm_size(comm, &size));
7838: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7840: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7842: if (rank > 0) {
7843: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7844: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7845: }
7846: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7847: for (i = 0; i < n; i++) {
7848: env = PetscMax(env, ja[ia[i + 1] - 1]);
7849: II = rstart + i;
7850: if (env == II) {
7851: starts[lblocks] = tbs;
7852: sizes[lblocks++] = 1 + II - tbs;
7853: tbs = 1 + II;
7854: }
7855: }
7856: if (rank < size - 1) {
7857: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7858: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7859: }
7861: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7862: if (!set || !flag) PetscCall(MatDestroy(&AA));
7863: PetscCall(MatDestroy(&A));
7865: PetscCall(PetscNew(&edata));
7866: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7867: edata->n = lblocks;
7868: /* create IS needed for extracting blocks from the original matrix */
7869: PetscCall(PetscMalloc1(lblocks, &edata->is));
7870: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7872: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7873: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7874: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7875: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7876: PetscCall(MatSetType(edata->C, MATAIJ));
7878: /* Communicate the start and end of each row, from each block to the correct rank */
7879: /* TODO: Use PetscSF instead of VecScatter */
7880: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7881: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7882: PetscCall(VecGetArrayWrite(seq, &seqv));
7883: for (PetscInt i = 0; i < lblocks; i++) {
7884: for (PetscInt j = 0; j < sizes[i]; j++) {
7885: seqv[cnt] = starts[i];
7886: seqv[cnt + 1] = starts[i] + sizes[i];
7887: cnt += 2;
7888: }
7889: }
7890: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7891: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7892: sc -= cnt;
7893: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7894: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7895: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7896: PetscCall(ISDestroy(&isglobal));
7897: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7898: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7899: PetscCall(VecScatterDestroy(&scatter));
7900: PetscCall(VecDestroy(&seq));
7901: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7902: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7903: PetscCall(VecGetArrayRead(par, &parv));
7904: cnt = 0;
7905: PetscCall(MatGetSize(mat, NULL, &n));
7906: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7907: PetscInt start, end, d = 0, od = 0;
7909: start = (PetscInt)PetscRealPart(parv[cnt]);
7910: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7911: cnt += 2;
7913: if (start < cstart) {
7914: od += cstart - start + n - cend;
7915: d += cend - cstart;
7916: } else if (start < cend) {
7917: od += n - cend;
7918: d += cend - start;
7919: } else od += n - start;
7920: if (end <= cstart) {
7921: od -= cstart - end + n - cend;
7922: d -= cend - cstart;
7923: } else if (end < cend) {
7924: od -= n - cend;
7925: d -= cend - end;
7926: } else od -= n - end;
7928: odiag[i] = od;
7929: diag[i] = d;
7930: }
7931: PetscCall(VecRestoreArrayRead(par, &parv));
7932: PetscCall(VecDestroy(&par));
7933: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7934: PetscCall(PetscFree2(diag, odiag));
7935: PetscCall(PetscFree2(sizes, starts));
7937: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7938: PetscCall(PetscContainerSetPointer(container, edata));
7939: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7940: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7941: PetscCall(PetscObjectDereference((PetscObject)container));
7942: PetscFunctionReturn(PETSC_SUCCESS);
7943: }
7945: /*@
7946: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7948: Collective
7950: Input Parameters:
7951: + A - the matrix
7952: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7954: Output Parameter:
7955: . C - matrix with inverted block diagonal of `A`
7957: Level: advanced
7959: Note:
7960: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7962: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7963: @*/
7964: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7965: {
7966: PetscContainer container;
7967: EnvelopeData *edata;
7968: PetscObjectState nonzerostate;
7970: PetscFunctionBegin;
7971: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7972: if (!container) {
7973: PetscCall(MatComputeVariableBlockEnvelope(A));
7974: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7975: }
7976: PetscCall(PetscContainerGetPointer(container, &edata));
7977: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7978: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7979: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7981: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7982: *C = edata->C;
7984: for (PetscInt i = 0; i < edata->n; i++) {
7985: Mat D;
7986: PetscScalar *dvalues;
7988: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7989: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7990: PetscCall(MatSeqDenseInvert(D));
7991: PetscCall(MatDenseGetArray(D, &dvalues));
7992: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7993: PetscCall(MatDestroy(&D));
7994: }
7995: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7996: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7997: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7998: PetscFunctionReturn(PETSC_SUCCESS);
7999: }
8001: /*@
8002: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
8004: Not Collective
8006: Input Parameters:
8007: + mat - the matrix
8008: . nblocks - the number of blocks on this process, each block can only exist on a single process
8009: - bsizes - the block sizes
8011: Level: intermediate
8013: Notes:
8014: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
8016: 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.
8018: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
8019: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
8020: @*/
8021: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
8022: {
8023: PetscInt ncnt = 0, nlocal;
8025: PetscFunctionBegin;
8027: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
8028: 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);
8029: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
8030: 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);
8031: PetscCall(PetscFree(mat->bsizes));
8032: mat->nblocks = nblocks;
8033: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
8034: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
8035: PetscFunctionReturn(PETSC_SUCCESS);
8036: }
8038: /*@C
8039: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
8041: Not Collective; No Fortran Support
8043: Input Parameter:
8044: . mat - the matrix
8046: Output Parameters:
8047: + nblocks - the number of blocks on this process
8048: - bsizes - the block sizes
8050: Level: intermediate
8052: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8053: @*/
8054: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
8055: {
8056: PetscFunctionBegin;
8058: if (nblocks) *nblocks = mat->nblocks;
8059: if (bsizes) *bsizes = mat->bsizes;
8060: PetscFunctionReturn(PETSC_SUCCESS);
8061: }
8063: /*@
8064: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
8066: Not Collective
8068: Input Parameter:
8069: + subA - the submatrix
8070: . A - the original matrix
8071: - isrow - The `IS` of selected rows for the submatrix, must be sorted
8073: Level: developer
8075: Notes:
8076: If the index set is not sorted or contains off-process entries, this function will do nothing.
8078: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8079: @*/
8080: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
8081: {
8082: const PetscInt *rows;
8083: PetscInt n, rStart, rEnd, Nb = 0;
8084: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
8086: PetscFunctionBegin;
8087: // The code for block size extraction does not support an unsorted IS
8088: if (flg) PetscCall(ISSorted(isrow, &flg));
8089: // We don't support originally off-diagonal blocks
8090: if (flg) {
8091: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
8092: PetscCall(ISGetLocalSize(isrow, &n));
8093: PetscCall(ISGetIndices(isrow, &rows));
8094: for (PetscInt i = 0; i < n && flg; ++i) {
8095: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
8096: }
8097: PetscCall(ISRestoreIndices(isrow, &rows));
8098: }
8099: // quiet return if we can't extract block size
8100: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
8101: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
8103: // extract block sizes
8104: PetscCall(ISGetIndices(isrow, &rows));
8105: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8106: PetscBool occupied = PETSC_FALSE;
8108: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8109: const PetscInt row = gr + br;
8111: if (i == n) break;
8112: if (rows[i] == row) {
8113: occupied = PETSC_TRUE;
8114: ++i;
8115: }
8116: while (i < n && rows[i] < row) ++i;
8117: }
8118: gr += A->bsizes[b];
8119: if (occupied) ++Nb;
8120: }
8121: subA->nblocks = Nb;
8122: PetscCall(PetscFree(subA->bsizes));
8123: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
8124: PetscInt sb = 0;
8125: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8126: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
8127: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8128: const PetscInt row = gr + br;
8130: if (i == n) break;
8131: if (rows[i] == row) {
8132: ++subA->bsizes[sb];
8133: ++i;
8134: }
8135: while (i < n && rows[i] < row) ++i;
8136: }
8137: gr += A->bsizes[b];
8138: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8139: }
8140: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8141: PetscInt nlocal, ncnt = 0;
8142: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8143: PetscCheck(subA->nblocks >= 0 && subA->nblocks <= nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks %" PetscInt_FMT " is not in [0, %" PetscInt_FMT "]", subA->nblocks, nlocal);
8144: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8145: 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);
8146: PetscCall(ISRestoreIndices(isrow, &rows));
8147: PetscFunctionReturn(PETSC_SUCCESS);
8148: }
8150: /*@
8151: MatSetBlockSizes - Sets the matrix block row and column sizes.
8153: Logically Collective
8155: Input Parameters:
8156: + mat - the matrix
8157: . rbs - row block size
8158: - cbs - column block size
8160: Level: intermediate
8162: Notes:
8163: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8164: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8165: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8167: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8168: are compatible with the matrix local sizes.
8170: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8172: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8173: @*/
8174: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8175: {
8176: PetscFunctionBegin;
8180: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8181: if (mat->rmap->refcnt) {
8182: ISLocalToGlobalMapping l2g = NULL;
8183: PetscLayout nmap = NULL;
8185: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8186: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8187: PetscCall(PetscLayoutDestroy(&mat->rmap));
8188: mat->rmap = nmap;
8189: mat->rmap->mapping = l2g;
8190: }
8191: if (mat->cmap->refcnt) {
8192: ISLocalToGlobalMapping l2g = NULL;
8193: PetscLayout nmap = NULL;
8195: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8196: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8197: PetscCall(PetscLayoutDestroy(&mat->cmap));
8198: mat->cmap = nmap;
8199: mat->cmap->mapping = l2g;
8200: }
8201: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8202: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8203: PetscFunctionReturn(PETSC_SUCCESS);
8204: }
8206: /*@
8207: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8209: Logically Collective
8211: Input Parameters:
8212: + mat - the matrix
8213: . fromRow - matrix from which to copy row block size
8214: - fromCol - matrix from which to copy column block size (can be same as `fromRow`)
8216: Level: developer
8218: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8219: @*/
8220: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8221: {
8222: PetscFunctionBegin;
8226: PetscTryTypeMethod(mat, setblocksizes, fromRow->rmap->bs, fromCol->cmap->bs);
8227: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8228: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8229: PetscFunctionReturn(PETSC_SUCCESS);
8230: }
8232: /*@
8233: MatResidual - Default routine to calculate the residual r = b - Ax
8235: Collective
8237: Input Parameters:
8238: + mat - the matrix
8239: . b - the right-hand-side
8240: - x - the approximate solution
8242: Output Parameter:
8243: . r - location to store the residual
8245: Level: developer
8247: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8248: @*/
8249: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8250: {
8251: PetscFunctionBegin;
8257: MatCheckPreallocated(mat, 1);
8258: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8259: if (!mat->ops->residual) {
8260: PetscCall(MatMult(mat, x, r));
8261: PetscCall(VecAYPX(r, -1.0, b));
8262: } else {
8263: PetscUseTypeMethod(mat, residual, b, x, r);
8264: }
8265: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8266: PetscFunctionReturn(PETSC_SUCCESS);
8267: }
8269: /*@C
8270: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8272: Collective
8274: Input Parameters:
8275: + mat - the matrix
8276: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8277: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8278: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8279: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8280: always used.
8282: Output Parameters:
8283: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8284: . 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
8285: . ja - the column indices, use `NULL` if not needed
8286: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8287: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8289: Level: developer
8291: Notes:
8292: You CANNOT change any of the ia[] or ja[] values.
8294: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8296: Fortran Notes:
8297: Use
8298: .vb
8299: PetscInt, pointer :: ia(:),ja(:)
8300: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8301: ! Access the ith and jth entries via ia(i) and ja(j)
8302: .ve
8304: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8305: @*/
8306: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8307: {
8308: PetscFunctionBegin;
8311: if (n) PetscAssertPointer(n, 5);
8312: if (ia) PetscAssertPointer(ia, 6);
8313: if (ja) PetscAssertPointer(ja, 7);
8314: if (done) PetscAssertPointer(done, 8);
8315: MatCheckPreallocated(mat, 1);
8316: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8317: else {
8318: if (done) *done = PETSC_TRUE;
8319: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8320: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8321: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8322: }
8323: PetscFunctionReturn(PETSC_SUCCESS);
8324: }
8326: /*@C
8327: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8329: Collective
8331: Input Parameters:
8332: + mat - the matrix
8333: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8334: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8335: symmetrized
8336: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8337: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8338: always used.
8340: Output Parameters:
8341: + n - number of columns in the (possibly compressed) matrix
8342: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8343: . ja - the row indices
8344: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8346: Level: developer
8348: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8349: @*/
8350: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8351: {
8352: PetscFunctionBegin;
8355: PetscAssertPointer(n, 5);
8356: if (ia) PetscAssertPointer(ia, 6);
8357: if (ja) PetscAssertPointer(ja, 7);
8358: PetscAssertPointer(done, 8);
8359: MatCheckPreallocated(mat, 1);
8360: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8361: else {
8362: *done = PETSC_TRUE;
8363: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8364: }
8365: PetscFunctionReturn(PETSC_SUCCESS);
8366: }
8368: /*@C
8369: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8371: Collective
8373: Input Parameters:
8374: + mat - the matrix
8375: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8376: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8377: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8378: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8379: always used.
8380: . n - size of (possibly compressed) matrix
8381: . ia - the row pointers
8382: - ja - the column indices
8384: Output Parameter:
8385: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8387: Level: developer
8389: Note:
8390: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8391: us of the array after it has been restored. If you pass `NULL`, it will
8392: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8394: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8395: @*/
8396: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8397: {
8398: PetscFunctionBegin;
8401: if (ia) PetscAssertPointer(ia, 6);
8402: if (ja) PetscAssertPointer(ja, 7);
8403: if (done) PetscAssertPointer(done, 8);
8404: MatCheckPreallocated(mat, 1);
8406: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8407: else {
8408: if (done) *done = PETSC_TRUE;
8409: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8410: if (n) *n = 0;
8411: if (ia) *ia = NULL;
8412: if (ja) *ja = NULL;
8413: }
8414: PetscFunctionReturn(PETSC_SUCCESS);
8415: }
8417: /*@C
8418: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8420: Collective
8422: Input Parameters:
8423: + mat - the matrix
8424: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8425: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8426: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8427: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8428: always used.
8430: Output Parameters:
8431: + n - size of (possibly compressed) matrix
8432: . ia - the column pointers
8433: . ja - the row indices
8434: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8436: Level: developer
8438: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8439: @*/
8440: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8441: {
8442: PetscFunctionBegin;
8445: if (ia) PetscAssertPointer(ia, 6);
8446: if (ja) PetscAssertPointer(ja, 7);
8447: PetscAssertPointer(done, 8);
8448: MatCheckPreallocated(mat, 1);
8450: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8451: else {
8452: *done = PETSC_TRUE;
8453: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8454: if (n) *n = 0;
8455: if (ia) *ia = NULL;
8456: if (ja) *ja = NULL;
8457: }
8458: PetscFunctionReturn(PETSC_SUCCESS);
8459: }
8461: /*@
8462: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8463: `MatGetColumnIJ()`.
8465: Collective
8467: Input Parameters:
8468: + mat - the matrix
8469: . ncolors - maximum color value
8470: . n - number of entries in colorarray
8471: - colorarray - array indicating color for each column
8473: Output Parameter:
8474: . iscoloring - coloring generated using colorarray information
8476: Level: developer
8478: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8479: @*/
8480: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8481: {
8482: PetscFunctionBegin;
8485: PetscAssertPointer(colorarray, 4);
8486: PetscAssertPointer(iscoloring, 5);
8487: MatCheckPreallocated(mat, 1);
8489: if (!mat->ops->coloringpatch) {
8490: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8491: } else {
8492: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8493: }
8494: PetscFunctionReturn(PETSC_SUCCESS);
8495: }
8497: /*@
8498: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8500: Logically Collective
8502: Input Parameter:
8503: . mat - the factored matrix to be reset
8505: Level: developer
8507: Notes:
8508: This routine should be used only with factored matrices formed by in-place
8509: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8510: format). This option can save memory, for example, when solving nonlinear
8511: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8512: ILU(0) preconditioner.
8514: One can specify in-place ILU(0) factorization by calling
8515: .vb
8516: PCType(pc,PCILU);
8517: PCFactorSeUseInPlace(pc);
8518: .ve
8519: or by using the options -pc_type ilu -pc_factor_in_place
8521: In-place factorization ILU(0) can also be used as a local
8522: solver for the blocks within the block Jacobi or additive Schwarz
8523: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8524: for details on setting local solver options.
8526: Most users should employ the `KSP` interface for linear solvers
8527: instead of working directly with matrix algebra routines such as this.
8528: See, e.g., `KSPCreate()`.
8530: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8531: @*/
8532: PetscErrorCode MatSetUnfactored(Mat mat)
8533: {
8534: PetscFunctionBegin;
8537: MatCheckPreallocated(mat, 1);
8538: mat->factortype = MAT_FACTOR_NONE;
8539: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8540: PetscUseTypeMethod(mat, setunfactored);
8541: PetscFunctionReturn(PETSC_SUCCESS);
8542: }
8544: /*@
8545: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8546: as the original matrix.
8548: Collective
8550: Input Parameters:
8551: + mat - the original matrix
8552: . isrow - parallel `IS` containing the rows this processor should obtain
8553: . 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.
8554: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8556: Output Parameter:
8557: . newmat - the new submatrix, of the same type as the original matrix
8559: Level: advanced
8561: Notes:
8562: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8564: Some matrix types place restrictions on the row and column indices, such
8565: 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;
8566: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8568: The index sets may not have duplicate entries.
8570: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8571: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8572: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8573: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8574: you are finished using it.
8576: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8577: the input matrix.
8579: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8581: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8582: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8584: Example usage:
8585: Consider the following 8x8 matrix with 34 non-zero values, that is
8586: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8587: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8588: as follows
8589: .vb
8590: 1 2 0 | 0 3 0 | 0 4
8591: Proc0 0 5 6 | 7 0 0 | 8 0
8592: 9 0 10 | 11 0 0 | 12 0
8593: -------------------------------------
8594: 13 0 14 | 15 16 17 | 0 0
8595: Proc1 0 18 0 | 19 20 21 | 0 0
8596: 0 0 0 | 22 23 0 | 24 0
8597: -------------------------------------
8598: Proc2 25 26 27 | 0 0 28 | 29 0
8599: 30 0 0 | 31 32 33 | 0 34
8600: .ve
8602: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8604: .vb
8605: 2 0 | 0 3 0 | 0
8606: Proc0 5 6 | 7 0 0 | 8
8607: -------------------------------
8608: Proc1 18 0 | 19 20 21 | 0
8609: -------------------------------
8610: Proc2 26 27 | 0 0 28 | 29
8611: 0 0 | 31 32 33 | 0
8612: .ve
8614: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8615: @*/
8616: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8617: {
8618: PetscMPIInt size;
8619: Mat *local;
8620: IS iscoltmp;
8621: PetscBool flg;
8623: PetscFunctionBegin;
8627: PetscAssertPointer(newmat, 5);
8630: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8631: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8632: PetscCheck(cll != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_INPLACE_MATRIX");
8634: MatCheckPreallocated(mat, 1);
8635: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8637: if (!iscol || isrow == iscol) {
8638: PetscBool stride;
8639: PetscMPIInt grabentirematrix = 0, grab;
8640: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8641: if (stride) {
8642: PetscInt first, step, n, rstart, rend;
8643: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8644: if (step == 1) {
8645: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8646: if (rstart == first) {
8647: PetscCall(ISGetLocalSize(isrow, &n));
8648: if (n == rend - rstart) grabentirematrix = 1;
8649: }
8650: }
8651: }
8652: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8653: if (grab) {
8654: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8655: if (cll == MAT_INITIAL_MATRIX) {
8656: *newmat = mat;
8657: PetscCall(PetscObjectReference((PetscObject)mat));
8658: }
8659: PetscFunctionReturn(PETSC_SUCCESS);
8660: }
8661: }
8663: if (!iscol) {
8664: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8665: } else {
8666: iscoltmp = iscol;
8667: }
8669: /* if original matrix is on just one processor then use submatrix generated */
8670: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8671: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8672: goto setproperties;
8673: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8674: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8675: *newmat = *local;
8676: PetscCall(PetscFree(local));
8677: goto setproperties;
8678: } else if (!mat->ops->createsubmatrix) {
8679: /* Create a new matrix type that implements the operation using the full matrix */
8680: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8681: switch (cll) {
8682: case MAT_INITIAL_MATRIX:
8683: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8684: break;
8685: case MAT_REUSE_MATRIX:
8686: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8687: break;
8688: default:
8689: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8690: }
8691: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8692: goto setproperties;
8693: }
8695: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8696: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8697: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8699: setproperties:
8700: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8701: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8702: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8703: }
8704: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8705: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8706: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
8707: PetscFunctionReturn(PETSC_SUCCESS);
8708: }
8710: /*@
8711: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8713: Not Collective
8715: Input Parameters:
8716: + A - the matrix we wish to propagate options from
8717: - B - the matrix we wish to propagate options to
8719: Level: beginner
8721: Note:
8722: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8724: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8725: @*/
8726: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8727: {
8728: PetscFunctionBegin;
8731: B->symmetry_eternal = A->symmetry_eternal;
8732: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8733: B->symmetric = A->symmetric;
8734: B->structurally_symmetric = A->structurally_symmetric;
8735: B->spd = A->spd;
8736: B->hermitian = A->hermitian;
8737: PetscFunctionReturn(PETSC_SUCCESS);
8738: }
8740: /*@
8741: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8742: used during the assembly process to store values that belong to
8743: other processors.
8745: Not Collective
8747: Input Parameters:
8748: + mat - the matrix
8749: . size - the initial size of the stash.
8750: - bsize - the initial size of the block-stash(if used).
8752: Options Database Keys:
8753: + -matstash_initial_size size or size0,size1,...,sizep-1 - set initial size
8754: - -matstash_block_initial_size bsize or bsize0,bsize1,...,bsizep-1 - set initial block size
8756: Level: intermediate
8758: Notes:
8759: The block-stash is used for values set with `MatSetValuesBlocked()` while
8760: the stash is used for values set with `MatSetValues()`
8762: Run with the option -info and look for output of the form
8763: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8764: to determine the appropriate value, MM, to use for size and
8765: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8766: to determine the value, BMM to use for bsize
8768: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8769: @*/
8770: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8771: {
8772: PetscFunctionBegin;
8775: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8776: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8777: PetscFunctionReturn(PETSC_SUCCESS);
8778: }
8780: /*@
8781: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8782: the matrix
8784: Neighbor-wise Collective
8786: Input Parameters:
8787: + A - the matrix
8788: . x - the vector to be multiplied by the interpolation operator
8789: - y - the vector to be added to the result
8791: Output Parameter:
8792: . w - the resulting vector
8794: Level: intermediate
8796: Notes:
8797: `w` may be the same vector as `y`.
8799: This allows one to use either the restriction or interpolation (its transpose)
8800: matrix to do the interpolation
8802: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8803: @*/
8804: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8805: {
8806: PetscInt M, N, Ny;
8808: PetscFunctionBegin;
8813: PetscCall(MatGetSize(A, &M, &N));
8814: PetscCall(VecGetSize(y, &Ny));
8815: if (M == Ny) PetscCall(MatMultAdd(A, x, y, w));
8816: else PetscCall(MatMultTransposeAdd(A, x, y, w));
8817: PetscFunctionReturn(PETSC_SUCCESS);
8818: }
8820: /*@
8821: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8822: the matrix
8824: Neighbor-wise Collective
8826: Input Parameters:
8827: + A - the matrix
8828: - x - the vector to be interpolated
8830: Output Parameter:
8831: . y - the resulting vector
8833: Level: intermediate
8835: Note:
8836: This allows one to use either the restriction or interpolation (its transpose)
8837: matrix to do the interpolation
8839: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8840: @*/
8841: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8842: {
8843: PetscInt M, N, Ny;
8845: PetscFunctionBegin;
8849: PetscCall(MatGetSize(A, &M, &N));
8850: PetscCall(VecGetSize(y, &Ny));
8851: if (M == Ny) PetscCall(MatMult(A, x, y));
8852: else PetscCall(MatMultTranspose(A, x, y));
8853: PetscFunctionReturn(PETSC_SUCCESS);
8854: }
8856: /*@
8857: MatRestrict - $y = A*x$ or $A^T*x$
8859: Neighbor-wise Collective
8861: Input Parameters:
8862: + A - the matrix
8863: - x - the vector to be restricted
8865: Output Parameter:
8866: . y - the resulting vector
8868: Level: intermediate
8870: Note:
8871: This allows one to use either the restriction or interpolation (its transpose)
8872: matrix to do the restriction
8874: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8875: @*/
8876: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8877: {
8878: PetscInt M, N, Nx;
8880: PetscFunctionBegin;
8884: PetscCall(MatGetSize(A, &M, &N));
8885: PetscCall(VecGetSize(x, &Nx));
8886: if (M == Nx) PetscCall(MatMultTranspose(A, x, y));
8887: else PetscCall(MatMult(A, x, y));
8888: PetscFunctionReturn(PETSC_SUCCESS);
8889: }
8891: /*@
8892: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8894: Neighbor-wise Collective
8896: Input Parameters:
8897: + A - the matrix
8898: . x - the input dense matrix to be multiplied
8899: - w - the input dense matrix to be added to the result
8901: Output Parameter:
8902: . y - the output dense matrix
8904: Level: intermediate
8906: Note:
8907: This allows one to use either the restriction or interpolation (its transpose)
8908: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8909: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8911: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8912: @*/
8913: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8914: {
8915: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8916: PetscBool trans = PETSC_TRUE;
8917: MatReuse reuse = MAT_INITIAL_MATRIX;
8919: PetscFunctionBegin;
8925: PetscCall(MatGetSize(A, &M, &N));
8926: PetscCall(MatGetSize(x, &Mx, &Nx));
8927: if (N == Mx) trans = PETSC_FALSE;
8928: 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);
8929: Mo = trans ? N : M;
8930: if (*y) {
8931: PetscCall(MatGetSize(*y, &My, &Ny));
8932: if (Mo == My && Nx == Ny) reuse = MAT_REUSE_MATRIX;
8933: else {
8934: 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);
8935: PetscCall(MatDestroy(y));
8936: }
8937: }
8939: if (w && *y == w) { /* this is to minimize changes in PCMG */
8940: PetscBool flg;
8942: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8943: if (w) {
8944: PetscInt My, Ny, Mw, Nw;
8946: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8947: PetscCall(MatGetSize(*y, &My, &Ny));
8948: PetscCall(MatGetSize(w, &Mw, &Nw));
8949: if (!flg || My != Mw || Ny != Nw) w = NULL;
8950: }
8951: if (!w) {
8952: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8953: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8954: PetscCall(PetscObjectDereference((PetscObject)w));
8955: } else PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8956: }
8957: if (!trans) PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8958: else PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8959: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8960: PetscFunctionReturn(PETSC_SUCCESS);
8961: }
8963: /*@
8964: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8966: Neighbor-wise Collective
8968: Input Parameters:
8969: + A - the matrix
8970: - x - the input dense matrix
8972: Output Parameter:
8973: . y - the output dense matrix
8975: Level: intermediate
8977: Note:
8978: This allows one to use either the restriction or interpolation (its transpose)
8979: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8980: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8982: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8983: @*/
8984: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8985: {
8986: PetscFunctionBegin;
8987: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8988: PetscFunctionReturn(PETSC_SUCCESS);
8989: }
8991: /*@
8992: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8994: Neighbor-wise Collective
8996: Input Parameters:
8997: + A - the matrix
8998: - x - the input dense matrix
9000: Output Parameter:
9001: . y - the output dense matrix
9003: Level: intermediate
9005: Note:
9006: This allows one to use either the restriction or interpolation (its transpose)
9007: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
9008: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9010: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
9011: @*/
9012: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
9013: {
9014: PetscFunctionBegin;
9015: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9016: PetscFunctionReturn(PETSC_SUCCESS);
9017: }
9019: /*@
9020: MatGetNullSpace - retrieves the null space of a matrix.
9022: Logically Collective
9024: Input Parameters:
9025: + mat - the matrix
9026: - nullsp - the null space object
9028: Level: developer
9030: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
9031: @*/
9032: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
9033: {
9034: PetscFunctionBegin;
9036: PetscAssertPointer(nullsp, 2);
9037: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
9038: PetscFunctionReturn(PETSC_SUCCESS);
9039: }
9041: /*@C
9042: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
9044: Logically Collective
9046: Input Parameters:
9047: + n - the number of matrices
9048: - mat - the array of matrices
9050: Output Parameters:
9051: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
9053: Level: developer
9055: Note:
9056: Call `MatRestoreNullspaces()` to provide these to another array of matrices
9058: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9059: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
9060: @*/
9061: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9062: {
9063: PetscFunctionBegin;
9064: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9065: PetscAssertPointer(mat, 2);
9066: PetscAssertPointer(nullsp, 3);
9068: PetscCall(PetscCalloc1(3 * n, nullsp));
9069: for (PetscInt i = 0; i < n; i++) {
9071: (*nullsp)[i] = mat[i]->nullsp;
9072: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
9073: (*nullsp)[n + i] = mat[i]->nearnullsp;
9074: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
9075: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
9076: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
9077: }
9078: PetscFunctionReturn(PETSC_SUCCESS);
9079: }
9081: /*@C
9082: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
9084: Logically Collective
9086: Input Parameters:
9087: + n - the number of matrices
9088: . mat - the array of matrices
9089: - nullsp - an array of null spaces
9091: Level: developer
9093: Note:
9094: Call `MatGetNullSpaces()` to create `nullsp`
9096: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9097: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
9098: @*/
9099: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9100: {
9101: PetscFunctionBegin;
9102: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9103: PetscAssertPointer(mat, 2);
9104: PetscAssertPointer(nullsp, 3);
9105: PetscAssertPointer(*nullsp, 3);
9107: for (PetscInt i = 0; i < n; i++) {
9109: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
9110: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9111: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9112: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9113: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9114: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9115: }
9116: PetscCall(PetscFree(*nullsp));
9117: PetscFunctionReturn(PETSC_SUCCESS);
9118: }
9120: /*@
9121: MatSetNullSpace - attaches a null space to a matrix.
9123: Logically Collective
9125: Input Parameters:
9126: + mat - the matrix
9127: - nullsp - the null space object
9129: Level: advanced
9131: Notes:
9132: This null space is used by the `KSP` linear solvers to solve singular systems.
9134: 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`
9136: 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
9137: to zero but the linear system will still be solved in a least squares sense.
9139: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9140: the domain of a matrix $A$ (from $R^n$ to $R^m$ ($m$ rows, $n$ columns) $R^n$ = the direct sum of the null space of $A$, $n(A)$, plus the range of $A^T$, $R(A^T)$.
9141: 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
9142: $n(A^T))$ 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
9143: 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 $n(A^T)$.
9144: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9146: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9147: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9148: routine also automatically calls `MatSetTransposeNullSpace()`.
9150: The user should call `MatNullSpaceDestroy()`.
9152: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9153: `KSPSetPCSide()`
9154: @*/
9155: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9156: {
9157: PetscFunctionBegin;
9160: PetscCall(PetscObjectReference((PetscObject)nullsp));
9161: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9162: mat->nullsp = nullsp;
9163: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9164: PetscFunctionReturn(PETSC_SUCCESS);
9165: }
9167: /*@
9168: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
9170: Logically Collective
9172: Input Parameters:
9173: + mat - the matrix
9174: - nullsp - the null space object
9176: Level: developer
9178: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9179: @*/
9180: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9181: {
9182: PetscFunctionBegin;
9185: PetscAssertPointer(nullsp, 2);
9186: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9187: PetscFunctionReturn(PETSC_SUCCESS);
9188: }
9190: /*@
9191: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9193: Logically Collective
9195: Input Parameters:
9196: + mat - the matrix
9197: - nullsp - the null space object
9199: Level: advanced
9201: Notes:
9202: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9204: See `MatSetNullSpace()`
9206: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9207: @*/
9208: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9209: {
9210: PetscFunctionBegin;
9213: PetscCall(PetscObjectReference((PetscObject)nullsp));
9214: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9215: mat->transnullsp = nullsp;
9216: PetscFunctionReturn(PETSC_SUCCESS);
9217: }
9219: /*@
9220: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9221: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9223: Logically Collective
9225: Input Parameters:
9226: + mat - the matrix
9227: - nullsp - the null space object
9229: Level: advanced
9231: Notes:
9232: Overwrites any previous near null space that may have been attached
9234: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9236: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9237: @*/
9238: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9239: {
9240: PetscFunctionBegin;
9244: MatCheckPreallocated(mat, 1);
9245: PetscCall(PetscObjectReference((PetscObject)nullsp));
9246: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9247: mat->nearnullsp = nullsp;
9248: PetscFunctionReturn(PETSC_SUCCESS);
9249: }
9251: /*@
9252: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9254: Not Collective
9256: Input Parameter:
9257: . mat - the matrix
9259: Output Parameter:
9260: . nullsp - the null space object, `NULL` if not set
9262: Level: advanced
9264: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9265: @*/
9266: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9267: {
9268: PetscFunctionBegin;
9271: PetscAssertPointer(nullsp, 2);
9272: MatCheckPreallocated(mat, 1);
9273: *nullsp = mat->nearnullsp;
9274: PetscFunctionReturn(PETSC_SUCCESS);
9275: }
9277: /*@
9278: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9280: Collective
9282: Input Parameters:
9283: + mat - the matrix
9284: . row - row/column permutation
9285: - info - information on desired factorization process
9287: Level: developer
9289: Notes:
9290: Probably really in-place only when level of fill is zero, otherwise allocates
9291: new space to store factored matrix and deletes previous memory.
9293: Most users should employ the `KSP` interface for linear solvers
9294: instead of working directly with matrix algebra routines such as this.
9295: See, e.g., `KSPCreate()`.
9297: Fortran Note:
9298: A valid (non-null) `info` argument must be provided
9300: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9301: @*/
9302: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9303: {
9304: PetscFunctionBegin;
9308: PetscAssertPointer(info, 3);
9309: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9310: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9311: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9312: MatCheckPreallocated(mat, 1);
9313: PetscUseTypeMethod(mat, iccfactor, row, info);
9314: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9315: PetscFunctionReturn(PETSC_SUCCESS);
9316: }
9318: /*@
9319: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9320: ghosted ones.
9322: Not Collective
9324: Input Parameters:
9325: + mat - the matrix
9326: - diag - the diagonal values, including ghost ones
9328: Level: developer
9330: Notes:
9331: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9333: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9335: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9336: @*/
9337: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9338: {
9339: PetscMPIInt size;
9341: PetscFunctionBegin;
9346: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9347: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9348: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9349: if (size == 1) {
9350: PetscInt n, m;
9351: PetscCall(VecGetSize(diag, &n));
9352: PetscCall(MatGetSize(mat, NULL, &m));
9353: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9354: PetscCall(MatDiagonalScale(mat, NULL, diag));
9355: } else PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9356: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9357: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9358: PetscFunctionReturn(PETSC_SUCCESS);
9359: }
9361: /*@
9362: MatGetInertia - Gets the inertia from a factored matrix
9364: Collective
9366: Input Parameter:
9367: . mat - the matrix
9369: Output Parameters:
9370: + nneg - number of negative eigenvalues
9371: . nzero - number of zero eigenvalues
9372: - npos - number of positive eigenvalues
9374: Level: advanced
9376: Note:
9377: Matrix must have been factored by `MatCholeskyFactor()`
9379: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9380: @*/
9381: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9382: {
9383: PetscFunctionBegin;
9386: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9387: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9388: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9389: PetscFunctionReturn(PETSC_SUCCESS);
9390: }
9392: /*@C
9393: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9395: Neighbor-wise Collective
9397: Input Parameters:
9398: + mat - the factored matrix obtained with `MatGetFactor()`
9399: - b - the right-hand-side vectors
9401: Output Parameter:
9402: . x - the result vectors
9404: Level: developer
9406: Note:
9407: The vectors `b` and `x` cannot be the same. I.e., one cannot
9408: call `MatSolves`(A,x,x).
9410: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9411: @*/
9412: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9413: {
9414: PetscFunctionBegin;
9417: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9418: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9419: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9421: MatCheckPreallocated(mat, 1);
9422: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9423: PetscUseTypeMethod(mat, solves, b, x);
9424: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9425: PetscFunctionReturn(PETSC_SUCCESS);
9426: }
9428: /*@
9429: MatIsSymmetric - Test whether a matrix is symmetric
9431: Collective
9433: Input Parameters:
9434: + A - the matrix to test
9435: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9437: Output Parameter:
9438: . flg - the result
9440: Level: intermediate
9442: Notes:
9443: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9445: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9447: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9448: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9450: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9451: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9452: @*/
9453: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9454: {
9455: PetscFunctionBegin;
9457: PetscAssertPointer(flg, 3);
9458: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9459: else {
9460: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9461: else PetscCall(MatIsTranspose(A, A, tol, flg));
9462: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9463: }
9464: PetscFunctionReturn(PETSC_SUCCESS);
9465: }
9467: /*@
9468: MatIsHermitian - Test whether a matrix is Hermitian
9470: Collective
9472: Input Parameters:
9473: + A - the matrix to test
9474: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9476: Output Parameter:
9477: . flg - the result
9479: Level: intermediate
9481: Notes:
9482: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9484: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9486: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9487: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9489: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9490: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9491: @*/
9492: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9493: {
9494: PetscFunctionBegin;
9496: PetscAssertPointer(flg, 3);
9497: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9498: else {
9499: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9500: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9501: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9502: }
9503: PetscFunctionReturn(PETSC_SUCCESS);
9504: }
9506: /*@
9507: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9509: Not Collective
9511: Input Parameter:
9512: . A - the matrix to check
9514: Output Parameters:
9515: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9516: - flg - the result (only valid if set is `PETSC_TRUE`)
9518: Level: advanced
9520: Notes:
9521: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9522: if you want it explicitly checked
9524: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9525: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9527: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9528: @*/
9529: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9530: {
9531: PetscFunctionBegin;
9533: PetscAssertPointer(set, 2);
9534: PetscAssertPointer(flg, 3);
9535: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9536: *set = PETSC_TRUE;
9537: *flg = PetscBool3ToBool(A->symmetric);
9538: } else *set = PETSC_FALSE;
9539: PetscFunctionReturn(PETSC_SUCCESS);
9540: }
9542: /*@
9543: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9545: Not Collective
9547: Input Parameter:
9548: . A - the matrix to check
9550: Output Parameters:
9551: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9552: - flg - the result (only valid if set is `PETSC_TRUE`)
9554: Level: advanced
9556: Notes:
9557: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9559: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9560: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9562: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9563: @*/
9564: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9565: {
9566: PetscFunctionBegin;
9568: PetscAssertPointer(set, 2);
9569: PetscAssertPointer(flg, 3);
9570: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9571: *set = PETSC_TRUE;
9572: *flg = PetscBool3ToBool(A->spd);
9573: } else *set = PETSC_FALSE;
9574: PetscFunctionReturn(PETSC_SUCCESS);
9575: }
9577: /*@
9578: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9580: Not Collective
9582: Input Parameter:
9583: . A - the matrix to check
9585: Output Parameters:
9586: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9587: - flg - the result (only valid if set is `PETSC_TRUE`)
9589: Level: advanced
9591: Notes:
9592: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9593: if you want it explicitly checked
9595: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9596: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9598: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9599: @*/
9600: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9601: {
9602: PetscFunctionBegin;
9604: PetscAssertPointer(set, 2);
9605: PetscAssertPointer(flg, 3);
9606: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9607: *set = PETSC_TRUE;
9608: *flg = PetscBool3ToBool(A->hermitian);
9609: } else *set = PETSC_FALSE;
9610: PetscFunctionReturn(PETSC_SUCCESS);
9611: }
9613: /*@
9614: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9616: Collective
9618: Input Parameter:
9619: . A - the matrix to test
9621: Output Parameter:
9622: . flg - the result
9624: Level: intermediate
9626: Notes:
9627: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9629: 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
9630: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9632: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9633: @*/
9634: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9635: {
9636: PetscFunctionBegin;
9638: PetscAssertPointer(flg, 2);
9639: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) *flg = PetscBool3ToBool(A->structurally_symmetric);
9640: else {
9641: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9642: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9643: }
9644: PetscFunctionReturn(PETSC_SUCCESS);
9645: }
9647: /*@
9648: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9650: Not Collective
9652: Input Parameter:
9653: . A - the matrix to check
9655: Output Parameters:
9656: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9657: - flg - the result (only valid if set is PETSC_TRUE)
9659: Level: advanced
9661: Notes:
9662: 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
9663: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9665: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9667: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9668: @*/
9669: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9670: {
9671: PetscFunctionBegin;
9673: PetscAssertPointer(set, 2);
9674: PetscAssertPointer(flg, 3);
9675: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9676: *set = PETSC_TRUE;
9677: *flg = PetscBool3ToBool(A->structurally_symmetric);
9678: } else *set = PETSC_FALSE;
9679: PetscFunctionReturn(PETSC_SUCCESS);
9680: }
9682: /*@
9683: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9684: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9686: Not Collective
9688: Input Parameter:
9689: . mat - the matrix
9691: Output Parameters:
9692: + nstash - the size of the stash
9693: . reallocs - the number of additional mallocs incurred.
9694: . bnstash - the size of the block stash
9695: - breallocs - the number of additional mallocs incurred.in the block stash
9697: Level: advanced
9699: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9700: @*/
9701: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9702: {
9703: PetscFunctionBegin;
9704: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9705: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9706: PetscFunctionReturn(PETSC_SUCCESS);
9707: }
9709: /*@
9710: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9711: parallel layout, `PetscLayout` for rows and columns
9713: Collective
9715: Input Parameter:
9716: . mat - the matrix
9718: Output Parameters:
9719: + right - (optional) vector that the matrix can be multiplied against
9720: - left - (optional) vector that the matrix vector product can be stored in
9722: Options Database Key:
9723: . -mat_vec_type type - set the `VecType` of the created vectors during `MatSetFromOptions()`
9725: Level: advanced
9727: Notes:
9728: 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()`.
9730: The `VecType` of the created vectors is determined by the `MatType` of `mat`. This can be overridden by using `MatSetVecType()` or the option `-mat_vec_type`.
9732: These are new vectors which are not owned by the `mat`, they should be destroyed with `VecDestroy()` when no longer needed.
9734: PETSc `Vec` always have all zero entries when created with `MatCreateVecs()` until routines such as `VecSet()` or `VecSetValues()`
9735: are used to change the values. There is no reason to call `VecZeroEntries()` after creation.
9737: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`, `MatSetVecType()`
9738: @*/
9739: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9740: {
9741: PetscFunctionBegin;
9744: if (mat->ops->getvecs) {
9745: PetscUseTypeMethod(mat, getvecs, right, left);
9746: } else {
9747: if (right) {
9748: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9749: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9750: PetscCall(VecSetType(*right, mat->defaultvectype));
9751: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9752: if (mat->boundtocpu && mat->bindingpropagates) {
9753: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9754: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9755: }
9756: #endif
9757: }
9758: if (left) {
9759: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9760: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9761: PetscCall(VecSetType(*left, mat->defaultvectype));
9762: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9763: if (mat->boundtocpu && mat->bindingpropagates) {
9764: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9765: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9766: }
9767: #endif
9768: }
9769: }
9770: PetscFunctionReturn(PETSC_SUCCESS);
9771: }
9773: /*@
9774: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9775: with default values.
9777: Not Collective
9779: Input Parameter:
9780: . info - the `MatFactorInfo` data structure
9782: Level: developer
9784: Notes:
9785: The solvers are generally used through the `KSP` and `PC` objects, for example
9786: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9788: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9790: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9791: @*/
9792: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9793: {
9794: PetscFunctionBegin;
9795: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9796: PetscFunctionReturn(PETSC_SUCCESS);
9797: }
9799: /*@
9800: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9802: Collective
9804: Input Parameters:
9805: + mat - the factored matrix
9806: - is - the index set defining the Schur indices (0-based)
9808: Level: advanced
9810: Notes:
9811: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9813: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9815: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9817: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9818: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9819: @*/
9820: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9821: {
9822: PetscErrorCode (*f)(Mat, IS);
9824: PetscFunctionBegin;
9829: PetscCheckSameComm(mat, 1, is, 2);
9830: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9831: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9832: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9833: PetscCall(MatDestroy(&mat->schur));
9834: PetscCall((*f)(mat, is));
9835: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9836: PetscFunctionReturn(PETSC_SUCCESS);
9837: }
9839: /*@
9840: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9842: Logically Collective
9844: Input Parameters:
9845: + F - the factored matrix obtained by calling `MatGetFactor()`
9846: . S - location where to return the Schur complement, can be `NULL`
9847: - status - the status of the Schur complement matrix, can be `NULL`
9849: Level: advanced
9851: Notes:
9852: You must call `MatFactorSetSchurIS()` before calling this routine.
9854: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9856: The routine provides a copy of the Schur matrix stored within the solver data structures.
9857: The caller must destroy the object when it is no longer needed.
9858: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9860: 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)
9862: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9864: Developer Note:
9865: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9866: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9868: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9869: @*/
9870: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9871: {
9872: PetscFunctionBegin;
9874: if (S) PetscAssertPointer(S, 2);
9875: if (status) PetscAssertPointer(status, 3);
9876: if (S) {
9877: PetscErrorCode (*f)(Mat, Mat *);
9879: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9880: if (f) PetscCall((*f)(F, S));
9881: else PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9882: }
9883: if (status) *status = F->schur_status;
9884: PetscFunctionReturn(PETSC_SUCCESS);
9885: }
9887: /*@
9888: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9890: Logically Collective
9892: Input Parameters:
9893: + F - the factored matrix obtained by calling `MatGetFactor()`
9894: . S - location where to return the Schur complement, can be `NULL`
9895: - status - the status of the Schur complement matrix, can be `NULL`
9897: Level: advanced
9899: Notes:
9900: You must call `MatFactorSetSchurIS()` before calling this routine.
9902: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9904: The routine returns a the Schur Complement stored within the data structures of the solver.
9906: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9908: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9910: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9912: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9914: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9915: @*/
9916: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9917: {
9918: PetscFunctionBegin;
9920: if (S) {
9921: PetscAssertPointer(S, 2);
9922: *S = F->schur;
9923: }
9924: if (status) {
9925: PetscAssertPointer(status, 3);
9926: *status = F->schur_status;
9927: }
9928: PetscFunctionReturn(PETSC_SUCCESS);
9929: }
9931: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9932: {
9933: Mat S = F->schur;
9935: PetscFunctionBegin;
9936: switch (F->schur_status) {
9937: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9938: case MAT_FACTOR_SCHUR_INVERTED:
9939: if (S) {
9940: S->ops->solve = NULL;
9941: S->ops->matsolve = NULL;
9942: S->ops->solvetranspose = NULL;
9943: S->ops->matsolvetranspose = NULL;
9944: S->ops->solveadd = NULL;
9945: S->ops->solvetransposeadd = NULL;
9946: S->factortype = MAT_FACTOR_NONE;
9947: PetscCall(PetscFree(S->solvertype));
9948: }
9949: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9950: break;
9951: default:
9952: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9953: }
9954: PetscFunctionReturn(PETSC_SUCCESS);
9955: }
9957: /*@
9958: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9960: Logically Collective
9962: Input Parameters:
9963: + F - the factored matrix obtained by calling `MatGetFactor()`
9964: . S - location where the Schur complement is stored
9965: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9967: Level: advanced
9969: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9970: @*/
9971: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9972: {
9973: PetscFunctionBegin;
9975: if (S) {
9977: *S = NULL;
9978: }
9979: F->schur_status = status;
9980: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9981: PetscFunctionReturn(PETSC_SUCCESS);
9982: }
9984: /*@
9985: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9987: Logically Collective
9989: Input Parameters:
9990: + F - the factored matrix obtained by calling `MatGetFactor()`
9991: . rhs - location where the right-hand side of the Schur complement system is stored
9992: - sol - location where the solution of the Schur complement system has to be returned
9994: Level: advanced
9996: Notes:
9997: The sizes of the vectors should match the size of the Schur complement
9999: Must be called after `MatFactorSetSchurIS()`
10001: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
10002: @*/
10003: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
10004: {
10005: PetscFunctionBegin;
10012: PetscCheckSameComm(F, 1, rhs, 2);
10013: PetscCheckSameComm(F, 1, sol, 3);
10014: PetscCall(MatFactorFactorizeSchurComplement(F));
10015: switch (F->schur_status) {
10016: case MAT_FACTOR_SCHUR_FACTORED:
10017: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
10018: break;
10019: case MAT_FACTOR_SCHUR_INVERTED:
10020: PetscCall(MatMultTranspose(F->schur, rhs, sol));
10021: break;
10022: default:
10023: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10024: }
10025: PetscFunctionReturn(PETSC_SUCCESS);
10026: }
10028: /*@
10029: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
10031: Logically Collective
10033: Input Parameters:
10034: + F - the factored matrix obtained by calling `MatGetFactor()`
10035: . rhs - location where the right-hand side of the Schur complement system is stored
10036: - sol - location where the solution of the Schur complement system has to be returned
10038: Level: advanced
10040: Notes:
10041: The sizes of the vectors should match the size of the Schur complement
10043: Must be called after `MatFactorSetSchurIS()`
10045: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
10046: @*/
10047: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
10048: {
10049: PetscFunctionBegin;
10056: PetscCheckSameComm(F, 1, rhs, 2);
10057: PetscCheckSameComm(F, 1, sol, 3);
10058: PetscCall(MatFactorFactorizeSchurComplement(F));
10059: switch (F->schur_status) {
10060: case MAT_FACTOR_SCHUR_FACTORED:
10061: PetscCall(MatSolve(F->schur, rhs, sol));
10062: break;
10063: case MAT_FACTOR_SCHUR_INVERTED:
10064: PetscCall(MatMult(F->schur, rhs, sol));
10065: break;
10066: default:
10067: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10068: }
10069: PetscFunctionReturn(PETSC_SUCCESS);
10070: }
10072: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
10073: #if PetscDefined(HAVE_CUDA)
10074: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
10075: #endif
10077: /* Schur status updated in the interface */
10078: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
10079: {
10080: Mat S = F->schur;
10082: PetscFunctionBegin;
10083: if (S) {
10084: PetscMPIInt size;
10085: PetscBool isdense, isdensecuda;
10087: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
10088: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
10089: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
10090: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
10091: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
10092: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
10093: if (isdense) {
10094: PetscCall(MatSeqDenseInvertFactors_Private(S));
10095: } else if (isdensecuda) {
10096: #if defined(PETSC_HAVE_CUDA)
10097: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
10098: #endif
10099: }
10100: // HIP??????????????
10101: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
10102: }
10103: PetscFunctionReturn(PETSC_SUCCESS);
10104: }
10106: /*@
10107: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
10109: Logically Collective
10111: Input Parameter:
10112: . F - the factored matrix obtained by calling `MatGetFactor()`
10114: Level: advanced
10116: Notes:
10117: Must be called after `MatFactorSetSchurIS()`.
10119: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
10121: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10122: @*/
10123: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10124: {
10125: PetscFunctionBegin;
10128: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10129: PetscCall(MatFactorFactorizeSchurComplement(F));
10130: PetscCall(MatFactorInvertSchurComplement_Private(F));
10131: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10132: PetscFunctionReturn(PETSC_SUCCESS);
10133: }
10135: /*@
10136: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10138: Logically Collective
10140: Input Parameter:
10141: . F - the factored matrix obtained by calling `MatGetFactor()`
10143: Level: advanced
10145: Note:
10146: Must be called after `MatFactorSetSchurIS()`
10148: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10149: @*/
10150: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10151: {
10152: MatFactorInfo info;
10154: PetscFunctionBegin;
10157: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10158: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10159: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10160: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10161: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10162: } else {
10163: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10164: }
10165: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10166: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10167: PetscFunctionReturn(PETSC_SUCCESS);
10168: }
10170: /*@
10171: MatPtAP - Creates the matrix product $C = P^T * A * P$
10173: Neighbor-wise Collective
10175: Input Parameters:
10176: + A - the matrix
10177: . P - the projection matrix
10178: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10179: - 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
10180: if the result is a dense matrix this is irrelevant
10182: Output Parameter:
10183: . C - the product matrix
10185: Level: intermediate
10187: Notes:
10188: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10190: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_PtAP`
10191: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10193: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10195: Developer Note:
10196: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10198: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10199: @*/
10200: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10201: {
10202: PetscFunctionBegin;
10203: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10204: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10206: if (scall == MAT_INITIAL_MATRIX) {
10207: PetscCall(MatProductCreate(A, P, NULL, C));
10208: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10209: PetscCall(MatProductSetAlgorithm(*C, "default"));
10210: PetscCall(MatProductSetFill(*C, fill));
10212: (*C)->product->api_user = PETSC_TRUE;
10213: PetscCall(MatProductSetFromOptions(*C));
10214: 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);
10215: PetscCall(MatProductSymbolic(*C));
10216: } else { /* scall == MAT_REUSE_MATRIX */
10217: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10218: }
10220: PetscCall(MatProductNumeric(*C));
10221: if (A->symmetric == PETSC_BOOL3_TRUE) {
10222: PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10223: (*C)->spd = A->spd;
10224: }
10225: PetscFunctionReturn(PETSC_SUCCESS);
10226: }
10228: /*@
10229: MatRARt - Creates the matrix product $C = R * A * R^T$
10231: Neighbor-wise Collective
10233: Input Parameters:
10234: + A - the matrix
10235: . R - the projection matrix
10236: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10237: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10238: if the result is a dense matrix this is irrelevant
10240: Output Parameter:
10241: . C - the product matrix
10243: Level: intermediate
10245: Notes:
10246: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10248: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_RARt`
10249: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10251: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10252: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10253: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10254: We recommend using `MatPtAP()` when possible.
10256: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10258: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10259: @*/
10260: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10261: {
10262: PetscFunctionBegin;
10263: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10264: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10266: if (scall == MAT_INITIAL_MATRIX) {
10267: PetscCall(MatProductCreate(A, R, NULL, C));
10268: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10269: PetscCall(MatProductSetAlgorithm(*C, "default"));
10270: PetscCall(MatProductSetFill(*C, fill));
10272: (*C)->product->api_user = PETSC_TRUE;
10273: PetscCall(MatProductSetFromOptions(*C));
10274: 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);
10275: PetscCall(MatProductSymbolic(*C));
10276: } else { /* scall == MAT_REUSE_MATRIX */
10277: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10278: }
10280: PetscCall(MatProductNumeric(*C));
10281: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10282: PetscFunctionReturn(PETSC_SUCCESS);
10283: }
10285: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10286: {
10287: PetscBool flg = PETSC_TRUE;
10289: PetscFunctionBegin;
10290: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10291: if (scall == MAT_INITIAL_MATRIX) {
10292: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10293: PetscCall(MatProductCreate(A, B, NULL, C));
10294: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10295: PetscCall(MatProductSetFill(*C, fill));
10296: } else { /* scall == MAT_REUSE_MATRIX */
10297: Mat_Product *product = (*C)->product;
10299: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10300: if (flg && product && product->type != ptype) {
10301: PetscCall(MatProductClear(*C));
10302: product = NULL;
10303: }
10304: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10305: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10306: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10307: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10308: product = (*C)->product;
10309: product->fill = fill;
10310: product->clear = PETSC_TRUE;
10311: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10312: flg = PETSC_FALSE;
10313: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10314: }
10315: }
10316: if (flg) {
10317: (*C)->product->api_user = PETSC_TRUE;
10318: PetscCall(MatProductSetType(*C, ptype));
10319: PetscCall(MatProductSetFromOptions(*C));
10320: PetscCall(MatProductSymbolic(*C));
10321: }
10322: PetscCall(MatProductNumeric(*C));
10323: PetscFunctionReturn(PETSC_SUCCESS);
10324: }
10326: /*@
10327: MatMatMult - Performs matrix-matrix multiplication $ C=A*B $.
10329: Neighbor-wise Collective
10331: Input Parameters:
10332: + A - the left matrix
10333: . B - the right matrix
10334: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10335: - 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
10336: if the result is a dense matrix this is irrelevant
10338: Output Parameter:
10339: . C - the product matrix
10341: Notes:
10342: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10344: `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
10345: call to this function with `MAT_INITIAL_MATRIX`.
10347: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10349: 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`,
10350: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10352: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10354: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AB`
10355: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10357: Example of Usage:
10358: .vb
10359: MatProductCreate(A,B,NULL,&C);
10360: MatProductSetType(C,MATPRODUCT_AB);
10361: MatProductSymbolic(C);
10362: MatProductNumeric(C); // compute C=A * B
10363: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10364: MatProductNumeric(C);
10365: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10366: MatProductNumeric(C);
10367: .ve
10369: Level: intermediate
10371: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10372: @*/
10373: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10374: {
10375: PetscFunctionBegin;
10376: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10377: PetscFunctionReturn(PETSC_SUCCESS);
10378: }
10380: /*@
10381: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10383: Neighbor-wise Collective
10385: Input Parameters:
10386: + A - the left matrix
10387: . B - the right matrix
10388: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10389: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10391: Output Parameter:
10392: . C - the product matrix
10394: Options Database Key:
10395: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10396: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10397: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10399: Level: intermediate
10401: Notes:
10402: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10404: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10406: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10407: actually needed.
10409: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10410: and for pairs of `MATMPIDENSE` matrices.
10412: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABt`
10413: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10415: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10417: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()`, `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10418: @*/
10419: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10420: {
10421: PetscFunctionBegin;
10422: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10423: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10424: PetscFunctionReturn(PETSC_SUCCESS);
10425: }
10427: /*@
10428: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10430: Neighbor-wise Collective
10432: Input Parameters:
10433: + A - the left matrix
10434: . B - the right matrix
10435: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10436: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10438: Output Parameter:
10439: . C - the product matrix
10441: Level: intermediate
10443: Notes:
10444: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10446: `MAT_REUSE_MATRIX` can only be used if `A` and `B` have the same nonzero pattern as in the previous call.
10448: This is a convenience routine that wraps the use of `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AtB`
10449: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10451: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10452: actually needed.
10454: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10455: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10457: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10459: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10460: @*/
10461: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10462: {
10463: PetscFunctionBegin;
10464: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10465: PetscFunctionReturn(PETSC_SUCCESS);
10466: }
10468: /*@
10469: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10471: Neighbor-wise Collective
10473: Input Parameters:
10474: + A - the left matrix
10475: . B - the middle matrix
10476: . C - the right matrix
10477: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10478: - 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
10479: if the result is a dense matrix this is irrelevant
10481: Output Parameter:
10482: . D - the product matrix
10484: Level: intermediate
10486: Notes:
10487: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10489: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10491: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABC`
10492: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10494: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10495: actually needed.
10497: If you have many matrices with the same non-zero structure to multiply, you
10498: should use `MAT_REUSE_MATRIX` in all calls but the first
10500: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10502: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10503: @*/
10504: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10505: {
10506: PetscFunctionBegin;
10507: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10508: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10510: if (scall == MAT_INITIAL_MATRIX) {
10511: PetscCall(MatProductCreate(A, B, C, D));
10512: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10513: PetscCall(MatProductSetAlgorithm(*D, "default"));
10514: PetscCall(MatProductSetFill(*D, fill));
10516: (*D)->product->api_user = PETSC_TRUE;
10517: PetscCall(MatProductSetFromOptions(*D));
10518: 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,
10519: ((PetscObject)C)->type_name);
10520: PetscCall(MatProductSymbolic(*D));
10521: } else { /* user may change input matrices when REUSE */
10522: PetscCall(MatProductReplaceMats(A, B, C, *D));
10523: }
10524: PetscCall(MatProductNumeric(*D));
10525: PetscFunctionReturn(PETSC_SUCCESS);
10526: }
10528: /*@
10529: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10531: Collective
10533: Input Parameters:
10534: + mat - the matrix
10535: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10536: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10537: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10539: Output Parameter:
10540: . matredundant - redundant matrix
10542: Level: advanced
10544: Notes:
10545: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10546: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10548: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10549: calling it.
10551: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10553: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10554: @*/
10555: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10556: {
10557: MPI_Comm comm;
10558: PetscMPIInt size;
10559: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10560: Mat_Redundant *redund = NULL;
10561: PetscSubcomm psubcomm = NULL;
10562: MPI_Comm subcomm_in = subcomm;
10563: Mat *matseq;
10564: IS isrow, iscol;
10565: PetscBool newsubcomm = PETSC_FALSE;
10567: PetscFunctionBegin;
10569: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10570: PetscAssertPointer(*matredundant, 5);
10572: }
10574: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10575: if (size == 1 || nsubcomm == 1) {
10576: if (reuse == MAT_INITIAL_MATRIX) {
10577: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10578: } else {
10579: 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");
10580: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10581: }
10582: PetscFunctionReturn(PETSC_SUCCESS);
10583: }
10585: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10586: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10587: MatCheckPreallocated(mat, 1);
10589: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10590: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10591: /* create psubcomm, then get subcomm */
10592: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10593: PetscCallMPI(MPI_Comm_size(comm, &size));
10594: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10596: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10597: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10598: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10599: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10600: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10601: newsubcomm = PETSC_TRUE;
10602: PetscCall(PetscSubcommDestroy(&psubcomm));
10603: }
10605: /* get isrow, iscol and a local sequential matrix matseq[0] */
10606: if (reuse == MAT_INITIAL_MATRIX) {
10607: mloc_sub = PETSC_DECIDE;
10608: nloc_sub = PETSC_DECIDE;
10609: if (bs < 1) {
10610: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10611: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10612: } else {
10613: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10614: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10615: }
10616: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10617: rstart = rend - mloc_sub;
10618: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10619: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10620: PetscCall(ISSetIdentity(iscol));
10621: } else { /* reuse == MAT_REUSE_MATRIX */
10622: 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");
10623: /* retrieve subcomm */
10624: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10625: redund = (*matredundant)->redundant;
10626: isrow = redund->isrow;
10627: iscol = redund->iscol;
10628: matseq = redund->matseq;
10629: }
10630: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10632: /* get matredundant over subcomm */
10633: if (reuse == MAT_INITIAL_MATRIX) {
10634: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10636: /* create a supporting struct and attach it to C for reuse */
10637: PetscCall(PetscNew(&redund));
10638: (*matredundant)->redundant = redund;
10639: redund->isrow = isrow;
10640: redund->iscol = iscol;
10641: redund->matseq = matseq;
10642: if (newsubcomm) {
10643: redund->subcomm = subcomm;
10644: } else {
10645: redund->subcomm = MPI_COMM_NULL;
10646: }
10647: } else {
10648: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10649: }
10650: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10651: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10652: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10653: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10654: }
10655: #endif
10656: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10657: PetscFunctionReturn(PETSC_SUCCESS);
10658: }
10660: /*@C
10661: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10662: a given `Mat`. Each submatrix can span multiple procs.
10664: Collective
10666: Input Parameters:
10667: + mat - the matrix
10668: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10669: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10671: Output Parameter:
10672: . subMat - parallel sub-matrices each spanning a given `subcomm`
10674: Level: advanced
10676: Notes:
10677: The submatrix partition across processors is dictated by `subComm` a
10678: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10679: is not restricted to be grouped with consecutive original MPI processes.
10681: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10682: map directly to the layout of the original matrix [wrt the local
10683: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10684: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10685: the `subMat`. However the offDiagMat looses some columns - and this is
10686: reconstructed with `MatSetValues()`
10688: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10690: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10691: @*/
10692: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10693: {
10694: PetscMPIInt commsize, subCommSize;
10696: PetscFunctionBegin;
10697: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10698: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10699: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10701: 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");
10702: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10703: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10704: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10705: PetscFunctionReturn(PETSC_SUCCESS);
10706: }
10708: /*@
10709: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10711: Not Collective
10713: Input Parameters:
10714: + mat - matrix to extract local submatrix from
10715: . isrow - local row indices for submatrix
10716: - iscol - local column indices for submatrix
10718: Output Parameter:
10719: . submat - the submatrix
10721: Level: intermediate
10723: Notes:
10724: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10726: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10727: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10729: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10730: `MatSetValuesBlockedLocal()` will also be implemented.
10732: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10733: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10735: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10736: @*/
10737: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10738: {
10739: PetscFunctionBegin;
10743: PetscCheckSameComm(isrow, 2, iscol, 3);
10744: PetscAssertPointer(submat, 4);
10745: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10747: if (mat->ops->getlocalsubmatrix) {
10748: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10749: } else {
10750: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10751: }
10752: (*submat)->assembled = mat->assembled;
10753: PetscFunctionReturn(PETSC_SUCCESS);
10754: }
10756: /*@
10757: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10759: Not Collective
10761: Input Parameters:
10762: + mat - matrix to extract local submatrix from
10763: . isrow - local row indices for submatrix
10764: . iscol - local column indices for submatrix
10765: - submat - the submatrix
10767: Level: intermediate
10769: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10770: @*/
10771: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10772: {
10773: PetscFunctionBegin;
10777: PetscCheckSameComm(isrow, 2, iscol, 3);
10778: PetscAssertPointer(submat, 4);
10781: if (mat->ops->restorelocalsubmatrix) {
10782: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10783: } else {
10784: PetscCall(MatDestroy(submat));
10785: }
10786: *submat = NULL;
10787: PetscFunctionReturn(PETSC_SUCCESS);
10788: }
10790: /*@
10791: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10793: Collective
10795: Input Parameter:
10796: . mat - the matrix
10798: Output Parameter:
10799: . is - if any rows have zero diagonals this contains the list of them
10801: Level: developer
10803: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10804: @*/
10805: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10806: {
10807: PetscFunctionBegin;
10810: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10811: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10813: if (!mat->ops->findzerodiagonals) {
10814: Vec diag;
10815: const PetscScalar *a;
10816: PetscInt *rows;
10817: PetscInt rStart, rEnd, r, nrow = 0;
10819: PetscCall(MatCreateVecs(mat, &diag, NULL));
10820: PetscCall(MatGetDiagonal(mat, diag));
10821: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10822: PetscCall(VecGetArrayRead(diag, &a));
10823: for (r = 0; r < rEnd - rStart; ++r)
10824: if (a[r] == 0.0) ++nrow;
10825: PetscCall(PetscMalloc1(nrow, &rows));
10826: nrow = 0;
10827: for (r = 0; r < rEnd - rStart; ++r)
10828: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10829: PetscCall(VecRestoreArrayRead(diag, &a));
10830: PetscCall(VecDestroy(&diag));
10831: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10832: } else {
10833: PetscUseTypeMethod(mat, findzerodiagonals, is);
10834: }
10835: PetscFunctionReturn(PETSC_SUCCESS);
10836: }
10838: /*@
10839: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10841: Collective
10843: Input Parameter:
10844: . mat - the matrix
10846: Output Parameter:
10847: . is - contains the list of rows with off block diagonal entries
10849: Level: developer
10851: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10852: @*/
10853: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10854: {
10855: PetscFunctionBegin;
10858: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10859: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10861: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10862: PetscFunctionReturn(PETSC_SUCCESS);
10863: }
10865: /*@C
10866: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10868: Collective; No Fortran Support
10870: Input Parameter:
10871: . mat - the matrix
10873: Output Parameter:
10874: . values - the block inverses in column major order (FORTRAN-like)
10876: Level: advanced
10878: Notes:
10879: The size of the blocks is determined by the block size of the matrix.
10881: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10883: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10885: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10886: @*/
10887: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10888: {
10889: PetscFunctionBegin;
10891: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10892: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10893: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10894: PetscFunctionReturn(PETSC_SUCCESS);
10895: }
10897: /*@
10898: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10900: Collective; No Fortran Support
10902: Input Parameters:
10903: + mat - the matrix
10904: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10905: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10907: Output Parameter:
10908: . values - the block inverses in column major order (FORTRAN-like)
10910: Level: advanced
10912: Notes:
10913: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10915: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10917: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10918: @*/
10919: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10920: {
10921: PetscFunctionBegin;
10923: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10924: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10925: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10926: PetscFunctionReturn(PETSC_SUCCESS);
10927: }
10929: /*@
10930: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10932: Collective
10934: Input Parameters:
10935: + A - the matrix
10936: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10938: Level: advanced
10940: Note:
10941: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10943: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10944: @*/
10945: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10946: {
10947: const PetscScalar *vals;
10948: PetscInt *dnnz;
10949: PetscInt m, rstart, rend, bs, i, j;
10951: PetscFunctionBegin;
10952: PetscCall(MatInvertBlockDiagonal(A, &vals));
10953: PetscCall(MatGetBlockSize(A, &bs));
10954: PetscCall(MatGetLocalSize(A, &m, NULL));
10955: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10956: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10957: PetscCall(PetscMalloc1(m / bs, &dnnz));
10958: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10959: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10960: PetscCall(PetscFree(dnnz));
10961: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10962: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10963: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10964: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
10965: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10966: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10967: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
10968: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10969: PetscFunctionReturn(PETSC_SUCCESS);
10970: }
10972: /*@
10973: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10974: via `MatTransposeColoringCreate()`.
10976: Collective
10978: Input Parameter:
10979: . c - coloring context
10981: Level: intermediate
10983: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10984: @*/
10985: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10986: {
10987: MatTransposeColoring matcolor = *c;
10989: PetscFunctionBegin;
10990: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10991: if (--((PetscObject)matcolor)->refct > 0) {
10992: matcolor = NULL;
10993: PetscFunctionReturn(PETSC_SUCCESS);
10994: }
10996: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10997: PetscCall(PetscFree(matcolor->rows));
10998: PetscCall(PetscFree(matcolor->den2sp));
10999: PetscCall(PetscFree(matcolor->colorforcol));
11000: PetscCall(PetscFree(matcolor->columns));
11001: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
11002: PetscCall(PetscHeaderDestroy(c));
11003: PetscFunctionReturn(PETSC_SUCCESS);
11004: }
11006: /*@
11007: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
11008: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
11009: `MatTransposeColoring` to sparse `B`.
11011: Collective
11013: Input Parameters:
11014: + coloring - coloring context created with `MatTransposeColoringCreate()`
11015: - B - sparse matrix
11017: Output Parameter:
11018: . Btdense - dense matrix $B^T$
11020: Level: developer
11022: Note:
11023: These are used internally for some implementations of `MatRARt()`
11025: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
11026: @*/
11027: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
11028: {
11029: PetscFunctionBegin;
11034: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
11035: PetscFunctionReturn(PETSC_SUCCESS);
11036: }
11038: /*@
11039: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
11040: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
11041: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
11042: $C_{sp}$ from $C_{den}$.
11044: Collective
11046: Input Parameters:
11047: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
11048: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
11050: Output Parameter:
11051: . Csp - sparse matrix
11053: Level: developer
11055: Note:
11056: These are used internally for some implementations of `MatRARt()`
11058: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
11059: @*/
11060: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
11061: {
11062: PetscFunctionBegin;
11067: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
11068: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
11069: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
11070: PetscFunctionReturn(PETSC_SUCCESS);
11071: }
11073: /*@
11074: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
11076: Collective
11078: Input Parameters:
11079: + mat - the matrix product C
11080: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
11082: Output Parameter:
11083: . color - the new coloring context
11085: Level: intermediate
11087: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
11088: `MatTransColoringApplyDenToSp()`
11089: @*/
11090: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
11091: {
11092: MatTransposeColoring c;
11093: MPI_Comm comm;
11095: PetscFunctionBegin;
11096: PetscAssertPointer(color, 3);
11098: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11099: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
11100: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
11101: c->ctype = iscoloring->ctype;
11102: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
11103: *color = c;
11104: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11105: PetscFunctionReturn(PETSC_SUCCESS);
11106: }
11108: /*@
11109: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
11110: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
11112: Not Collective
11114: Input Parameter:
11115: . mat - the matrix
11117: Output Parameter:
11118: . state - the current state
11120: Level: intermediate
11122: Notes:
11123: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11124: different matrices
11126: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
11128: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
11130: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11131: @*/
11132: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11133: {
11134: PetscFunctionBegin;
11136: *state = mat->nonzerostate;
11137: PetscFunctionReturn(PETSC_SUCCESS);
11138: }
11140: /*@
11141: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11142: matrices from each processor
11144: Collective
11146: Input Parameters:
11147: + comm - the communicators the parallel matrix will live on
11148: . seqmat - the input sequential matrices
11149: . n - number of local columns (or `PETSC_DECIDE`)
11150: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11152: Output Parameter:
11153: . mpimat - the parallel matrix generated
11155: Level: developer
11157: Note:
11158: The number of columns of the matrix in EACH processor MUST be the same.
11160: .seealso: [](ch_matrices), `Mat`
11161: @*/
11162: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11163: {
11164: PetscMPIInt size;
11166: PetscFunctionBegin;
11167: PetscCallMPI(MPI_Comm_size(comm, &size));
11168: if (size == 1) {
11169: if (reuse == MAT_INITIAL_MATRIX) {
11170: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11171: } else {
11172: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11173: }
11174: PetscFunctionReturn(PETSC_SUCCESS);
11175: }
11177: 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");
11179: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11180: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11181: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11182: PetscFunctionReturn(PETSC_SUCCESS);
11183: }
11185: /*@
11186: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11188: Collective
11190: Input Parameters:
11191: + A - the matrix to create subdomains from
11192: - N - requested number of subdomains
11194: Output Parameters:
11195: + n - number of subdomains resulting on this MPI process
11196: - iss - `IS` list with indices of subdomains on this MPI process
11198: Level: advanced
11200: Note:
11201: The number of subdomains must be smaller than the communicator size
11203: .seealso: [](ch_matrices), `Mat`, `IS`
11204: @*/
11205: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11206: {
11207: MPI_Comm comm, subcomm;
11208: PetscMPIInt size, rank, color;
11209: PetscInt rstart, rend, k;
11211: PetscFunctionBegin;
11212: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11213: PetscCallMPI(MPI_Comm_size(comm, &size));
11214: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11215: 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);
11216: *n = 1;
11217: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11218: color = rank / k;
11219: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11220: PetscCall(PetscMalloc1(1, iss));
11221: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11222: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11223: PetscCallMPI(MPI_Comm_free(&subcomm));
11224: PetscFunctionReturn(PETSC_SUCCESS);
11225: }
11227: /*@
11228: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11230: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11231: If they are not the same, uses `MatMatMatMult()`.
11233: Once the coarse grid problem is constructed, correct for interpolation operators
11234: that are not of full rank, which can legitimately happen in the case of non-nested
11235: geometric multigrid.
11237: Input Parameters:
11238: + restrct - restriction operator
11239: . dA - fine grid matrix
11240: . interpolate - interpolation operator
11241: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11242: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11244: Output Parameter:
11245: . A - the Galerkin coarse matrix
11247: Options Database Key:
11248: . -pc_mg_galerkin (both|pmat|mat|none) - for what matrices the Galerkin process should be used
11250: Level: developer
11252: Note:
11253: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11255: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11256: @*/
11257: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11258: {
11259: IS zerorows;
11260: Vec diag;
11262: PetscFunctionBegin;
11263: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11264: /* Construct the coarse grid matrix */
11265: if (interpolate == restrct) {
11266: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11267: } else {
11268: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11269: }
11271: /* If the interpolation matrix is not of full rank, A will have zero rows.
11272: This can legitimately happen in the case of non-nested geometric multigrid.
11273: In that event, we set the rows of the matrix to the rows of the identity,
11274: ignoring the equations (as the RHS will also be zero). */
11276: PetscCall(MatFindZeroRows(*A, &zerorows));
11278: if (zerorows != NULL) { /* if there are any zero rows */
11279: PetscCall(MatCreateVecs(*A, &diag, NULL));
11280: PetscCall(MatGetDiagonal(*A, diag));
11281: PetscCall(VecISSet(diag, zerorows, 1.0));
11282: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11283: PetscCall(VecDestroy(&diag));
11284: PetscCall(ISDestroy(&zerorows));
11285: }
11286: PetscFunctionReturn(PETSC_SUCCESS);
11287: }
11289: /*@C
11290: MatSetOperation - Allows user to set a matrix operation for any matrix type
11292: Logically Collective
11294: Input Parameters:
11295: + mat - the matrix
11296: . op - the name of the operation
11297: - f - the function that provides the operation
11299: Level: developer
11301: Example Usage:
11302: .vb
11303: extern PetscErrorCode usermult(Mat, Vec, Vec);
11305: PetscCall(MatCreateXXX(comm, ..., &A));
11306: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11307: .ve
11309: Notes:
11310: See the file `include/petscmat.h` for a complete list of matrix
11311: operations, which all have the form MATOP_<OPERATION>, where
11312: <OPERATION> is the name (in all capital letters) of the
11313: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11315: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11316: sequence as the usual matrix interface routines, since they
11317: are intended to be accessed via the usual matrix interface
11318: routines, e.g.,
11319: .vb
11320: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11321: .ve
11323: In particular each function MUST return `PETSC_SUCCESS` on success and
11324: nonzero on failure.
11326: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11328: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11329: @*/
11330: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11331: {
11332: PetscFunctionBegin;
11334: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11335: (((PetscErrorCodeFn **)mat->ops)[op]) = f;
11336: PetscFunctionReturn(PETSC_SUCCESS);
11337: }
11339: /*@C
11340: MatGetOperation - Gets a matrix operation for any matrix type.
11342: Not Collective
11344: Input Parameters:
11345: + mat - the matrix
11346: - op - the name of the operation
11348: Output Parameter:
11349: . f - the function that provides the operation
11351: Level: developer
11353: Example Usage:
11354: .vb
11355: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11357: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11358: .ve
11360: Notes:
11361: See the file `include/petscmat.h` for a complete list of matrix
11362: operations, which all have the form MATOP_<OPERATION>, where
11363: <OPERATION> is the name (in all capital letters) of the
11364: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11366: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11368: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11369: @*/
11370: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11371: {
11372: PetscFunctionBegin;
11374: *f = (((PetscErrorCodeFn **)mat->ops)[op]);
11375: PetscFunctionReturn(PETSC_SUCCESS);
11376: }
11378: /*@
11379: MatHasOperation - Determines whether the given matrix supports the particular operation.
11381: Not Collective
11383: Input Parameters:
11384: + mat - the matrix
11385: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11387: Output Parameter:
11388: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11390: Level: advanced
11392: Note:
11393: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11395: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11396: @*/
11397: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11398: {
11399: PetscFunctionBegin;
11401: PetscAssertPointer(has, 3);
11402: if (mat->ops->hasoperation) {
11403: PetscUseTypeMethod(mat, hasoperation, op, has);
11404: } else {
11405: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11406: else {
11407: *has = PETSC_FALSE;
11408: if (op == MATOP_CREATE_SUBMATRIX) {
11409: PetscMPIInt size;
11411: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11412: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11413: }
11414: }
11415: }
11416: PetscFunctionReturn(PETSC_SUCCESS);
11417: }
11419: /*@
11420: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11422: Collective
11424: Input Parameter:
11425: . mat - the matrix
11427: Output Parameter:
11428: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11430: Level: beginner
11432: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11433: @*/
11434: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11435: {
11436: PetscFunctionBegin;
11439: PetscAssertPointer(cong, 2);
11440: if (!mat->rmap || !mat->cmap) {
11441: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11442: PetscFunctionReturn(PETSC_SUCCESS);
11443: }
11444: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11445: PetscCall(PetscLayoutSetUp(mat->rmap));
11446: PetscCall(PetscLayoutSetUp(mat->cmap));
11447: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11448: if (*cong) mat->congruentlayouts = 1;
11449: else mat->congruentlayouts = 0;
11450: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11451: PetscFunctionReturn(PETSC_SUCCESS);
11452: }
11454: PetscErrorCode MatSetInf(Mat A)
11455: {
11456: PetscFunctionBegin;
11457: PetscUseTypeMethod(A, setinf);
11458: PetscFunctionReturn(PETSC_SUCCESS);
11459: }
11461: /*@
11462: 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
11463: and possibly removes small values from the graph structure.
11465: Collective
11467: Input Parameters:
11468: + A - the matrix
11469: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11470: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11471: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11472: . num_idx - size of `index` array
11473: - index - array of block indices to use for graph strength of connection weight
11475: Output Parameter:
11476: . graph - the resulting graph
11478: Level: advanced
11480: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11481: @*/
11482: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11483: {
11484: PetscFunctionBegin;
11488: PetscAssertPointer(graph, 7);
11489: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11490: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11491: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11492: PetscFunctionReturn(PETSC_SUCCESS);
11493: }
11495: /*@
11496: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11497: meaning the same memory is used for the matrix, and no new memory is allocated.
11499: Collective
11501: Input Parameters:
11502: + A - the matrix
11503: - 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
11505: Level: intermediate
11507: Developer Note:
11508: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11509: of the arrays in the data structure are unneeded.
11511: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11512: @*/
11513: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11514: {
11515: PetscFunctionBegin;
11517: PetscUseTypeMethod(A, eliminatezeros, keep);
11518: PetscFunctionReturn(PETSC_SUCCESS);
11519: }
11521: /*@C
11522: MatGetCurrentMemType - Get the memory location of the matrix
11524: Not Collective, but the result will be the same on all MPI processes
11526: Input Parameter:
11527: . A - the matrix whose memory type we are checking
11529: Output Parameter:
11530: . m - the memory type
11532: Level: intermediate
11534: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11535: @*/
11536: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11537: {
11538: PetscFunctionBegin;
11540: PetscAssertPointer(m, 2);
11541: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11542: else *m = PETSC_MEMTYPE_HOST;
11543: PetscFunctionReturn(PETSC_SUCCESS);
11544: }