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: MatGetMultPetscSF - Returns the `PetscSF` that communicates to each MPI process the values held on other MPI processes that are coupled to it by the `Mat`
400: Not Collective
402: Input Parameter:
403: . A - the matrix
405: Output Parameter:
406: . sf - the `PetscSF`.
408: Level: advanced
410: Notes:
411: The returned `PetscSF` is owned by the matrix; do not destroy it.
413: It is only valid if this function is called after the matrix has been assembled
414: (and for `MATMPIDENSE` after a `MatMult()` has been additionally called).
416: This is only implemented for the matrix types listed below; calling it on a sequential matrix or a type that does not
417: build such a `PetscSF` raises an error.
419: For `MATMPIAIJ`, `MATMPIBAIJ`, `MATMPIDENSE`, and `MATMPISELL`, this `PetscSF` is used within
420: `MatMult()` to provide the contribution of vector entries that are not local to each MPI process to the matrix-vector product.
421: For `MATMPISBAIJ` the returned `PetscSF` is instead the off-process column gather used by operations such as
422: `MatDiagonalScale()`; `MatMult_MPISBAIJ()` uses a separate, augmented scatter context.
423: In all cases the `PetscSF` maps the global vector layout (the matrix column layout) onto the off-process columns that the local rows couple to,
424: so it can be reused to communicate any per-column data, for example with `PetscSFBcastBegin()`.
426: For `MATMPIDENSE` this `PetscSF` is an allgather: every process gathers all columns, including its own, in the natural global
427: order rather than a sparse `garray` order. The leaf set and ordering therefore differ across matrix types, so callers should use
428: `PetscSFGetGraph()` rather than assume a particular leaf layout.
430: .seealso: [](ch_matrices), `Mat`, `PetscSF`, `MatGetDiagonalBlock()`, `MatMPIAIJGetSeqAIJ()`, `PetscSFBcastBegin()`, `MatMult()`
431: @*/
432: PetscErrorCode MatGetMultPetscSF(Mat A, PetscSF *sf)
433: {
434: PetscFunctionBegin;
437: PetscAssertPointer(sf, 2);
438: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
439: *sf = NULL;
440: PetscUseMethod(A, "MatGetMultPetscSF_C", (Mat, PetscSF *), (A, sf));
441: PetscFunctionReturn(PETSC_SUCCESS);
442: }
444: /*@
445: MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.
447: Collective
449: Input Parameter:
450: . mat - the matrix
452: Output Parameter:
453: . trace - the sum of the diagonal entries
455: Level: advanced
457: .seealso: [](ch_matrices), `Mat`
458: @*/
459: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
460: {
461: Vec diag;
463: PetscFunctionBegin;
465: PetscAssertPointer(trace, 2);
466: PetscCall(MatCreateVecs(mat, &diag, NULL));
467: PetscCall(MatGetDiagonal(mat, diag));
468: PetscCall(VecSum(diag, trace));
469: PetscCall(VecDestroy(&diag));
470: PetscFunctionReturn(PETSC_SUCCESS);
471: }
473: /*@
474: MatRealPart - Zeros out the imaginary part of the matrix
476: Logically Collective
478: Input Parameter:
479: . mat - the matrix
481: Level: advanced
483: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
484: @*/
485: PetscErrorCode MatRealPart(Mat mat)
486: {
487: PetscFunctionBegin;
490: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
491: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
492: MatCheckPreallocated(mat, 1);
493: PetscUseTypeMethod(mat, realpart);
494: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
495: PetscFunctionReturn(PETSC_SUCCESS);
496: }
498: /*@
499: MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix
501: Collective
503: Input Parameter:
504: . mat - the matrix
506: Output Parameters:
507: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
508: - ghosts - the global indices of the ghost points
510: Level: advanced
512: Note:
513: `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`
515: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
516: @*/
517: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
518: {
519: PetscFunctionBegin;
522: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
523: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
524: if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
525: else {
526: if (nghosts) *nghosts = 0;
527: if (ghosts) *ghosts = NULL;
528: }
529: PetscFunctionReturn(PETSC_SUCCESS);
530: }
532: /*@
533: MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part
535: Logically Collective
537: Input Parameter:
538: . mat - the matrix
540: Level: advanced
542: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
543: @*/
544: PetscErrorCode MatImaginaryPart(Mat mat)
545: {
546: PetscFunctionBegin;
549: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
550: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
551: MatCheckPreallocated(mat, 1);
552: PetscUseTypeMethod(mat, imaginarypart);
553: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
554: PetscFunctionReturn(PETSC_SUCCESS);
555: }
557: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
558: /*@
559: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
560: for each row that you get to ensure that your application does
561: not bleed memory.
563: Not Collective
565: Input Parameters:
566: + mat - the matrix
567: - row - the row to get
569: Output Parameters:
570: + ncols - if not `NULL`, the number of nonzeros in `row`
571: . cols - if not `NULL`, the column numbers
572: - vals - if not `NULL`, the numerical values
574: Level: advanced
576: Notes:
577: This routine is provided for people who need to have direct access
578: to the structure of a matrix. We hope that we provide enough
579: high-level matrix routines that few users will need it.
581: `MatGetRow()` always returns 0-based column indices, regardless of
582: whether the internal representation is 0-based (default) or 1-based.
584: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
585: not wish to extract these quantities. `vals` must be `NULL` for a matrix with
586: the `MAT_STRUCTURE_ONLY` option set to true, since no numerical values are stored.
588: The user can only examine the values extracted with `MatGetRow()`;
589: the values CANNOT be altered. To change the matrix entries, one
590: must use `MatSetValues()`.
592: You can only have one call to `MatGetRow()` outstanding for a particular
593: matrix at a time, per process. `MatGetRow()` can only obtain rows
594: associated with the given process, it cannot get rows from the
595: other processes; for that we suggest using `MatCreateSubMatrices()`, then
596: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
597: is in the global number of rows.
599: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
601: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
603: Fortran Note:
604: .vb
605: PetscInt, pointer :: cols(:)
606: PetscScalar, pointer :: vals(:)
607: .ve
609: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
610: @*/
611: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
612: {
613: PetscInt incols;
615: PetscFunctionBegin;
618: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
619: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
620: PetscCheck(!mat->structure_only || vals == NULL, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for matrix with MAT_STRUCTURE_ONLY");
621: MatCheckPreallocated(mat, 1);
622: 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);
623: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
624: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
625: if (ncols) *ncols = incols;
626: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
627: PetscFunctionReturn(PETSC_SUCCESS);
628: }
630: /*@
631: MatConjugate - replaces the matrix values with their complex conjugates
633: Logically Collective
635: Input Parameter:
636: . mat - the matrix
638: Level: advanced
640: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
641: @*/
642: PetscErrorCode MatConjugate(Mat mat)
643: {
644: PetscFunctionBegin;
646: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
647: if (PetscDefined(USE_COMPLEX) && !(mat->symmetric == PETSC_BOOL3_TRUE && mat->hermitian == PETSC_BOOL3_TRUE)) {
648: PetscUseTypeMethod(mat, conjugate);
649: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
650: }
651: PetscFunctionReturn(PETSC_SUCCESS);
652: }
654: /*@
655: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
657: Not Collective
659: Input Parameters:
660: + mat - the matrix
661: . row - the row to get
662: . ncols - the number of nonzeros
663: . cols - the columns of the nonzeros
664: - vals - if nonzero the column values
666: Level: advanced
668: Notes:
669: This routine should be called after you have finished examining the entries.
671: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
672: us of the array after it has been restored. If you pass `NULL`, it will
673: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
675: Fortran Note:
676: .vb
677: PetscInt, pointer :: cols(:)
678: PetscScalar, pointer :: vals(:)
679: .ve
681: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
682: @*/
683: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
684: {
685: PetscFunctionBegin;
687: if (ncols) PetscAssertPointer(ncols, 3);
688: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
689: PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
690: if (ncols) *ncols = 0;
691: if (cols) *cols = NULL;
692: if (vals) *vals = NULL;
693: PetscFunctionReturn(PETSC_SUCCESS);
694: }
696: /*@
697: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
698: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
700: Not Collective
702: Input Parameter:
703: . mat - the matrix
705: Level: advanced
707: Note:
708: 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.
710: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
711: @*/
712: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
713: {
714: PetscFunctionBegin;
717: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
718: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
719: MatCheckPreallocated(mat, 1);
720: PetscTryTypeMethod(mat, getrowuppertriangular);
721: PetscFunctionReturn(PETSC_SUCCESS);
722: }
724: /*@
725: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
727: Not Collective
729: Input Parameter:
730: . mat - the matrix
732: Level: advanced
734: Note:
735: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
737: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
738: @*/
739: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
740: {
741: PetscFunctionBegin;
744: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
745: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
746: MatCheckPreallocated(mat, 1);
747: PetscTryTypeMethod(mat, restorerowuppertriangular);
748: PetscFunctionReturn(PETSC_SUCCESS);
749: }
751: /*@
752: MatSetOptionsPrefix - Sets the prefix used for searching for all
753: `Mat` options in the database.
755: Logically Collective
757: Input Parameters:
758: + A - the matrix
759: - prefix - the prefix to prepend to all option names
761: Level: advanced
763: Notes:
764: A hyphen (-) must NOT be given at the beginning of the prefix name.
765: The first character of all runtime options is AUTOMATICALLY the hyphen.
767: This is NOT used for options for the factorization of the matrix. Normally the
768: prefix is automatically passed in from the PC calling the factorization. To set
769: it directly use `MatSetOptionsPrefixFactor()`
771: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
772: @*/
773: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
774: {
775: PetscFunctionBegin;
777: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
778: PetscTryMethod(A, "MatSetOptionsPrefix_C", (Mat, const char[]), (A, prefix));
779: PetscFunctionReturn(PETSC_SUCCESS);
780: }
782: /*@
783: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
784: for matrices created with `MatGetFactor()`
786: Logically Collective
788: Input Parameters:
789: + A - the matrix
790: - prefix - the prefix to prepend to all option names for the factored matrix
792: Level: developer
794: Notes:
795: A hyphen (-) must NOT be given at the beginning of the prefix name.
796: The first character of all runtime options is AUTOMATICALLY the hyphen.
798: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
799: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
801: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
802: @*/
803: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
804: {
805: PetscFunctionBegin;
807: if (prefix) {
808: PetscAssertPointer(prefix, 2);
809: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
810: if (prefix != A->factorprefix) {
811: PetscCall(PetscFree(A->factorprefix));
812: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
813: }
814: } else PetscCall(PetscFree(A->factorprefix));
815: PetscFunctionReturn(PETSC_SUCCESS);
816: }
818: /*@
819: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
820: for matrices created with `MatGetFactor()`
822: Logically Collective
824: Input Parameters:
825: + A - the matrix
826: - prefix - the prefix to prepend to all option names for the factored matrix
828: Level: developer
830: Notes:
831: A hyphen (-) must NOT be given at the beginning of the prefix name.
832: The first character of all runtime options is AUTOMATICALLY the hyphen.
834: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
835: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
837: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
838: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
839: `MatSetOptionsPrefix()`
840: @*/
841: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
842: {
843: size_t len1, len2, new_len;
845: PetscFunctionBegin;
847: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
848: if (!A->factorprefix) {
849: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
850: PetscFunctionReturn(PETSC_SUCCESS);
851: }
852: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
854: PetscCall(PetscStrlen(A->factorprefix, &len1));
855: PetscCall(PetscStrlen(prefix, &len2));
856: new_len = len1 + len2 + 1;
857: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
858: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
859: PetscFunctionReturn(PETSC_SUCCESS);
860: }
862: /*@
863: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
864: matrix options in the database.
866: Logically Collective
868: Input Parameters:
869: + A - the matrix
870: - prefix - the prefix to prepend to all option names
872: Level: advanced
874: Note:
875: A hyphen (-) must NOT be given at the beginning of the prefix name.
876: The first character of all runtime options is AUTOMATICALLY the hyphen.
878: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
879: @*/
880: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
881: {
882: PetscFunctionBegin;
884: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
885: PetscTryMethod(A, "MatAppendOptionsPrefix_C", (Mat, const char[]), (A, prefix));
886: PetscFunctionReturn(PETSC_SUCCESS);
887: }
889: /*@
890: MatGetOptionsPrefix - Gets the prefix used for searching for all
891: matrix options in the database.
893: Not Collective
895: Input Parameter:
896: . A - the matrix
898: Output Parameter:
899: . prefix - pointer to the prefix string used
901: Level: advanced
903: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
904: @*/
905: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
906: {
907: PetscFunctionBegin;
909: PetscAssertPointer(prefix, 2);
910: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
911: PetscFunctionReturn(PETSC_SUCCESS);
912: }
914: /*@
915: MatGetState - Gets a snapshot of the state of a `Mat`
917: Not Collective, No Fortran Support
919: Input Parameter:
920: . A - the matrix
922: Output Parameter:
923: . state - the matrix state
925: Level: developer
927: Notes:
928: The snapshot includes the matrix identity, object state, and nonzero state. Use `MatStateCompare()` to determine whether two snapshots are the same, or `MatStateCompareUpdate()` to compare and update a saved snapshot.
930: .seealso: [](ch_matrices), `Mat`, `MatState`, `MatStateCompare()`, `MatStateCompareUpdate()`, `MatStateInvalidate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
931: @*/
932: PetscErrorCode MatGetState(Mat A, MatState *state)
933: {
934: PetscFunctionBegin;
936: PetscAssertPointer(state, 2);
937: state->id = ((PetscObject)A)->id;
938: state->state = ((PetscObject)A)->state;
939: state->nonzerostate = A->nonzerostate;
940: PetscFunctionReturn(PETSC_SUCCESS);
941: }
943: /*@
944: MatStateCompare - Compares two matrix state snapshots
946: Not Collective, No Fortran Support
948: Input Parameters:
949: + state1 - the first matrix state
950: - state2 - the second matrix state
952: Output Parameter:
953: . same - `PETSC_TRUE` if the matrix identity, object state, and nonzero state are the same, `PETSC_FALSE` otherwise
955: Level: developer
957: .seealso: [](ch_matrices), `Mat`, `MatState`, `MatGetState()`, `MatStateCompareUpdate()`, `MatStateInvalidate()`
958: @*/
959: PetscErrorCode MatStateCompare(MatState state1, MatState state2, PetscBool *same)
960: {
961: PetscFunctionBegin;
962: PetscAssertPointer(same, 3);
963: *same = (PetscBool)(state1.id == state2.id && state1.state == state2.state && state1.nonzerostate == state2.nonzerostate);
964: PetscFunctionReturn(PETSC_SUCCESS);
965: }
967: /*@
968: MatStateCompareUpdate - Compares a matrix with a state snapshot, then updates the snapshot
970: Not Collective, No Fortran Support
972: Input Parameter:
973: . A - the matrix
975: Input/Output Parameter:
976: . state - the matrix state snapshot to compare with and update
978: Output Parameter:
979: . same - `PETSC_TRUE` if the matrix state matched the snapshot before it was updated, `PETSC_FALSE` otherwise
981: Level: developer
983: .seealso: [](ch_matrices), `Mat`, `MatState`, `MatGetState()`, `MatStateCompare()`, `MatStateInvalidate()`
984: @*/
985: PetscErrorCode MatStateCompareUpdate(Mat A, MatState *state, PetscBool *same)
986: {
987: MatState current;
989: PetscFunctionBegin;
991: PetscAssertPointer(state, 2);
992: PetscAssertPointer(same, 3);
993: PetscCall(MatGetState(A, ¤t));
994: PetscCall(MatStateCompare(current, *state, same));
995: *state = current;
996: PetscFunctionReturn(PETSC_SUCCESS);
997: }
999: /*@
1000: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
1002: Collective
1004: Input Parameter:
1005: . A - the matrix
1007: Level: beginner
1009: Notes:
1010: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
1011: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
1012: makes all of the preallocation space available
1014: Current values in the matrix are lost in this call
1016: Currently only supported for `MATAIJ` matrices.
1018: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
1019: @*/
1020: PetscErrorCode MatResetPreallocation(Mat A)
1021: {
1022: PetscFunctionBegin;
1025: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
1026: PetscFunctionReturn(PETSC_SUCCESS);
1027: }
1029: /*@
1030: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
1032: Collective
1034: Input Parameter:
1035: . A - the matrix
1037: Level: intermediate
1039: Notes:
1040: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
1042: Currently only supported for `MATAIJ` matrices.
1044: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
1045: @*/
1046: PetscErrorCode MatResetHash(Mat A)
1047: {
1048: PetscFunctionBegin;
1051: 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()");
1052: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
1053: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
1054: /* These flags are used to determine whether certain setups occur */
1055: A->was_assembled = PETSC_FALSE;
1056: A->assembled = PETSC_FALSE;
1057: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
1058: PetscCall(PetscObjectStateIncrease((PetscObject)A));
1059: PetscFunctionReturn(PETSC_SUCCESS);
1060: }
1062: /*@
1063: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
1065: Collective
1067: Input Parameter:
1068: . A - the matrix
1070: Level: advanced
1072: Notes:
1073: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
1074: setting values in the matrix.
1076: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
1078: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1079: @*/
1080: PetscErrorCode MatSetUp(Mat A)
1081: {
1082: PetscFunctionBegin;
1084: if (!((PetscObject)A)->type_name) {
1085: PetscMPIInt size;
1087: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1088: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1089: }
1090: if (!A->preallocated) PetscTryTypeMethod(A, setup);
1091: PetscCall(PetscLayoutSetUp(A->rmap));
1092: PetscCall(PetscLayoutSetUp(A->cmap));
1093: A->preallocated = PETSC_TRUE;
1094: PetscFunctionReturn(PETSC_SUCCESS);
1095: }
1097: #if PetscDefined(HAVE_SAWS)
1098: #include <petscviewersaws.h>
1099: #endif
1101: /*
1102: If threadsafety is on extraneous matrices may be printed
1104: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1105: */
1106: #if !PetscDefined(HAVE_THREADSAFETY)
1107: static PetscInt insidematview = 0;
1108: #endif
1110: /*@
1111: MatViewFromOptions - View properties of the matrix based on options set in the options database
1113: Collective
1115: Input Parameters:
1116: + A - the matrix
1117: . obj - optional additional object that provides the options prefix to use
1118: - name - command line option
1120: Options Database Key:
1121: . -name viewer_specification - See `PetscOptionsCreateViewer()` for the values of `viewer_specification`
1123: Level: intermediate
1125: Note:
1126: This checks the options database, creates the viewer on-the-fly, uses it and then destroys it. Hence it should not be called in heavily used routines,
1127: rather `PetscOptionsCreateViewer()` should be used to construct the viewer once which can then be utilized in the heavily used routine.
1129: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`, `PetscOptionsCreateViewer()`
1130: @*/
1131: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1132: {
1133: PetscFunctionBegin;
1135: #if !PetscDefined(HAVE_THREADSAFETY)
1136: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1137: #endif
1138: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1139: PetscFunctionReturn(PETSC_SUCCESS);
1140: }
1142: /*@
1143: MatView - display information about a matrix in a variety ways
1145: Collective on viewer
1147: Input Parameters:
1148: + mat - the matrix
1149: - viewer - visualization context
1151: Options Database Key:
1152: . -mat_view viewer_specification - Call `MatView()` at the conclusion of `MatAssemblyEnd()` or other routines that have changed the matrix values.
1153: See `PetscOptionsCreateViewer()` for the values of `viewer_specification`.
1155: Level: beginner
1157: Notes:
1158: The available visualization contexts include
1159: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1160: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1161: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1162: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1164: The user can open alternative visualization contexts with
1165: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1166: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1167: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1168: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1170: The user can call `PetscViewerPushFormat()` to specify the output
1171: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1172: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1173: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1174: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1175: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1176: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1177: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1178: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1179: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1181: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1182: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1184: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1186: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1187: viewer is used.
1189: `MatViewFromOptions()` provides an alternative to this routine that only views the matrix if the requested value
1190: is provided in the options database.
1192: See `share/petsc/matlab/PetscBinaryRead.m` for a MATLAB code that can read in the binary file when the binary
1193: viewer is used and `lib/petsc/bin/PetscBinaryIO.py` for loading them into Python.
1195: One can use `-mat_view draw -draw_pause -1` to pause the graphical display of matrix nonzero structure,
1196: and then use the following mouse functions.
1197: .vb
1198: left mouse: zoom in
1199: middle mouse: zoom out
1200: right mouse: continue with the simulation
1201: .ve
1203: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1204: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`, `PetscOptionsCreateViewer()`
1205: @*/
1206: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1207: {
1208: PetscInt rows, cols, rbs, cbs;
1209: PetscBool isascii, isstring, issaws;
1210: PetscViewerFormat format;
1211: PetscMPIInt size;
1213: PetscFunctionBegin;
1216: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1219: PetscCall(PetscViewerGetFormat(viewer, &format));
1220: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1221: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1223: #if !PetscDefined(HAVE_THREADSAFETY)
1224: insidematview++;
1225: #endif
1226: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1227: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1228: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1229: 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");
1231: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1232: if (isascii) {
1233: if (!mat->preallocated) {
1234: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1235: #if !PetscDefined(HAVE_THREADSAFETY)
1236: insidematview--;
1237: #endif
1238: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1239: PetscFunctionReturn(PETSC_SUCCESS);
1240: }
1241: if (!mat->assembled) {
1242: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1243: #if !PetscDefined(HAVE_THREADSAFETY)
1244: insidematview--;
1245: #endif
1246: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1247: PetscFunctionReturn(PETSC_SUCCESS);
1248: }
1249: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1250: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1251: MatNullSpace nullsp, transnullsp;
1252: PetscBool nz_factor = PETSC_TRUE;
1254: PetscCall(PetscViewerASCIIPushTab(viewer));
1255: PetscCall(MatGetSize(mat, &rows, &cols));
1256: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1257: if (rbs != 1 || cbs != 1) {
1258: 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" : ""));
1259: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1260: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1261: if (mat->factortype) {
1262: MatSolverType solver;
1264: PetscCall(MatFactorGetSolverType(mat, &solver));
1265: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1266: PetscCall(PetscStrcmpAny(solver, &nz_factor, MATSOLVERUMFPACK, MATSOLVERCHOLMOD, MATSOLVERSUPERLU, MATSOLVERSUPERLU_DIST, MATSOLVERSTRUMPACK, MATSOLVERHTOOL, ""));
1267: nz_factor = !nz_factor;
1268: }
1269: if (mat->ops->getinfo) {
1270: PetscBool is_constant_or_diagonal;
1272: // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1273: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1274: if (!is_constant_or_diagonal && nz_factor) {
1275: MatInfo info;
1277: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1278: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1279: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1280: }
1281: }
1282: PetscCall(MatGetNullSpace(mat, &nullsp));
1283: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1284: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1285: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1286: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1287: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1288: PetscCall(PetscViewerASCIIPushTab(viewer));
1289: PetscCall(MatProductView(mat, viewer));
1290: PetscCall(PetscViewerASCIIPopTab(viewer));
1291: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1292: IS tmp;
1294: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1295: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1296: PetscCall(PetscViewerASCIIPushTab(viewer));
1297: PetscCall(ISView(tmp, viewer));
1298: PetscCall(PetscViewerASCIIPopTab(viewer));
1299: PetscCall(ISDestroy(&tmp));
1300: }
1301: }
1302: } else if (issaws) {
1303: #if PetscDefined(HAVE_SAWS)
1304: PetscMPIInt rank;
1306: PetscCall(PetscObjectName((PetscObject)mat));
1307: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1308: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1309: #endif
1310: } else if (isstring) {
1311: const char *type;
1312: PetscCall(MatGetType(mat, &type));
1313: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1314: PetscTryTypeMethod(mat, view, viewer);
1315: }
1316: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1317: PetscCall(PetscViewerASCIIPushTab(viewer));
1318: PetscUseTypeMethod(mat, viewnative, viewer);
1319: PetscCall(PetscViewerASCIIPopTab(viewer));
1320: } else if (mat->ops->view) {
1321: PetscCall(PetscViewerASCIIPushTab(viewer));
1322: PetscUseTypeMethod(mat, view, viewer);
1323: PetscCall(PetscViewerASCIIPopTab(viewer));
1324: }
1325: if (isascii) {
1326: PetscCall(PetscViewerGetFormat(viewer, &format));
1327: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1328: }
1329: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1330: #if !PetscDefined(HAVE_THREADSAFETY)
1331: insidematview--;
1332: #endif
1333: PetscFunctionReturn(PETSC_SUCCESS);
1334: }
1336: #if PetscDefined(USE_DEBUG)
1337: #include <../src/sys/totalview/tv_data_display.h>
1338: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1339: {
1340: TV_add_row("Local rows", "int", &mat->rmap->n);
1341: TV_add_row("Local columns", "int", &mat->cmap->n);
1342: TV_add_row("Global rows", "int", &mat->rmap->N);
1343: TV_add_row("Global columns", "int", &mat->cmap->N);
1344: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1345: return TV_format_OK;
1346: }
1347: #endif
1349: /*@
1350: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1351: with `MatView()`. The matrix format is determined from the options database.
1352: Generates a parallel MPI matrix if the communicator has more than one
1353: process. The default matrix type is `MATAIJ`.
1355: Collective
1357: Input Parameters:
1358: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1359: or some related function before a call to `MatLoad()`
1360: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1362: Options Database Key:
1363: . -matload_block_size bs - set block size
1365: Level: beginner
1367: Notes:
1368: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1369: `Mat` before calling this routine if you wish to set it from the options database.
1371: `MatLoad()` automatically loads into the options database any options
1372: given in the file filename.info where filename is the name of the file
1373: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1374: file will be ignored if you use the `-viewer_binary_skip_info` option.
1376: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1377: sets the default matrix type AIJ and sets the local and global sizes.
1378: If type and/or size is already set, then the same are used.
1380: In parallel, each process can load a subset of rows (or the
1381: entire matrix). This routine is especially useful when a large
1382: matrix is stored on disk and only part of it is desired on each
1383: process. For example, a parallel solver may access only some of
1384: the rows from each process. The algorithm used here reads
1385: relatively small blocks of data rather than reading the entire
1386: matrix and then subsetting it.
1388: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1389: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1390: or the sequence like
1391: .vb
1392: PetscViewer v;
1393: PetscViewerCreate(PETSC_COMM_WORLD, &v);
1394: PetscViewerSetType(v, PETSCVIEWERBINARY);
1395: PetscViewerSetFromOptions(v);
1396: PetscViewerFileSetMode(v, FILE_MODE_READ);
1397: PetscViewerFileSetName(v, "datafile");
1398: .ve
1399: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1400: .vb
1401: -viewer_type (binary|hdf5)
1402: .ve
1404: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1405: and src/mat/tutorials/ex10.c with the second approach.
1407: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1408: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1409: Multiple objects, both matrices and vectors, can be stored within the same file.
1410: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1412: Most users should not need to know the details of the binary storage
1413: format, since `MatLoad()` and `MatView()` completely hide these details.
1414: But for anyone who is interested, the standard binary matrix storage
1415: format is
1417: .vb
1418: PetscInt MAT_FILE_CLASSID
1419: PetscInt number of rows
1420: PetscInt number of columns
1421: PetscInt total number of nonzeros
1422: PetscInt *number nonzeros in each row
1423: PetscInt *column indices of all nonzeros (starting index is zero)
1424: PetscScalar *values of all nonzeros
1425: .ve
1426: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1427: 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
1428: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1430: PETSc automatically does the byte swapping for
1431: machines that store the bytes reversed. Thus if you write your own binary
1432: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1433: and `PetscBinaryWrite()` to see how this may be done.
1435: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1436: Each process's chunk is loaded independently by its owning MPI process.
1437: Multiple objects, both matrices and vectors, can be stored within the same file.
1438: They are looked up by their PetscObject name.
1440: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1441: by default the same structure and naming of the AIJ arrays and column count
1442: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1443: .vb
1444: save example.mat A b -v7.3
1445: .ve
1446: can be directly read by this routine (see Reference 1 for details).
1448: Depending on your MATLAB version, this format might be a default,
1449: otherwise you can set it as default in Preferences.
1451: Unless `-nocompression` flag is used to save the file in MATLAB,
1452: PETSc must be configured with ZLIB package.
1454: See also examples `src/mat/tutorials/ex10.c` and `src/ksp/ksp/tutorials/ex27.c`
1456: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE`, and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1458: Corresponding `MatView()` is not yet implemented.
1460: The loaded matrix is actually a transpose of the original one in MATLAB,
1461: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1462: With this format, matrix is automatically transposed by PETSc,
1463: unless the matrix is marked as SPD or symmetric
1464: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1466: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1468: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1469: @*/
1470: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1471: {
1472: PetscBool flg;
1474: PetscFunctionBegin;
1478: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1480: flg = PETSC_FALSE;
1481: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1482: if (flg) {
1483: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1484: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1485: }
1486: flg = PETSC_FALSE;
1487: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1488: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1490: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1491: PetscUseTypeMethod(mat, load, viewer);
1492: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1493: PetscFunctionReturn(PETSC_SUCCESS);
1494: }
1496: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1497: {
1498: Mat_Redundant *redund = *redundant;
1500: PetscFunctionBegin;
1501: if (redund) {
1502: if (redund->matseq) { /* via MatCreateSubMatrices() */
1503: PetscCall(ISDestroy(&redund->isrow));
1504: PetscCall(ISDestroy(&redund->iscol));
1505: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1506: } else {
1507: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1508: PetscCall(PetscFree(redund->sbuf_j));
1509: PetscCall(PetscFree(redund->sbuf_a));
1510: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1511: PetscCall(PetscFree(redund->rbuf_j[i]));
1512: PetscCall(PetscFree(redund->rbuf_a[i]));
1513: }
1514: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1515: }
1517: PetscCall(PetscCommDestroy(&redund->subcomm));
1518: PetscCall(PetscFree(redund));
1519: }
1520: PetscFunctionReturn(PETSC_SUCCESS);
1521: }
1523: /*@
1524: MatDestroy - Frees space taken by a matrix.
1526: Collective
1528: Input Parameter:
1529: . A - the matrix
1531: Level: beginner
1533: Developer Note:
1534: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1535: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1536: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1537: if changes are needed here.
1539: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1540: @*/
1541: PetscErrorCode MatDestroy(Mat *A)
1542: {
1543: PetscFunctionBegin;
1544: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1546: if (--((PetscObject)*A)->refct > 0) {
1547: *A = NULL;
1548: PetscFunctionReturn(PETSC_SUCCESS);
1549: }
1551: /* if memory was published with SAWs then destroy it */
1552: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1553: PetscTryTypeMethod(*A, destroy);
1555: PetscCall(PetscFree((*A)->factorprefix));
1556: PetscCall(PetscFree((*A)->defaultvectype));
1557: PetscCall(PetscFree((*A)->defaultrandtype));
1558: PetscCall(PetscFree((*A)->bsizes));
1559: PetscCall(PetscFree((*A)->solvertype));
1560: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1561: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1562: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1563: PetscCall(MatProductClear(*A));
1564: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1565: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1566: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1567: PetscCall(MatDestroy(&(*A)->schur));
1568: PetscCall(VecDestroy(&(*A)->dot_vec));
1569: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1570: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1571: PetscCall(PetscHeaderDestroy(A));
1572: PetscFunctionReturn(PETSC_SUCCESS);
1573: }
1575: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1576: /*@
1577: MatSetValues - Inserts or adds a block of values into a matrix.
1578: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1579: MUST be called after all calls to `MatSetValues()` have been completed.
1581: Not Collective
1583: Input Parameters:
1584: + mat - the matrix
1585: . m - the number of rows
1586: . idxm - the global indices of the rows
1587: . n - the number of columns
1588: . idxn - the global indices of the columns
1589: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1590: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1591: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1593: Level: beginner
1595: Notes:
1596: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1597: options cannot be mixed without intervening calls to the assembly
1598: routines.
1600: `MatSetValues()` uses 0-based row and column numbers in Fortran
1601: as well as in C.
1603: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are simply ignored. This allows easily inserting element stiffness matrices
1604: with homogeneous Dirichlet boundary conditions that you don't want represented
1605: in the matrix.
1607: Efficiency Alert:
1608: The routine `MatSetValuesBlocked()` may offer much better efficiency
1609: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1611: Fortran Notes:
1612: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1613: .vb
1614: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1615: .ve
1617: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1618: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1620: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1621: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1622: @*/
1623: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1624: {
1625: PetscFunctionBeginHot;
1628: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1629: PetscAssertPointer(idxm, 3);
1630: PetscAssertPointer(idxn, 5);
1631: MatCheckPreallocated(mat, 1);
1633: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1634: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1636: if (PetscDefined(USE_DEBUG)) {
1637: PetscInt i, j;
1639: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1640: if (v) {
1641: for (i = 0; i < m; i++) {
1642: for (j = 0; j < n; j++) {
1643: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1644: #if PetscDefined(USE_COMPLEX)
1645: 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]);
1646: #else
1647: 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]);
1648: #endif
1649: }
1650: }
1651: }
1652: 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);
1653: 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);
1654: }
1656: if (mat->assembled) {
1657: mat->was_assembled = PETSC_TRUE;
1658: mat->assembled = PETSC_FALSE;
1659: }
1660: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1661: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1662: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1663: PetscFunctionReturn(PETSC_SUCCESS);
1664: }
1666: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1667: /*@
1668: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1669: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1670: MUST be called after all calls to `MatSetValues()` have been completed.
1672: Not Collective
1674: Input Parameters:
1675: + mat - the matrix
1676: . ism - the rows to provide
1677: . isn - the columns to provide
1678: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1679: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1680: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1682: Level: beginner
1684: Notes:
1685: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1687: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1688: options cannot be mixed without intervening calls to the assembly
1689: routines.
1691: `MatSetValues()` uses 0-based row and column numbers in Fortran
1692: as well as in C.
1694: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1695: simply ignored. This allows easily inserting element stiffness matrices
1696: with homogeneous Dirichlet boundary conditions that you don't want represented
1697: in the matrix.
1699: Fortran Note:
1700: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1701: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1703: Efficiency Alert:
1704: The routine `MatSetValuesBlocked()` may offer much better efficiency
1705: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1707: This is currently not optimized for any particular `ISType`
1709: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1710: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1711: @*/
1712: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1713: {
1714: PetscInt m, n;
1715: const PetscInt *rows, *cols;
1717: PetscFunctionBeginHot;
1719: PetscCall(ISGetIndices(ism, &rows));
1720: PetscCall(ISGetIndices(isn, &cols));
1721: PetscCall(ISGetLocalSize(ism, &m));
1722: PetscCall(ISGetLocalSize(isn, &n));
1723: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1724: PetscCall(ISRestoreIndices(ism, &rows));
1725: PetscCall(ISRestoreIndices(isn, &cols));
1726: PetscFunctionReturn(PETSC_SUCCESS);
1727: }
1729: /*@
1730: MatSetValuesRowLocal - Inserts a row of nonzero values into a matrix
1732: Not Collective
1734: Input Parameters:
1735: + mat - the matrix
1736: . row - the row to set
1737: - v - a one-dimensional array that contains the values
1739: Level: intermediate
1741: Notes:
1742: Currently only supported for `MATAIJ`.
1744: All the nonzero values in `row` must be provided
1746: The matrix must have previously had its column indices set, likely by having been assembled.
1748: `row` must belong to this MPI process
1750: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1751: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`, `MATAIJ`
1752: @*/
1753: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1754: {
1755: PetscInt globalrow;
1757: PetscFunctionBegin;
1760: PetscAssertPointer(v, 3);
1761: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1762: PetscCall(MatSetValuesRow(mat, globalrow, v));
1763: PetscFunctionReturn(PETSC_SUCCESS);
1764: }
1766: /*@
1767: MatSetValuesRow - Inserts a row of nonzero values into a matrix
1769: Not Collective
1771: Input Parameters:
1772: + mat - the matrix
1773: . row - the row to set
1774: - v - a one dimensional array of values
1776: Level: advanced
1778: Notes:
1779: Currently only supported for `MATAIJ`.
1781: All the nonzeros in `row` must be provided
1783: The matrix must have previously had its column indices set, likely by having been assembled.
1785: `row` must belong to this process
1787: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1788: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MATAIJ`
1789: @*/
1790: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1791: {
1792: PetscFunctionBeginHot;
1795: MatCheckPreallocated(mat, 1);
1796: PetscAssertPointer(v, 3);
1797: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1798: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1799: mat->insertmode = INSERT_VALUES;
1801: if (mat->assembled) {
1802: mat->was_assembled = PETSC_TRUE;
1803: mat->assembled = PETSC_FALSE;
1804: }
1805: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1806: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1807: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1808: PetscFunctionReturn(PETSC_SUCCESS);
1809: }
1811: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1812: /*@
1813: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1814: Using structured grid indexing
1816: Not Collective
1818: Input Parameters:
1819: + mat - the matrix
1820: . m - number of rows being entered
1821: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1822: . n - number of columns being entered
1823: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1824: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1825: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1826: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1828: Level: beginner
1830: Notes:
1831: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1833: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1834: options cannot be mixed without intervening calls to the assembly
1835: routines.
1837: The grid coordinates are across the entire grid, not just the local portion
1839: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1840: as well as in C.
1842: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1844: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1845: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1847: The columns and rows in the stencil passed in MUST be contained within the
1848: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1849: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1850: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1851: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1853: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1854: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1855: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1856: `DM_BOUNDARY_PERIODIC` boundary type.
1858: 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
1859: a single value per point) you can skip filling those indices.
1861: Inspired by the structured grid interface to the HYPRE package
1862: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1864: Fortran Notes:
1865: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1866: .vb
1867: call MatSetValuesStencil(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1868: .ve
1870: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1871: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1873: Efficiency Alert:
1874: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1875: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1877: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1878: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1879: @*/
1880: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1881: {
1882: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1883: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1884: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1886: PetscFunctionBegin;
1887: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1890: PetscAssertPointer(idxm, 3);
1891: PetscAssertPointer(idxn, 5);
1893: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1894: jdxm = buf;
1895: jdxn = buf + m;
1896: } else {
1897: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1898: jdxm = bufm;
1899: jdxn = bufn;
1900: }
1901: for (i = 0; i < m; i++) {
1902: for (j = 0; j < 3 - sdim; j++) dxm++;
1903: tmp = *dxm++ - starts[0];
1904: for (j = 0; j < dim - 1; j++) {
1905: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1906: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1907: }
1908: if (mat->stencil.noc) dxm++;
1909: jdxm[i] = tmp;
1910: }
1911: for (i = 0; i < n; i++) {
1912: for (j = 0; j < 3 - sdim; j++) dxn++;
1913: tmp = *dxn++ - starts[0];
1914: for (j = 0; j < dim - 1; j++) {
1915: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1916: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1917: }
1918: if (mat->stencil.noc) dxn++;
1919: jdxn[i] = tmp;
1920: }
1921: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1922: PetscCall(PetscFree2(bufm, bufn));
1923: PetscFunctionReturn(PETSC_SUCCESS);
1924: }
1926: /*@
1927: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1928: Using structured grid indexing
1930: Not Collective
1932: Input Parameters:
1933: + mat - the matrix
1934: . m - number of rows being entered
1935: . idxm - grid coordinates for matrix rows being entered
1936: . n - number of columns being entered
1937: . idxn - grid coordinates for matrix columns being entered
1938: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1939: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1940: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1942: Level: beginner
1944: Notes:
1945: By default the values, `v`, are row-oriented and unsorted.
1946: See `MatSetOption()` for other options.
1948: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1949: options cannot be mixed without intervening calls to the assembly
1950: routines.
1952: The grid coordinates are across the entire grid, not just the local portion
1954: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1955: as well as in C.
1957: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1959: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1960: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1962: The columns and rows in the stencil passed in MUST be contained within the
1963: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1964: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1965: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1966: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1968: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1969: simply ignored. This allows easily inserting element stiffness matrices
1970: with homogeneous Dirichlet boundary conditions that you don't want represented
1971: in the matrix.
1973: Inspired by the structured grid interface to the HYPRE package
1974: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1976: Fortran Notes:
1977: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1978: .vb
1979: call MatSetValuesBlockedStencil(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1980: .ve
1982: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1983: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1985: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1986: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1987: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1988: @*/
1989: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1990: {
1991: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1992: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1993: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1995: PetscFunctionBegin;
1996: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1999: PetscAssertPointer(idxm, 3);
2000: PetscAssertPointer(idxn, 5);
2001: PetscAssertPointer(v, 6);
2003: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2004: jdxm = buf;
2005: jdxn = buf + m;
2006: } else {
2007: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
2008: jdxm = bufm;
2009: jdxn = bufn;
2010: }
2011: for (i = 0; i < m; i++) {
2012: for (j = 0; j < 3 - sdim; j++) dxm++;
2013: tmp = *dxm++ - starts[0];
2014: for (j = 0; j < sdim - 1; j++) {
2015: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
2016: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
2017: }
2018: dxm++;
2019: jdxm[i] = tmp;
2020: }
2021: for (i = 0; i < n; i++) {
2022: for (j = 0; j < 3 - sdim; j++) dxn++;
2023: tmp = *dxn++ - starts[0];
2024: for (j = 0; j < sdim - 1; j++) {
2025: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
2026: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
2027: }
2028: dxn++;
2029: jdxn[i] = tmp;
2030: }
2031: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
2032: PetscCall(PetscFree2(bufm, bufn));
2033: PetscFunctionReturn(PETSC_SUCCESS);
2034: }
2036: /*@
2037: MatSetStencil - Sets the grid information for setting values into a matrix via
2038: `MatSetValuesStencil()`
2040: Not Collective
2042: Input Parameters:
2043: + mat - the matrix
2044: . dim - dimension of the grid 1, 2, or 3
2045: . dims - number of grid points in x, y, and z direction, including ghost points on your process
2046: . starts - starting point of ghost nodes on your process in x, y, and z direction
2047: - dof - number of degrees of freedom per node
2049: Level: beginner
2051: Notes:
2052: Inspired by the structured grid interface to the HYPRE package
2053: (www.llnl.gov/CASC/hyper)
2055: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
2056: user.
2058: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2059: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
2060: @*/
2061: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
2062: {
2063: PetscFunctionBegin;
2065: PetscAssertPointer(dims, 3);
2066: PetscAssertPointer(starts, 4);
2068: mat->stencil.dim = dim + (dof > 1);
2069: for (PetscInt i = 0; i < dim; i++) {
2070: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
2071: mat->stencil.starts[i] = starts[dim - i - 1];
2072: }
2073: mat->stencil.dims[dim] = dof;
2074: mat->stencil.starts[dim] = 0;
2075: mat->stencil.noc = (PetscBool)(dof == 1);
2076: PetscFunctionReturn(PETSC_SUCCESS);
2077: }
2079: /*@
2080: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
2082: Not Collective
2084: Input Parameters:
2085: + mat - the matrix
2086: . m - the number of block rows
2087: . idxm - the global block indices
2088: . n - the number of block columns
2089: . idxn - the global block indices
2090: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2091: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2092: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
2094: Level: intermediate
2096: Notes:
2097: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2098: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2100: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2101: NOT the total number of rows/columns; for example, if the block size is 2 and
2102: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2103: The values in `idxm` would be 1 2; that is the first index for each block divided by
2104: the block size.
2106: You must call `MatSetBlockSize()` when constructing this matrix (before
2107: preallocating it).
2109: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2111: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2112: options cannot be mixed without intervening calls to the assembly
2113: routines.
2115: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2116: as well as in C.
2118: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2119: simply ignored. This allows easily inserting element stiffness matrices
2120: with homogeneous Dirichlet boundary conditions that you don't want represented
2121: in the matrix.
2123: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2124: internal searching must be done to determine where to place the
2125: data in the matrix storage space. By instead inserting blocks of
2126: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2127: reduced.
2129: Example:
2130: .vb
2131: Suppose m=n=2 and block size(bs) = 2 The array is
2133: 1 2 | 3 4
2134: 5 6 | 7 8
2135: - - - | - - -
2136: 9 10 | 11 12
2137: 13 14 | 15 16
2139: v[] should be passed in like
2140: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2142: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2143: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2144: .ve
2146: Fortran Notes:
2147: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2148: .vb
2149: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2150: .ve
2152: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2153: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2155: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2156: @*/
2157: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2158: {
2159: PetscFunctionBeginHot;
2162: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2163: PetscAssertPointer(idxm, 3);
2164: PetscAssertPointer(idxn, 5);
2165: MatCheckPreallocated(mat, 1);
2166: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2167: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2168: if (PetscDefined(USE_DEBUG)) {
2169: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2170: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2171: }
2172: if (PetscDefined(USE_DEBUG)) {
2173: PetscInt rbs, cbs, M, N, i;
2174: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2175: PetscCall(MatGetSize(mat, &M, &N));
2176: 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);
2177: for (i = 0; i < n; i++)
2178: 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);
2179: }
2180: if (mat->assembled) {
2181: mat->was_assembled = PETSC_TRUE;
2182: mat->assembled = PETSC_FALSE;
2183: }
2184: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2185: if (mat->ops->setvaluesblocked) PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2186: else {
2187: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2188: PetscInt i, j, bs, cbs;
2190: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2191: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2192: iidxm = buf;
2193: iidxn = buf + m * bs;
2194: } else {
2195: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2196: iidxm = bufr;
2197: iidxn = bufc;
2198: }
2199: for (i = 0; i < m; i++) {
2200: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2201: }
2202: if (m != n || bs != cbs || idxm != idxn) {
2203: for (i = 0; i < n; i++) {
2204: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2205: }
2206: } else iidxn = iidxm;
2207: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2208: PetscCall(PetscFree2(bufr, bufc));
2209: }
2210: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2211: PetscFunctionReturn(PETSC_SUCCESS);
2212: }
2214: /*@
2215: MatGetValues - Gets a block of local values from a matrix.
2217: Not Collective; can only return values that are owned by the give process
2219: Input Parameters:
2220: + mat - the matrix
2221: . v - a logically two-dimensional array for storing the values
2222: . m - the number of rows
2223: . idxm - the global indices of the rows
2224: . n - the number of columns
2225: - idxn - the global indices of the columns
2227: Level: advanced
2229: Notes:
2230: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2232: The values, `v`, are returned in a row-oriented format, analogous to that used by default in `MatSetValues()`,
2233: unless `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE)` is called in which case they are returned column oriented.
2235: `MatGetValues()` uses 0-based row and column numbers in
2236: Fortran as well as in C.
2238: For `MATSBAIJ` matrices only the block upper triangular entries will be set.
2240: `MatGetValues()` requires that the matrix has been assembled
2241: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2242: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2243: without intermediate matrix assembly.
2245: Negative row or column indices will be ignored and those locations in `v` will be
2246: left unchanged.
2248: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2249: That is, rows with global index greater than or equal to `rstart` and less than `rend` where `rstart` and `rend` are obtainable
2250: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2252: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2253: @*/
2254: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2255: {
2256: PetscFunctionBegin;
2259: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2260: PetscAssertPointer(idxm, 3);
2261: PetscAssertPointer(idxn, 5);
2262: PetscAssertPointer(v, 6);
2263: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2264: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2265: MatCheckPreallocated(mat, 1);
2267: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2268: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2269: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2270: PetscFunctionReturn(PETSC_SUCCESS);
2271: }
2273: /*@
2274: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2275: defined previously by `MatSetLocalToGlobalMapping()`
2277: Not Collective
2279: Input Parameters:
2280: + mat - the matrix
2281: . nrow - number of rows
2282: . irow - the row local indices
2283: . ncol - number of columns
2284: - icol - the column local indices
2286: Output Parameter:
2287: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2288: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2290: Level: advanced
2292: Notes:
2293: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2295: 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,
2296: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2297: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2298: with `MatSetLocalToGlobalMapping()`.
2300: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2301: `MatSetValuesLocal()`, `MatGetValues()`
2302: @*/
2303: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2304: {
2305: PetscFunctionBeginHot;
2308: MatCheckPreallocated(mat, 1);
2309: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2310: PetscAssertPointer(irow, 3);
2311: PetscAssertPointer(icol, 5);
2312: if (PetscDefined(USE_DEBUG)) {
2313: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2314: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2315: }
2316: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2317: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2318: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2319: else {
2320: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2321: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2322: irowm = buf;
2323: icolm = buf + nrow;
2324: } else {
2325: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2326: irowm = bufr;
2327: icolm = bufc;
2328: }
2329: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2330: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2331: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2332: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2333: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2334: PetscCall(PetscFree2(bufr, bufc));
2335: }
2336: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2337: PetscFunctionReturn(PETSC_SUCCESS);
2338: }
2340: /*@
2341: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2342: the same size. Currently, this can only be called once and creates the given matrix.
2344: Not Collective
2346: Input Parameters:
2347: + mat - the matrix
2348: . nb - the number of blocks
2349: . bs - the number of rows (and columns) in each block
2350: . rows - a concatenation of the rows for each block
2351: - v - a concatenation of logically two-dimensional arrays of values
2353: Level: advanced
2355: Notes:
2356: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2358: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2360: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2361: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2362: @*/
2363: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2364: {
2365: PetscFunctionBegin;
2368: PetscAssertPointer(rows, 4);
2369: PetscAssertPointer(v, 5);
2370: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2372: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2373: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2374: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2375: PetscFunctionReturn(PETSC_SUCCESS);
2376: }
2378: /*@
2379: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2380: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2381: using a local (per-process) numbering.
2383: Not Collective
2385: Input Parameters:
2386: + x - the matrix
2387: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2388: - cmapping - column mapping
2390: Level: intermediate
2392: Note:
2393: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2395: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2396: @*/
2397: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2398: {
2399: PetscFunctionBegin;
2404: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2405: else {
2406: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2407: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2408: }
2409: PetscFunctionReturn(PETSC_SUCCESS);
2410: }
2412: /*@
2413: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2415: Not Collective
2417: Input Parameter:
2418: . A - the matrix
2420: Output Parameters:
2421: + rmapping - row mapping
2422: - cmapping - column mapping
2424: Level: advanced
2426: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2427: @*/
2428: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2429: {
2430: PetscFunctionBegin;
2433: if (rmapping) {
2434: PetscAssertPointer(rmapping, 2);
2435: *rmapping = A->rmap->mapping;
2436: }
2437: if (cmapping) {
2438: PetscAssertPointer(cmapping, 3);
2439: *cmapping = A->cmap->mapping;
2440: }
2441: PetscFunctionReturn(PETSC_SUCCESS);
2442: }
2444: /*@
2445: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2447: Logically Collective
2449: Input Parameters:
2450: + A - the matrix
2451: . rmap - row layout
2452: - cmap - column layout
2454: Level: advanced
2456: Note:
2457: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2459: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2460: @*/
2461: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2462: {
2463: PetscFunctionBegin;
2465: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2466: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2467: PetscFunctionReturn(PETSC_SUCCESS);
2468: }
2470: /*@
2471: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2473: Not Collective
2475: Input Parameter:
2476: . A - the matrix
2478: Output Parameters:
2479: + rmap - row layout
2480: - cmap - column layout
2482: Level: advanced
2484: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2485: @*/
2486: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2487: {
2488: PetscFunctionBegin;
2491: if (rmap) {
2492: PetscAssertPointer(rmap, 2);
2493: *rmap = A->rmap;
2494: }
2495: if (cmap) {
2496: PetscAssertPointer(cmap, 3);
2497: *cmap = A->cmap;
2498: }
2499: PetscFunctionReturn(PETSC_SUCCESS);
2500: }
2502: /*@
2503: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2504: using a local numbering of the rows and columns.
2506: Not Collective
2508: Input Parameters:
2509: + mat - the matrix
2510: . nrow - number of rows
2511: . irow - the row local indices
2512: . ncol - number of columns
2513: . icol - the column local indices
2514: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2515: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2516: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2518: Level: intermediate
2520: Notes:
2521: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2523: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2524: options cannot be mixed without intervening calls to the assembly
2525: routines.
2527: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2528: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2530: Fortran Notes:
2531: If any of `irow`, `icol`, and `v` are scalars pass them using, for example,
2532: .vb
2533: call MatSetValuesLocal(mat, one, [irow], one, [icol], [v], INSERT_VALUES, ierr)
2534: .ve
2536: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2537: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2539: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2540: `MatGetValuesLocal()`
2541: @*/
2542: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar v[], InsertMode addv)
2543: {
2544: PetscFunctionBeginHot;
2547: MatCheckPreallocated(mat, 1);
2548: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2549: PetscAssertPointer(irow, 3);
2550: PetscAssertPointer(icol, 5);
2551: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2552: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2553: if (PetscDefined(USE_DEBUG)) {
2554: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2555: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2556: }
2558: if (mat->assembled) {
2559: mat->was_assembled = PETSC_TRUE;
2560: mat->assembled = PETSC_FALSE;
2561: }
2562: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2563: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, v, addv);
2564: else {
2565: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2566: const PetscInt *irowm, *icolm;
2568: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2569: bufr = buf;
2570: bufc = buf + nrow;
2571: irowm = bufr;
2572: icolm = bufc;
2573: } else {
2574: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2575: irowm = bufr;
2576: icolm = bufc;
2577: }
2578: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2579: else irowm = irow;
2580: if (mat->cmap->mapping) {
2581: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2582: else icolm = irowm;
2583: } else icolm = icol;
2584: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, v, addv));
2585: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2586: }
2587: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2588: PetscFunctionReturn(PETSC_SUCCESS);
2589: }
2591: /*@
2592: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2593: using a local ordering of the nodes a block at a time.
2595: Not Collective
2597: Input Parameters:
2598: + mat - the matrix
2599: . nrow - number of rows
2600: . irow - the row local indices
2601: . ncol - number of columns
2602: . icol - the column local indices
2603: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2604: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2605: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2607: Level: intermediate
2609: Notes:
2610: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2611: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2613: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2614: options cannot be mixed without intervening calls to the assembly
2615: routines.
2617: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2618: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2620: Fortran Notes:
2621: If any of `irow`, `icol`, and `v` are scalars pass them using, for example,
2622: .vb
2623: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [v], INSERT_VALUES, ierr)
2624: .ve
2626: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2627: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2629: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2630: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2631: @*/
2632: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar v[], InsertMode addv)
2633: {
2634: PetscFunctionBeginHot;
2637: MatCheckPreallocated(mat, 1);
2638: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2639: PetscAssertPointer(irow, 3);
2640: PetscAssertPointer(icol, 5);
2641: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2642: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2643: if (PetscDefined(USE_DEBUG)) {
2644: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2645: 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);
2646: }
2648: if (mat->assembled) {
2649: mat->was_assembled = PETSC_TRUE;
2650: mat->assembled = PETSC_FALSE;
2651: }
2652: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2653: PetscInt irbs, rbs;
2654: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2655: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2656: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2657: }
2658: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2659: PetscInt icbs, cbs;
2660: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2661: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2662: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2663: }
2664: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2665: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, v, addv);
2666: else {
2667: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2668: const PetscInt *irowm, *icolm;
2670: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2671: bufr = buf;
2672: bufc = buf + nrow;
2673: irowm = bufr;
2674: icolm = bufc;
2675: } else {
2676: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2677: irowm = bufr;
2678: icolm = bufc;
2679: }
2680: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2681: else irowm = irow;
2682: if (mat->cmap->mapping) {
2683: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2684: else icolm = irowm;
2685: } else icolm = icol;
2686: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, v, addv));
2687: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2688: }
2689: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2690: PetscFunctionReturn(PETSC_SUCCESS);
2691: }
2693: /*@
2694: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2696: Collective
2698: Input Parameters:
2699: + mat - the matrix
2700: - x - the vector to be multiplied
2702: Output Parameter:
2703: . y - the result
2705: Level: developer
2707: Note:
2708: The vectors `x` and `y` cannot be the same. I.e., one cannot
2709: call `MatMultDiagonalBlock`(A,y,y).
2711: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2712: @*/
2713: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2714: {
2715: PetscFunctionBegin;
2721: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2722: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2723: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2724: MatCheckPreallocated(mat, 1);
2726: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2727: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2728: PetscFunctionReturn(PETSC_SUCCESS);
2729: }
2731: /*@
2732: MatMult - Computes the matrix-vector product, $y = Ax$.
2734: Neighbor-wise Collective
2736: Input Parameters:
2737: + mat - the matrix
2738: - x - the vector to be multiplied
2740: Output Parameter:
2741: . y - the result
2743: Level: beginner
2745: Note:
2746: The vectors `x` and `y` cannot be the same. I.e., one cannot
2747: call `MatMult`(A,y,y).
2749: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2750: @*/
2751: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2752: {
2753: PetscFunctionBegin;
2757: VecCheckAssembled(x);
2759: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2760: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2761: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2762: 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);
2763: 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);
2764: 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);
2765: 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);
2766: PetscCall(VecSetErrorIfLocked(y, 3));
2767: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2768: MatCheckPreallocated(mat, 1);
2770: PetscCall(VecLockReadPush(x));
2771: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2772: PetscUseTypeMethod(mat, mult, x, y);
2773: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2774: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2775: PetscCall(VecLockReadPop(x));
2776: PetscFunctionReturn(PETSC_SUCCESS);
2777: }
2779: /*@
2780: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2782: Neighbor-wise Collective
2784: Input Parameters:
2785: + mat - the matrix
2786: - x - the vector to be multiplied
2788: Output Parameter:
2789: . y - the result
2791: Level: beginner
2793: Notes:
2794: The vectors `x` and `y` cannot be the same. I.e., one cannot
2795: call `MatMultTranspose`(A,y,y).
2797: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2798: use `MatMultHermitianTranspose()`
2800: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2801: @*/
2802: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2803: {
2804: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2806: PetscFunctionBegin;
2810: VecCheckAssembled(x);
2813: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2814: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2815: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2816: 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);
2817: 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);
2818: 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);
2819: 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);
2820: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2821: MatCheckPreallocated(mat, 1);
2823: if (!mat->ops->multtranspose) {
2824: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2825: 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);
2826: } else op = mat->ops->multtranspose;
2827: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2828: PetscCall(VecLockReadPush(x));
2829: PetscCall((*op)(mat, x, y));
2830: PetscCall(VecLockReadPop(x));
2831: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2832: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2833: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2834: PetscFunctionReturn(PETSC_SUCCESS);
2835: }
2837: /*@
2838: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2840: Neighbor-wise Collective
2842: Input Parameters:
2843: + mat - the matrix
2844: - x - the vector to be multiplied
2846: Output Parameter:
2847: . y - the result
2849: Level: beginner
2851: Notes:
2852: The vectors `x` and `y` cannot be the same. I.e., one cannot
2853: call `MatMultHermitianTranspose`(A,y,y).
2855: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2857: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2859: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2860: @*/
2861: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2862: {
2863: PetscFunctionBegin;
2869: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2870: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2871: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2872: 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);
2873: 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);
2874: 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);
2875: 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);
2876: MatCheckPreallocated(mat, 1);
2878: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2879: if (PetscDefined(USE_COMPLEX)) {
2880: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2881: PetscCall(VecLockReadPush(x));
2882: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2883: else PetscUseTypeMethod(mat, mult, x, y);
2884: PetscCall(VecLockReadPop(x));
2885: } else {
2886: Vec w;
2887: PetscCall(VecDuplicate(x, &w));
2888: PetscCall(VecCopy(x, w));
2889: PetscCall(VecConjugate(w));
2890: PetscCall(MatMultTranspose(mat, w, y));
2891: PetscCall(VecDestroy(&w));
2892: PetscCall(VecConjugate(y));
2893: }
2894: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2895: } else PetscCall(MatMultTranspose(mat, x, y));
2896: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2897: PetscFunctionReturn(PETSC_SUCCESS);
2898: }
2900: /*@
2901: MatMultAdd - Computes $v3 = v2 + A * v1$.
2903: Neighbor-wise Collective
2905: Input Parameters:
2906: + mat - the matrix
2907: . v1 - the vector to be multiplied by `mat`
2908: - v2 - the vector to be added to the result
2910: Output Parameter:
2911: . v3 - the result
2913: Level: beginner
2915: Note:
2916: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2917: call `MatMultAdd`(A,v1,v2,v1).
2919: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2920: @*/
2921: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2922: {
2923: PetscFunctionBegin;
2930: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2931: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2932: 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);
2933: /* 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);
2934: 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); */
2935: 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);
2936: 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);
2937: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2938: MatCheckPreallocated(mat, 1);
2940: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2941: PetscCall(VecLockReadPush(v1));
2942: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2943: PetscCall(VecLockReadPop(v1));
2944: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2945: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2946: PetscFunctionReturn(PETSC_SUCCESS);
2947: }
2949: /*@
2950: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2952: Neighbor-wise Collective
2954: Input Parameters:
2955: + mat - the matrix
2956: . v1 - the vector to be multiplied by the transpose of the matrix
2957: - v2 - the vector to be added to the result
2959: Output Parameter:
2960: . v3 - the result
2962: Level: beginner
2964: Note:
2965: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2966: call `MatMultTransposeAdd`(A,v1,v2,v1).
2968: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2969: @*/
2970: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2971: {
2972: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2974: PetscFunctionBegin;
2981: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2982: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2983: 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);
2984: 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);
2985: 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);
2986: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2987: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2988: MatCheckPreallocated(mat, 1);
2990: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2991: PetscCall(VecLockReadPush(v1));
2992: PetscCall((*op)(mat, v1, v2, v3));
2993: PetscCall(VecLockReadPop(v1));
2994: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2995: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2996: PetscFunctionReturn(PETSC_SUCCESS);
2997: }
2999: /*@
3000: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
3002: Neighbor-wise Collective
3004: Input Parameters:
3005: + mat - the matrix
3006: . v1 - the vector to be multiplied by the Hermitian transpose
3007: - v2 - the vector to be added to the result
3009: Output Parameter:
3010: . v3 - the result
3012: Level: beginner
3014: Note:
3015: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
3016: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
3018: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
3019: @*/
3020: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
3021: {
3022: PetscFunctionBegin;
3029: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3030: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3031: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
3032: 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);
3033: 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);
3034: 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);
3035: MatCheckPreallocated(mat, 1);
3037: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
3038: PetscCall(VecLockReadPush(v1));
3039: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
3040: else {
3041: Vec w, z;
3042: PetscCall(VecDuplicate(v1, &w));
3043: PetscCall(VecCopy(v1, w));
3044: PetscCall(VecConjugate(w));
3045: PetscCall(VecDuplicate(v3, &z));
3046: PetscCall(MatMultTranspose(mat, w, z));
3047: PetscCall(VecDestroy(&w));
3048: PetscCall(VecConjugate(z));
3049: if (v2 != v3) PetscCall(VecWAXPY(v3, 1.0, v2, z));
3050: else PetscCall(VecAXPY(v3, 1.0, z));
3051: PetscCall(VecDestroy(&z));
3052: }
3053: PetscCall(VecLockReadPop(v1));
3054: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
3055: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
3056: PetscFunctionReturn(PETSC_SUCCESS);
3057: }
3059: static PetscErrorCode MatADot_Default(Mat mat, Vec x, Vec y, PetscScalar *val)
3060: {
3061: PetscFunctionBegin;
3062: if (!mat->dot_vec) PetscCall(MatCreateVecs(mat, NULL, &mat->dot_vec));
3063: PetscCall(MatMult(mat, x, mat->dot_vec));
3064: PetscCall(VecDot(mat->dot_vec, y, val));
3065: PetscFunctionReturn(PETSC_SUCCESS);
3066: }
3068: static PetscErrorCode MatANorm_Default(Mat mat, Vec x, PetscReal *val)
3069: {
3070: PetscScalar sval;
3072: PetscFunctionBegin;
3073: PetscCall(MatADot(mat, x, x, &sval));
3074: PetscCheck(PetscRealPart(sval) >= 0.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not positive definite");
3075: PetscCheck(PetscAbsReal(PetscImaginaryPart(sval)) <= 100 * PETSC_MACHINE_EPSILON * PetscMax(1.0, PetscAbsScalar(sval)), PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not Hermitian");
3076: *val = PetscSqrtReal(PetscRealPart(sval));
3077: PetscFunctionReturn(PETSC_SUCCESS);
3078: }
3080: /*@
3081: 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)
3082: positive definite.
3084: Collective
3086: Input Parameters:
3087: + mat - matrix used to define the inner product
3088: . x - first vector
3089: - y - second vector
3091: Output Parameter:
3092: . val - the dot product with respect to `A`
3094: Level: intermediate
3096: Note:
3097: For complex vectors, `MatADot()` computes
3098: $$
3099: val = (x,y)_A = y^H A x,
3100: $$
3101: where $y^H$ denotes the conjugate transpose of `y`. Note that this corresponds to the "mathematicians" complex
3102: inner product where the SECOND argument gets the complex conjugate.
3104: .seealso: [](ch_matrices), `Mat`, `MatANorm()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3105: @*/
3106: PetscErrorCode MatADot(Mat mat, Vec x, Vec y, PetscScalar *val)
3107: {
3108: PetscFunctionBegin;
3112: VecCheckAssembled(x);
3114: VecCheckAssembled(y);
3117: PetscAssertPointer(val, 4);
3118: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3119: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3120: 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);
3121: 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);
3122: 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);
3123: 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);
3124: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3125: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_TRUE));
3126: MatCheckPreallocated(mat, 1);
3128: PetscCall(VecLockReadPush(x));
3129: PetscCall(VecLockReadPush(y));
3130: PetscCall(PetscLogEventBegin(MAT_ADot, mat, x, y, 0));
3131: if (mat->ops->adot) PetscUseTypeMethod(mat, adot, x, y, val);
3132: else PetscCall(MatADot_Default(mat, x, y, val));
3133: PetscCall(PetscLogEventEnd(MAT_ADot, mat, x, y, 0));
3134: PetscCall(VecLockReadPop(y));
3135: PetscCall(VecLockReadPop(x));
3136: PetscFunctionReturn(PETSC_SUCCESS);
3137: }
3139: /*@
3140: 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)
3141: positive definite.
3143: Collective
3145: Input Parameters:
3146: + mat - matrix used to define norm
3147: - x - the vector to compute the norm of
3149: Output Parameter:
3150: . val - the norm with respect to `A`
3152: Level: intermediate
3154: Note:
3155: For complex vectors, `MatANorm()` computes
3156: $$
3157: val = (x,x)_A^{1/2} = (x^H A x)^{1/2},
3158: $$
3159: where $x^H$ denotes the conjugate transpose of `x`.
3161: .seealso: [](ch_matrices), `Mat`, `MatADot()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3162: @*/
3163: PetscErrorCode MatANorm(Mat mat, Vec x, PetscReal *val)
3164: {
3165: PetscFunctionBegin;
3169: VecCheckAssembled(x);
3171: PetscAssertPointer(val, 3);
3172: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3173: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3174: 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);
3175: 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);
3176: 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);
3177: 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);
3178: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3179: MatCheckPreallocated(mat, 1);
3181: PetscCall(VecLockReadPush(x));
3182: PetscCall(PetscLogEventBegin(MAT_ANorm, mat, x, 0, 0));
3183: if (mat->ops->anorm) PetscUseTypeMethod(mat, anorm, x, val);
3184: else PetscCall(MatANorm_Default(mat, x, val));
3185: PetscCall(PetscLogEventEnd(MAT_ANorm, mat, x, 0, 0));
3186: PetscCall(VecLockReadPop(x));
3187: PetscFunctionReturn(PETSC_SUCCESS);
3188: }
3190: /*@
3191: MatGetFactorType - gets the type of factorization a matrix is
3193: Not Collective
3195: Input Parameter:
3196: . mat - the matrix
3198: Output Parameter:
3199: . 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`
3201: Level: intermediate
3203: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3204: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3205: @*/
3206: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3207: {
3208: PetscFunctionBegin;
3211: PetscAssertPointer(t, 2);
3212: *t = mat->factortype;
3213: PetscFunctionReturn(PETSC_SUCCESS);
3214: }
3216: /*@
3217: MatSetFactorType - sets the type of factorization a matrix is
3219: Logically Collective
3221: Input Parameters:
3222: + mat - the matrix
3223: - 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`
3225: Level: intermediate
3227: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3228: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3229: @*/
3230: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3231: {
3232: PetscFunctionBegin;
3235: mat->factortype = t;
3236: PetscFunctionReturn(PETSC_SUCCESS);
3237: }
3239: /*@
3240: MatGetInfo - Returns information about matrix storage (number of
3241: nonzeros, memory, etc.).
3243: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3245: Input Parameters:
3246: + mat - the matrix
3247: - flag - flag indicating the type of parameters to be returned (`MAT_LOCAL` - local matrix, `MAT_GLOBAL_MAX` - maximum over all processes, `MAT_GLOBAL_SUM` - sum over all processes)
3249: Output Parameter:
3250: . info - matrix information context
3252: Options Database Key:
3253: . -mat_view :[filename]:ascii_info - print the matrix information to `filename` or `stdout`, see `MatView()`
3255: Level: intermediate
3257: Notes:
3258: The `MatInfo` context contains a variety of matrix data, including
3259: number of nonzeros allocated and used, number of mallocs during
3260: matrix assembly, etc. Additional information for factored matrices
3261: is provided (such as the fill ratio, number of mallocs during
3262: factorization, etc.).
3264: Example:
3265: See the file `${PETSC_DIR}/include/petscmat.h` for a complete list of
3266: data within the `MatInfo` context. For example,
3267: .vb
3268: MatInfo info;
3269: Mat A;
3270: double mal, nz_a, nz_u;
3272: MatGetInfo(A, MAT_LOCAL, &info);
3273: mal = info.mallocs;
3274: nz_a = info.nz_allocated;
3275: .ve
3277: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3278: @*/
3279: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3280: {
3281: PetscFunctionBegin;
3284: PetscAssertPointer(info, 3);
3285: MatCheckPreallocated(mat, 1);
3286: PetscUseTypeMethod(mat, getinfo, flag, info);
3287: PetscFunctionReturn(PETSC_SUCCESS);
3288: }
3290: /*
3291: This is used by external packages where it is not easy to get the info from the actual
3292: matrix factorization.
3293: */
3294: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3295: {
3296: PetscFunctionBegin;
3297: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3298: PetscFunctionReturn(PETSC_SUCCESS);
3299: }
3301: /*@
3302: MatLUFactor - Performs in-place LU factorization of matrix.
3304: Collective
3306: Input Parameters:
3307: + mat - the matrix
3308: . row - row permutation
3309: . col - column permutation
3310: - info - options for factorization, includes
3311: .vb
3312: fill - expected fill as ratio of original fill.
3313: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3314: Run with the option -info to determine an optimal value to use
3315: .ve
3317: Level: developer
3319: Notes:
3320: Most users should employ the `KSP` interface for linear solvers
3321: instead of working directly with matrix algebra routines such as this.
3322: See, e.g., `KSPCreate()`.
3324: This changes the state of the matrix to a factored matrix; it cannot be used
3325: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3327: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3328: when not using `KSP`.
3330: Fortran Note:
3331: A valid (non-null) `info` argument must be provided
3333: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3334: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3335: @*/
3336: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3337: {
3338: MatFactorInfo tinfo;
3340: PetscFunctionBegin;
3344: if (info) PetscAssertPointer(info, 4);
3346: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3347: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3348: MatCheckPreallocated(mat, 1);
3349: if (!info) {
3350: PetscCall(MatFactorInfoInitialize(&tinfo));
3351: info = &tinfo;
3352: }
3354: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3355: PetscUseTypeMethod(mat, lufactor, row, col, info);
3356: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3357: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3358: PetscFunctionReturn(PETSC_SUCCESS);
3359: }
3361: /*@
3362: MatILUFactor - Performs in-place ILU factorization of matrix.
3364: Collective
3366: Input Parameters:
3367: + mat - the matrix
3368: . row - row permutation
3369: . col - column permutation
3370: - info - structure containing
3371: .vb
3372: levels - number of levels of fill.
3373: expected fill - as ratio of original fill.
3374: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3375: missing diagonal entries)
3376: .ve
3378: Level: developer
3380: Notes:
3381: Most users should employ the `KSP` interface for linear solvers
3382: instead of working directly with matrix algebra routines such as this.
3383: See, e.g., `KSPCreate()`.
3385: Probably really in-place only when level of fill is zero, otherwise allocates
3386: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatLUFactorNumeric()`
3387: when not using `KSP`.
3389: Fortran Note:
3390: A valid (non-null) `info` argument must be provided
3392: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3393: @*/
3394: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3395: {
3396: PetscFunctionBegin;
3400: PetscAssertPointer(info, 4);
3402: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3403: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3404: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3405: MatCheckPreallocated(mat, 1);
3407: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3408: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3409: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3410: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3411: PetscFunctionReturn(PETSC_SUCCESS);
3412: }
3414: /*@
3415: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3416: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3418: Collective
3420: Input Parameters:
3421: + fact - the factor matrix obtained with `MatGetFactor()`
3422: . mat - the matrix
3423: . row - the row permutation
3424: . col - the column permutation
3425: - info - options for factorization, includes
3426: .vb
3427: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3428: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3429: .ve
3431: Level: developer
3433: Notes:
3434: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3436: Most users should employ the simplified `KSP` interface for linear solvers
3437: instead of working directly with matrix algebra routines such as this.
3438: See, e.g., `KSPCreate()`.
3440: Fortran Note:
3441: A valid (non-null) `info` argument must be provided
3443: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3444: @*/
3445: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3446: {
3447: MatFactorInfo tinfo;
3449: PetscFunctionBegin;
3454: if (info) PetscAssertPointer(info, 5);
3457: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3458: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3459: MatCheckPreallocated(mat, 2);
3460: if (!info) {
3461: PetscCall(MatFactorInfoInitialize(&tinfo));
3462: info = &tinfo;
3463: }
3465: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3466: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3467: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3468: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3469: PetscFunctionReturn(PETSC_SUCCESS);
3470: }
3472: /*@
3473: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3474: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3476: Collective
3478: Input Parameters:
3479: + fact - the factor matrix obtained with `MatGetFactor()`
3480: . mat - the matrix
3481: - info - options for factorization
3483: Level: developer
3485: Notes:
3486: See `MatLUFactor()` for in-place factorization. See
3487: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3489: Most users should employ the `KSP` interface for linear solvers
3490: instead of working directly with matrix algebra routines such as this.
3491: See, e.g., `KSPCreate()`.
3493: Fortran Note:
3494: A valid (non-null) `info` argument must be provided
3496: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3497: @*/
3498: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3499: {
3500: MatFactorInfo tinfo;
3502: PetscFunctionBegin;
3507: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3508: 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,
3509: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3511: MatCheckPreallocated(mat, 2);
3512: if (!info) {
3513: PetscCall(MatFactorInfoInitialize(&tinfo));
3514: info = &tinfo;
3515: }
3517: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3518: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3519: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3520: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3521: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3522: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3523: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3524: PetscFunctionReturn(PETSC_SUCCESS);
3525: }
3527: /*@
3528: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3529: symmetric matrix.
3531: Collective
3533: Input Parameters:
3534: + mat - the matrix
3535: . perm - row and column permutations
3536: - info - expected fill as ratio of original fill
3538: Level: developer
3540: Notes:
3541: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3542: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3544: Most users should employ the `KSP` interface for linear solvers
3545: instead of working directly with matrix algebra routines such as this.
3546: See, e.g., `KSPCreate()`.
3548: Fortran Note:
3549: A valid (non-null) `info` argument must be provided
3551: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`,
3552: `MatGetOrdering()`
3553: @*/
3554: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3555: {
3556: MatFactorInfo tinfo;
3558: PetscFunctionBegin;
3561: if (info) PetscAssertPointer(info, 3);
3563: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3564: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3565: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3566: MatCheckPreallocated(mat, 1);
3567: if (!info) {
3568: PetscCall(MatFactorInfoInitialize(&tinfo));
3569: info = &tinfo;
3570: }
3572: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3573: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3574: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3575: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3576: PetscFunctionReturn(PETSC_SUCCESS);
3577: }
3579: /*@
3580: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3581: of a symmetric matrix.
3583: Collective
3585: Input Parameters:
3586: + fact - the factor matrix obtained with `MatGetFactor()`
3587: . mat - the matrix
3588: . perm - row and column permutations
3589: - info - options for factorization, includes
3590: .vb
3591: fill - expected fill as ratio of original fill.
3592: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3593: Run with the option -info to determine an optimal value to use
3594: .ve
3596: Level: developer
3598: Notes:
3599: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3600: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3602: Most users should employ the `KSP` interface for linear solvers
3603: instead of working directly with matrix algebra routines such as this.
3604: See, e.g., `KSPCreate()`.
3606: Fortran Note:
3607: A valid (non-null) `info` argument must be provided
3609: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`,
3610: `MatGetOrdering()`
3611: @*/
3612: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3613: {
3614: MatFactorInfo tinfo;
3616: PetscFunctionBegin;
3620: if (info) PetscAssertPointer(info, 4);
3623: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3624: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3625: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3626: MatCheckPreallocated(mat, 2);
3627: if (!info) {
3628: PetscCall(MatFactorInfoInitialize(&tinfo));
3629: info = &tinfo;
3630: }
3632: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3633: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3634: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3635: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3636: PetscFunctionReturn(PETSC_SUCCESS);
3637: }
3639: /*@
3640: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3641: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3642: `MatCholeskyFactorSymbolic()`.
3644: Collective
3646: Input Parameters:
3647: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3648: . mat - the initial matrix that is to be factored
3649: - info - options for factorization
3651: Level: developer
3653: Note:
3654: Most users should employ the `KSP` interface for linear solvers
3655: instead of working directly with matrix algebra routines such as this.
3656: See, e.g., `KSPCreate()`.
3658: Fortran Note:
3659: A valid (non-null) `info` argument must be provided
3661: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3662: @*/
3663: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3664: {
3665: MatFactorInfo tinfo;
3667: PetscFunctionBegin;
3672: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3673: 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,
3674: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3675: MatCheckPreallocated(mat, 2);
3676: if (!info) {
3677: PetscCall(MatFactorInfoInitialize(&tinfo));
3678: info = &tinfo;
3679: }
3681: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3682: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3683: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3684: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3685: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3686: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3687: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3688: PetscFunctionReturn(PETSC_SUCCESS);
3689: }
3691: /*@
3692: MatQRFactor - Performs in-place QR factorization of matrix.
3694: Collective
3696: Input Parameters:
3697: + mat - the matrix
3698: . col - column permutation
3699: - info - options for factorization, includes
3700: .vb
3701: fill - expected fill as ratio of original fill.
3702: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3703: Run with the option -info to determine an optimal value to use
3704: .ve
3706: Level: developer
3708: Notes:
3709: Most users should employ the `KSP` interface for linear solvers
3710: instead of working directly with matrix algebra routines such as this.
3711: See, e.g., `KSPCreate()`.
3713: This changes the state of the matrix to a factored matrix; it cannot be used
3714: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3716: Fortran Note:
3717: A valid (non-null) `info` argument must be provided
3719: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3720: `MatSetUnfactored()`
3721: @*/
3722: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3723: {
3724: PetscFunctionBegin;
3727: if (info) PetscAssertPointer(info, 3);
3729: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3730: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3731: MatCheckPreallocated(mat, 1);
3732: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3733: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3734: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3735: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3736: PetscFunctionReturn(PETSC_SUCCESS);
3737: }
3739: /*@
3740: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3741: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3743: Collective
3745: Input Parameters:
3746: + fact - the factor matrix obtained with `MatGetFactor()`
3747: . mat - the matrix
3748: . col - column permutation
3749: - info - options for factorization, includes
3750: .vb
3751: fill - expected fill as ratio of original fill.
3752: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3753: Run with the option -info to determine an optimal value to use
3754: .ve
3756: Level: developer
3758: Note:
3759: Most users should employ the `KSP` interface for linear solvers
3760: instead of working directly with matrix algebra routines such as this.
3761: See, e.g., `KSPCreate()`.
3763: Fortran Note:
3764: A valid (non-null) `info` argument must be provided
3766: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3767: @*/
3768: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3769: {
3770: MatFactorInfo tinfo;
3772: PetscFunctionBegin;
3776: if (info) PetscAssertPointer(info, 4);
3779: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3780: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3781: MatCheckPreallocated(mat, 2);
3782: if (!info) {
3783: PetscCall(MatFactorInfoInitialize(&tinfo));
3784: info = &tinfo;
3785: }
3787: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3788: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3789: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3790: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3791: PetscFunctionReturn(PETSC_SUCCESS);
3792: }
3794: /*@
3795: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3796: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3798: Collective
3800: Input Parameters:
3801: + fact - the factor matrix obtained with `MatGetFactor()`
3802: . mat - the matrix
3803: - info - options for factorization
3805: Level: developer
3807: Notes:
3808: See `MatQRFactor()` for in-place factorization.
3810: Most users should employ the `KSP` interface for linear solvers
3811: instead of working directly with matrix algebra routines such as this.
3812: See, e.g., `KSPCreate()`.
3814: Fortran Note:
3815: A valid (non-null) `info` argument must be provided
3817: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3818: @*/
3819: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3820: {
3821: MatFactorInfo tinfo;
3823: PetscFunctionBegin;
3828: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3829: 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,
3830: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3832: MatCheckPreallocated(mat, 2);
3833: if (!info) {
3834: PetscCall(MatFactorInfoInitialize(&tinfo));
3835: info = &tinfo;
3836: }
3838: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3839: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3840: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3841: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3842: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3843: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3844: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3845: PetscFunctionReturn(PETSC_SUCCESS);
3846: }
3848: /*@
3849: MatSolve - Solves $A x = b$, given a factored matrix.
3851: Neighbor-wise Collective
3853: Input Parameters:
3854: + mat - the factored matrix
3855: - b - the right-hand-side vector
3857: Output Parameter:
3858: . x - the result vector
3860: Level: developer
3862: Notes:
3863: The vectors `b` and `x` cannot be the same. I.e., one cannot
3864: call `MatSolve`(A,x,x).
3866: Most users should employ the `KSP` interface for linear solvers
3867: instead of working directly with matrix algebra routines such as this.
3868: See, e.g., `KSPCreate()`.
3870: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3871: @*/
3872: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3873: {
3874: PetscFunctionBegin;
3879: PetscCheckSameComm(mat, 1, b, 2);
3880: PetscCheckSameComm(mat, 1, x, 3);
3881: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3882: 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);
3883: 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);
3884: 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);
3885: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3886: MatCheckPreallocated(mat, 1);
3888: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3889: PetscCall(VecFlag(x, mat->factorerrortype));
3890: if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3891: else PetscUseTypeMethod(mat, solve, b, x);
3892: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3893: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3894: PetscFunctionReturn(PETSC_SUCCESS);
3895: }
3897: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3898: {
3899: Vec b, x;
3900: PetscInt N;
3901: PetscErrorCode (*f)(Mat, Vec, Vec);
3902: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3904: PetscFunctionBegin;
3905: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3906: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3907: PetscCall(MatBoundToCPU(A, &Abound));
3908: if (!Abound) {
3909: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3910: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3911: }
3912: #if PetscDefined(HAVE_CUDA)
3913: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3914: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3915: #elif PetscDefined(HAVE_HIP)
3916: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3917: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3918: #endif
3919: PetscCall(MatGetSize(B, NULL, &N));
3920: for (PetscInt i = 0; i < N; i++) {
3921: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3922: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3923: PetscCall((*f)(A, b, x));
3924: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3925: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3926: }
3927: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3928: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3929: PetscFunctionReturn(PETSC_SUCCESS);
3930: }
3932: /*@
3933: MatMatSolve - Solves $A X = B$, given a factored matrix.
3935: Neighbor-wise Collective
3937: Input Parameters:
3938: + A - the factored matrix
3939: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3941: Output Parameter:
3942: . X - the result matrix (dense matrix)
3944: Level: developer
3946: Note:
3947: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3948: otherwise, `B` and `X` cannot be the same.
3950: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3951: @*/
3952: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3953: {
3954: PetscFunctionBegin;
3959: PetscCheckSameComm(A, 1, B, 2);
3960: PetscCheckSameComm(A, 1, X, 3);
3961: 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);
3962: 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);
3963: 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");
3964: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3965: MatCheckPreallocated(A, 1);
3967: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3968: if (A->factorerrortype) {
3969: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3970: PetscCall(MatFlag(X, 1));
3971: } else if (!A->ops->matsolve) {
3972: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3973: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3974: } else PetscUseTypeMethod(A, matsolve, B, X);
3975: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3976: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3977: PetscFunctionReturn(PETSC_SUCCESS);
3978: }
3980: /*@
3981: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3983: Neighbor-wise Collective
3985: Input Parameters:
3986: + A - the factored matrix
3987: - B - the right-hand-side matrix (`MATDENSE` matrix)
3989: Output Parameter:
3990: . X - the result matrix (dense matrix)
3992: Level: developer
3994: Note:
3995: The matrices `B` and `X` cannot be the same. I.e., one cannot
3996: call `MatMatSolveTranspose`(A,X,X).
3998: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3999: @*/
4000: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
4001: {
4002: PetscFunctionBegin;
4007: PetscCheckSameComm(A, 1, B, 2);
4008: PetscCheckSameComm(A, 1, X, 3);
4009: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
4010: 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);
4011: 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);
4012: 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);
4013: 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");
4014: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4015: MatCheckPreallocated(A, 1);
4017: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
4018: if (A->factorerrortype) {
4019: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
4020: PetscCall(MatFlag(X, 1));
4021: } else if (!A->ops->matsolvetranspose) {
4022: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
4023: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
4024: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
4025: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
4026: PetscCall(PetscObjectStateIncrease((PetscObject)X));
4027: PetscFunctionReturn(PETSC_SUCCESS);
4028: }
4030: /*@
4031: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
4033: Neighbor-wise Collective
4035: Input Parameters:
4036: + A - the factored matrix
4037: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
4039: Output Parameter:
4040: . X - the result matrix (dense matrix)
4042: Level: developer
4044: Note:
4045: For MUMPS, it only supports centralized sparse compressed column format on the host process for right-hand side matrix. User must create `Bt` in sparse compressed row
4046: format on the host process and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
4048: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
4049: @*/
4050: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
4051: {
4052: PetscFunctionBegin;
4057: PetscCheckSameComm(A, 1, Bt, 2);
4058: PetscCheckSameComm(A, 1, X, 3);
4060: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
4061: 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);
4062: 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);
4063: 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");
4064: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4065: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
4066: MatCheckPreallocated(A, 1);
4068: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
4069: if (A->factorerrortype) {
4070: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
4071: PetscCall(MatFlag(X, 1));
4072: } else PetscUseTypeMethod(A, mattransposesolve, Bt, X);
4073: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
4074: PetscCall(PetscObjectStateIncrease((PetscObject)X));
4075: PetscFunctionReturn(PETSC_SUCCESS);
4076: }
4078: /*@
4079: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
4080: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4082: Neighbor-wise Collective
4084: Input Parameters:
4085: + mat - the factored matrix
4086: - b - the right-hand-side vector
4088: Output Parameter:
4089: . x - the result vector
4091: Level: developer
4093: Notes:
4094: `MatSolve()` should be used for most applications, as it performs
4095: a forward solve followed by a backward solve.
4097: The vectors `b` and `x` cannot be the same, i.e., one cannot
4098: call `MatForwardSolve`(A,x,x).
4100: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4101: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4102: `MatForwardSolve()` solves $U^T*D y = b$, and
4103: `MatBackwardSolve()` solves $U x = y$.
4104: Thus they do not provide a symmetric preconditioner.
4106: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
4107: @*/
4108: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
4109: {
4110: PetscFunctionBegin;
4115: PetscCheckSameComm(mat, 1, b, 2);
4116: PetscCheckSameComm(mat, 1, x, 3);
4117: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4118: 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);
4119: 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);
4120: 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);
4121: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4122: MatCheckPreallocated(mat, 1);
4124: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
4125: PetscUseTypeMethod(mat, forwardsolve, b, x);
4126: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
4127: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4128: PetscFunctionReturn(PETSC_SUCCESS);
4129: }
4131: /*@
4132: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
4133: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4135: Neighbor-wise Collective
4137: Input Parameters:
4138: + mat - the factored matrix
4139: - b - the right-hand-side vector
4141: Output Parameter:
4142: . x - the result vector
4144: Level: developer
4146: Notes:
4147: `MatSolve()` should be used for most applications, as it performs
4148: a forward solve followed by a backward solve.
4150: The vectors `b` and `x` cannot be the same. I.e., one cannot
4151: call `MatBackwardSolve`(A,x,x).
4153: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4154: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4155: `MatForwardSolve()` solves $U^T*D y = b$, and
4156: `MatBackwardSolve()` solves $U x = y$.
4157: Thus they do not provide a symmetric preconditioner.
4159: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
4160: @*/
4161: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
4162: {
4163: PetscFunctionBegin;
4168: PetscCheckSameComm(mat, 1, b, 2);
4169: PetscCheckSameComm(mat, 1, x, 3);
4170: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4171: 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);
4172: 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);
4173: 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);
4174: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4175: MatCheckPreallocated(mat, 1);
4177: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
4178: PetscUseTypeMethod(mat, backwardsolve, b, x);
4179: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
4180: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4181: PetscFunctionReturn(PETSC_SUCCESS);
4182: }
4184: /*@
4185: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
4187: Neighbor-wise Collective
4189: Input Parameters:
4190: + mat - the factored matrix
4191: . b - the right-hand-side vector
4192: - y - the vector to be added to
4194: Output Parameter:
4195: . x - the result vector
4197: Level: developer
4199: Note:
4200: The vectors `b` and `x` cannot be the same. I.e., one cannot
4201: call `MatSolveAdd`(A,x,y,x).
4203: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4204: @*/
4205: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4206: {
4207: PetscScalar one = 1.0;
4208: Vec tmp;
4210: PetscFunctionBegin;
4216: PetscCheckSameComm(mat, 1, b, 2);
4217: PetscCheckSameComm(mat, 1, y, 3);
4218: PetscCheckSameComm(mat, 1, x, 4);
4219: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4220: PetscCheck(mat->cmap->N == x->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec x: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, x->map->N);
4221: PetscCheck(mat->rmap->N == b->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec b: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, b->map->N);
4222: PetscCheck(mat->rmap->N == 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);
4223: 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);
4224: 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);
4225: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4226: MatCheckPreallocated(mat, 1);
4228: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4229: PetscCall(VecFlag(x, mat->factorerrortype));
4230: if (mat->factorerrortype) {
4231: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4232: } else if (mat->ops->solveadd) {
4233: PetscUseTypeMethod(mat, solveadd, b, y, x);
4234: } else {
4235: /* do the solve then the add manually */
4236: if (x != y) {
4237: PetscCall(MatSolve(mat, b, x));
4238: PetscCall(VecAXPY(x, one, y));
4239: } else {
4240: PetscCall(VecDuplicate(x, &tmp));
4241: PetscCall(VecCopy(x, tmp));
4242: PetscCall(MatSolve(mat, b, x));
4243: PetscCall(VecAXPY(x, one, tmp));
4244: PetscCall(VecDestroy(&tmp));
4245: }
4246: }
4247: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4248: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4249: PetscFunctionReturn(PETSC_SUCCESS);
4250: }
4252: /*@
4253: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4255: Neighbor-wise Collective
4257: Input Parameters:
4258: + mat - the factored matrix
4259: - b - the right-hand-side vector
4261: Output Parameter:
4262: . x - the result vector
4264: Level: developer
4266: Notes:
4267: The vectors `b` and `x` cannot be the same. I.e., one cannot
4268: call `MatSolveTranspose`(A,x,x).
4270: Most users should employ the `KSP` interface for linear solvers
4271: instead of working directly with matrix algebra routines such as this.
4272: See, e.g., `KSPCreate()`.
4274: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4275: @*/
4276: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4277: {
4278: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4280: PetscFunctionBegin;
4285: PetscCheckSameComm(mat, 1, b, 2);
4286: PetscCheckSameComm(mat, 1, x, 3);
4287: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4288: 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);
4289: 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);
4290: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4291: MatCheckPreallocated(mat, 1);
4292: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4293: PetscCall(VecFlag(x, mat->factorerrortype));
4294: if (mat->factorerrortype) {
4295: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4296: } else {
4297: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4298: PetscCall((*f)(mat, b, x));
4299: }
4300: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4301: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4302: PetscFunctionReturn(PETSC_SUCCESS);
4303: }
4305: /*@
4306: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4307: factored matrix.
4309: Neighbor-wise Collective
4311: Input Parameters:
4312: + mat - the factored matrix
4313: . b - the right-hand-side vector
4314: - y - the vector to be added to
4316: Output Parameter:
4317: . x - the result vector
4319: Level: developer
4321: Note:
4322: The vectors `b` and `x` cannot be the same. I.e., one cannot
4323: call `MatSolveTransposeAdd`(A,x,y,x).
4325: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4326: @*/
4327: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4328: {
4329: PetscScalar one = 1.0;
4330: Vec tmp;
4331: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4333: PetscFunctionBegin;
4339: PetscCheckSameComm(mat, 1, b, 2);
4340: PetscCheckSameComm(mat, 1, y, 3);
4341: PetscCheckSameComm(mat, 1, x, 4);
4342: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4343: 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);
4344: 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);
4345: 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);
4346: 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);
4347: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4348: MatCheckPreallocated(mat, 1);
4350: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4351: PetscCall(VecFlag(x, mat->factorerrortype));
4352: if (mat->factorerrortype) {
4353: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4354: } else if (f) {
4355: PetscCall((*f)(mat, b, y, x));
4356: } else {
4357: /* do the solve then the add manually */
4358: if (x != y) {
4359: PetscCall(MatSolveTranspose(mat, b, x));
4360: PetscCall(VecAXPY(x, one, y));
4361: } else {
4362: PetscCall(VecDuplicate(x, &tmp));
4363: PetscCall(VecCopy(x, tmp));
4364: PetscCall(MatSolveTranspose(mat, b, x));
4365: PetscCall(VecAXPY(x, one, tmp));
4366: PetscCall(VecDestroy(&tmp));
4367: }
4368: }
4369: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4370: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4371: PetscFunctionReturn(PETSC_SUCCESS);
4372: }
4374: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4375: /*@
4376: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4378: Neighbor-wise Collective
4380: Input Parameters:
4381: + mat - the matrix
4382: . b - the right-hand side
4383: . omega - the relaxation factor
4384: . flag - flag indicating the type of SOR (see below)
4385: . shift - diagonal shift
4386: . its - the number of iterations
4387: - lits - the number of local iterations
4389: Output Parameter:
4390: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4392: SOR Flags:
4393: + `SOR_FORWARD_SWEEP` - forward SOR
4394: . `SOR_BACKWARD_SWEEP` - backward SOR
4395: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4396: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4397: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4398: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4399: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4400: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies upper/lower triangular part of matrix to vector (with `omega`)
4401: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4403: Level: developer
4405: Notes:
4406: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4407: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4408: on each process.
4410: Application programmers will not generally use `MatSOR()` directly,
4411: but instead will employ `PCSOR` or `PCEISENSTAT`
4413: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with inodes, this does a block SOR smoothing, otherwise it does a pointwise smoothing.
4414: For `MATAIJ` matrices with inodes, the block sizes are determined by the inode sizes, not the block size set with `MatSetBlockSize()`
4416: Vectors `x` and `b` CANNOT be the same
4418: The flags are implemented as bitwise inclusive or operations.
4419: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4420: to specify a zero initial guess for SSOR.
4422: Developer Note:
4423: We should add block SOR support for `MATAIJ` matrices with block size set to greater than one and no inodes
4425: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4426: @*/
4427: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4428: {
4429: PetscFunctionBegin;
4434: PetscCheckSameComm(mat, 1, b, 2);
4435: PetscCheckSameComm(mat, 1, x, 8);
4436: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4437: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4438: 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);
4439: 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);
4440: 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);
4441: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4442: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4443: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4445: MatCheckPreallocated(mat, 1);
4446: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4447: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4448: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4449: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4450: PetscFunctionReturn(PETSC_SUCCESS);
4451: }
4453: /*
4454: Default matrix copy routine.
4455: */
4456: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4457: {
4458: PetscInt i, rstart = 0, rend = 0, nz;
4459: const PetscInt *cwork;
4460: const PetscScalar *vwork;
4462: PetscFunctionBegin;
4463: if (B->assembled) PetscCall(MatZeroEntries(B));
4464: if (str == SAME_NONZERO_PATTERN) {
4465: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4466: for (i = rstart; i < rend; i++) {
4467: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4468: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4469: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4470: }
4471: } else {
4472: PetscCall(MatAYPX(B, 0.0, A, str));
4473: }
4474: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4475: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4476: PetscFunctionReturn(PETSC_SUCCESS);
4477: }
4479: /*@
4480: MatCopy - Copies a matrix to another matrix.
4482: Collective
4484: Input Parameters:
4485: + A - the matrix
4486: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4488: Output Parameter:
4489: . B - where the copy is put
4491: Level: intermediate
4493: Notes:
4494: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4496: `MatCopy()` copies the matrix entries of a matrix to another existing
4497: matrix (after first zeroing the second matrix). A related routine is
4498: `MatConvert()`, which first creates a new matrix and then copies the data.
4500: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4501: @*/
4502: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4503: {
4504: PetscInt i;
4506: PetscFunctionBegin;
4511: PetscCheckSameComm(A, 1, B, 2);
4512: MatCheckPreallocated(B, 2);
4513: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4514: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4515: 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,
4516: A->cmap->N, B->cmap->N);
4517: MatCheckPreallocated(A, 1);
4518: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4520: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4521: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4522: else PetscCall(MatCopy_Basic(A, B, str));
4524: B->stencil.dim = A->stencil.dim;
4525: B->stencil.noc = A->stencil.noc;
4526: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4527: B->stencil.dims[i] = A->stencil.dims[i];
4528: B->stencil.starts[i] = A->stencil.starts[i];
4529: }
4531: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4532: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4533: PetscFunctionReturn(PETSC_SUCCESS);
4534: }
4536: /*@
4537: MatConvert - Converts a matrix to another matrix, either of the same
4538: or different type.
4540: Collective
4542: Input Parameters:
4543: + mat - the matrix
4544: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4545: same type as the original matrix.
4546: - reuse - denotes if the destination matrix is to be created or reused.
4547: 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
4548: `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).
4550: Output Parameter:
4551: . M - pointer to place new matrix
4553: Level: intermediate
4555: Notes:
4556: `MatConvert()` first creates a new matrix and then copies the data from
4557: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4558: entries of one matrix to another already existing matrix context.
4560: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4561: the MPI communicator of the generated matrix is always the same as the communicator
4562: of the input matrix.
4564: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4565: @*/
4566: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4567: {
4568: PetscBool sametype, issame, flg;
4569: PetscBool3 issymmetric, ishermitian, isspd;
4570: char convname[256], mtype[256];
4571: Mat B;
4573: PetscFunctionBegin;
4576: PetscAssertPointer(M, 4);
4577: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4578: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4579: MatCheckPreallocated(mat, 1);
4581: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4582: if (flg) newtype = mtype;
4584: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4585: PetscCall(PetscStrcmp(newtype, "same", &issame));
4586: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4587: if (reuse == MAT_REUSE_MATRIX) {
4589: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4590: }
4592: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4593: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4594: PetscFunctionReturn(PETSC_SUCCESS);
4595: }
4597: /* Cache Mat options because some converters use MatHeaderReplace() */
4598: issymmetric = mat->symmetric;
4599: ishermitian = mat->hermitian;
4600: isspd = mat->spd;
4602: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4603: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4604: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4605: } else {
4606: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4607: const char *prefix[3] = {"seq", "mpi", ""};
4608: PetscInt i;
4609: /*
4610: Order of precedence:
4611: 0) See if newtype is a superclass of the current matrix.
4612: 1) See if a specialized converter is known to the current matrix.
4613: 2) See if a specialized converter is known to the desired matrix class.
4614: 3) See if a good general converter is registered for the desired class
4615: (as of 6/27/03 only MATMPIADJ falls into this category).
4616: 4) See if a good general converter is known for the current matrix.
4617: 5) Use a really basic converter.
4618: */
4620: /* 0) See if newtype is a superclass of the current matrix.
4621: i.e mat is mpiaij and newtype is aij */
4622: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4623: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4624: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4625: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4626: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4627: if (flg) {
4628: if (reuse == MAT_INPLACE_MATRIX) {
4629: PetscCall(PetscInfo(mat, "Early return\n"));
4630: PetscFunctionReturn(PETSC_SUCCESS);
4631: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4632: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4633: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4634: PetscFunctionReturn(PETSC_SUCCESS);
4635: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4636: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4637: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4638: PetscFunctionReturn(PETSC_SUCCESS);
4639: }
4640: }
4641: }
4642: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4643: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4644: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4645: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4646: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4647: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4648: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4649: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4650: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4651: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4652: if (conv) goto foundconv;
4653: }
4655: /* 2) See if a specialized converter is known to the desired matrix class. */
4656: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4657: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4658: PetscCall(MatSetType(B, newtype));
4659: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4660: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4661: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4662: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4663: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4664: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4665: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4666: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4667: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4668: if (conv) {
4669: PetscCall(MatDestroy(&B));
4670: goto foundconv;
4671: }
4672: }
4674: /* 3) See if a good general converter is registered for the desired class */
4675: conv = B->ops->convertfrom;
4676: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4677: PetscCall(MatDestroy(&B));
4678: if (conv) goto foundconv;
4680: /* 4) See if a good general converter is known for the current matrix */
4681: if (mat->ops->convert) conv = mat->ops->convert;
4682: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4683: if (conv) goto foundconv;
4685: /* 5) Use a really basic converter. */
4686: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4687: conv = MatConvert_Basic;
4689: foundconv:
4690: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4691: PetscCall((*conv)(mat, newtype, reuse, M));
4692: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4693: /* the block sizes must be same if the mappings are copied over */
4694: (*M)->rmap->bs = mat->rmap->bs;
4695: (*M)->cmap->bs = mat->cmap->bs;
4696: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4697: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4698: (*M)->rmap->mapping = mat->rmap->mapping;
4699: (*M)->cmap->mapping = mat->cmap->mapping;
4700: }
4701: (*M)->stencil.dim = mat->stencil.dim;
4702: (*M)->stencil.noc = mat->stencil.noc;
4703: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4704: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4705: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4706: }
4707: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4708: }
4709: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4711: /* Reset Mat options */
4712: if (issymmetric != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PetscBool3ToBool(issymmetric)));
4713: if (ishermitian != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PetscBool3ToBool(ishermitian)));
4714: if (isspd != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SPD, PetscBool3ToBool(isspd)));
4715: PetscFunctionReturn(PETSC_SUCCESS);
4716: }
4718: /*@
4719: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4721: Not Collective
4723: Input Parameter:
4724: . mat - the matrix, must be a factored matrix
4726: Output Parameter:
4727: . type - the string name of the package (do not free this string)
4729: Level: intermediate
4731: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4732: @*/
4733: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4734: {
4735: PetscErrorCode (*conv)(Mat, MatSolverType *);
4737: PetscFunctionBegin;
4740: PetscAssertPointer(type, 2);
4741: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4742: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4743: if (conv) PetscCall((*conv)(mat, type));
4744: else *type = MATSOLVERPETSC;
4745: PetscFunctionReturn(PETSC_SUCCESS);
4746: }
4748: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4749: struct _MatSolverTypeForSpecifcType {
4750: MatType mtype;
4751: /* no entry for MAT_FACTOR_NONE */
4752: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4753: MatSolverTypeForSpecifcType next;
4754: };
4756: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4757: struct _MatSolverTypeHolder {
4758: char *name;
4759: MatSolverTypeForSpecifcType handlers;
4760: MatSolverTypeHolder next;
4761: };
4763: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4765: /*@
4766: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4768: Logically Collective, No Fortran Support
4770: Input Parameters:
4771: + package - name of the package, for example `petsc` or `superlu`
4772: . mtype - the matrix type that works with this package
4773: . ftype - the type of factorization supported by the package
4774: - createfactor - routine that will create the factored matrix ready to be used
4776: Level: developer
4778: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4779: `MatGetFactor()`
4780: @*/
4781: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4782: {
4783: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4784: PetscBool flg;
4785: MatSolverTypeForSpecifcType inext, iprev = NULL;
4787: PetscFunctionBegin;
4788: PetscCall(MatInitializePackage());
4789: if (!next) {
4790: PetscCall(PetscNew(&MatSolverTypeHolders));
4791: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4792: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4793: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4794: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4795: PetscFunctionReturn(PETSC_SUCCESS);
4796: }
4797: while (next) {
4798: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4799: if (flg) {
4800: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4801: inext = next->handlers;
4802: while (inext) {
4803: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4804: if (flg) {
4805: inext->createfactor[(int)ftype - 1] = createfactor;
4806: PetscFunctionReturn(PETSC_SUCCESS);
4807: }
4808: iprev = inext;
4809: inext = inext->next;
4810: }
4811: PetscCall(PetscNew(&iprev->next));
4812: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4813: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4814: PetscFunctionReturn(PETSC_SUCCESS);
4815: }
4816: prev = next;
4817: next = next->next;
4818: }
4819: PetscCall(PetscNew(&prev->next));
4820: PetscCall(PetscStrallocpy(package, &prev->next->name));
4821: PetscCall(PetscNew(&prev->next->handlers));
4822: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4823: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4824: PetscFunctionReturn(PETSC_SUCCESS);
4825: }
4827: /*@
4828: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4830: Input Parameters:
4831: + 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
4832: . ftype - the type of factorization supported by the type
4833: - mtype - the matrix type that works with this type
4835: Output Parameters:
4836: + foundtype - `PETSC_TRUE` if the type was registered
4837: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4838: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4840: Calling sequence of `createfactor`:
4841: + A - the matrix providing the factor matrix
4842: . ftype - the `MatFactorType` of the factor requested
4843: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4845: Level: developer
4847: Note:
4848: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4849: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4850: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4852: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4853: `MatInitializePackage()`
4854: @*/
4855: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4856: {
4857: MatSolverTypeHolder next = MatSolverTypeHolders;
4858: PetscBool flg;
4859: MatSolverTypeForSpecifcType inext;
4861: PetscFunctionBegin;
4862: if (foundtype) *foundtype = PETSC_FALSE;
4863: if (foundmtype) *foundmtype = PETSC_FALSE;
4864: if (createfactor) *createfactor = NULL;
4866: if (type) {
4867: while (next) {
4868: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4869: if (flg) {
4870: if (foundtype) *foundtype = PETSC_TRUE;
4871: inext = next->handlers;
4872: while (inext) {
4873: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4874: if (flg) {
4875: if (foundmtype) *foundmtype = PETSC_TRUE;
4876: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4877: PetscFunctionReturn(PETSC_SUCCESS);
4878: }
4879: inext = inext->next;
4880: }
4881: }
4882: next = next->next;
4883: }
4884: } else {
4885: while (next) {
4886: inext = next->handlers;
4887: while (inext) {
4888: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4889: if (flg && inext->createfactor[(int)ftype - 1]) {
4890: if (foundtype) *foundtype = PETSC_TRUE;
4891: if (foundmtype) *foundmtype = PETSC_TRUE;
4892: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4893: PetscFunctionReturn(PETSC_SUCCESS);
4894: }
4895: inext = inext->next;
4896: }
4897: next = next->next;
4898: }
4899: /* try with base classes inext->mtype */
4900: next = MatSolverTypeHolders;
4901: while (next) {
4902: inext = next->handlers;
4903: while (inext) {
4904: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4905: if (flg && inext->createfactor[(int)ftype - 1]) {
4906: if (foundtype) *foundtype = PETSC_TRUE;
4907: if (foundmtype) *foundmtype = PETSC_TRUE;
4908: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4909: PetscFunctionReturn(PETSC_SUCCESS);
4910: }
4911: inext = inext->next;
4912: }
4913: next = next->next;
4914: }
4915: }
4916: PetscFunctionReturn(PETSC_SUCCESS);
4917: }
4919: PetscErrorCode MatSolverTypeDestroy(void)
4920: {
4921: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4922: MatSolverTypeForSpecifcType inext, iprev;
4924: PetscFunctionBegin;
4925: while (next) {
4926: PetscCall(PetscFree(next->name));
4927: inext = next->handlers;
4928: while (inext) {
4929: PetscCall(PetscFree(inext->mtype));
4930: iprev = inext;
4931: inext = inext->next;
4932: PetscCall(PetscFree(iprev));
4933: }
4934: prev = next;
4935: next = next->next;
4936: PetscCall(PetscFree(prev));
4937: }
4938: MatSolverTypeHolders = NULL;
4939: PetscFunctionReturn(PETSC_SUCCESS);
4940: }
4942: static PetscErrorCode MatGetFactor_Private(Mat mat, MatFactorType ftype, PetscBool exact, PetscBool *found, Mat *f)
4943: {
4944: MatSolverTypeHolder next = MatSolverTypeHolders;
4945: MatSolverTypeForSpecifcType inext;
4946: PetscBool flg, same;
4948: PetscFunctionBegin;
4949: *found = PETSC_FALSE;
4950: *f = NULL;
4951: /* When no solver type is requested, MatGetFactor() must honor registration order, but a registered
4952: MatSolverType may only be able to reject a particular MatType at runtime by returning NULL in *f.
4953: Keep walking the registry until a matching backend actually creates a factor. */
4954: while (next) {
4955: inext = next->handlers;
4956: while (inext) {
4957: PetscCall(PetscStrcmp(((PetscObject)mat)->type_name, inext->mtype, &same));
4958: if (exact) flg = same;
4959: else {
4960: /* Do the base-type pass separately from the exact pass so exact registrations for the MatType
4961: are all tried before broader registrations such as implementation base classes. */
4962: PetscCall(PetscStrbeginswith(((PetscObject)mat)->type_name, inext->mtype, &flg));
4963: flg = (PetscBool)(flg && !same);
4964: }
4965: if (flg && inext->createfactor[(int)ftype - 1]) {
4966: *found = PETSC_TRUE;
4967: PetscCall((*inext->createfactor[(int)ftype - 1])(mat, ftype, f));
4968: if (*f) PetscFunctionReturn(PETSC_SUCCESS);
4969: }
4970: inext = inext->next;
4971: }
4972: next = next->next;
4973: }
4974: PetscFunctionReturn(PETSC_SUCCESS);
4975: }
4977: /*@
4978: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4980: Logically Collective
4982: Input Parameter:
4983: . mat - the matrix
4985: Output Parameter:
4986: . flg - `PETSC_TRUE` if uses the ordering
4988: Level: developer
4990: Note:
4991: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4992: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4994: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4995: @*/
4996: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4997: {
4998: PetscFunctionBegin;
4999: *flg = mat->canuseordering;
5000: PetscFunctionReturn(PETSC_SUCCESS);
5001: }
5003: /*@
5004: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
5006: Logically Collective
5008: Input Parameters:
5009: + mat - the matrix obtained with `MatGetFactor()`
5010: - ftype - the factorization type to be used
5012: Output Parameter:
5013: . otype - the preferred ordering type
5015: Level: developer
5017: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
5018: @*/
5019: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
5020: {
5021: PetscFunctionBegin;
5022: *otype = mat->preferredordering[ftype];
5023: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
5024: PetscFunctionReturn(PETSC_SUCCESS);
5025: }
5027: /*@
5028: MatGetFactor - Returns a matrix suitable to calls to routines such as `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
5029: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, and `MatCholeskyFactorNumeric()`
5031: Collective
5033: Input Parameters:
5034: + mat - the matrix
5035: . 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
5036: the other criteria is returned
5037: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
5039: Output Parameter:
5040: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
5042: Options Database Keys:
5043: + -pc_factor_mat_solver_type type - choose the type at run time. When using `KSP` solvers
5044: . -pc_factor_mat_factor_on_host (true|false) - do matrix factorization on host (with device matrices). Default is doing it on device
5045: - -pc_factor_mat_solve_on_host (true|false) - do matrix solve on host (with device matrices). Default is doing it on device
5047: Level: intermediate
5049: Notes:
5050: Some of the packages, such as MUMPS, have options for controlling the factorization, these are in the form `-prefix_mat_packagename_packageoption`
5051: (for example, `-mat_mumps_icntl_6 1`) where `prefix` is normally set automatically from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly,
5052: without using a `PC`, one can set the prefix by
5053: calling `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
5055: Some PETSc matrix formats have alternative solvers available that are provided by alternative packages
5056: such as PaStiX, SuperLU_DIST, MUMPS etc. PETSc must have been configured to use the external solver,
5057: using the corresponding `./configure` option such as `--download-package` or `--with-package-dir`.
5059: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
5060: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
5061: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
5063: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
5064: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
5066: Developer Note:
5067: This should actually be called `MatCreateFactor()` since it creates a new factor object
5069: The `MatGetFactor()` implementations should not be accessing the PETSc options database or making other decisions about solver options,
5070: that should be delayed until the later operations. This is to ensure the correct options prefix has been set in the factor matrix.
5072: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
5073: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`,
5074: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`,
5075: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
5076: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactorNumeric()`
5077: @*/
5078: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
5079: {
5080: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
5081: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
5083: PetscFunctionBegin;
5087: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5088: MatCheckPreallocated(mat, 1);
5090: PetscCall(MatIsShell(mat, &shell));
5091: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
5092: if (hasop) {
5093: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
5094: PetscFunctionReturn(PETSC_SUCCESS);
5095: }
5097: if (!type) {
5098: PetscBool foundbase;
5100: /* First try exact MatType registrations in solver registration order. If all matching backends
5101: decline this matrix instance by returning NULL, then try base-type registrations. */
5102: PetscCall(MatGetFactor_Private(mat, ftype, PETSC_TRUE, &foundtype, f));
5103: if (!*f) {
5104: PetscCall(MatGetFactor_Private(mat, ftype, PETSC_FALSE, &foundbase, f));
5105: foundtype = (PetscBool)(foundtype || foundbase);
5106: }
5107: PetscCheck(foundtype, 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);
5108: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
5109: PetscFunctionReturn(PETSC_SUCCESS);
5110: }
5112: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
5113: PetscCheck(foundtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "Could not locate%s solver type%s%s for factorization type %s and matrix type %s.%s%s", !type ? " a" : "", type ? " " : "", type ? type : "", MatFactorTypes[ftype],
5114: ((PetscObject)mat)->type_name, type ? " Perhaps you must ./configure with --download-" : "", type ? type : "");
5115: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
5116: 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);
5118: PetscCall((*conv)(mat, ftype, f));
5119: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
5120: PetscFunctionReturn(PETSC_SUCCESS);
5121: }
5123: /*@
5124: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
5126: Not Collective
5128: Input Parameters:
5129: + mat - the matrix
5130: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
5131: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
5133: Output Parameter:
5134: . flg - `PETSC_TRUE` if the factorization is available
5136: Level: intermediate
5138: Notes:
5139: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
5140: such as `pastix`, `superlu`, `mumps`, etc.
5142: PETSc must have been configured with `./configure` to use the external solver using the option `--download-package` where package is the name of the package
5144: Developer Note:
5145: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
5147: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
5148: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
5149: @*/
5150: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
5151: {
5152: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
5154: PetscFunctionBegin;
5156: PetscAssertPointer(flg, 4);
5158: *flg = PETSC_FALSE;
5159: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
5161: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5162: MatCheckPreallocated(mat, 1);
5164: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
5165: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
5166: PetscFunctionReturn(PETSC_SUCCESS);
5167: }
5169: /*@
5170: MatDuplicate - Duplicates a matrix including the non-zero structure.
5172: Collective
5174: Input Parameters:
5175: + mat - the matrix
5176: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
5177: See the manual page for `MatDuplicateOption()` for an explanation of these options.
5179: Output Parameter:
5180: . M - pointer to place new matrix
5182: Level: intermediate
5184: Notes:
5185: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
5187: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
5189: 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.
5191: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
5192: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
5193: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
5195: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
5196: @*/
5197: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
5198: {
5199: Mat B;
5200: VecType vtype;
5201: PetscInt i;
5202: PetscObject dm, container_h, container_d;
5203: PetscErrorCodeFn *viewf;
5205: PetscFunctionBegin;
5208: PetscAssertPointer(M, 3);
5209: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
5210: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5211: MatCheckPreallocated(mat, 1);
5213: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
5214: PetscUseTypeMethod(mat, duplicate, op, M);
5215: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
5216: B = *M;
5218: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
5219: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
5220: PetscCall(MatGetVecType(mat, &vtype));
5221: PetscCall(MatSetVecType(B, vtype));
5223: B->stencil.dim = mat->stencil.dim;
5224: B->stencil.noc = mat->stencil.noc;
5225: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
5226: B->stencil.dims[i] = mat->stencil.dims[i];
5227: B->stencil.starts[i] = mat->stencil.starts[i];
5228: }
5230: B->nooffproczerorows = mat->nooffproczerorows;
5231: B->nooffprocentries = mat->nooffprocentries;
5233: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5234: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5235: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5236: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5237: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5238: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5239: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5240: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5241: PetscFunctionReturn(PETSC_SUCCESS);
5242: }
5244: /*@
5245: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5247: Logically Collective
5249: Input Parameter:
5250: . mat - the matrix
5252: Output Parameter:
5253: . v - the diagonal of the matrix
5255: Level: intermediate
5257: Note:
5258: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5259: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5260: is larger than `ndiag`, the values of the remaining entries are unspecified.
5262: Currently only correct in parallel for square matrices.
5264: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5265: @*/
5266: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5267: {
5268: PetscFunctionBegin;
5272: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5273: MatCheckPreallocated(mat, 1);
5274: if (PetscDefined(USE_DEBUG)) {
5275: PetscInt nv, row, col, ndiag;
5277: PetscCall(VecGetLocalSize(v, &nv));
5278: PetscCall(MatGetLocalSize(mat, &row, &col));
5279: ndiag = PetscMin(row, col);
5280: 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);
5281: }
5283: PetscUseTypeMethod(mat, getdiagonal, v);
5284: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5285: PetscFunctionReturn(PETSC_SUCCESS);
5286: }
5288: /*@
5289: MatGetRowMin - Gets the minimum value (of the real part) of each
5290: row of the matrix
5292: Logically Collective
5294: Input Parameter:
5295: . mat - the matrix
5297: Output Parameters:
5298: + v - the vector for storing the maximums
5299: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5301: Level: intermediate
5303: Note:
5304: The result of this call are the same as if one converted the matrix to dense format
5305: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5307: This code is only implemented for a couple of matrix formats.
5309: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5310: `MatGetRowMax()`
5311: @*/
5312: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5313: {
5314: PetscFunctionBegin;
5318: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5320: if (!mat->cmap->N) {
5321: PetscCall(VecSet(v, PETSC_MAX_REAL));
5322: if (idx) {
5323: PetscInt i, m = mat->rmap->n;
5324: for (i = 0; i < m; i++) idx[i] = -1;
5325: }
5326: } else {
5327: MatCheckPreallocated(mat, 1);
5328: }
5329: PetscUseTypeMethod(mat, getrowmin, v, idx);
5330: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5331: PetscFunctionReturn(PETSC_SUCCESS);
5332: }
5334: /*@
5335: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5336: row of the matrix
5338: Logically Collective
5340: Input Parameter:
5341: . mat - the matrix
5343: Output Parameters:
5344: + v - the vector for storing the minimums
5345: - idx - the indices of the column found for each row (or `NULL` if not needed)
5347: Level: intermediate
5349: Notes:
5350: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5351: row is 0 (the first column).
5353: This code is only implemented for a couple of matrix formats.
5355: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5356: @*/
5357: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5358: {
5359: PetscFunctionBegin;
5363: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5364: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5366: if (!mat->cmap->N) {
5367: PetscCall(VecSet(v, 0.0));
5368: if (idx) {
5369: PetscInt i, m = mat->rmap->n;
5370: for (i = 0; i < m; i++) idx[i] = -1;
5371: }
5372: } else {
5373: MatCheckPreallocated(mat, 1);
5374: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5375: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5376: }
5377: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5378: PetscFunctionReturn(PETSC_SUCCESS);
5379: }
5381: /*@
5382: MatGetRowMax - Gets the maximum value (of the real part) of each
5383: row of the matrix
5385: Logically Collective
5387: Input Parameter:
5388: . mat - the matrix
5390: Output Parameters:
5391: + v - the vector for storing the maximums
5392: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5394: Level: intermediate
5396: Notes:
5397: The result of this call are the same as if one converted the matrix to dense format
5398: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5400: This code is only implemented for a couple of matrix formats.
5402: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5403: @*/
5404: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5405: {
5406: PetscFunctionBegin;
5410: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5412: if (!mat->cmap->N) {
5413: PetscCall(VecSet(v, PETSC_MIN_REAL));
5414: if (idx) {
5415: PetscInt i, m = mat->rmap->n;
5416: for (i = 0; i < m; i++) idx[i] = -1;
5417: }
5418: } else {
5419: MatCheckPreallocated(mat, 1);
5420: PetscUseTypeMethod(mat, getrowmax, v, idx);
5421: }
5422: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5423: PetscFunctionReturn(PETSC_SUCCESS);
5424: }
5426: /*@
5427: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5428: row of the matrix
5430: Logically Collective
5432: Input Parameter:
5433: . mat - the matrix
5435: Output Parameters:
5436: + v - the vector for storing the maximums
5437: - idx - the indices of the column found for each row (or `NULL` if not needed)
5439: Level: intermediate
5441: Notes:
5442: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5443: row is 0 (the first column).
5445: This code is only implemented for a couple of matrix formats.
5447: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5448: @*/
5449: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5450: {
5451: PetscFunctionBegin;
5455: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5457: if (!mat->cmap->N) {
5458: PetscCall(VecSet(v, 0.0));
5459: if (idx) {
5460: PetscInt i, m = mat->rmap->n;
5461: for (i = 0; i < m; i++) idx[i] = -1;
5462: }
5463: } else {
5464: MatCheckPreallocated(mat, 1);
5465: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5466: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5467: }
5468: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5469: PetscFunctionReturn(PETSC_SUCCESS);
5470: }
5472: /*@
5473: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5475: Logically Collective
5477: Input Parameter:
5478: . mat - the matrix
5480: Output Parameter:
5481: . v - the vector for storing the sum
5483: Level: intermediate
5485: This code is only implemented for a couple of matrix formats.
5487: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5488: @*/
5489: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5490: {
5491: PetscFunctionBegin;
5495: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5497: if (!mat->cmap->N) PetscCall(VecSet(v, 0.0));
5498: else {
5499: MatCheckPreallocated(mat, 1);
5500: PetscUseTypeMethod(mat, getrowsumabs, v);
5501: }
5502: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5503: PetscFunctionReturn(PETSC_SUCCESS);
5504: }
5506: /*@
5507: MatGetRowSum - Gets the sum of each row of the matrix
5509: Logically or Neighborhood Collective
5511: Input Parameter:
5512: . mat - the matrix
5514: Output Parameter:
5515: . v - the vector for storing the sum of rows
5517: Level: intermediate
5519: Note:
5520: This code is slow since it is not currently specialized for different formats
5522: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5523: @*/
5524: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5525: {
5526: Vec ones;
5528: PetscFunctionBegin;
5532: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5533: MatCheckPreallocated(mat, 1);
5534: PetscCall(MatCreateVecs(mat, &ones, NULL));
5535: PetscCall(VecSet(ones, 1.));
5536: PetscCall(MatMult(mat, ones, v));
5537: PetscCall(VecDestroy(&ones));
5538: PetscFunctionReturn(PETSC_SUCCESS);
5539: }
5541: /*@
5542: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5543: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5545: Collective
5547: Input Parameter:
5548: . mat - the matrix to provide the transpose
5550: Output Parameter:
5551: . 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
5553: Level: advanced
5555: Note:
5556: 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
5557: routine allows bypassing that call.
5559: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5560: @*/
5561: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5562: {
5563: MatState *rb = NULL;
5565: PetscFunctionBegin;
5566: PetscCall(PetscNew(&rb));
5567: rb->id = ((PetscObject)mat)->id;
5568: rb->state = 0;
5569: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5570: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5571: PetscFunctionReturn(PETSC_SUCCESS);
5572: }
5574: static PetscErrorCode MatTranspose_Private(Mat mat, MatReuse reuse, Mat *B, PetscBool conjugate)
5575: {
5576: PetscContainer rB = NULL;
5577: MatState *rb = NULL;
5578: PetscErrorCode (*f)(Mat, MatReuse, Mat *) = NULL;
5580: PetscFunctionBegin;
5583: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5584: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5585: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5586: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5587: MatCheckPreallocated(mat, 1);
5588: if (reuse == MAT_REUSE_MATRIX) {
5589: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5590: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5591: PetscCall(PetscContainerGetPointer(rB, &rb));
5592: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5593: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5594: }
5596: if (conjugate) {
5597: f = mat->ops->hermitiantranspose;
5598: if (f) PetscCall((*f)(mat, reuse, B));
5599: }
5600: if (!f && !(reuse == MAT_INPLACE_MATRIX && mat->hermitian == PETSC_BOOL3_TRUE && conjugate)) {
5601: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5602: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5603: PetscUseTypeMethod(mat, transpose, reuse, B);
5604: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5605: }
5606: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5607: if (conjugate) PetscCall(MatConjugate(*B));
5608: }
5610: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5611: if (reuse != MAT_INPLACE_MATRIX) {
5612: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5613: PetscCall(PetscContainerGetPointer(rB, &rb));
5614: rb->state = ((PetscObject)mat)->state;
5615: rb->nonzerostate = mat->nonzerostate;
5616: }
5617: PetscFunctionReturn(PETSC_SUCCESS);
5618: }
5620: /*@
5621: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5623: Collective
5625: Input Parameters:
5626: + mat - the matrix to transpose
5627: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5629: Output Parameter:
5630: . B - the transpose of the matrix
5632: Level: intermediate
5634: Notes:
5635: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5637: `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
5638: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5640: 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.
5642: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5643: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5645: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5647: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5649: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5650: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5651: @*/
5652: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5653: {
5654: PetscFunctionBegin;
5655: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_FALSE));
5656: PetscFunctionReturn(PETSC_SUCCESS);
5657: }
5659: /*@
5660: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5662: Collective
5664: Input Parameter:
5665: . A - the matrix to transpose
5667: Output Parameter:
5668: . 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
5669: numerical portion.
5671: Level: intermediate
5673: Note:
5674: This is not supported for many matrix types, use `MatTranspose()` in those cases
5676: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5677: @*/
5678: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5679: {
5680: PetscFunctionBegin;
5683: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5684: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5685: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5686: PetscUseTypeMethod(A, transposesymbolic, B);
5687: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5689: PetscCall(MatTransposeSetPrecursor(A, *B));
5690: PetscFunctionReturn(PETSC_SUCCESS);
5691: }
5693: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5694: {
5695: PetscContainer rB;
5696: MatState *rb;
5698: PetscFunctionBegin;
5701: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5702: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5703: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5704: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5705: PetscCall(PetscContainerGetPointer(rB, &rb));
5706: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5707: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5708: PetscFunctionReturn(PETSC_SUCCESS);
5709: }
5711: /*@
5712: MatIsTranspose - Test whether a matrix is another one's transpose,
5713: or its own, in which case it tests symmetry.
5715: Collective
5717: Input Parameters:
5718: + A - the matrix to test
5719: . B - the matrix to test against, this can equal the first parameter
5720: - tol - tolerance, differences between entries smaller than this are counted as zero
5722: Output Parameter:
5723: . flg - the result
5725: Level: intermediate
5727: Notes:
5728: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5729: test involves parallel copies of the block off-diagonal parts of the matrix.
5731: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5732: @*/
5733: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5734: {
5735: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5737: PetscFunctionBegin;
5740: PetscAssertPointer(flg, 4);
5741: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5742: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5743: *flg = PETSC_FALSE;
5744: if (f && g) {
5745: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5746: PetscCall((*f)(A, B, tol, flg));
5747: } else {
5748: MatType mattype;
5750: PetscCall(MatGetType(f ? B : A, &mattype));
5751: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5752: }
5753: PetscFunctionReturn(PETSC_SUCCESS);
5754: }
5756: /*@
5757: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5759: Collective
5761: Input Parameters:
5762: + mat - the matrix to transpose and complex conjugate
5763: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5765: Output Parameter:
5766: . B - the Hermitian transpose
5768: Level: intermediate
5770: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5771: @*/
5772: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5773: {
5774: PetscFunctionBegin;
5775: PetscCall(MatTranspose_Private(mat, reuse, B, PetscDefined(USE_COMPLEX) ? PETSC_TRUE : PETSC_FALSE));
5776: PetscFunctionReturn(PETSC_SUCCESS);
5777: }
5779: /*@
5780: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5782: Collective
5784: Input Parameters:
5785: + A - the matrix to test
5786: . B - the matrix to test against, this can equal the first parameter
5787: - tol - tolerance, differences between entries smaller than this are counted as zero
5789: Output Parameter:
5790: . flg - the result
5792: Level: intermediate
5794: Notes:
5795: Only available for `MATAIJ` matrices.
5797: The sequential algorithm
5798: has a running time of the order of the number of nonzeros; the parallel
5799: test involves parallel copies of the block off-diagonal parts of the matrix.
5801: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5802: @*/
5803: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5804: {
5805: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5807: PetscFunctionBegin;
5810: PetscAssertPointer(flg, 4);
5811: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5812: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5813: if (f && g) {
5814: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5815: PetscCall((*f)(A, B, tol, flg));
5816: } else {
5817: MatType mattype;
5819: PetscCall(MatGetType(f ? B : A, &mattype));
5820: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for Hermitian transpose", mattype);
5821: }
5822: PetscFunctionReturn(PETSC_SUCCESS);
5823: }
5825: /*@
5826: MatPermute - Creates a new matrix with rows and columns permuted from the
5827: original.
5829: Collective
5831: Input Parameters:
5832: + mat - the matrix to permute
5833: . row - row permutation, each process supplies only the permutation for its rows
5834: - col - column permutation, each process supplies only the permutation for its columns
5836: Output Parameter:
5837: . B - the permuted matrix
5839: Level: advanced
5841: Note:
5842: The index sets map from `row`/`col` of permuted matrix to `row`/`col` of original matrix.
5843: The index sets should be on the same communicator as mat and have the same local sizes.
5844: `MATSEQSBAIJ` inputs may produce a `MATSEQBAIJ` matrix when the permutation does not preserve symmetry.
5846: Developer Note:
5847: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5848: exploit the fact that `row` and `col` are permutations, consider implementing the
5849: more general `MatCreateSubMatrix()` instead.
5851: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5852: @*/
5853: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5854: {
5855: PetscFunctionBegin;
5860: PetscAssertPointer(B, 4);
5861: PetscCheckSameComm(mat, 1, row, 2);
5862: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5863: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5864: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5865: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5866: MatCheckPreallocated(mat, 1);
5868: if (mat->ops->permute) {
5869: PetscUseTypeMethod(mat, permute, row, col, B);
5870: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5871: } else {
5872: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5873: }
5874: PetscFunctionReturn(PETSC_SUCCESS);
5875: }
5877: /*@
5878: MatEqual - Compares two matrices.
5880: Collective
5882: Input Parameters:
5883: + A - the first matrix
5884: - B - the second matrix
5886: Output Parameter:
5887: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5889: Level: intermediate
5891: Note:
5892: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5893: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5895: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5896: @*/
5897: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5898: {
5899: PetscFunctionBegin;
5904: PetscAssertPointer(flg, 3);
5905: PetscCheckSameComm(A, 1, B, 2);
5906: MatCheckPreallocated(A, 1);
5907: MatCheckPreallocated(B, 2);
5908: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5909: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5910: 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,
5911: B->cmap->N);
5912: if (A->ops->equal && A->ops->equal == B->ops->equal) PetscUseTypeMethod(A, equal, B, flg);
5913: else PetscCall(MatMultEqual(A, B, 10, flg));
5914: PetscFunctionReturn(PETSC_SUCCESS);
5915: }
5917: /*@
5918: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5919: matrices that are stored as vectors. Either of the two scaling
5920: matrices can be `NULL`.
5922: Collective
5924: Input Parameters:
5925: + mat - the matrix to be scaled
5926: . l - the left scaling vector (or `NULL`)
5927: - r - the right scaling vector (or `NULL`)
5929: Level: intermediate
5931: Note:
5932: `MatDiagonalScale()` computes $A = LAR$, where
5933: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5934: The L scales the rows of the matrix, the R scales the columns of the matrix.
5935: For `MATSEQSBAIJ`, if `l` and `r` are different `Vec` objects, `mat` changes to type `MATSEQBAIJ` because the result is not necessarily symmetric.
5937: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5938: @*/
5939: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5940: {
5941: PetscBool flg = PETSC_FALSE;
5943: PetscFunctionBegin;
5946: if (l) {
5948: PetscCheckSameComm(mat, 1, l, 2);
5949: }
5950: if (r) {
5952: PetscCheckSameComm(mat, 1, r, 3);
5953: }
5954: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5955: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5956: MatCheckPreallocated(mat, 1);
5957: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5959: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5960: PetscUseTypeMethod(mat, diagonalscale, l, r);
5961: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5962: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5963: if (l != r && (PetscBool3ToBool(mat->symmetric) || PetscBool3ToBool(mat->hermitian))) {
5964: if (!PetscDefined(USE_COMPLEX) || PetscBool3ToBool(mat->symmetric)) {
5965: if (l && r) PetscCall(VecEqual(l, r, &flg));
5966: if (!flg) {
5967: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &flg));
5968: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For MATMPISBAIJ, left and right scaling vectors must be the same");
5969: mat->symmetric = mat->spd = PETSC_BOOL3_FALSE;
5970: if (!PetscDefined(USE_COMPLEX)) mat->hermitian = PETSC_BOOL3_FALSE;
5971: else mat->hermitian = PETSC_BOOL3_UNKNOWN;
5972: }
5973: }
5974: if (PetscDefined(USE_COMPLEX) && PetscBool3ToBool(mat->hermitian)) {
5975: flg = PETSC_FALSE;
5976: if (l && r) {
5977: Vec conjugate;
5979: PetscCall(VecDuplicate(l, &conjugate));
5980: PetscCall(VecCopy(l, conjugate));
5981: PetscCall(VecConjugate(conjugate));
5982: PetscCall(VecEqual(conjugate, r, &flg));
5983: PetscCall(VecDestroy(&conjugate));
5984: }
5985: if (!flg) {
5986: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &flg));
5987: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For Hermitian MATMPISBAIJ, left and right scaling vectors must be conjugate one of the other");
5988: mat->hermitian = PETSC_BOOL3_FALSE;
5989: mat->symmetric = mat->spd = PETSC_BOOL3_UNKNOWN;
5990: }
5991: }
5992: }
5993: PetscFunctionReturn(PETSC_SUCCESS);
5994: }
5996: /*@
5997: MatScale - Scales all elements of a matrix by a given number.
5999: Logically Collective
6001: Input Parameters:
6002: + mat - the matrix to be scaled
6003: - a - the scaling value
6005: Level: intermediate
6007: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
6008: @*/
6009: PetscErrorCode MatScale(Mat mat, PetscScalar a)
6010: {
6011: PetscFunctionBegin;
6014: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6015: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6017: MatCheckPreallocated(mat, 1);
6019: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
6020: if (a != (PetscScalar)1.0) {
6021: PetscUseTypeMethod(mat, scale, a);
6022: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6023: }
6024: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
6025: PetscFunctionReturn(PETSC_SUCCESS);
6026: }
6028: /*@
6029: MatNorm - Calculates various norms of a matrix.
6031: Collective
6033: Input Parameters:
6034: + mat - the matrix
6035: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
6037: Output Parameter:
6038: . nrm - the resulting norm
6040: Level: intermediate
6042: .seealso: [](ch_matrices), `Mat`, `MatNormApproximate()`
6043: @*/
6044: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
6045: {
6046: PetscFunctionBegin;
6050: PetscAssertPointer(nrm, 3);
6052: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6053: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6054: MatCheckPreallocated(mat, 1);
6056: PetscUseTypeMethod(mat, norm, type, nrm);
6057: PetscFunctionReturn(PETSC_SUCCESS);
6058: }
6060: static PetscErrorCode VecSetFinalNormApp_Private(Vec x)
6061: {
6062: PetscScalar *ax, nm1;
6063: PetscInt st, en, n;
6065: PetscFunctionBegin;
6066: PetscCall(VecGetSize(x, &n));
6067: if (n < 2) PetscFunctionReturn(PETSC_SUCCESS);
6068: nm1 = n - 1;
6069: PetscCall(VecGetOwnershipRange(x, &st, &en));
6070: PetscCall(VecGetArrayWrite(x, &ax));
6071: for (PetscInt i = st; i < en; i++) {
6072: const PetscInt ii = i - st;
6073: const PetscScalar s = i % 2 ? -1.0 : 1.0;
6075: ax[ii] = s * (1.0 + i / nm1);
6076: }
6077: PetscCall(VecRestoreArrayWrite(x, &ax));
6078: PetscFunctionReturn(PETSC_SUCCESS);
6079: }
6081: static PetscErrorCode MatNormApproximateForwardOnly_Private(Mat A, NormType normtype, PetscInt maxit, PetscBool boundtocpu, PetscReal *n)
6082: {
6083: Vec x, y;
6084: PetscReal normx, normy;
6085: PetscInt i, N;
6086: PetscRandom rnd;
6088: PetscFunctionBegin;
6089: if (maxit < 0) maxit = 1;
6090: PetscCall(PetscRandomCreate(PetscObjectComm((PetscObject)A), &rnd));
6091: PetscCall(PetscRandomSetFromOptions(rnd));
6092: PetscCall(MatCreateVecs(A, &x, &y));
6093: PetscCall(VecBindToCPU(x, boundtocpu));
6094: PetscCall(VecBindToCPU(y, boundtocpu));
6095: PetscCall(VecGetSize(x, &N));
6096: *n = 0.0;
6097: for (i = 0; i < maxit; i++) {
6098: PetscCall(VecSetRandom(x, rnd));
6099: switch (normtype) {
6100: case NORM_1:
6101: PetscCall(VecNorm(x, NORM_1, &normx));
6102: if (normx > 0.0) PetscCall(VecScale(x, 1.0 / normx));
6103: break;
6104: case NORM_INFINITY:
6105: PetscCall(VecShift(x, -0.5));
6106: PetscCall(VecPointwiseSign(x, x, VEC_SIGN_ZERO_TO_SIGNED_UNIT));
6107: break;
6108: case NORM_2:
6109: PetscCall(VecNormalize(x, NULL));
6110: break;
6111: default:
6112: PetscUnreachable();
6113: }
6114: PetscCall(MatMult(A, x, y));
6115: PetscCall(VecNorm(y, normtype, &normy));
6116: *n = PetscMax(*n, normy);
6117: PetscCall(PetscInfo(A, "%s norm forward-only sample %" PetscInt_FMT " -> %g\n", NormTypes[normtype], i, (double)normy));
6118: }
6119: PetscCall(VecDestroy(&x));
6120: PetscCall(VecDestroy(&y));
6121: PetscCall(PetscRandomDestroy(&rnd));
6122: PetscFunctionReturn(PETSC_SUCCESS);
6123: }
6125: /*@
6126: MatNormApproximate - Approximate the norm of a matrix.
6128: Collective
6130: Input Parameters:
6131: + A - the matrix
6132: . normtype - the `NormType`
6133: - maxit - maximum number of iterations to use
6135: Output Parameter:
6136: . n - the norm estimate
6138: Level: intermediate
6140: Notes:
6141: Does not need access to the matrix entries; it just performs matrix-vector and transposed matrix-vector products {cite}`Higham1992`, {cite}`doi:10.1137/S0895479899356080`.
6143: If `maxit` is negative, a default number of iterations (10 for `NORM_1` and `NORM_INFINITY` and 20 for `NORM_2`) is performed.
6145: .seealso: [](ch_matrices), `Mat`, `MatNorm()`
6146: @*/
6147: PetscErrorCode MatNormApproximate(Mat A, NormType normtype, PetscInt maxit, PetscReal *n)
6148: {
6149: Vec x, y, w, z;
6150: PetscReal normz, adot;
6151: PetscScalar dot;
6152: PetscInt i, j, N, jold = -1;
6153: PetscBool boundtocpu = PETSC_TRUE, setherm, isherm, hasop;
6155: PetscFunctionBegin;
6160: PetscAssertPointer(n, 4);
6161: #if PetscDefined(HAVE_DEVICE)
6162: boundtocpu = A->boundtocpu;
6163: #endif
6164: PetscCall(MatHasOperation(A, MATOP_MULT_HERMITIAN_TRANSPOSE, &hasop));
6165: switch (normtype) {
6166: case NORM_INFINITY:
6167: case NORM_1:
6168: if (!hasop) {
6169: PetscCall(MatNormApproximateForwardOnly_Private(A, normtype, maxit, boundtocpu, n));
6170: i = maxit;
6171: break;
6172: } else {
6173: PetscCall(MatIsHermitianKnown(A, &setherm, &isherm));
6174: if ((setherm && isherm) || normtype == NORM_1) PetscCall(PetscObjectReference((PetscObject)A));
6175: else {
6176: Mat B;
6178: PetscCall(MatCreateHermitianTranspose(A, &B));
6179: A = B;
6180: }
6181: }
6182: if (maxit < 0) maxit = 10; /* pure guess */
6183: PetscCall(MatCreateVecs(A, &x, &y));
6184: PetscCall(MatCreateVecs(A, &z, &w));
6185: PetscCall(VecBindToCPU(x, boundtocpu));
6186: PetscCall(VecBindToCPU(y, boundtocpu));
6187: PetscCall(VecBindToCPU(z, boundtocpu));
6188: PetscCall(VecBindToCPU(w, boundtocpu));
6189: PetscCall(VecGetSize(x, &N));
6190: PetscCall(VecSet(x, 1. / N));
6191: *n = 0.0;
6192: for (i = 0; i < maxit; i++) {
6193: PetscCall(MatMult(A, x, y));
6194: PetscCall(VecNorm(y, NORM_1, n));
6195: if (PetscDefined(USE_COMPLEX)) {
6196: PetscCall(VecCopy(y, w));
6197: PetscCall(VecAbs(w));
6198: PetscCall(VecPointwiseDivide(w, y, w));
6199: } else PetscCall(VecPointwiseSign(w, y, VEC_SIGN_ZERO_TO_SIGNED_UNIT));
6200: PetscCall(MatMultHermitianTranspose(A, w, z));
6201: PetscCall(VecRealPart(z));
6202: PetscCall(VecNorm(z, NORM_INFINITY, &normz));
6203: PetscCall(VecDot(x, z, &dot));
6204: adot = PetscAbsScalar(dot);
6205: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> %g (%g %g)\n", NormTypes[normtype], i, (double)*n, (double)normz, (double)adot));
6206: if (normz <= adot && i > 0) {
6207: PetscCall(PetscInfo(A, "%s norm converged\n", NormTypes[normtype]));
6208: break;
6209: }
6210: PetscCall(VecAbs(z));
6211: PetscCall(VecMax(z, &j, &normz));
6212: if (j == jold) {
6213: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> breakdown (j==jold)\n", NormTypes[normtype], i));
6214: break;
6215: }
6216: jold = j;
6217: if (i < maxit - 1) PetscCall(VecSetStdBasis(x, j));
6218: }
6219: /* last check */
6220: if (N > 1) {
6221: PetscReal ny;
6223: PetscCall(VecSetFinalNormApp_Private(x));
6224: PetscCall(MatMult(A, x, y));
6225: PetscCall(VecNorm(y, NORM_1, &ny));
6226: ny = 2 * ny / (3 * N);
6227: PetscCall(PetscInfo(A, "%s norm final check: current %g test %g\n", NormTypes[normtype], (double)*n, (double)ny));
6228: *n = PetscMax(*n, ny);
6229: }
6230: PetscCall(MatDestroy(&A));
6231: PetscCall(VecDestroy(&x));
6232: PetscCall(VecDestroy(&w));
6233: PetscCall(VecDestroy(&y));
6234: PetscCall(VecDestroy(&z));
6235: break;
6236: case NORM_2:
6237: if (!hasop) {
6238: PetscCall(MatNormApproximateForwardOnly_Private(A, normtype, maxit, boundtocpu, n));
6239: i = maxit;
6240: break;
6241: }
6242: if (maxit < 0) maxit = 20; /* pure guess */
6243: PetscCall(MatCreateVecs(A, &x, &y));
6244: PetscCall(MatCreateVecs(A, &z, NULL));
6245: PetscCall(VecBindToCPU(x, boundtocpu));
6246: PetscCall(VecBindToCPU(y, boundtocpu));
6247: PetscCall(VecBindToCPU(z, boundtocpu));
6248: PetscCall(VecSetRandom(x, NULL));
6249: PetscCall(VecNormalize(x, NULL));
6250: *n = 0.0;
6251: for (i = 0; i < maxit; i++) {
6252: PetscCall(MatMult(A, x, y));
6253: PetscCall(VecNormalize(y, n));
6254: PetscCall(MatMultHermitianTranspose(A, y, z));
6255: PetscCall(VecNorm(z, NORM_2, &normz));
6256: PetscCall(VecDot(x, z, &dot));
6257: adot = PetscAbsScalar(dot);
6258: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> %g (%g %g)\n", NormTypes[normtype], i, (double)*n, (double)normz, (double)adot));
6259: if (normz <= adot) {
6260: PetscCall(PetscInfo(A, "%s norm converged\n", NormTypes[normtype]));
6261: break;
6262: }
6263: if (i < maxit - 1) {
6264: Vec t;
6266: PetscCall(VecNormalize(z, NULL));
6267: t = x;
6268: x = z;
6269: z = t;
6270: }
6271: }
6272: PetscCall(VecDestroy(&x));
6273: PetscCall(VecDestroy(&y));
6274: PetscCall(VecDestroy(&z));
6275: break;
6276: default:
6277: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "%s norm not supported", NormTypes[normtype]);
6278: }
6279: PetscCall(PetscInfo(A, "%s norm %g computed in %" PetscInt_FMT " iterations\n", NormTypes[normtype], (double)*n, i));
6280: PetscFunctionReturn(PETSC_SUCCESS);
6281: }
6283: /*
6284: This variable is used to prevent counting of MatAssemblyBegin() that
6285: are called from within a MatAssemblyEnd().
6286: */
6287: static PetscInt MatAssemblyEnd_InUse = 0;
6288: /*@
6289: MatAssemblyBegin - Begins assembling the matrix. This routine should
6290: be called after completing all calls to `MatSetValues()`.
6292: Collective
6294: Input Parameters:
6295: + mat - the matrix
6296: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
6298: Level: beginner
6300: Notes:
6301: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
6302: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
6304: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
6305: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
6306: using the matrix.
6308: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
6309: 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
6310: a global collective operation requiring all processes that share the matrix.
6312: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
6313: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
6314: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
6316: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
6317: @*/
6318: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
6319: {
6320: PetscFunctionBegin;
6323: MatCheckPreallocated(mat, 1);
6324: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
6325: if (mat->assembled) {
6326: mat->was_assembled = PETSC_TRUE;
6327: mat->assembled = PETSC_FALSE;
6328: }
6330: if (!MatAssemblyEnd_InUse) {
6331: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
6332: PetscTryTypeMethod(mat, assemblybegin, type);
6333: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
6334: } else PetscTryTypeMethod(mat, assemblybegin, type);
6335: PetscFunctionReturn(PETSC_SUCCESS);
6336: }
6338: /*@
6339: MatAssembled - Indicates if a matrix has been assembled and is ready for
6340: use; for example, in matrix-vector product.
6342: Not Collective
6344: Input Parameter:
6345: . mat - the matrix
6347: Output Parameter:
6348: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
6350: Level: advanced
6352: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
6353: @*/
6354: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
6355: {
6356: PetscFunctionBegin;
6358: PetscAssertPointer(assembled, 2);
6359: *assembled = mat->assembled;
6360: PetscFunctionReturn(PETSC_SUCCESS);
6361: }
6363: /*@
6364: MatAssemblyEnd - Completes assembling the matrix. This routine should
6365: be called after `MatAssemblyBegin()`.
6367: Collective
6369: Input Parameters:
6370: + mat - the matrix
6371: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
6373: Options Database Key:
6374: . -mat_view viewer_specification - Displays the matrix during this function call. See `PetscOptionsCreateViewer()` for the values of `viewer_specification`
6376: Level: beginner
6378: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`,
6379: `MatViewFromOptions()`, `PetscObjectViewFromOptions()`, `PetscOptionsCreateViewer()`
6380: @*/
6381: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
6382: {
6383: static PetscInt inassm = 0;
6384: PetscBool flg = PETSC_FALSE;
6386: PetscFunctionBegin;
6390: inassm++;
6391: MatAssemblyEnd_InUse++;
6392: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
6393: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
6394: PetscTryTypeMethod(mat, assemblyend, type);
6395: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
6396: } else PetscTryTypeMethod(mat, assemblyend, type);
6398: /* Flush assembly is not a true assembly */
6399: if (type != MAT_FLUSH_ASSEMBLY) {
6400: if (mat->num_ass) {
6401: if (!mat->symmetry_eternal) {
6402: mat->symmetric = PETSC_BOOL3_UNKNOWN;
6403: mat->hermitian = PETSC_BOOL3_UNKNOWN;
6404: }
6405: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
6406: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
6407: }
6408: mat->num_ass++;
6409: mat->assembled = PETSC_TRUE;
6410: mat->ass_nonzerostate = mat->nonzerostate;
6411: }
6413: mat->insertmode = NOT_SET_VALUES;
6414: MatAssemblyEnd_InUse--;
6415: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6416: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
6417: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6419: if (mat->checksymmetryonassembly) {
6420: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
6421: if (flg) {
6422: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6423: } else {
6424: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6425: }
6426: }
6427: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
6428: }
6429: inassm--;
6430: PetscFunctionReturn(PETSC_SUCCESS);
6431: }
6433: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
6434: /*@
6435: MatSetOption - Sets a parameter option for a matrix. Some options
6436: may be specific to certain storage formats. Some options
6437: determine how values will be inserted (or added). Sorted,
6438: row-oriented input will generally assemble the fastest. The default
6439: is row-oriented.
6441: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
6443: Input Parameters:
6444: + mat - the matrix
6445: . op - the option, one of those listed below (and possibly others),
6446: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6448: Options Describing Matrix Structure:
6449: + `MAT_SPD` - symmetric positive definite
6450: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
6451: . `MAT_HERMITIAN` - transpose is the complex conjugation
6452: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
6453: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
6454: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
6455: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
6457: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
6458: do not need to be computed (usually at a high cost)
6460: Options For Use with `MatSetValues()` and `MatGetValues()`:
6461: Insert a logically dense subblock, which can be
6462: . `MAT_ROW_ORIENTED` - row-oriented (default)
6464: These options reflect the data you pass in with `MatSetValues()` or receive with `MatGetValues()`; it has
6465: nothing to do with how the data is stored internally in the matrix
6466: data structure.
6468: When (re)assembling a matrix, we can restrict the input for
6469: efficiency/debugging purposes. These options include
6470: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
6471: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
6472: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-process entries
6473: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
6474: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
6475: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
6476: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
6477: performance for very large process counts.
6478: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
6479: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
6480: functions, instead sending only neighbor messages.
6482: Level: intermediate
6484: Notes:
6485: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
6487: Some options are relevant only for particular matrix types and
6488: are thus ignored by others. Other options are not supported by
6489: certain matrix types and will generate an error message if set.
6491: Once `MAT_STRUCTURE_ONLY` has been set to `PETSC_TRUE`, it cannot be set back to `PETSC_FALSE`.
6493: If using Fortran to compute a matrix, one may need to
6494: use the column-oriented option (or convert to the row-oriented
6495: format).
6497: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6498: that would generate a new entry in the nonzero structure is instead
6499: ignored. Thus, if memory has not already been allocated for this particular
6500: data, then the insertion is ignored. For dense matrices, in which
6501: the entire array is allocated, no entries are ever ignored.
6502: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6504: `MAT_NEW_NONZERO_LOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6505: that would generate a new entry in the nonzero structure instead produces
6506: 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
6508: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6509: that would generate a new entry that has not been preallocated will
6510: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6511: only.) This is a useful flag when debugging matrix memory preallocation.
6512: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6514: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6515: other processes should be dropped, rather than stashed.
6516: This is useful if you know that the "owning" process is also
6517: always generating the correct matrix entries, so that PETSc need
6518: not transfer duplicate entries generated on another process.
6520: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6521: searches during matrix assembly. When this flag is set, the hash table
6522: is created during the first matrix assembly. This hash table is
6523: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6524: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6525: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6526: supported by `MATMPIBAIJ` format only.
6528: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6529: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6531: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ`, `MATSELL`, and `MATIS` matrices this will stop zero values
6532: from creating a zero location in the matrix. A zero on the diagonal is exempt and still creates its
6533: location, so that operations needing a full diagonal, such as `MatSOR()`, keep working. The exemption
6534: covers the diagonal portion of the owning process's rows; a zero added with `ADD_VALUES` to a row owned
6535: by another process is dropped as it is stashed and never reaches that test. A matrix assembled without
6536: preallocation, through `MatSetUp()`, decides the exemption from process-local indices, so it does not
6537: hold there when the row and column layouts differ
6539: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6541: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6542: zero row routines and thus improves performance for very large process counts.
6544: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6545: part of the matrix (since they should match the upper triangular part).
6547: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6548: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6549: with finite difference schemes with non-periodic boundary conditions.
6551: Developer Note:
6552: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6553: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6554: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6555: not changed.
6557: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6558: @*/
6559: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6560: {
6561: PetscFunctionBegin;
6563: if (op > 0) {
6566: }
6568: 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);
6570: switch (op) {
6571: case MAT_FORCE_DIAGONAL_ENTRIES:
6572: mat->force_diagonals = flg;
6573: PetscFunctionReturn(PETSC_SUCCESS);
6574: case MAT_NO_OFF_PROC_ENTRIES:
6575: mat->nooffprocentries = flg;
6576: PetscFunctionReturn(PETSC_SUCCESS);
6577: case MAT_SUBSET_OFF_PROC_ENTRIES:
6578: mat->assembly_subset = flg;
6579: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6580: #if !PetscDefined(HAVE_MPIUNI)
6581: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6582: #endif
6583: mat->stash.first_assembly_done = PETSC_FALSE;
6584: }
6585: PetscFunctionReturn(PETSC_SUCCESS);
6586: case MAT_NO_OFF_PROC_ZERO_ROWS:
6587: mat->nooffproczerorows = flg;
6588: PetscFunctionReturn(PETSC_SUCCESS);
6589: case MAT_SPD:
6590: if (flg) {
6591: mat->spd = PETSC_BOOL3_TRUE;
6592: mat->symmetric = PETSC_BOOL3_TRUE;
6593: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6594: #if !PetscDefined(USE_COMPLEX)
6595: mat->hermitian = PETSC_BOOL3_TRUE;
6596: #endif
6597: } else {
6598: mat->spd = PETSC_BOOL3_FALSE;
6599: }
6600: break;
6601: case MAT_SYMMETRIC:
6602: mat->symmetric = PetscBoolToBool3(flg);
6603: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6604: #if !PetscDefined(USE_COMPLEX)
6605: mat->hermitian = PetscBoolToBool3(flg);
6606: #endif
6607: break;
6608: case MAT_HERMITIAN:
6609: mat->hermitian = PetscBoolToBool3(flg);
6610: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6611: #if !PetscDefined(USE_COMPLEX)
6612: mat->symmetric = PetscBoolToBool3(flg);
6613: #endif
6614: break;
6615: case MAT_STRUCTURALLY_SYMMETRIC:
6616: mat->structurally_symmetric = PetscBoolToBool3(flg);
6617: break;
6618: case MAT_SYMMETRY_ETERNAL:
6619: 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");
6620: mat->symmetry_eternal = flg;
6621: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6622: break;
6623: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6624: 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");
6625: mat->structural_symmetry_eternal = flg;
6626: break;
6627: case MAT_SPD_ETERNAL:
6628: 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");
6629: mat->spd_eternal = flg;
6630: if (flg) {
6631: mat->structural_symmetry_eternal = PETSC_TRUE;
6632: mat->symmetry_eternal = PETSC_TRUE;
6633: }
6634: break;
6635: case MAT_STRUCTURE_ONLY:
6636: PetscCheck(flg || !mat->structure_only, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot set MAT_STRUCTURE_ONLY to PETSC_FALSE after it has been set to PETSC_TRUE");
6637: mat->structure_only = flg;
6638: break;
6639: case MAT_SORTED_FULL:
6640: mat->sortedfull = flg;
6641: break;
6642: default:
6643: break;
6644: }
6645: PetscCheck((op != MAT_ROW_ORIENTED) || ((PetscObject)mat)->type_name, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "The matrix type must be set (MatSetType()) before setting this option");
6646: PetscTryTypeMethod(mat, setoption, op, flg);
6647: PetscFunctionReturn(PETSC_SUCCESS);
6648: }
6650: /*@
6651: MatGetOption - Gets a parameter option that has been set for a matrix.
6653: Logically Collective
6655: Input Parameters:
6656: + mat - the matrix
6657: - op - the option, this only responds to certain options, check the code for which ones
6659: Output Parameter:
6660: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6662: Level: intermediate
6664: Notes:
6665: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6667: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6668: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6670: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6671: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6672: @*/
6673: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6674: {
6675: PetscFunctionBegin;
6679: 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);
6680: 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()");
6682: switch (op) {
6683: case MAT_NO_OFF_PROC_ENTRIES:
6684: *flg = mat->nooffprocentries;
6685: break;
6686: case MAT_NO_OFF_PROC_ZERO_ROWS:
6687: *flg = mat->nooffproczerorows;
6688: break;
6689: case MAT_SYMMETRIC:
6690: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6691: break;
6692: case MAT_HERMITIAN:
6693: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6694: break;
6695: case MAT_STRUCTURALLY_SYMMETRIC:
6696: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6697: break;
6698: case MAT_SPD:
6699: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6700: break;
6701: case MAT_SYMMETRY_ETERNAL:
6702: *flg = mat->symmetry_eternal;
6703: break;
6704: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6705: *flg = mat->symmetry_eternal;
6706: break;
6707: default:
6708: break;
6709: }
6710: PetscFunctionReturn(PETSC_SUCCESS);
6711: }
6713: /*@
6714: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6715: this routine retains the old nonzero structure.
6717: Logically Collective
6719: Input Parameter:
6720: . mat - the matrix
6722: Level: intermediate
6724: Note:
6725: 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.
6726: See the Performance chapter of the users manual for information on preallocating matrices.
6727: For matrices with the `MAT_STRUCTURE_ONLY` option set to true, this routine leaves the structure and object state unchanged because no numerical values are stored.
6729: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6730: @*/
6731: PetscErrorCode MatZeroEntries(Mat mat)
6732: {
6733: PetscFunctionBegin;
6736: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6737: 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");
6738: MatCheckPreallocated(mat, 1);
6740: if (mat->structure_only == PETSC_FALSE) {
6741: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6742: PetscUseTypeMethod(mat, zeroentries);
6743: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6744: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6745: }
6746: PetscFunctionReturn(PETSC_SUCCESS);
6747: }
6749: /*@
6750: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6751: of a set of rows and columns of a matrix.
6753: Collective
6755: Input Parameters:
6756: + mat - the matrix
6757: . numRows - the number of rows/columns to zero
6758: . rows - the global row indices
6759: . diag - value put in the diagonal of the eliminated rows
6760: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6761: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6763: Level: intermediate
6765: Notes:
6766: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6768: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6769: 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
6771: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6772: Krylov method to take advantage of the known solution on the zeroed rows.
6774: For the parallel case, all processes that share the matrix (i.e.,
6775: those in the communicator used for matrix creation) MUST call this
6776: routine, regardless of whether any rows being zeroed are owned by
6777: them.
6779: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6780: 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
6781: missing.
6783: Each process can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6784: list only rows local to itself).
6786: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6788: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6789: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6790: @*/
6791: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6792: {
6793: PetscFunctionBegin;
6796: if (numRows) PetscAssertPointer(rows, 3);
6797: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6798: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6799: MatCheckPreallocated(mat, 1);
6801: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6802: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6803: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6804: PetscFunctionReturn(PETSC_SUCCESS);
6805: }
6807: /*@
6808: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6809: of a set of rows and columns of a matrix.
6811: Collective
6813: Input Parameters:
6814: + mat - the matrix
6815: . is - the rows to zero
6816: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6817: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6818: - b - optional vector of right-hand side, that will be adjusted by provided solution
6820: Level: intermediate
6822: Note:
6823: See `MatZeroRowsColumns()` for details on how this routine operates.
6825: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6826: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6827: @*/
6828: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6829: {
6830: PetscInt numRows;
6831: const PetscInt *rows;
6833: PetscFunctionBegin;
6838: PetscCall(ISGetLocalSize(is, &numRows));
6839: PetscCall(ISGetIndices(is, &rows));
6840: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6841: PetscCall(ISRestoreIndices(is, &rows));
6842: PetscFunctionReturn(PETSC_SUCCESS);
6843: }
6845: /*@
6846: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6847: of a set of rows of a matrix.
6849: Collective
6851: Input Parameters:
6852: + mat - the matrix
6853: . numRows - the number of rows to zero
6854: . rows - the global row indices
6855: . diag - value put in the diagonal of the zeroed rows
6856: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6857: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6859: Level: intermediate
6861: Notes:
6862: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6864: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6866: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6867: Krylov method to take advantage of the known solution on the zeroed rows.
6869: 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)
6870: from the matrix.
6872: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6873: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6874: formats this does not alter the nonzero structure.
6876: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6877: of the matrix is not changed the values are
6878: merely zeroed.
6880: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6881: formats can optionally remove the main diagonal entry from the
6882: nonzero structure as well, by passing 0.0 as the final argument).
6884: For the parallel case, all processes that share the matrix (i.e.,
6885: those in the communicator used for matrix creation) MUST call this
6886: routine, regardless of whether any rows being zeroed are owned by
6887: them.
6889: Each process can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6890: list only rows local to itself).
6892: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6893: owns that are to be zeroed. This saves a global synchronization in the implementation.
6895: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6896: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6897: @*/
6898: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6899: {
6900: PetscFunctionBegin;
6903: if (numRows) PetscAssertPointer(rows, 3);
6904: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6905: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6906: MatCheckPreallocated(mat, 1);
6908: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6909: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6910: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6911: PetscFunctionReturn(PETSC_SUCCESS);
6912: }
6914: /*@
6915: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6916: of a set of rows of a matrix indicated by an `IS`
6918: Collective
6920: Input Parameters:
6921: + mat - the matrix
6922: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6923: . diag - value put in all diagonals of eliminated rows
6924: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6925: - b - optional vector of right-hand side, that will be adjusted by provided solution
6927: Level: intermediate
6929: Note:
6930: See `MatZeroRows()` for details on how this routine operates.
6932: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6933: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6934: @*/
6935: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6936: {
6937: PetscInt numRows = 0;
6938: const PetscInt *rows = NULL;
6940: PetscFunctionBegin;
6943: if (is) {
6945: PetscCall(ISGetLocalSize(is, &numRows));
6946: PetscCall(ISGetIndices(is, &rows));
6947: }
6948: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6949: if (is) PetscCall(ISRestoreIndices(is, &rows));
6950: PetscFunctionReturn(PETSC_SUCCESS);
6951: }
6953: /*@
6954: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6955: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6957: Collective
6959: Input Parameters:
6960: + mat - the matrix
6961: . numRows - the number of rows to remove
6962: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6963: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6964: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6965: - b - optional vector of right-hand side, that will be adjusted by provided solution
6967: Level: intermediate
6969: Notes:
6970: See `MatZeroRows()` for details on how this routine operates.
6972: The grid coordinates are across the entire grid, not just the local portion
6974: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6975: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6976: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6977: `DM_BOUNDARY_PERIODIC` boundary type.
6979: 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
6980: a single value per point) you can skip filling those indices.
6982: Fortran Note:
6983: `idxm` and `idxn` should be declared as
6984: .vb
6985: MatStencil idxm(4, m)
6986: .ve
6987: and the values inserted using
6988: .vb
6989: idxm(MatStencil_i, 1) = i
6990: idxm(MatStencil_j, 1) = j
6991: idxm(MatStencil_k, 1) = k
6992: idxm(MatStencil_c, 1) = c
6993: etc
6994: .ve
6996: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6997: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6998: @*/
6999: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
7000: {
7001: PetscInt dim = mat->stencil.dim;
7002: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
7003: PetscInt *dims = mat->stencil.dims + 1;
7004: PetscInt *starts = mat->stencil.starts;
7005: PetscInt *dxm = (PetscInt *)rows;
7006: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
7008: PetscFunctionBegin;
7011: if (numRows) PetscAssertPointer(rows, 3);
7013: PetscCall(PetscMalloc1(numRows, &jdxm));
7014: for (i = 0; i < numRows; ++i) {
7015: /* Skip unused dimensions (they are ordered k, j, i, c) */
7016: for (j = 0; j < 3 - sdim; ++j) dxm++;
7017: /* Local index in X dir */
7018: tmp = *dxm++ - starts[0];
7019: /* Loop over remaining dimensions */
7020: for (j = 0; j < dim - 1; ++j) {
7021: /* If nonlocal, set index to be negative */
7022: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
7023: /* Update local index */
7024: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
7025: }
7026: /* Skip component slot if necessary */
7027: if (mat->stencil.noc) dxm++;
7028: /* Local row number */
7029: if (tmp >= 0) jdxm[numNewRows++] = tmp;
7030: }
7031: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
7032: PetscCall(PetscFree(jdxm));
7033: PetscFunctionReturn(PETSC_SUCCESS);
7034: }
7036: /*@
7037: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
7038: of a set of rows and columns of a matrix.
7040: Collective
7042: Input Parameters:
7043: + mat - the matrix
7044: . numRows - the number of rows/columns to remove
7045: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
7046: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
7047: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7048: - b - optional vector of right-hand side, that will be adjusted by provided solution
7050: Level: intermediate
7052: Notes:
7053: See `MatZeroRowsColumns()` for details on how this routine operates.
7055: The grid coordinates are across the entire grid, not just the local portion
7057: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
7058: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
7059: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
7060: `DM_BOUNDARY_PERIODIC` boundary type.
7062: 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
7063: a single value per point) you can skip filling those indices.
7065: Fortran Note:
7066: `idxm` and `idxn` should be declared as
7067: .vb
7068: MatStencil idxm(4, m)
7069: .ve
7070: and the values inserted using
7071: .vb
7072: idxm(MatStencil_i, 1) = i
7073: idxm(MatStencil_j, 1) = j
7074: idxm(MatStencil_k, 1) = k
7075: idxm(MatStencil_c, 1) = c
7076: etc
7077: .ve
7079: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7080: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
7081: @*/
7082: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
7083: {
7084: PetscInt dim = mat->stencil.dim;
7085: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
7086: PetscInt *dims = mat->stencil.dims + 1;
7087: PetscInt *starts = mat->stencil.starts;
7088: PetscInt *dxm = (PetscInt *)rows;
7089: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
7091: PetscFunctionBegin;
7094: if (numRows) PetscAssertPointer(rows, 3);
7096: PetscCall(PetscMalloc1(numRows, &jdxm));
7097: for (i = 0; i < numRows; ++i) {
7098: /* Skip unused dimensions (they are ordered k, j, i, c) */
7099: for (j = 0; j < 3 - sdim; ++j) dxm++;
7100: /* Local index in X dir */
7101: tmp = *dxm++ - starts[0];
7102: /* Loop over remaining dimensions */
7103: for (j = 0; j < dim - 1; ++j) {
7104: /* If nonlocal, set index to be negative */
7105: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
7106: /* Update local index */
7107: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
7108: }
7109: /* Skip component slot if necessary */
7110: if (mat->stencil.noc) dxm++;
7111: /* Local row number */
7112: if (tmp >= 0) jdxm[numNewRows++] = tmp;
7113: }
7114: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
7115: PetscCall(PetscFree(jdxm));
7116: PetscFunctionReturn(PETSC_SUCCESS);
7117: }
7119: /*@
7120: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
7121: of a set of rows of a matrix; using local numbering of rows.
7123: Collective
7125: Input Parameters:
7126: + mat - the matrix
7127: . numRows - the number of rows to remove
7128: . rows - the local row indices
7129: . diag - value put in all diagonals of eliminated rows
7130: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7131: - b - optional vector of right-hand side, that will be adjusted by provided solution
7133: Level: intermediate
7135: Notes:
7136: Before calling `MatZeroRowsLocal()`, the user must first set the
7137: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
7139: See `MatZeroRows()` for details on how this routine operates.
7141: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
7142: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7143: @*/
7144: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
7145: {
7146: PetscFunctionBegin;
7149: if (numRows) PetscAssertPointer(rows, 3);
7150: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7151: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7152: MatCheckPreallocated(mat, 1);
7154: if (mat->ops->zerorowslocal) {
7155: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
7156: } else {
7157: IS is, newis;
7158: PetscInt *newRows, nl = 0;
7160: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
7161: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
7162: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
7163: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
7164: for (PetscInt i = 0; i < numRows; i++)
7165: if (newRows[i] > -1) newRows[nl++] = newRows[i];
7166: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
7167: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
7168: PetscCall(ISDestroy(&newis));
7169: PetscCall(ISDestroy(&is));
7170: }
7171: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
7172: PetscFunctionReturn(PETSC_SUCCESS);
7173: }
7175: /*@
7176: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
7177: of a set of rows of a matrix; using local numbering of rows.
7179: Collective
7181: Input Parameters:
7182: + mat - the matrix
7183: . is - index set of rows to remove
7184: . diag - value put in all diagonals of eliminated rows
7185: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7186: - b - optional vector of right-hand side, that will be adjusted by provided solution
7188: Level: intermediate
7190: Notes:
7191: Before calling `MatZeroRowsLocalIS()`, the user must first set the
7192: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7194: See `MatZeroRows()` for details on how this routine operates.
7196: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7197: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7198: @*/
7199: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
7200: {
7201: PetscInt numRows;
7202: const PetscInt *rows;
7204: PetscFunctionBegin;
7208: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7209: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7210: MatCheckPreallocated(mat, 1);
7212: PetscCall(ISGetLocalSize(is, &numRows));
7213: PetscCall(ISGetIndices(is, &rows));
7214: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
7215: PetscCall(ISRestoreIndices(is, &rows));
7216: PetscFunctionReturn(PETSC_SUCCESS);
7217: }
7219: /*@
7220: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
7221: of a set of rows and columns of a matrix; using local numbering of rows.
7223: Collective
7225: Input Parameters:
7226: + mat - the matrix
7227: . numRows - the number of rows to remove
7228: . rows - the global row indices
7229: . diag - value put in all diagonals of eliminated rows
7230: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7231: - b - optional vector of right-hand side, that will be adjusted by provided solution
7233: Level: intermediate
7235: Notes:
7236: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
7237: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7239: See `MatZeroRowsColumns()` for details on how this routine operates.
7241: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7242: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7243: @*/
7244: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
7245: {
7246: PetscFunctionBegin;
7249: if (numRows) PetscAssertPointer(rows, 3);
7250: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7251: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7252: MatCheckPreallocated(mat, 1);
7254: if (mat->ops->zerorowscolumnslocal) {
7255: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
7256: } else {
7257: IS is, newis;
7258: PetscInt *newRows, nl = 0;
7260: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
7261: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
7262: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
7263: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
7264: for (PetscInt i = 0; i < numRows; i++)
7265: if (newRows[i] > -1) newRows[nl++] = newRows[i];
7266: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
7267: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
7268: PetscCall(ISDestroy(&newis));
7269: PetscCall(ISDestroy(&is));
7270: }
7271: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
7272: PetscFunctionReturn(PETSC_SUCCESS);
7273: }
7275: /*@
7276: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
7277: of a set of rows and columns of a matrix; using local numbering of rows.
7279: Collective
7281: Input Parameters:
7282: + mat - the matrix
7283: . is - index set of rows to remove
7284: . diag - value put in all diagonals of eliminated rows
7285: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7286: - b - optional vector of right-hand side, that will be adjusted by provided solution
7288: Level: intermediate
7290: Notes:
7291: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
7292: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7294: See `MatZeroRowsColumns()` for details on how this routine operates.
7296: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7297: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7298: @*/
7299: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
7300: {
7301: PetscInt numRows;
7302: const PetscInt *rows;
7304: PetscFunctionBegin;
7308: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7309: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7310: MatCheckPreallocated(mat, 1);
7312: PetscCall(ISGetLocalSize(is, &numRows));
7313: PetscCall(ISGetIndices(is, &rows));
7314: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
7315: PetscCall(ISRestoreIndices(is, &rows));
7316: PetscFunctionReturn(PETSC_SUCCESS);
7317: }
7319: /*@
7320: MatGetSize - Returns the numbers of rows and columns in a matrix.
7322: Not Collective
7324: Input Parameter:
7325: . mat - the matrix
7327: Output Parameters:
7328: + m - the number of global rows
7329: - n - the number of global columns
7331: Level: beginner
7333: Note:
7334: Both output parameters can be `NULL` on input.
7336: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
7337: @*/
7338: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
7339: {
7340: PetscFunctionBegin;
7342: if (m) *m = mat->rmap->N;
7343: if (n) *n = mat->cmap->N;
7344: PetscFunctionReturn(PETSC_SUCCESS);
7345: }
7347: /*@
7348: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
7349: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
7351: Not Collective
7353: Input Parameter:
7354: . mat - the matrix
7356: Output Parameters:
7357: + m - the number of local rows, use `NULL` to not obtain this value
7358: - n - the number of local columns, use `NULL` to not obtain this value
7360: Level: beginner
7362: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
7363: @*/
7364: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
7365: {
7366: PetscFunctionBegin;
7368: if (m) PetscAssertPointer(m, 2);
7369: if (n) PetscAssertPointer(n, 3);
7370: if (m) *m = mat->rmap->n;
7371: if (n) *n = mat->cmap->n;
7372: PetscFunctionReturn(PETSC_SUCCESS);
7373: }
7375: /*@
7376: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
7377: vector one multiplies this matrix by that are owned by this process.
7379: Not Collective, unless matrix has not been allocated, then collective
7381: Input Parameter:
7382: . mat - the matrix
7384: Output Parameters:
7385: + m - the global index of the first local column, use `NULL` to not obtain this value
7386: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
7388: Level: developer
7390: Notes:
7391: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7393: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7394: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7396: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7397: the local values in the matrix.
7399: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
7400: Layouts](sec_matlayout) for details on matrix layouts.
7402: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7403: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7404: @*/
7405: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
7406: {
7407: PetscFunctionBegin;
7410: if (m) PetscAssertPointer(m, 2);
7411: if (n) PetscAssertPointer(n, 3);
7412: MatCheckPreallocated(mat, 1);
7413: if (m) *m = mat->cmap->rstart;
7414: if (n) *n = mat->cmap->rend;
7415: PetscFunctionReturn(PETSC_SUCCESS);
7416: }
7418: /*@
7419: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
7420: this MPI process.
7422: Not Collective
7424: Input Parameter:
7425: . mat - the matrix
7427: Output Parameters:
7428: + m - the global index of the first local row, use `NULL` to not obtain this value
7429: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
7431: Level: beginner
7433: Notes:
7434: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7436: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7437: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7439: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7440: the local values in the matrix.
7442: The high argument is one more than the last element stored locally.
7444: For all matrices it returns the range of matrix rows associated with rows of a vector that
7445: would contain the result of a matrix vector product with this matrix. See [Matrix
7446: Layouts](sec_matlayout) for details on matrix layouts.
7448: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
7449: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7450: @*/
7451: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
7452: {
7453: PetscFunctionBegin;
7456: if (m) PetscAssertPointer(m, 2);
7457: if (n) PetscAssertPointer(n, 3);
7458: MatCheckPreallocated(mat, 1);
7459: if (m) *m = mat->rmap->rstart;
7460: if (n) *n = mat->rmap->rend;
7461: PetscFunctionReturn(PETSC_SUCCESS);
7462: }
7464: /*@
7465: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
7466: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
7468: Not Collective, unless matrix has not been allocated
7470: Input Parameter:
7471: . mat - the matrix
7473: Output Parameter:
7474: . ranges - start of each process's portion plus one more than the total length at the end, of length `size` + 1
7475: where `size` is the number of MPI processes used by `mat`
7477: Level: beginner
7479: Notes:
7480: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7482: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7483: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7485: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7486: the local values in the matrix.
7488: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
7489: would contain the result of a matrix vector product with this matrix. See [Matrix
7490: Layouts](sec_matlayout) for details on matrix layouts.
7492: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7493: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
7494: `DMDAGetGhostCorners()`, `DM`
7495: @*/
7496: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
7497: {
7498: PetscFunctionBegin;
7501: MatCheckPreallocated(mat, 1);
7502: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
7503: PetscFunctionReturn(PETSC_SUCCESS);
7504: }
7506: /*@
7507: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
7508: vector one multiplies this vector by that are owned by each process.
7510: Not Collective, unless matrix has not been allocated
7512: Input Parameter:
7513: . mat - the matrix
7515: Output Parameter:
7516: . ranges - start of each process's portion plus one more than the total length at the end
7518: Level: beginner
7520: Notes:
7521: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7523: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7524: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7526: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7527: the local values in the matrix.
7529: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7530: Layouts](sec_matlayout) for details on matrix layouts.
7532: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7533: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7534: `DMDAGetGhostCorners()`, `DM`
7535: @*/
7536: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7537: {
7538: PetscFunctionBegin;
7541: MatCheckPreallocated(mat, 1);
7542: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7543: PetscFunctionReturn(PETSC_SUCCESS);
7544: }
7546: /*@
7547: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7549: Not Collective
7551: Input Parameter:
7552: . A - matrix
7554: Output Parameters:
7555: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7556: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7558: Level: intermediate
7560: Note:
7561: You should call `ISDestroy()` on the returned `IS`
7563: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7564: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7565: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7566: details on matrix layouts.
7568: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7569: @*/
7570: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7571: {
7572: PetscErrorCode (*f)(Mat, IS *, IS *);
7574: PetscFunctionBegin;
7577: MatCheckPreallocated(A, 1);
7578: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7579: if (f) {
7580: PetscCall((*f)(A, rows, cols));
7581: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7582: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7583: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7584: }
7585: PetscFunctionReturn(PETSC_SUCCESS);
7586: }
7588: /*@
7589: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7590: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7591: to complete the factorization.
7593: Collective
7595: Input Parameters:
7596: + fact - the factorized matrix obtained with `MatGetFactor()`
7597: . mat - the matrix
7598: . row - row permutation
7599: . col - column permutation
7600: - info - structure containing
7601: .vb
7602: levels - number of levels of fill.
7603: expected fill - as ratio of original fill.
7604: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7605: missing diagonal entries)
7606: .ve
7608: Level: developer
7610: Notes:
7611: See [Matrix Factorization](sec_matfactor) for additional information.
7613: Most users should employ the `KSP` interface for linear solvers
7614: instead of working directly with matrix algebra routines such as this.
7615: See, e.g., `KSPCreate()`.
7617: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7619: Fortran Note:
7620: A valid (non-null) `info` argument must be provided
7622: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
7623: `MatGetOrdering()`, `MatFactorInfo`
7624: @*/
7625: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7626: {
7627: PetscFunctionBegin;
7632: PetscAssertPointer(info, 5);
7633: PetscAssertPointer(fact, 1);
7634: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7635: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7636: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7637: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7638: MatCheckPreallocated(mat, 2);
7640: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7641: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7642: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7643: PetscFunctionReturn(PETSC_SUCCESS);
7644: }
7646: /*@
7647: MatICCFactorSymbolic - Performs symbolic incomplete
7648: Cholesky factorization for a symmetric matrix. Use
7649: `MatCholeskyFactorNumeric()` to complete the factorization.
7651: Collective
7653: Input Parameters:
7654: + fact - the factorized matrix obtained with `MatGetFactor()`
7655: . mat - the matrix to be factored
7656: . perm - row and column permutation
7657: - info - structure containing
7658: .vb
7659: levels - number of levels of fill.
7660: expected fill - as ratio of original fill.
7661: .ve
7663: Level: developer
7665: Notes:
7666: Most users should employ the `KSP` interface for linear solvers
7667: instead of working directly with matrix algebra routines such as this.
7668: See, e.g., `KSPCreate()`.
7670: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7672: Fortran Note:
7673: A valid (non-null) `info` argument must be provided
7675: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7676: @*/
7677: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7678: {
7679: PetscFunctionBegin;
7683: PetscAssertPointer(info, 4);
7684: PetscAssertPointer(fact, 1);
7685: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7686: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7687: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7688: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7689: MatCheckPreallocated(mat, 2);
7691: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7692: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7693: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7694: PetscFunctionReturn(PETSC_SUCCESS);
7695: }
7697: /*@
7698: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7699: points to an array of valid matrices, they may be reused to store the new
7700: submatrices.
7702: Collective
7704: Input Parameters:
7705: + mat - the matrix
7706: . n - the number of submatrixes to be extracted (on this process, may be zero)
7707: . irow - index set of rows to extract
7708: . icol - index set of columns to extract
7709: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7711: Output Parameter:
7712: . submat - the array of submatrices
7714: Level: advanced
7716: Notes:
7717: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7718: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7719: to extract a parallel submatrix.
7721: Some matrix types place restrictions on the row and column
7722: indices, such as that they be sorted or that they be equal to each other.
7723: `MATSEQSBAIJ` inputs may produce `MATSEQBAIJ` submatrices when the row and column index sets do not preserve symmetry.
7725: The index sets may not have duplicate entries.
7727: When extracting submatrices from a parallel matrix, each process can
7728: form a different submatrix by setting the rows and columns of its
7729: individual index sets according to the local submatrix desired.
7731: When finished using the submatrices, the user should destroy
7732: them with `MatDestroySubMatrices()`.
7734: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7735: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7737: This routine creates the matrices in submat; you should NOT create them before
7738: calling it. It also allocates the array of matrix pointers submat.
7740: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7741: request one row/column in a block, they must request all rows/columns that are in
7742: that block. For example, if the block size is 2 you cannot request just row 0 and
7743: column 0.
7745: Fortran Note:
7746: .vb
7747: Mat, pointer :: submat(:)
7748: .ve
7750: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7751: @*/
7752: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7753: {
7754: PetscInt i;
7755: PetscBool eq;
7757: PetscFunctionBegin;
7760: if (n) {
7761: PetscAssertPointer(irow, 3);
7763: PetscAssertPointer(icol, 4);
7765: }
7766: PetscAssertPointer(submat, 6);
7767: if (n && scall == MAT_REUSE_MATRIX) {
7768: PetscAssertPointer(*submat, 6);
7770: }
7771: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7772: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7773: MatCheckPreallocated(mat, 1);
7774: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7775: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7776: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7777: for (i = 0; i < n; i++) {
7778: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7779: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7780: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7781: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
7782: if (mat->boundtocpu && mat->bindingpropagates) {
7783: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7784: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7785: }
7786: #endif
7787: }
7788: PetscFunctionReturn(PETSC_SUCCESS);
7789: }
7791: /*@
7792: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7794: Collective
7796: Input Parameters:
7797: + mat - the matrix
7798: . n - the number of submatrixes to be extracted
7799: . irow - index set of rows to extract
7800: . icol - index set of columns to extract
7801: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7803: Output Parameter:
7804: . submat - the array of submatrices
7806: Level: advanced
7808: Note:
7809: This is used by `PCGASM`
7811: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7812: @*/
7813: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7814: {
7815: PetscInt i;
7816: PetscBool eq;
7818: PetscFunctionBegin;
7821: if (n) {
7822: PetscAssertPointer(irow, 3);
7824: PetscAssertPointer(icol, 4);
7826: }
7827: PetscAssertPointer(submat, 6);
7828: if (n && scall == MAT_REUSE_MATRIX) {
7829: PetscAssertPointer(*submat, 6);
7831: }
7832: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7833: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7834: MatCheckPreallocated(mat, 1);
7836: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7837: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7838: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7839: for (i = 0; i < n; i++) {
7840: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7841: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7842: }
7843: PetscFunctionReturn(PETSC_SUCCESS);
7844: }
7846: /*@
7847: MatDestroyMatrices - Destroys an array of matrices
7849: Collective
7851: Input Parameters:
7852: + n - the number of local matrices
7853: - mat - the matrices (this is a pointer to the array of matrices)
7855: Level: advanced
7857: Notes:
7858: Frees not only the matrices, but also the array that contains the matrices
7860: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7862: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7863: @*/
7864: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7865: {
7866: PetscInt i;
7868: PetscFunctionBegin;
7869: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7870: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7871: PetscAssertPointer(mat, 2);
7873: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7875: /* memory is allocated even if n = 0 */
7876: PetscCall(PetscFree(*mat));
7877: PetscFunctionReturn(PETSC_SUCCESS);
7878: }
7880: /*@
7881: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7883: Collective
7885: Input Parameters:
7886: + n - the number of local matrices
7887: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7889: Level: advanced
7891: Note:
7892: Frees not only the matrices, but also the array that contains the matrices
7894: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7895: @*/
7896: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7897: {
7898: Mat mat0;
7900: PetscFunctionBegin;
7901: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7902: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7903: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7904: PetscAssertPointer(mat, 2);
7906: mat0 = (*mat)[0];
7907: if (mat0 && mat0->ops->destroysubmatrices) {
7908: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7909: } else {
7910: PetscCall(MatDestroyMatrices(n, mat));
7911: }
7912: PetscFunctionReturn(PETSC_SUCCESS);
7913: }
7915: /*@
7916: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7918: Collective
7920: Input Parameter:
7921: . mat - the matrix
7923: Output Parameter:
7924: . matstruct - the sequential matrix with the nonzero structure of `mat`
7926: Level: developer
7928: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7929: @*/
7930: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7931: {
7932: PetscFunctionBegin;
7934: PetscAssertPointer(matstruct, 2);
7937: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7938: MatCheckPreallocated(mat, 1);
7940: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7941: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7942: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7943: PetscFunctionReturn(PETSC_SUCCESS);
7944: }
7946: /*@
7947: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7949: Collective
7951: Input Parameter:
7952: . mat - the matrix
7954: Level: advanced
7956: Note:
7957: This is not needed, one can just call `MatDestroy()`
7959: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7960: @*/
7961: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7962: {
7963: PetscFunctionBegin;
7964: PetscAssertPointer(mat, 1);
7965: PetscCall(MatDestroy(mat));
7966: PetscFunctionReturn(PETSC_SUCCESS);
7967: }
7969: /*@
7970: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7971: replaces the index sets by larger ones that represent submatrices with
7972: additional overlap.
7974: Collective
7976: Input Parameters:
7977: + mat - the matrix
7978: . n - the number of index sets
7979: . is - the array of index sets (these index sets will changed during the call)
7980: - ov - the additional overlap requested
7982: Options Database Key:
7983: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7985: Level: developer
7987: Note:
7988: The computed overlap preserves the matrix block sizes when the blocks are square.
7989: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7990: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7992: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7993: @*/
7994: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7995: {
7996: PetscInt i, bs, cbs;
7998: PetscFunctionBegin;
8002: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
8003: if (n) {
8004: PetscAssertPointer(is, 3);
8006: }
8007: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
8008: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8009: MatCheckPreallocated(mat, 1);
8011: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
8012: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
8013: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
8014: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
8015: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
8016: if (bs == cbs) {
8017: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
8018: }
8019: PetscFunctionReturn(PETSC_SUCCESS);
8020: }
8022: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
8024: /*@
8025: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
8026: a sub communicator, replaces the index sets by larger ones that represent submatrices with
8027: additional overlap.
8029: Collective
8031: Input Parameters:
8032: + mat - the matrix
8033: . n - the number of index sets
8034: . is - the array of index sets (these index sets will changed during the call)
8035: - ov - the additional overlap requested
8037: ` Options Database Key:
8038: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
8040: Level: developer
8042: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
8043: @*/
8044: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
8045: {
8046: PetscInt i;
8048: PetscFunctionBegin;
8051: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
8052: if (n) {
8053: PetscAssertPointer(is, 3);
8055: }
8056: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
8057: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8058: MatCheckPreallocated(mat, 1);
8059: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
8060: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
8061: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
8062: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
8063: PetscFunctionReturn(PETSC_SUCCESS);
8064: }
8066: /*@
8067: MatGetBlockSize - Returns the matrix block size.
8069: Not Collective
8071: Input Parameter:
8072: . mat - the matrix
8074: Output Parameter:
8075: . bs - block size
8077: Level: intermediate
8079: Notes:
8080: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
8082: If the block size has not been set yet this routine returns 1.
8084: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
8085: @*/
8086: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
8087: {
8088: PetscFunctionBegin;
8090: PetscAssertPointer(bs, 2);
8091: *bs = mat->rmap->bs;
8092: PetscFunctionReturn(PETSC_SUCCESS);
8093: }
8095: /*@
8096: MatGetBlockSizes - Returns the matrix block row and column sizes.
8098: Not Collective
8100: Input Parameter:
8101: . mat - the matrix
8103: Output Parameters:
8104: + rbs - row block size
8105: - cbs - column block size
8107: Level: intermediate
8109: Notes:
8110: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
8111: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8113: If a block size has not been set yet this routine returns 1.
8115: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
8116: @*/
8117: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
8118: {
8119: PetscFunctionBegin;
8121: if (rbs) PetscAssertPointer(rbs, 2);
8122: if (cbs) PetscAssertPointer(cbs, 3);
8123: if (rbs) *rbs = mat->rmap->bs;
8124: if (cbs) *cbs = mat->cmap->bs;
8125: PetscFunctionReturn(PETSC_SUCCESS);
8126: }
8128: /*@
8129: MatSetBlockSize - Sets the matrix block size.
8131: Logically Collective
8133: Input Parameters:
8134: + mat - the matrix
8135: - bs - block size
8137: Level: intermediate
8139: Notes:
8140: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
8141: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8143: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
8144: is compatible with the matrix local sizes.
8146: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
8147: @*/
8148: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
8149: {
8150: PetscFunctionBegin;
8153: PetscCall(MatSetBlockSizes(mat, bs, bs));
8154: PetscFunctionReturn(PETSC_SUCCESS);
8155: }
8157: typedef struct {
8158: PetscInt n;
8159: IS *is;
8160: Mat *mat;
8161: PetscObjectState nonzerostate;
8162: Mat C;
8163: } EnvelopeData;
8165: static PetscErrorCode EnvelopeDataDestroy(PetscCtxRt ptr)
8166: {
8167: EnvelopeData *edata = *(EnvelopeData **)ptr;
8169: PetscFunctionBegin;
8170: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
8171: PetscCall(PetscFree(edata->is));
8172: PetscCall(PetscFree(edata));
8173: PetscFunctionReturn(PETSC_SUCCESS);
8174: }
8176: /*@
8177: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
8178: the sizes of these blocks in the matrix. An individual block may lie over several processes.
8180: Collective
8182: Input Parameter:
8183: . mat - the matrix
8185: Level: intermediate
8187: Notes:
8188: There can be zeros within the blocks
8190: The blocks can overlap between processes, including laying on more than two processes
8192: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
8193: @*/
8194: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
8195: {
8196: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
8197: PetscInt *diag, *odiag, sc;
8198: VecScatter scatter;
8199: PetscScalar *seqv;
8200: const PetscScalar *parv;
8201: const PetscInt *ia, *ja;
8202: PetscBool set, flag, done;
8203: Mat AA = mat, A;
8204: MPI_Comm comm;
8205: PetscMPIInt rank, size, tag;
8206: MPI_Status status;
8207: PetscContainer container;
8208: EnvelopeData *edata;
8209: Vec seq, par;
8210: IS isglobal;
8212: PetscFunctionBegin;
8214: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
8215: if (!set || !flag) {
8216: /* TODO: only needs nonzero structure of transpose */
8217: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
8218: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
8219: }
8220: PetscCall(MatAIJGetLocalMat(AA, &A));
8221: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
8222: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
8224: PetscCall(MatGetLocalSize(mat, &n, NULL));
8225: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
8226: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
8227: PetscCallMPI(MPI_Comm_size(comm, &size));
8228: PetscCallMPI(MPI_Comm_rank(comm, &rank));
8230: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
8232: if (rank > 0) {
8233: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
8234: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
8235: }
8236: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
8237: for (i = 0; i < n; i++) {
8238: env = PetscMax(env, ja[ia[i + 1] - 1]);
8239: II = rstart + i;
8240: if (env == II) {
8241: starts[lblocks] = tbs;
8242: sizes[lblocks++] = 1 + II - tbs;
8243: tbs = 1 + II;
8244: }
8245: }
8246: if (rank < size - 1) {
8247: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
8248: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
8249: }
8251: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
8252: if (!set || !flag) PetscCall(MatDestroy(&AA));
8253: PetscCall(MatDestroy(&A));
8255: PetscCall(PetscNew(&edata));
8256: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
8257: edata->n = lblocks;
8258: /* create IS needed for extracting blocks from the original matrix */
8259: PetscCall(PetscMalloc1(lblocks, &edata->is));
8260: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
8262: /* Create the resulting inverse matrix nonzero structure with preallocation information */
8263: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
8264: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
8265: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
8266: PetscCall(MatSetType(edata->C, MATAIJ));
8268: /* Communicate the start and end of each row, from each block to the correct rank */
8269: /* TODO: Use PetscSF instead of VecScatter */
8270: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
8271: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
8272: PetscCall(VecGetArrayWrite(seq, &seqv));
8273: for (PetscInt i = 0; i < lblocks; i++) {
8274: for (PetscInt j = 0; j < sizes[i]; j++) {
8275: seqv[cnt] = starts[i];
8276: seqv[cnt + 1] = starts[i] + sizes[i];
8277: cnt += 2;
8278: }
8279: }
8280: PetscCall(VecRestoreArrayWrite(seq, &seqv));
8281: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
8282: sc -= cnt;
8283: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
8284: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
8285: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
8286: PetscCall(ISDestroy(&isglobal));
8287: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
8288: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
8289: PetscCall(VecScatterDestroy(&scatter));
8290: PetscCall(VecDestroy(&seq));
8291: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
8292: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
8293: PetscCall(VecGetArrayRead(par, &parv));
8294: cnt = 0;
8295: PetscCall(MatGetSize(mat, NULL, &n));
8296: for (PetscInt i = 0; i < mat->rmap->n; i++) {
8297: PetscInt start, end, d = 0, od = 0;
8299: start = (PetscInt)PetscRealPart(parv[cnt]);
8300: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
8301: cnt += 2;
8303: if (start < cstart) {
8304: od += cstart - start + n - cend;
8305: d += cend - cstart;
8306: } else if (start < cend) {
8307: od += n - cend;
8308: d += cend - start;
8309: } else od += n - start;
8310: if (end <= cstart) {
8311: od -= cstart - end + n - cend;
8312: d -= cend - cstart;
8313: } else if (end < cend) {
8314: od -= n - cend;
8315: d -= cend - end;
8316: } else od -= n - end;
8318: odiag[i] = od;
8319: diag[i] = d;
8320: }
8321: PetscCall(VecRestoreArrayRead(par, &parv));
8322: PetscCall(VecDestroy(&par));
8323: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
8324: PetscCall(PetscFree2(diag, odiag));
8325: PetscCall(PetscFree2(sizes, starts));
8327: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
8328: PetscCall(PetscContainerSetPointer(container, edata));
8329: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
8330: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
8331: PetscCall(PetscObjectDereference((PetscObject)container));
8332: PetscFunctionReturn(PETSC_SUCCESS);
8333: }
8335: /*@
8336: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
8338: Collective
8340: Input Parameters:
8341: + A - the matrix
8342: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
8344: Output Parameter:
8345: . C - matrix with inverted block diagonal of `A`
8347: Level: advanced
8349: Note:
8350: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
8352: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
8353: @*/
8354: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
8355: {
8356: PetscContainer container;
8357: EnvelopeData *edata;
8358: PetscObjectState nonzerostate;
8360: PetscFunctionBegin;
8361: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
8362: if (!container) {
8363: PetscCall(MatComputeVariableBlockEnvelope(A));
8364: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
8365: }
8366: PetscCall(PetscContainerGetPointer(container, &edata));
8367: PetscCall(MatGetNonzeroState(A, &nonzerostate));
8368: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
8369: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
8371: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
8372: *C = edata->C;
8374: for (PetscInt i = 0; i < edata->n; i++) {
8375: Mat D;
8376: PetscScalar *dvalues;
8378: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
8379: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
8380: PetscCall(MatSeqDenseInvert(D));
8381: PetscCall(MatDenseGetArray(D, &dvalues));
8382: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
8383: PetscCall(MatDestroy(&D));
8384: }
8385: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
8386: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
8387: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
8388: PetscFunctionReturn(PETSC_SUCCESS);
8389: }
8391: /*@
8392: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
8394: Not Collective
8396: Input Parameters:
8397: + mat - the matrix
8398: . nblocks - the number of blocks on this process, each block can only exist on a single MPI process
8399: - bsizes - the block sizes
8401: Level: intermediate
8403: Note:
8404: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
8406: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
8407: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
8408: @*/
8409: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
8410: {
8411: PetscInt ncnt = 0, nlocal;
8413: PetscFunctionBegin;
8415: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
8416: 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);
8417: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
8418: 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);
8419: PetscCall(PetscFree(mat->bsizes));
8420: mat->nblocks = nblocks;
8421: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
8422: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
8423: PetscFunctionReturn(PETSC_SUCCESS);
8424: }
8426: /*@
8427: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
8429: Not Collective; No Fortran Support
8431: Input Parameter:
8432: . mat - the matrix
8434: Output Parameters:
8435: + nblocks - the number of blocks on this process
8436: - bsizes - the block sizes
8438: Level: intermediate
8440: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8441: @*/
8442: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
8443: {
8444: PetscFunctionBegin;
8446: if (nblocks) *nblocks = mat->nblocks;
8447: if (bsizes) *bsizes = mat->bsizes;
8448: PetscFunctionReturn(PETSC_SUCCESS);
8449: }
8451: /*@
8452: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
8454: Not Collective
8456: Input Parameters:
8457: + subA - the submatrix
8458: . A - the original matrix
8459: - isrow - The `IS` of selected rows for the submatrix, must be sorted
8461: Level: developer
8463: Note:
8464: If the index set is not sorted or contains off-process entries, this function will do nothing.
8466: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8467: @*/
8468: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
8469: {
8470: const PetscInt *rows;
8471: PetscInt n, rStart, rEnd, Nb = 0;
8472: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
8474: PetscFunctionBegin;
8475: // The code for block size extraction does not support an unsorted IS
8476: if (flg) PetscCall(ISSorted(isrow, &flg));
8477: // We don't support originally off-diagonal blocks
8478: if (flg) {
8479: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
8480: PetscCall(ISGetLocalSize(isrow, &n));
8481: PetscCall(ISGetIndices(isrow, &rows));
8482: for (PetscInt i = 0; i < n && flg; ++i) {
8483: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
8484: }
8485: PetscCall(ISRestoreIndices(isrow, &rows));
8486: }
8487: // quiet return if we can't extract block size
8488: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
8489: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
8491: // extract block sizes
8492: PetscCall(ISGetIndices(isrow, &rows));
8493: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8494: PetscBool occupied = PETSC_FALSE;
8496: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8497: const PetscInt row = gr + br;
8499: if (i == n) break;
8500: if (rows[i] == row) {
8501: occupied = PETSC_TRUE;
8502: ++i;
8503: }
8504: while (i < n && rows[i] < row) ++i;
8505: }
8506: gr += A->bsizes[b];
8507: if (occupied) ++Nb;
8508: }
8509: subA->nblocks = Nb;
8510: PetscCall(PetscFree(subA->bsizes));
8511: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
8512: PetscInt sb = 0;
8513: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8514: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
8515: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8516: const PetscInt row = gr + br;
8518: if (i == n) break;
8519: if (rows[i] == row) {
8520: ++subA->bsizes[sb];
8521: ++i;
8522: }
8523: while (i < n && rows[i] < row) ++i;
8524: }
8525: gr += A->bsizes[b];
8526: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8527: }
8528: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8529: PetscInt nlocal, ncnt = 0;
8530: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8531: 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);
8532: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8533: 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);
8534: PetscCall(ISRestoreIndices(isrow, &rows));
8535: PetscFunctionReturn(PETSC_SUCCESS);
8536: }
8538: /*@
8539: MatSetBlockSizes - Sets the matrix block row and column sizes.
8541: Logically Collective
8543: Input Parameters:
8544: + mat - the matrix
8545: . rbs - row block size
8546: - cbs - column block size
8548: Level: intermediate
8550: Notes:
8551: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8552: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8553: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8555: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8556: are compatible with the matrix local sizes.
8558: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8560: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8561: @*/
8562: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8563: {
8564: PetscFunctionBegin;
8568: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8569: if (mat->rmap->refcnt) {
8570: ISLocalToGlobalMapping l2g = NULL;
8571: PetscLayout nmap = NULL;
8573: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8574: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8575: PetscCall(PetscLayoutDestroy(&mat->rmap));
8576: mat->rmap = nmap;
8577: mat->rmap->mapping = l2g;
8578: }
8579: if (mat->cmap->refcnt) {
8580: ISLocalToGlobalMapping l2g = NULL;
8581: PetscLayout nmap = NULL;
8583: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8584: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8585: PetscCall(PetscLayoutDestroy(&mat->cmap));
8586: mat->cmap = nmap;
8587: mat->cmap->mapping = l2g;
8588: }
8589: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8590: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8591: PetscFunctionReturn(PETSC_SUCCESS);
8592: }
8594: /*@
8595: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8597: Logically Collective
8599: Input Parameters:
8600: + mat - the matrix
8601: . fromRow - matrix from which to copy row block size
8602: - fromCol - matrix from which to copy column block size (can be same as `fromRow`)
8604: Level: developer
8606: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8607: @*/
8608: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8609: {
8610: PetscFunctionBegin;
8614: PetscTryTypeMethod(mat, setblocksizes, fromRow->rmap->bs, fromCol->cmap->bs);
8615: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8616: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8617: PetscFunctionReturn(PETSC_SUCCESS);
8618: }
8620: /*@
8621: MatResidual - Default routine to calculate the residual $r = b - Ax$
8623: Collective
8625: Input Parameters:
8626: + mat - the matrix
8627: . b - the right-hand-side
8628: - x - the approximate solution
8630: Output Parameter:
8631: . r - location to store the residual
8633: Level: developer
8635: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8636: @*/
8637: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8638: {
8639: PetscFunctionBegin;
8645: MatCheckPreallocated(mat, 1);
8646: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8647: if (!mat->ops->residual) {
8648: PetscCall(MatMult(mat, x, r));
8649: PetscCall(VecAYPX(r, -1.0, b));
8650: } else {
8651: PetscUseTypeMethod(mat, residual, b, x, r);
8652: }
8653: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8654: PetscFunctionReturn(PETSC_SUCCESS);
8655: }
8657: /*@
8658: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8660: Collective
8662: Input Parameters:
8663: + mat - the matrix
8664: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8665: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8666: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8667: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8668: always used.
8670: Output Parameters:
8671: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8672: . 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
8673: . ja - the column indices, use `NULL` if not needed
8674: - done - indicates if the routine actually worked and returned appropriate `ia` and `ja` arrays; callers
8675: are responsible for handling the case when done is `PETSC_FALSE` and `ia` and `ja` are not provided
8677: Level: developer
8679: Notes:
8680: You CANNOT change any of the `ia` or `ja` values.
8682: Use `MatRestoreRowIJ()` when you are finished accessing the `ia` and `ja` values.
8684: Fortran Notes:
8685: Use
8686: .vb
8687: PetscInt, pointer :: ia(:),ja(:)
8688: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8689: ! Access the ith and jth entries via ia(i) and ja(j)
8690: .ve
8692: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8693: @*/
8694: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8695: {
8696: PetscFunctionBegin;
8699: if (n) PetscAssertPointer(n, 5);
8700: if (ia) PetscAssertPointer(ia, 6);
8701: if (ja) PetscAssertPointer(ja, 7);
8702: if (done) PetscAssertPointer(done, 8);
8703: MatCheckPreallocated(mat, 1);
8704: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8705: else {
8706: if (done) *done = PETSC_TRUE;
8707: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8708: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8709: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8710: }
8711: PetscFunctionReturn(PETSC_SUCCESS);
8712: }
8714: /*@
8715: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8717: Collective
8719: Input Parameters:
8720: + mat - the matrix
8721: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8722: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8723: symmetrized
8724: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8725: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8726: always used.
8728: Output Parameters:
8729: + n - number of columns in the (possibly compressed) matrix
8730: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8731: . ja - the row indices
8732: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8734: Level: developer
8736: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8737: @*/
8738: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8739: {
8740: PetscFunctionBegin;
8743: PetscAssertPointer(n, 5);
8744: if (ia) PetscAssertPointer(ia, 6);
8745: if (ja) PetscAssertPointer(ja, 7);
8746: PetscAssertPointer(done, 8);
8747: MatCheckPreallocated(mat, 1);
8748: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8749: else {
8750: *done = PETSC_TRUE;
8751: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8752: }
8753: PetscFunctionReturn(PETSC_SUCCESS);
8754: }
8756: /*@
8757: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8759: Collective
8761: Input Parameters:
8762: + mat - the matrix
8763: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8764: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8765: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8766: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8767: always used.
8768: . n - size of (possibly compressed) matrix, or `NULL`
8769: . ia - the row pointers, or `NULL`
8770: - ja - the column indices, or `NULL`
8772: Output Parameter:
8773: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8775: Level: developer
8777: Note:
8778: This routine zeros out `n`, `ia`, and `ja` if they are provided. Use of `ia` or `ja` after `MatRestoreRowIJ()` is always invalid.
8780: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8781: @*/
8782: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8783: {
8784: PetscFunctionBegin;
8787: if (ia) PetscAssertPointer(ia, 6);
8788: if (ja) PetscAssertPointer(ja, 7);
8789: if (done) PetscAssertPointer(done, 8);
8790: MatCheckPreallocated(mat, 1);
8792: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8793: else {
8794: if (done) *done = PETSC_TRUE;
8795: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8796: if (n) *n = 0;
8797: if (ia) *ia = NULL;
8798: if (ja) *ja = NULL;
8799: }
8800: PetscFunctionReturn(PETSC_SUCCESS);
8801: }
8803: /*@
8804: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8806: Collective
8808: Input Parameters:
8809: + mat - the matrix
8810: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8811: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8812: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8813: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8814: always used.
8816: Output Parameters:
8817: + n - size of (possibly compressed) matrix
8818: . ia - the column pointers
8819: . ja - the row indices
8820: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8822: Level: developer
8824: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8825: @*/
8826: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8827: {
8828: PetscFunctionBegin;
8831: if (ia) PetscAssertPointer(ia, 6);
8832: if (ja) PetscAssertPointer(ja, 7);
8833: PetscAssertPointer(done, 8);
8834: MatCheckPreallocated(mat, 1);
8836: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8837: else {
8838: *done = PETSC_TRUE;
8839: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8840: if (n) *n = 0;
8841: if (ia) *ia = NULL;
8842: if (ja) *ja = NULL;
8843: }
8844: PetscFunctionReturn(PETSC_SUCCESS);
8845: }
8847: /*@
8848: MatColoringPatch - Utility routine used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8849: `MatGetColumnIJ()`.
8851: Collective
8853: Input Parameters:
8854: + mat - the matrix
8855: . ncolors - maximum color value
8856: . n - number of entries in `colorarray`
8857: - colorarray - array indicating color for each column
8859: Output Parameter:
8860: . iscoloring - coloring generated using `colorarray` information
8862: Level: developer
8864: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8865: @*/
8866: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8867: {
8868: PetscFunctionBegin;
8871: PetscAssertPointer(colorarray, 4);
8872: PetscAssertPointer(iscoloring, 5);
8873: MatCheckPreallocated(mat, 1);
8875: if (!mat->ops->coloringpatch) {
8876: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8877: } else {
8878: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8879: }
8880: PetscFunctionReturn(PETSC_SUCCESS);
8881: }
8883: /*@
8884: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8886: Logically Collective
8888: Input Parameter:
8889: . mat - the factored matrix to be reset
8891: Level: developer
8893: Notes:
8894: This routine should be used only with factored matrices formed by in-place
8895: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8896: format). This option can save memory, for example, when solving nonlinear
8897: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8898: ILU(0) preconditioner.
8900: One can specify in-place ILU(0) factorization by calling
8901: .vb
8902: PCType(pc,PCILU);
8903: PCFactorSeUseInPlace(pc);
8904: .ve
8905: or by using the options -pc_type ilu -pc_factor_in_place
8907: In-place factorization ILU(0) can also be used as a local
8908: solver for the blocks within the block Jacobi or additive Schwarz
8909: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8910: for details on setting local solver options.
8912: Most users should employ the `KSP` interface for linear solvers
8913: instead of working directly with matrix algebra routines such as this.
8914: See, e.g., `KSPCreate()`.
8916: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8917: @*/
8918: PetscErrorCode MatSetUnfactored(Mat mat)
8919: {
8920: PetscFunctionBegin;
8923: MatCheckPreallocated(mat, 1);
8924: mat->factortype = MAT_FACTOR_NONE;
8925: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8926: PetscUseTypeMethod(mat, setunfactored);
8927: PetscFunctionReturn(PETSC_SUCCESS);
8928: }
8930: /*@
8931: MatCreateSubMatrix - Gets a single submatrix on the same number of processes
8932: as the original matrix.
8934: Collective
8936: Input Parameters:
8937: + mat - the original matrix
8938: . isrow - parallel `IS` containing the rows this process should obtain
8939: . 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.
8940: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8942: Output Parameter:
8943: . newmat - the new submatrix, of the same type as the original matrix (except potentially for `MATSEQSBAIJ`)
8945: Level: advanced
8947: Notes:
8948: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8950: Some matrix types place restrictions on the row and column indices, such
8951: 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;
8952: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8953: `MATSEQSBAIJ` inputs may produce a `MATSEQBAIJ` matrix when the row and column index sets do not preserve symmetry.
8955: The index sets may not have duplicate entries.
8957: The first time this is called you should use a `cll` of `MAT_INITIAL_MATRIX`,
8958: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8959: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8960: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8961: you are finished using it.
8963: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8964: the input matrix.
8966: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8968: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8969: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8971: Example usage:
8972: Consider the following 8x8 matrix with 34 non-zero values, that is
8973: assembled across 3 processes. Let's assume that proc0 owns 3 rows,
8974: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8975: as follows
8976: .vb
8977: 1 2 0 | 0 3 0 | 0 4
8978: Proc0 0 5 6 | 7 0 0 | 8 0
8979: 9 0 10 | 11 0 0 | 12 0
8980: -------------------------------------
8981: 13 0 14 | 15 16 17 | 0 0
8982: Proc1 0 18 0 | 19 20 21 | 0 0
8983: 0 0 0 | 22 23 0 | 24 0
8984: -------------------------------------
8985: Proc2 25 26 27 | 0 0 28 | 29 0
8986: 30 0 0 | 31 32 33 | 0 34
8987: .ve
8989: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8991: .vb
8992: 2 0 | 0 3 0 | 0
8993: Proc0 5 6 | 7 0 0 | 8
8994: -------------------------------
8995: Proc1 18 0 | 19 20 21 | 0
8996: -------------------------------
8997: Proc2 26 27 | 0 0 28 | 29
8998: 0 0 | 31 32 33 | 0
8999: .ve
9001: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
9002: @*/
9003: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
9004: {
9005: PetscMPIInt size;
9006: Mat *local;
9007: IS iscoltmp;
9008: PetscBool flg;
9010: PetscFunctionBegin;
9014: PetscAssertPointer(newmat, 5);
9017: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9018: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
9019: PetscCheck(cll != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_INPLACE_MATRIX");
9021: MatCheckPreallocated(mat, 1);
9022: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9024: if (!iscol || isrow == iscol) {
9025: PetscBool stride;
9026: PetscMPIInt grab = 0;
9027: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
9028: if (stride) {
9029: PetscInt first, step, n, rstart, rend;
9030: PetscCall(ISStrideGetInfo(isrow, &first, &step));
9031: if (step == 1) {
9032: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
9033: if (rstart == first) {
9034: PetscCall(ISGetLocalSize(isrow, &n));
9035: if (n == rend - rstart) grab = 1;
9036: }
9037: }
9038: }
9039: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
9040: if (grab) {
9041: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
9042: if (cll == MAT_INITIAL_MATRIX) {
9043: *newmat = mat;
9044: PetscCall(PetscObjectReference((PetscObject)mat));
9045: }
9046: PetscFunctionReturn(PETSC_SUCCESS);
9047: }
9048: }
9050: if (!iscol) {
9051: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
9052: } else {
9053: iscoltmp = iscol;
9054: }
9056: /* if original matrix is on just one process then use submatrix generated */
9057: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
9058: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
9059: goto setproperties;
9060: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
9061: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
9062: *newmat = *local;
9063: PetscCall(PetscFree(local));
9064: goto setproperties;
9065: } else if (!mat->ops->createsubmatrix) {
9066: /* Create a new matrix type that implements the operation using the full matrix */
9067: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
9068: switch (cll) {
9069: case MAT_INITIAL_MATRIX:
9070: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
9071: break;
9072: case MAT_REUSE_MATRIX:
9073: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
9074: break;
9075: default:
9076: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
9077: }
9078: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
9079: goto setproperties;
9080: }
9082: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
9083: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
9084: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
9086: setproperties:
9087: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
9088: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
9089: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
9090: }
9091: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
9092: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
9093: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
9094: PetscFunctionReturn(PETSC_SUCCESS);
9095: }
9097: /*@
9098: MatPropagateSymmetryOptions - Propagates symmetry properties set on a matrix to another matrix
9100: Not Collective
9102: Input Parameters:
9103: + A - the matrix we wish to propagate properties from
9104: - B - the matrix we wish to propagate properties to
9106: Level: beginner
9108: Note:
9109: Propagates the properties associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
9111: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
9112: @*/
9113: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
9114: {
9115: PetscFunctionBegin;
9118: B->symmetry_eternal = A->symmetry_eternal;
9119: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
9120: B->symmetric = A->symmetric;
9121: B->structurally_symmetric = A->structurally_symmetric;
9122: B->spd = A->spd;
9123: B->hermitian = A->hermitian;
9124: PetscFunctionReturn(PETSC_SUCCESS);
9125: }
9127: /*@
9128: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
9129: used during the assembly process to store values that belong to
9130: other processes.
9132: Not Collective
9134: Input Parameters:
9135: + mat - the matrix
9136: . size - the initial size of the stash.
9137: - bsize - the initial size of the block-stash(if used).
9139: Options Database Key:
9140: . -matstash_initial_size (size|size0,size1,...,sizep-1) - set initial size of stash for all or each of the MPI processes, sets both block and non-block stash sizes
9142: Level: intermediate
9144: Notes:
9145: The block-stash is used for values set with `MatSetValuesBlocked()` while
9146: the stash is used for values set with `MatSetValues()`
9148: Run with the option `-info` and look for output of the form
9149: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
9150: to determine the appropriate value, MM, to use for size and
9151: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
9152: to determine the value, BMM to use for bsize
9154: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
9155: @*/
9156: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
9157: {
9158: PetscFunctionBegin;
9161: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
9162: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
9163: PetscFunctionReturn(PETSC_SUCCESS);
9164: }
9166: /*@
9167: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
9168: the matrix
9170: Neighbor-wise Collective
9172: Input Parameters:
9173: + A - the matrix
9174: . x - the vector to be multiplied by the interpolation operator
9175: - y - the vector to be added to the result
9177: Output Parameter:
9178: . w - the resulting vector
9180: Level: intermediate
9182: Notes:
9183: `w` may be the same vector as `y`.
9185: This allows one to use either the restriction or interpolation (its transpose)
9186: matrix to do the interpolation
9188: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
9189: @*/
9190: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
9191: {
9192: PetscInt M, N, Ny;
9194: PetscFunctionBegin;
9199: PetscCall(MatGetSize(A, &M, &N));
9200: PetscCall(VecGetSize(y, &Ny));
9201: if (M == Ny) PetscCall(MatMultAdd(A, x, y, w));
9202: else PetscCall(MatMultTransposeAdd(A, x, y, w));
9203: PetscFunctionReturn(PETSC_SUCCESS);
9204: }
9206: /*@
9207: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
9208: the matrix
9210: Neighbor-wise Collective
9212: Input Parameters:
9213: + A - the matrix
9214: - x - the vector to be interpolated
9216: Output Parameter:
9217: . y - the resulting vector
9219: Level: intermediate
9221: Note:
9222: This allows one to use either the restriction or interpolation (its transpose)
9223: matrix to do the interpolation
9225: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
9226: @*/
9227: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
9228: {
9229: PetscInt M, N, Ny;
9231: PetscFunctionBegin;
9235: PetscCall(MatGetSize(A, &M, &N));
9236: PetscCall(VecGetSize(y, &Ny));
9237: if (M == Ny) PetscCall(MatMult(A, x, y));
9238: else PetscCall(MatMultTranspose(A, x, y));
9239: PetscFunctionReturn(PETSC_SUCCESS);
9240: }
9242: /*@
9243: MatRestrict - $y = A*x$ or $A^T*x$
9245: Neighbor-wise Collective
9247: Input Parameters:
9248: + A - the matrix
9249: - x - the vector to be restricted
9251: Output Parameter:
9252: . y - the resulting vector
9254: Level: intermediate
9256: Note:
9257: This allows one to use either the restriction or interpolation (its transpose)
9258: matrix to do the restriction
9260: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
9261: @*/
9262: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
9263: {
9264: PetscInt M, N, Nx;
9266: PetscFunctionBegin;
9270: PetscCall(MatGetSize(A, &M, &N));
9271: PetscCall(VecGetSize(x, &Nx));
9272: if (M == Nx) PetscCall(MatMultTranspose(A, x, y));
9273: else PetscCall(MatMult(A, x, y));
9274: PetscFunctionReturn(PETSC_SUCCESS);
9275: }
9277: /*@
9278: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
9280: Neighbor-wise Collective
9282: Input Parameters:
9283: + A - the matrix
9284: . x - the input dense matrix to be multiplied
9285: - w - the input dense matrix to be added to the result
9287: Output Parameter:
9288: . y - the output dense matrix
9290: Level: intermediate
9292: Note:
9293: This allows one to use either the restriction or interpolation (its transpose)
9294: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
9295: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9297: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
9298: @*/
9299: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
9300: {
9301: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
9302: PetscBool trans = PETSC_TRUE;
9303: MatReuse reuse = MAT_INITIAL_MATRIX;
9305: PetscFunctionBegin;
9311: PetscCall(MatGetSize(A, &M, &N));
9312: PetscCall(MatGetSize(x, &Mx, &Nx));
9313: if (N == Mx) trans = PETSC_FALSE;
9314: 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);
9315: Mo = trans ? N : M;
9316: if (*y) {
9317: PetscCall(MatGetSize(*y, &My, &Ny));
9318: if (Mo == My && Nx == Ny) reuse = MAT_REUSE_MATRIX;
9319: else {
9320: 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);
9321: PetscCall(MatDestroy(y));
9322: }
9323: }
9325: if (w && *y == w) { /* this is to minimize changes in PCMG */
9326: PetscBool flg;
9328: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
9329: if (w) {
9330: PetscInt My, Ny, Mw, Nw;
9332: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
9333: PetscCall(MatGetSize(*y, &My, &Ny));
9334: PetscCall(MatGetSize(w, &Mw, &Nw));
9335: if (!flg || My != Mw || Ny != Nw) w = NULL;
9336: }
9337: if (!w) {
9338: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
9339: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
9340: PetscCall(PetscObjectDereference((PetscObject)w));
9341: } else PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
9342: }
9343: if (!trans) PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
9344: else PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
9345: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
9346: PetscFunctionReturn(PETSC_SUCCESS);
9347: }
9349: /*@
9350: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
9352: Neighbor-wise Collective
9354: Input Parameters:
9355: + A - the matrix
9356: - x - the input dense matrix
9358: Output Parameter:
9359: . y - the output dense matrix
9361: Level: intermediate
9363: Note:
9364: This allows one to use either the restriction or interpolation (its transpose)
9365: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
9366: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9368: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
9369: @*/
9370: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
9371: {
9372: PetscFunctionBegin;
9373: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9374: PetscFunctionReturn(PETSC_SUCCESS);
9375: }
9377: /*@
9378: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
9380: Neighbor-wise Collective
9382: Input Parameters:
9383: + A - the matrix
9384: - x - the input dense matrix
9386: Output Parameter:
9387: . y - the output dense matrix
9389: Level: intermediate
9391: Note:
9392: This allows one to use either the restriction or interpolation (its transpose)
9393: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
9394: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9396: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
9397: @*/
9398: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
9399: {
9400: PetscFunctionBegin;
9401: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9402: PetscFunctionReturn(PETSC_SUCCESS);
9403: }
9405: /*@
9406: MatGetNullSpace - retrieves the null space of a matrix that was provided with `MatSetNullSpace()`
9408: Logically Collective
9410: Input Parameters:
9411: + mat - the matrix
9412: - nullsp - the null space object
9414: Level: developer
9416: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
9417: @*/
9418: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
9419: {
9420: PetscFunctionBegin;
9422: PetscAssertPointer(nullsp, 2);
9423: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
9424: PetscFunctionReturn(PETSC_SUCCESS);
9425: }
9427: /*@
9428: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices that were supplied with `MatSetNullSpace()`,
9429: `MatSetTransposeNullSpace()`, and `MatSetNearNullSpace()`
9431: Logically Collective
9433: Input Parameters:
9434: + n - the number of matrices
9435: - mat - the array of matrices
9437: Output Parameters:
9438: . nullsp - an array of null spaces, `NULL` will be inserted for each matrix that does not have a null space, length 3 * `n`
9440: Level: developer
9442: Note:
9443: Call `MatRestoreNullspaces()` to provide these to another array of matrices
9445: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9446: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`, `MatNullSpace`
9447: @*/
9448: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9449: {
9450: PetscFunctionBegin;
9451: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9452: PetscAssertPointer(mat, 2);
9453: PetscAssertPointer(nullsp, 3);
9455: PetscCall(PetscCalloc1(3 * n, nullsp));
9456: for (PetscInt i = 0; i < n; i++) {
9458: (*nullsp)[i] = mat[i]->nullsp;
9459: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
9460: (*nullsp)[n + i] = mat[i]->nearnullsp;
9461: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
9462: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
9463: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
9464: }
9465: PetscFunctionReturn(PETSC_SUCCESS);
9466: }
9468: /*@
9469: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
9471: Logically Collective
9473: Input Parameters:
9474: + n - the number of matrices
9475: . mat - the array of matrices
9476: - nullsp - an array of null spaces, of length 3 * `n`
9478: Level: developer
9480: Notes:
9481: Call `MatGetNullSpaces()` to create `nullsp`.
9483: Frees `nullsp`.
9485: Developer Note:
9486: The name of this function is confusing. Traditionally in PETSc, a restore operation undoes something that was previously done on an object (or objects) with a get operation.
9487: This restore routine does something to a new set of objects using the results of a get operation on a previous set of objects. Perhaps this routine
9488: should have simply been called `MatSetNullSpaces()`
9490: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9491: `MatNullSpaceRemove()`, `MatGetNullSpaces()`, `MatNullSpace`
9492: @*/
9493: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9494: {
9495: PetscFunctionBegin;
9496: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9497: PetscAssertPointer(mat, 2);
9498: PetscAssertPointer(nullsp, 3);
9499: PetscAssertPointer(*nullsp, 3);
9501: for (PetscInt i = 0; i < n; i++) {
9503: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
9504: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9505: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9506: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9507: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9508: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9509: }
9510: PetscCall(PetscFree(*nullsp));
9511: PetscFunctionReturn(PETSC_SUCCESS);
9512: }
9514: /*@
9515: MatSetNullSpace - attaches a null space to a matrix.
9517: Logically Collective
9519: Input Parameters:
9520: + mat - the matrix
9521: - nullsp - the null space object
9523: Level: advanced
9525: Notes:
9526: This null space is used by the `KSP` linear solvers to solve singular systems.
9528: 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`
9530: 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
9531: to zero but the linear system will still be solved in a least squares sense.
9533: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9534: 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 $A^T$, $R(A^T)$.
9535: 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
9536: $n(A^T))$ 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
9537: 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 $n(A^T)$.
9538: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9540: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9541: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9542: routine also automatically calls `MatSetTransposeNullSpace()`.
9544: The user should call `MatNullSpaceDestroy()`.
9546: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9547: `KSPSetPCSide()`, `MatNullSpace`
9548: @*/
9549: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9550: {
9551: PetscFunctionBegin;
9554: PetscCall(PetscObjectReference((PetscObject)nullsp));
9555: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9556: mat->nullsp = nullsp;
9557: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9558: PetscFunctionReturn(PETSC_SUCCESS);
9559: }
9561: /*@
9562: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix that was set with `MatSetTransposeNullSpace()`
9564: Logically Collective
9566: Input Parameters:
9567: + mat - the matrix
9568: - nullsp - the null space object
9570: Level: developer
9572: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9573: @*/
9574: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9575: {
9576: PetscFunctionBegin;
9579: PetscAssertPointer(nullsp, 2);
9580: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9581: PetscFunctionReturn(PETSC_SUCCESS);
9582: }
9584: /*@
9585: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9587: Logically Collective
9589: Input Parameters:
9590: + mat - the matrix
9591: - nullsp - the null space object
9593: Level: advanced
9595: Notes:
9596: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9598: See `MatSetNullSpace()`
9600: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9601: @*/
9602: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9603: {
9604: PetscFunctionBegin;
9607: PetscCall(PetscObjectReference((PetscObject)nullsp));
9608: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9609: mat->transnullsp = nullsp;
9610: PetscFunctionReturn(PETSC_SUCCESS);
9611: }
9613: /*@
9614: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9615: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9617: Logically Collective
9619: Input Parameters:
9620: + mat - the matrix
9621: - nullsp - the null space object
9623: Level: advanced
9625: Notes:
9626: Overwrites any previous near null space that may have been attached
9628: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9630: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9631: @*/
9632: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9633: {
9634: PetscFunctionBegin;
9638: MatCheckPreallocated(mat, 1);
9639: PetscCall(PetscObjectReference((PetscObject)nullsp));
9640: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9641: mat->nearnullsp = nullsp;
9642: PetscFunctionReturn(PETSC_SUCCESS);
9643: }
9645: /*@
9646: MatGetNearNullSpace - Get null space from a matrix that was attached with `MatSetNearNullSpace()`
9648: Not Collective
9650: Input Parameter:
9651: . mat - the matrix
9653: Output Parameter:
9654: . nullsp - the null space object, `NULL` if not set
9656: Level: advanced
9658: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9659: @*/
9660: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9661: {
9662: PetscFunctionBegin;
9665: PetscAssertPointer(nullsp, 2);
9666: MatCheckPreallocated(mat, 1);
9667: *nullsp = mat->nearnullsp;
9668: PetscFunctionReturn(PETSC_SUCCESS);
9669: }
9671: /*@
9672: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9674: Collective
9676: Input Parameters:
9677: + mat - the matrix
9678: . row - row/column permutation
9679: - info - information on desired factorization process
9681: Level: developer
9683: Notes:
9684: Probably really in-place only when level of fill is zero, otherwise allocates
9685: new space to store factored matrix and deletes previous memory.
9687: Most users should employ the `KSP` interface for linear solvers
9688: instead of working directly with matrix algebra routines such as this.
9689: See, e.g., `KSPCreate()`.
9691: Fortran Note:
9692: A valid (non-null) `info` argument must be provided
9694: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9695: @*/
9696: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9697: {
9698: PetscFunctionBegin;
9702: PetscAssertPointer(info, 3);
9703: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9704: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9705: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9706: MatCheckPreallocated(mat, 1);
9707: PetscUseTypeMethod(mat, iccfactor, row, info);
9708: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9709: PetscFunctionReturn(PETSC_SUCCESS);
9710: }
9712: /*@
9713: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9714: ghosted ones.
9716: Not Collective
9718: Input Parameters:
9719: + mat - the matrix
9720: - diag - the diagonal values, including ghost ones
9722: Level: developer
9724: Notes:
9725: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9727: `diag` is a sequential vector that has a length which is the same as the local (ghosted) length of the vector associated with
9728: the matrix's `ISLocalToGlobalMapping` set with `MatSetLocalToGlobalMapping()`.
9730: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`.
9732: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`, `MatSetLocalToGlobalMapping()`, `ISLocalToGlobalMapping`
9733: @*/
9734: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9735: {
9736: PetscMPIInt size;
9738: PetscFunctionBegin;
9743: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9744: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9745: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9746: if (size == 1) {
9747: PetscInt n, m;
9748: PetscCall(VecGetSize(diag, &n));
9749: PetscCall(MatGetSize(mat, NULL, &m));
9750: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9751: PetscCall(MatDiagonalScale(mat, NULL, diag));
9752: } else PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9753: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9754: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9755: PetscFunctionReturn(PETSC_SUCCESS);
9756: }
9758: /*@
9759: MatGetInertia - Gets the inertia from a factored matrix
9761: Collective
9763: Input Parameter:
9764: . mat - the matrix
9766: Output Parameters:
9767: + nneg - number of negative eigenvalues
9768: . nzero - number of zero eigenvalues
9769: - npos - number of positive eigenvalues
9771: Level: advanced
9773: Note:
9774: Matrix must have been factored by `MatCholeskyFactor()`
9776: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9777: @*/
9778: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9779: {
9780: PetscFunctionBegin;
9783: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9784: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9785: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9786: PetscFunctionReturn(PETSC_SUCCESS);
9787: }
9789: /*@
9790: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9792: Neighbor-wise Collective
9794: Input Parameters:
9795: + mat - the factored matrix obtained with `MatGetFactor()`
9796: - b - the right-hand-side vectors
9798: Output Parameter:
9799: . x - the result vectors
9801: Level: developer
9803: Note:
9804: The vectors `b` and `x` cannot be the same. I.e., one cannot
9805: call `MatSolves`(A,x,x).
9807: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatGetFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9808: @*/
9809: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9810: {
9811: PetscFunctionBegin;
9814: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9815: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9816: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9818: MatCheckPreallocated(mat, 1);
9819: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9820: PetscUseTypeMethod(mat, solves, b, x);
9821: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9822: PetscFunctionReturn(PETSC_SUCCESS);
9823: }
9825: /*@
9826: MatIsSymmetric - Test whether a matrix is symmetric
9828: Collective
9830: Input Parameters:
9831: + A - the matrix to test
9832: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9834: Output Parameter:
9835: . flg - the result
9837: Level: intermediate
9839: Notes:
9840: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9842: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9844: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9845: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`). If these properties
9846: have been set then `MatIsSymmetric()` does not need to perform any computations and returns immediately with the result.
9848: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9849: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9850: @*/
9851: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9852: {
9853: PetscFunctionBegin;
9855: PetscAssertPointer(flg, 3);
9856: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9857: else {
9858: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9859: else PetscCall(MatIsTranspose(A, A, tol, flg));
9860: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9861: }
9862: PetscFunctionReturn(PETSC_SUCCESS);
9863: }
9865: /*@
9866: MatIsHermitian - Test whether a matrix is Hermitian
9868: Collective
9870: Input Parameters:
9871: + A - the matrix to test
9872: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9874: Output Parameter:
9875: . flg - the result
9877: Level: intermediate
9879: Notes:
9880: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9882: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9884: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9885: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`). If these properties
9886: have been set then `MatIsHermitian()` does not need to perform any computations and returns immediately with the result.
9888: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9889: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9890: @*/
9891: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9892: {
9893: PetscFunctionBegin;
9895: PetscAssertPointer(flg, 3);
9896: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9897: else {
9898: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9899: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9900: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9901: }
9902: PetscFunctionReturn(PETSC_SUCCESS);
9903: }
9905: /*@
9906: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9908: Not Collective
9910: Input Parameter:
9911: . A - the matrix to check
9913: Output Parameters:
9914: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9915: - flg - the result (only valid if set is `PETSC_TRUE`)
9917: Level: advanced
9919: Notes:
9920: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9921: if you want it explicitly checked
9923: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9924: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9926: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9927: @*/
9928: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9929: {
9930: PetscFunctionBegin;
9932: PetscAssertPointer(set, 2);
9933: PetscAssertPointer(flg, 3);
9934: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9935: *set = PETSC_TRUE;
9936: *flg = PetscBool3ToBool(A->symmetric);
9937: } else *set = PETSC_FALSE;
9938: PetscFunctionReturn(PETSC_SUCCESS);
9939: }
9941: /*@
9942: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9944: Not Collective
9946: Input Parameter:
9947: . A - the matrix to check
9949: Output Parameters:
9950: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9951: - flg - the result (only valid if set is `PETSC_TRUE`)
9953: Level: advanced
9955: Notes:
9956: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9958: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9959: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9961: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9962: @*/
9963: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9964: {
9965: PetscFunctionBegin;
9967: PetscAssertPointer(set, 2);
9968: PetscAssertPointer(flg, 3);
9969: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9970: *set = PETSC_TRUE;
9971: *flg = PetscBool3ToBool(A->spd);
9972: } else *set = PETSC_FALSE;
9973: PetscFunctionReturn(PETSC_SUCCESS);
9974: }
9976: /*@
9977: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9979: Not Collective
9981: Input Parameter:
9982: . A - the matrix to check
9984: Output Parameters:
9985: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9986: - flg - the result (only valid if set is `PETSC_TRUE`)
9988: Level: advanced
9990: Notes:
9991: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9992: if you want it explicitly checked
9994: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9995: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9997: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9998: @*/
9999: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
10000: {
10001: PetscFunctionBegin;
10003: PetscAssertPointer(set, 2);
10004: PetscAssertPointer(flg, 3);
10005: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
10006: *set = PETSC_TRUE;
10007: *flg = PetscBool3ToBool(A->hermitian);
10008: } else *set = PETSC_FALSE;
10009: PetscFunctionReturn(PETSC_SUCCESS);
10010: }
10012: /*@
10013: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
10015: Collective
10017: Input Parameter:
10018: . A - the matrix to test
10020: Output Parameter:
10021: . flg - the result
10023: Level: intermediate
10025: Notes:
10026: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
10028: 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
10029: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`). If these properties
10030: have been set then `MatIsStructurallySymmetric()` does not need to perform any computations and returns immediately with the result.
10032: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
10033: @*/
10034: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
10035: {
10036: PetscFunctionBegin;
10038: PetscAssertPointer(flg, 2);
10039: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) *flg = PetscBool3ToBool(A->structurally_symmetric);
10040: else {
10041: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
10042: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
10043: }
10044: PetscFunctionReturn(PETSC_SUCCESS);
10045: }
10047: /*@
10048: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
10050: Not Collective
10052: Input Parameter:
10053: . A - the matrix to check
10055: Output Parameters:
10056: + set - `PETSC_TRUE` if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
10057: - flg - the result (only valid if set is `PETSC_TRUE`)
10059: Level: advanced
10061: Notes:
10062: 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
10063: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
10065: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
10067: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
10068: @*/
10069: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
10070: {
10071: PetscFunctionBegin;
10073: PetscAssertPointer(set, 2);
10074: PetscAssertPointer(flg, 3);
10075: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
10076: *set = PETSC_TRUE;
10077: *flg = PetscBool3ToBool(A->structurally_symmetric);
10078: } else *set = PETSC_FALSE;
10079: PetscFunctionReturn(PETSC_SUCCESS);
10080: }
10082: /*@
10083: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
10084: to be communicated to other processes during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
10086: Not Collective
10088: Input Parameter:
10089: . mat - the matrix
10091: Output Parameters:
10092: + nstash - the size of the stash
10093: . reallocs - the number of additional mallocs incurred.
10094: . bnstash - the size of the block stash
10095: - breallocs - the number of additional mallocs incurred.in the block stash
10097: Level: advanced
10099: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
10100: @*/
10101: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
10102: {
10103: PetscFunctionBegin;
10104: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
10105: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
10106: PetscFunctionReturn(PETSC_SUCCESS);
10107: }
10109: /*@
10110: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
10111: parallel layout, `PetscLayout` for rows and columns
10113: Collective
10115: Input Parameter:
10116: . mat - the matrix
10118: Output Parameters:
10119: + right - (optional) vector that the matrix can be multiplied against
10120: - left - (optional) vector that the matrix vector product can be stored in
10122: Options Database Key:
10123: . -mat_vec_type type - set the `VecType` of the created vectors during `MatSetFromOptions()`
10125: Level: advanced
10127: Notes:
10128: 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()`.
10130: 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`.
10132: These are new vectors which are not owned by the `mat`, they should be destroyed with `VecDestroy()` when no longer needed.
10134: PETSc `Vec` always have all zero entries when created with `MatCreateVecs()` until routines such as `VecSet()` or `VecSetValues()`
10135: are used to change the values. There is no reason to call `VecZeroEntries()` after creation.
10137: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`, `MatSetVecType()`
10138: @*/
10139: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
10140: {
10141: PetscFunctionBegin;
10144: if (mat->ops->getvecs) {
10145: PetscUseTypeMethod(mat, getvecs, right, left);
10146: } else {
10147: if (right) {
10148: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
10149: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
10150: PetscCall(VecSetType(*right, mat->defaultvectype));
10151: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
10152: if (mat->boundtocpu && mat->bindingpropagates) {
10153: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
10154: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
10155: }
10156: #endif
10157: }
10158: if (left) {
10159: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
10160: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
10161: PetscCall(VecSetType(*left, mat->defaultvectype));
10162: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
10163: if (mat->boundtocpu && mat->bindingpropagates) {
10164: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
10165: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
10166: }
10167: #endif
10168: }
10169: }
10170: PetscFunctionReturn(PETSC_SUCCESS);
10171: }
10173: /*@
10174: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
10175: with default values.
10177: Not Collective
10179: Input Parameter:
10180: . info - the `MatFactorInfo` data structure
10182: Level: developer
10184: Notes:
10185: The solvers are generally used through the `KSP` and `PC` objects, for example
10186: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
10188: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
10190: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
10191: @*/
10192: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
10193: {
10194: PetscFunctionBegin;
10195: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
10196: PetscFunctionReturn(PETSC_SUCCESS);
10197: }
10199: /*@
10200: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
10202: Collective
10204: Input Parameters:
10205: + mat - the factored matrix
10206: - is - the index set defining the Schur indices (0-based)
10208: Level: advanced
10210: Notes:
10211: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
10213: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
10215: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
10217: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
10218: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
10219: @*/
10220: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
10221: {
10222: PetscErrorCode (*f)(Mat, IS);
10224: PetscFunctionBegin;
10229: PetscCheckSameComm(mat, 1, is, 2);
10230: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
10231: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
10232: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
10233: PetscCall((*f)(mat, is));
10234: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
10235: PetscFunctionReturn(PETSC_SUCCESS);
10236: }
10238: /*@
10239: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
10241: Logically Collective
10243: Input Parameters:
10244: + F - the factored matrix obtained by calling `MatGetFactor()`
10245: . S - location where to return the Schur complement, can be `NULL`
10246: - status - the status of the Schur complement matrix, can be `NULL`
10248: Level: advanced
10250: Notes:
10251: You must call `MatFactorSetSchurIS()` before calling this routine.
10253: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
10255: The routine provides a copy of the Schur matrix stored within the solver data structures.
10256: The caller must destroy the object when it is no longer needed.
10257: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
10259: 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)
10261: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
10263: Developer Note:
10264: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
10265: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
10267: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
10268: @*/
10269: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
10270: {
10271: PetscFunctionBegin;
10273: if (S) PetscAssertPointer(S, 2);
10274: if (status) PetscAssertPointer(status, 3);
10275: if (S) {
10276: PetscErrorCode (*f)(Mat, Mat *);
10278: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
10279: if (f) PetscCall((*f)(F, S));
10280: else PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
10281: }
10282: if (status) *status = F->schur_status;
10283: PetscFunctionReturn(PETSC_SUCCESS);
10284: }
10286: /*@
10287: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
10289: Logically Collective
10291: Input Parameters:
10292: + F - the factored matrix obtained by calling `MatGetFactor()`
10293: . S - location where to return the Schur complement, can be `NULL`
10294: - status - the status of the Schur complement matrix, can be `NULL`
10296: Level: advanced
10298: Notes:
10299: You must call `MatFactorSetSchurIS()` before calling this routine.
10301: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
10303: The routine returns a the Schur Complement stored within the data structures of the solver.
10305: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
10307: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
10309: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
10311: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
10313: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
10314: @*/
10315: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
10316: {
10317: PetscFunctionBegin;
10319: if (S) {
10320: PetscAssertPointer(S, 2);
10321: *S = F->schur;
10322: }
10323: if (status) {
10324: PetscAssertPointer(status, 3);
10325: *status = F->schur_status;
10326: }
10327: PetscFunctionReturn(PETSC_SUCCESS);
10328: }
10330: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
10331: {
10332: Mat S = F->schur;
10334: PetscFunctionBegin;
10335: switch (F->schur_status) {
10336: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
10337: case MAT_FACTOR_SCHUR_INVERTED:
10338: if (S) {
10339: S->ops->solve = NULL;
10340: S->ops->matsolve = NULL;
10341: S->ops->solvetranspose = NULL;
10342: S->ops->matsolvetranspose = NULL;
10343: S->ops->solveadd = NULL;
10344: S->ops->solvetransposeadd = NULL;
10345: S->factortype = MAT_FACTOR_NONE;
10346: PetscCall(PetscFree(S->solvertype));
10347: }
10348: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
10349: break;
10350: default:
10351: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10352: }
10353: PetscFunctionReturn(PETSC_SUCCESS);
10354: }
10356: /*@
10357: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
10359: Logically Collective
10361: Input Parameters:
10362: + F - the factored matrix obtained by calling `MatGetFactor()`
10363: . S - location where the Schur complement is stored
10364: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
10366: Level: advanced
10368: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
10369: @*/
10370: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
10371: {
10372: PetscFunctionBegin;
10374: if (S) {
10376: *S = NULL;
10377: }
10378: F->schur_status = status;
10379: PetscCall(MatFactorUpdateSchurStatus_Private(F));
10380: PetscFunctionReturn(PETSC_SUCCESS);
10381: }
10383: /*@
10384: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
10386: Logically Collective
10388: Input Parameters:
10389: + F - the factored matrix obtained by calling `MatGetFactor()`
10390: . rhs - location where the right-hand side of the Schur complement system is stored
10391: - sol - location where the solution of the Schur complement system has to be returned
10393: Level: advanced
10395: Notes:
10396: The sizes of the vectors should match the size of the Schur complement
10398: Must be called after `MatFactorSetSchurIS()`
10400: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
10401: @*/
10402: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
10403: {
10404: PetscFunctionBegin;
10411: PetscCheckSameComm(F, 1, rhs, 2);
10412: PetscCheckSameComm(F, 1, sol, 3);
10413: PetscCall(MatFactorFactorizeSchurComplement(F));
10414: switch (F->schur_status) {
10415: case MAT_FACTOR_SCHUR_FACTORED:
10416: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
10417: break;
10418: case MAT_FACTOR_SCHUR_INVERTED:
10419: PetscCall(MatMultTranspose(F->schur, rhs, sol));
10420: break;
10421: default:
10422: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10423: }
10424: PetscFunctionReturn(PETSC_SUCCESS);
10425: }
10427: /*@
10428: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
10430: Logically Collective
10432: Input Parameters:
10433: + F - the factored matrix obtained by calling `MatGetFactor()`
10434: . rhs - location where the right-hand side of the Schur complement system is stored
10435: - sol - location where the solution of the Schur complement system has to be returned
10437: Level: advanced
10439: Notes:
10440: The sizes of the vectors should match the size of the Schur complement
10442: Must be called after `MatFactorSetSchurIS()`
10444: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
10445: @*/
10446: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
10447: {
10448: PetscFunctionBegin;
10455: PetscCheckSameComm(F, 1, rhs, 2);
10456: PetscCheckSameComm(F, 1, sol, 3);
10457: PetscCall(MatFactorFactorizeSchurComplement(F));
10458: switch (F->schur_status) {
10459: case MAT_FACTOR_SCHUR_FACTORED:
10460: PetscCall(MatSolve(F->schur, rhs, sol));
10461: break;
10462: case MAT_FACTOR_SCHUR_INVERTED:
10463: PetscCall(MatMult(F->schur, rhs, sol));
10464: break;
10465: default:
10466: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10467: }
10468: PetscFunctionReturn(PETSC_SUCCESS);
10469: }
10471: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
10472: #if PetscDefined(HAVE_CUDA)
10473: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
10474: #endif
10476: /* Schur status updated in the interface */
10477: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
10478: {
10479: Mat S = F->schur;
10481: PetscFunctionBegin;
10482: if (S) {
10483: PetscMPIInt size;
10484: PetscBool isdense, isdensecuda;
10486: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
10487: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
10488: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
10489: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
10490: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
10491: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
10492: if (isdense) {
10493: PetscCall(MatSeqDenseInvertFactors_Private(S));
10494: } else if (isdensecuda) {
10495: #if PetscDefined(HAVE_CUDA)
10496: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
10497: #endif
10498: }
10499: // HIP??????????????
10500: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
10501: }
10502: PetscFunctionReturn(PETSC_SUCCESS);
10503: }
10505: /*@
10506: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
10508: Logically Collective
10510: Input Parameter:
10511: . F - the factored matrix obtained by calling `MatGetFactor()`
10513: Level: advanced
10515: Notes:
10516: Must be called after `MatFactorSetSchurIS()`.
10518: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
10520: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10521: @*/
10522: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10523: {
10524: PetscFunctionBegin;
10527: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10528: PetscCall(MatFactorFactorizeSchurComplement(F));
10529: PetscCall(MatFactorInvertSchurComplement_Private(F));
10530: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10531: PetscFunctionReturn(PETSC_SUCCESS);
10532: }
10534: /*@
10535: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10537: Logically Collective
10539: Input Parameter:
10540: . F - the factored matrix obtained by calling `MatGetFactor()`
10542: Level: advanced
10544: Note:
10545: Must be called after `MatFactorSetSchurIS()`
10547: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10548: @*/
10549: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10550: {
10551: MatFactorInfo info;
10553: PetscFunctionBegin;
10556: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10557: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10558: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10559: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10560: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10561: } else {
10562: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10563: }
10564: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10565: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10566: PetscFunctionReturn(PETSC_SUCCESS);
10567: }
10569: /*@
10570: MatPtAP - Creates the matrix product $C = P^T * A * P$
10572: Neighbor-wise Collective
10574: Input Parameters:
10575: + A - the matrix
10576: . P - the projection matrix
10577: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10578: - 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
10579: if the result is a dense matrix this is irrelevant
10581: Output Parameter:
10582: . C - the product matrix
10584: Level: intermediate
10586: Notes:
10587: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10589: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_PtAP`
10590: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10592: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10594: Developer Note:
10595: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10597: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10598: @*/
10599: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10600: {
10601: PetscFunctionBegin;
10602: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10603: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10605: if (scall == MAT_INITIAL_MATRIX) {
10606: PetscCall(MatProductCreate(A, P, NULL, C));
10607: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10608: PetscCall(MatProductSetAlgorithm(*C, "default"));
10609: PetscCall(MatProductSetFill(*C, fill));
10611: (*C)->product->api_user = PETSC_TRUE;
10612: PetscCall(MatProductSetFromOptions(*C));
10613: 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);
10614: PetscCall(MatProductSymbolic(*C));
10615: } else { /* scall == MAT_REUSE_MATRIX */
10616: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10617: }
10619: PetscCall(MatProductNumeric(*C));
10620: if (A->symmetric == PETSC_BOOL3_TRUE) {
10621: PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10622: (*C)->spd = A->spd;
10623: }
10624: PetscFunctionReturn(PETSC_SUCCESS);
10625: }
10627: /*@
10628: MatRARt - Creates the matrix product $C = R * A * R^T$
10630: Neighbor-wise Collective
10632: Input Parameters:
10633: + A - the matrix
10634: . R - the projection matrix
10635: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10636: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10637: if the result is a dense matrix this is irrelevant
10639: Output Parameter:
10640: . C - the product matrix
10642: Level: intermediate
10644: Notes:
10645: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10647: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_RARt`
10648: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10650: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10651: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10652: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10653: We recommend using `MatPtAP()` when possible.
10655: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10657: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10658: @*/
10659: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10660: {
10661: PetscFunctionBegin;
10662: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10663: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10665: if (scall == MAT_INITIAL_MATRIX) {
10666: PetscCall(MatProductCreate(A, R, NULL, C));
10667: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10668: PetscCall(MatProductSetAlgorithm(*C, "default"));
10669: PetscCall(MatProductSetFill(*C, fill));
10671: (*C)->product->api_user = PETSC_TRUE;
10672: PetscCall(MatProductSetFromOptions(*C));
10673: 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);
10674: PetscCall(MatProductSymbolic(*C));
10675: } else { /* scall == MAT_REUSE_MATRIX */
10676: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10677: }
10679: PetscCall(MatProductNumeric(*C));
10680: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10681: PetscFunctionReturn(PETSC_SUCCESS);
10682: }
10684: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10685: {
10686: PetscBool flg = PETSC_TRUE;
10688: PetscFunctionBegin;
10689: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10690: if (scall == MAT_INITIAL_MATRIX) {
10691: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10692: PetscCall(MatProductCreate(A, B, NULL, C));
10693: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10694: PetscCall(MatProductSetFill(*C, fill));
10695: } else { /* scall == MAT_REUSE_MATRIX */
10696: Mat_Product *product = (*C)->product;
10698: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10699: if (flg && product && product->type != ptype) {
10700: PetscCall(MatProductClear(*C));
10701: product = NULL;
10702: }
10703: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10704: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10705: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10706: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10707: product = (*C)->product;
10708: product->fill = fill;
10709: product->clear = PETSC_TRUE;
10710: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10711: flg = PETSC_FALSE;
10712: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10713: }
10714: }
10715: if (flg) {
10716: (*C)->product->api_user = PETSC_TRUE;
10717: PetscCall(MatProductSetType(*C, ptype));
10718: PetscCall(MatProductSetFromOptions(*C));
10719: PetscCall(MatProductSymbolic(*C));
10720: }
10721: PetscCall(MatProductNumeric(*C));
10722: PetscFunctionReturn(PETSC_SUCCESS);
10723: }
10725: /*@
10726: MatMatMult - Performs matrix-matrix multiplication $ C=A*B $.
10728: Neighbor-wise Collective
10730: Input Parameters:
10731: + A - the left matrix
10732: . B - the right matrix
10733: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10734: - 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
10735: if the result is a dense matrix this is irrelevant
10737: Output Parameter:
10738: . C - the product matrix
10740: Notes:
10741: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10743: `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
10744: call to this function with `MAT_INITIAL_MATRIX`.
10746: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10748: 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`,
10749: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10751: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10753: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AB`
10754: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10756: Example of Usage:
10757: .vb
10758: MatProductCreate(A,B,NULL,&C);
10759: MatProductSetType(C,MATPRODUCT_AB);
10760: MatProductSymbolic(C);
10761: MatProductNumeric(C); // compute C=A * B
10762: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10763: MatProductNumeric(C);
10764: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10765: MatProductNumeric(C);
10766: .ve
10768: Level: intermediate
10770: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10771: @*/
10772: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10773: {
10774: PetscFunctionBegin;
10775: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10776: PetscFunctionReturn(PETSC_SUCCESS);
10777: }
10779: /*@
10780: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10782: Neighbor-wise Collective
10784: Input Parameters:
10785: + A - the left matrix
10786: . B - the right matrix
10787: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10788: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10790: Output Parameter:
10791: . C - the product matrix
10793: Options Database Key:
10794: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10795: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10796: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10798: Level: intermediate
10800: Notes:
10801: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10803: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10805: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10806: actually needed.
10808: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10809: and for pairs of `MATMPIDENSE` matrices.
10811: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABt`
10812: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10814: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10816: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()`, `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10817: @*/
10818: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10819: {
10820: PetscFunctionBegin;
10821: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10822: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10823: PetscFunctionReturn(PETSC_SUCCESS);
10824: }
10826: /*@
10827: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10829: Neighbor-wise Collective
10831: Input Parameters:
10832: + A - the left matrix
10833: . B - the right matrix
10834: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10835: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10837: Output Parameter:
10838: . C - the product matrix
10840: Level: intermediate
10842: Notes:
10843: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10845: `MAT_REUSE_MATRIX` can only be used if `A` and `B` have the same nonzero pattern as in the previous call.
10847: This is a convenience routine that wraps the use of `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AtB`
10848: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10850: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10851: actually needed.
10853: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10854: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10856: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10858: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10859: @*/
10860: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10861: {
10862: PetscFunctionBegin;
10863: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10864: PetscFunctionReturn(PETSC_SUCCESS);
10865: }
10867: /*@
10868: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10870: Neighbor-wise Collective
10872: Input Parameters:
10873: + A - the left matrix
10874: . B - the middle matrix
10875: . C - the right matrix
10876: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10877: - 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
10878: if the result is a dense matrix this is irrelevant
10880: Output Parameter:
10881: . D - the product matrix
10883: Level: intermediate
10885: Notes:
10886: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10888: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10890: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABC`
10891: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10893: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10894: actually needed.
10896: If you have many matrices with the same non-zero structure to multiply, you
10897: should use `MAT_REUSE_MATRIX` in all calls but the first
10899: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10901: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10902: @*/
10903: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10904: {
10905: PetscFunctionBegin;
10906: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10907: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10909: if (scall == MAT_INITIAL_MATRIX) {
10910: PetscCall(MatProductCreate(A, B, C, D));
10911: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10912: PetscCall(MatProductSetAlgorithm(*D, "default"));
10913: PetscCall(MatProductSetFill(*D, fill));
10915: (*D)->product->api_user = PETSC_TRUE;
10916: PetscCall(MatProductSetFromOptions(*D));
10917: 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,
10918: ((PetscObject)C)->type_name);
10919: PetscCall(MatProductSymbolic(*D));
10920: } else { /* user may change input matrices when REUSE */
10921: PetscCall(MatProductReplaceMats(A, B, C, *D));
10922: }
10923: PetscCall(MatProductNumeric(*D));
10924: PetscFunctionReturn(PETSC_SUCCESS);
10925: }
10927: /*@
10928: MatCreateRedundantMatrix - Create redundant matrices and put them into processes of subcommunicators.
10930: Collective
10932: Input Parameters:
10933: + mat - the matrix
10934: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10935: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10936: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10938: Output Parameter:
10939: . matredundant - redundant matrix
10941: Level: advanced
10943: Notes:
10944: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10945: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10947: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10948: calling it.
10950: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10952: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10953: @*/
10954: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10955: {
10956: MPI_Comm comm;
10957: PetscMPIInt size;
10958: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10959: Mat_Redundant *redund = NULL;
10960: PetscSubcomm psubcomm = NULL;
10961: MPI_Comm subcomm_in = subcomm;
10962: Mat *matseq;
10963: IS isrow, iscol;
10964: PetscBool newsubcomm = PETSC_FALSE;
10966: PetscFunctionBegin;
10968: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10969: PetscAssertPointer(*matredundant, 5);
10971: }
10973: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10974: if (size == 1 || nsubcomm == 1) {
10975: if (reuse == MAT_INITIAL_MATRIX) {
10976: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10977: } else {
10978: 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");
10979: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10980: }
10981: PetscFunctionReturn(PETSC_SUCCESS);
10982: }
10984: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10985: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10986: MatCheckPreallocated(mat, 1);
10988: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10989: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10990: /* create psubcomm, then get subcomm */
10991: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10992: PetscCallMPI(MPI_Comm_size(comm, &size));
10993: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10995: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10996: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10997: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10998: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10999: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
11000: newsubcomm = PETSC_TRUE;
11001: PetscCall(PetscSubcommDestroy(&psubcomm));
11002: }
11004: /* get isrow, iscol and a local sequential matrix matseq[0] */
11005: if (reuse == MAT_INITIAL_MATRIX) {
11006: mloc_sub = PETSC_DECIDE;
11007: nloc_sub = PETSC_DECIDE;
11008: if (bs < 1) {
11009: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
11010: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
11011: } else {
11012: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
11013: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
11014: }
11015: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
11016: rstart = rend - mloc_sub;
11017: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
11018: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
11019: PetscCall(ISSetIdentity(iscol));
11020: } else { /* reuse == MAT_REUSE_MATRIX */
11021: 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");
11022: /* retrieve subcomm */
11023: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
11024: redund = (*matredundant)->redundant;
11025: isrow = redund->isrow;
11026: iscol = redund->iscol;
11027: matseq = redund->matseq;
11028: }
11029: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
11031: /* get matredundant over subcomm */
11032: if (reuse == MAT_INITIAL_MATRIX) {
11033: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
11035: /* create a supporting struct and attach it to C for reuse */
11036: PetscCall(PetscNew(&redund));
11037: (*matredundant)->redundant = redund;
11038: redund->isrow = isrow;
11039: redund->iscol = iscol;
11040: redund->matseq = matseq;
11041: if (newsubcomm) {
11042: redund->subcomm = subcomm;
11043: } else {
11044: redund->subcomm = MPI_COMM_NULL;
11045: }
11046: } else {
11047: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
11048: }
11049: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
11050: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
11051: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
11052: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
11053: }
11054: #endif
11055: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
11056: PetscFunctionReturn(PETSC_SUCCESS);
11057: }
11059: /*@
11060: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
11061: a given `Mat`. Each submatrix can span multiple procs.
11063: Collective
11065: Input Parameters:
11066: + mat - the matrix
11067: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
11068: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11070: Output Parameter:
11071: . subMat - parallel sub-matrices each spanning a given `subcomm`
11073: Level: advanced
11075: Notes:
11076: The submatrix partition across processes is dictated by `subComm` a
11077: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
11078: is not restricted to be grouped with consecutive original MPI processes.
11080: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
11081: map directly to the layout of the original matrix [wrt the local
11082: row,col partitioning]. So the original 'DiagonalMat' naturally maps
11083: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
11084: the `subMat`. However the offDiagMat looses some columns - and this is
11085: reconstructed with `MatSetValues()`
11087: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
11089: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
11090: @*/
11091: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
11092: {
11093: PetscMPIInt commsize, subCommSize;
11095: PetscFunctionBegin;
11096: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
11097: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
11098: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
11100: 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");
11101: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
11102: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
11103: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
11104: PetscFunctionReturn(PETSC_SUCCESS);
11105: }
11107: /*@
11108: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
11110: Not Collective
11112: Input Parameters:
11113: + mat - matrix to extract local submatrix from
11114: . isrow - local row indices for submatrix
11115: - iscol - local column indices for submatrix
11117: Output Parameter:
11118: . submat - the submatrix
11120: Level: intermediate
11122: Notes:
11123: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
11125: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
11126: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
11128: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
11129: `MatSetValuesBlockedLocal()` will also be implemented.
11131: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
11132: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
11134: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
11135: @*/
11136: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
11137: {
11138: PetscFunctionBegin;
11142: PetscCheckSameComm(isrow, 2, iscol, 3);
11143: PetscAssertPointer(submat, 4);
11144: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
11146: if (mat->ops->getlocalsubmatrix) {
11147: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
11148: } else {
11149: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
11150: }
11151: (*submat)->assembled = mat->assembled;
11152: PetscFunctionReturn(PETSC_SUCCESS);
11153: }
11155: /*@
11156: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
11158: Not Collective
11160: Input Parameters:
11161: + mat - matrix to extract local submatrix from
11162: . isrow - local row indices for submatrix
11163: . iscol - local column indices for submatrix
11164: - submat - the submatrix
11166: Level: intermediate
11168: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
11169: @*/
11170: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
11171: {
11172: PetscFunctionBegin;
11176: PetscCheckSameComm(isrow, 2, iscol, 3);
11177: PetscAssertPointer(submat, 4);
11180: if (mat->ops->restorelocalsubmatrix) {
11181: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
11182: } else {
11183: PetscCall(MatDestroy(submat));
11184: }
11185: *submat = NULL;
11186: PetscFunctionReturn(PETSC_SUCCESS);
11187: }
11189: /*@
11190: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
11192: Collective
11194: Input Parameter:
11195: . mat - the matrix
11197: Output Parameter:
11198: . is - if any rows have zero diagonals this contains the list of them
11200: Level: developer
11202: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
11203: @*/
11204: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
11205: {
11206: PetscFunctionBegin;
11209: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11210: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11212: if (!mat->ops->findzerodiagonals) {
11213: Vec diag;
11214: const PetscScalar *a;
11215: PetscInt *rows;
11216: PetscInt rStart, rEnd, r, nrow = 0;
11218: PetscCall(MatCreateVecs(mat, &diag, NULL));
11219: PetscCall(MatGetDiagonal(mat, diag));
11220: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
11221: PetscCall(VecGetArrayRead(diag, &a));
11222: for (r = 0; r < rEnd - rStart; ++r)
11223: if (a[r] == 0.0) ++nrow;
11224: PetscCall(PetscMalloc1(nrow, &rows));
11225: nrow = 0;
11226: for (r = 0; r < rEnd - rStart; ++r)
11227: if (a[r] == 0.0) rows[nrow++] = r + rStart;
11228: PetscCall(VecRestoreArrayRead(diag, &a));
11229: PetscCall(VecDestroy(&diag));
11230: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
11231: } else {
11232: PetscUseTypeMethod(mat, findzerodiagonals, is);
11233: }
11234: PetscFunctionReturn(PETSC_SUCCESS);
11235: }
11237: /*@
11238: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
11240: Collective
11242: Input Parameter:
11243: . mat - the matrix
11245: Output Parameter:
11246: . is - contains the list of rows with off block diagonal entries
11248: Level: developer
11250: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
11251: @*/
11252: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
11253: {
11254: PetscFunctionBegin;
11257: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11258: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11260: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
11261: PetscFunctionReturn(PETSC_SUCCESS);
11262: }
11264: /*@
11265: MatInvertBlockDiagonal - Inverts the block diagonal entries.
11267: Collective; No Fortran Support
11269: Input Parameter:
11270: . mat - the matrix
11272: Output Parameter:
11273: . values - the block inverses in column major order (FORTRAN-like)
11275: Level: advanced
11277: Notes:
11278: The size of the blocks is determined by the block size of the matrix.
11280: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
11282: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
11284: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
11285: @*/
11286: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
11287: {
11288: PetscFunctionBegin;
11290: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11291: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11292: PetscUseTypeMethod(mat, invertblockdiagonal, values);
11293: PetscFunctionReturn(PETSC_SUCCESS);
11294: }
11296: /*@
11297: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
11299: Collective; No Fortran Support
11301: Input Parameters:
11302: + mat - the matrix
11303: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
11304: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
11306: Output Parameter:
11307: . values - the block inverses in column major order (FORTRAN-like)
11309: Level: advanced
11311: Notes:
11312: Use `MatInvertBlockDiagonal()` if all blocks have the same size
11314: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
11316: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
11317: @*/
11318: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
11319: {
11320: PetscFunctionBegin;
11322: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11323: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11324: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
11325: PetscFunctionReturn(PETSC_SUCCESS);
11326: }
11328: /*@
11329: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
11331: Collective
11333: Input Parameters:
11334: + A - the matrix
11335: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
11337: Level: advanced
11339: Note:
11340: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
11342: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
11343: @*/
11344: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
11345: {
11346: const PetscScalar *vals;
11347: PetscInt *dnnz;
11348: PetscInt m, rstart, rend, bs, i, j;
11350: PetscFunctionBegin;
11351: PetscCall(MatInvertBlockDiagonal(A, &vals));
11352: PetscCall(MatGetBlockSize(A, &bs));
11353: PetscCall(MatGetLocalSize(A, &m, NULL));
11354: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
11355: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
11356: PetscCall(PetscMalloc1(m / bs, &dnnz));
11357: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
11358: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
11359: PetscCall(PetscFree(dnnz));
11360: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
11361: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
11362: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
11363: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
11364: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
11365: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
11366: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
11367: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
11368: PetscFunctionReturn(PETSC_SUCCESS);
11369: }
11371: /*@
11372: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
11373: via `MatTransposeColoringCreate()`.
11375: Collective
11377: Input Parameter:
11378: . c - coloring context
11380: Level: intermediate
11382: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
11383: @*/
11384: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
11385: {
11386: MatTransposeColoring matcolor = *c;
11388: PetscFunctionBegin;
11389: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
11390: if (--((PetscObject)matcolor)->refct > 0) {
11391: matcolor = NULL;
11392: PetscFunctionReturn(PETSC_SUCCESS);
11393: }
11395: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
11396: PetscCall(PetscFree(matcolor->rows));
11397: PetscCall(PetscFree(matcolor->den2sp));
11398: PetscCall(PetscFree(matcolor->colorforcol));
11399: PetscCall(PetscFree(matcolor->columns));
11400: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
11401: PetscCall(PetscHeaderDestroy(c));
11402: PetscFunctionReturn(PETSC_SUCCESS);
11403: }
11405: /*@
11406: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
11407: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
11408: `MatTransposeColoring` to sparse `B`.
11410: Collective
11412: Input Parameters:
11413: + coloring - coloring context created with `MatTransposeColoringCreate()`
11414: - B - sparse matrix
11416: Output Parameter:
11417: . Btdense - dense matrix $B^T$
11419: Level: developer
11421: Note:
11422: These are used internally for some implementations of `MatRARt()`
11424: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
11425: @*/
11426: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
11427: {
11428: PetscFunctionBegin;
11433: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
11434: PetscFunctionReturn(PETSC_SUCCESS);
11435: }
11437: /*@
11438: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
11439: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
11440: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
11441: $C_{sp}$ from $C_{den}$.
11443: Collective
11445: Input Parameters:
11446: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
11447: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
11449: Output Parameter:
11450: . Csp - sparse matrix
11452: Level: developer
11454: Note:
11455: These are used internally for some implementations of `MatRARt()`
11457: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
11458: @*/
11459: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
11460: {
11461: PetscFunctionBegin;
11466: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
11467: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
11468: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
11469: PetscFunctionReturn(PETSC_SUCCESS);
11470: }
11472: /*@
11473: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
11475: Collective
11477: Input Parameters:
11478: + mat - the matrix product C
11479: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
11481: Output Parameter:
11482: . color - the new coloring context
11484: Level: intermediate
11486: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
11487: `MatTransColoringApplyDenToSp()`
11488: @*/
11489: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
11490: {
11491: MatTransposeColoring c;
11492: MPI_Comm comm;
11494: PetscFunctionBegin;
11495: PetscAssertPointer(color, 3);
11497: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11498: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
11499: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
11500: c->ctype = iscoloring->ctype;
11501: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
11502: *color = c;
11503: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11504: PetscFunctionReturn(PETSC_SUCCESS);
11505: }
11507: /*@
11508: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
11509: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
11511: Not Collective
11513: Input Parameter:
11514: . mat - the matrix
11516: Output Parameter:
11517: . state - the current state
11519: Level: intermediate
11521: Notes:
11522: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11523: different matrices
11525: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
11527: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
11529: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11530: @*/
11531: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11532: {
11533: PetscFunctionBegin;
11535: *state = mat->nonzerostate;
11536: PetscFunctionReturn(PETSC_SUCCESS);
11537: }
11539: /*@
11540: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11541: matrices from each process
11543: Collective
11545: Input Parameters:
11546: + comm - the communicators the parallel matrix will live on
11547: . seqmat - the input sequential matrices
11548: . n - number of local columns (or `PETSC_DECIDE`)
11549: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11551: Output Parameter:
11552: . mpimat - the parallel matrix generated
11554: Level: developer
11556: Note:
11557: The number of columns of the matrix in EACH process MUST be the same.
11559: .seealso: [](ch_matrices), `Mat`
11560: @*/
11561: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11562: {
11563: PetscMPIInt size;
11565: PetscFunctionBegin;
11566: PetscCallMPI(MPI_Comm_size(comm, &size));
11567: if (size == 1) {
11568: if (reuse == MAT_INITIAL_MATRIX) {
11569: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11570: } else {
11571: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11572: }
11573: PetscFunctionReturn(PETSC_SUCCESS);
11574: }
11576: 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");
11578: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11579: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11580: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11581: PetscFunctionReturn(PETSC_SUCCESS);
11582: }
11584: /*@
11585: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11587: Collective
11589: Input Parameters:
11590: + A - the matrix to create subdomains from
11591: - N - requested number of subdomains
11593: Output Parameters:
11594: + n - number of subdomains resulting on this MPI process
11595: - iss - `IS` list with indices of subdomains on this MPI process
11597: Level: advanced
11599: Note:
11600: The number of subdomains must be smaller than the communicator size
11602: .seealso: [](ch_matrices), `Mat`, `IS`
11603: @*/
11604: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11605: {
11606: MPI_Comm comm, subcomm;
11607: PetscMPIInt size, rank, color;
11608: PetscInt rstart, rend, k;
11610: PetscFunctionBegin;
11611: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11612: PetscCallMPI(MPI_Comm_size(comm, &size));
11613: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11614: 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);
11615: *n = 1;
11616: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11617: color = rank / k;
11618: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11619: PetscCall(PetscMalloc1(1, iss));
11620: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11621: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11622: PetscCallMPI(MPI_Comm_free(&subcomm));
11623: PetscFunctionReturn(PETSC_SUCCESS);
11624: }
11626: /*@
11627: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11629: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11630: If they are not the same, uses `MatMatMatMult()`.
11632: Once the coarse grid problem is constructed, correct for interpolation operators
11633: that are not of full rank, which can legitimately happen in the case of non-nested
11634: geometric multigrid.
11636: Input Parameters:
11637: + restrct - restriction operator
11638: . dA - fine grid matrix
11639: . interpolate - interpolation operator
11640: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11641: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11643: Output Parameter:
11644: . A - the Galerkin coarse matrix
11646: Options Database Key:
11647: . -pc_mg_galerkin (both|pmat|mat|none) - for what matrices the Galerkin process should be used
11649: Level: developer
11651: Note:
11652: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11654: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11655: @*/
11656: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11657: {
11658: IS zerorows;
11659: Vec diag;
11661: PetscFunctionBegin;
11662: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11663: /* Construct the coarse grid matrix */
11664: if (interpolate == restrct) {
11665: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11666: } else {
11667: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11668: }
11670: /* If the interpolation matrix is not of full rank, A will have zero rows.
11671: This can legitimately happen in the case of non-nested geometric multigrid.
11672: In that event, we set the rows of the matrix to the rows of the identity,
11673: ignoring the equations (as the RHS will also be zero). */
11675: PetscCall(MatFindZeroRows(*A, &zerorows));
11677: if (zerorows != NULL) { /* if there are any zero rows */
11678: PetscCall(MatCreateVecs(*A, &diag, NULL));
11679: PetscCall(MatGetDiagonal(*A, diag));
11680: PetscCall(VecISSet(diag, zerorows, 1.0));
11681: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11682: PetscCall(VecDestroy(&diag));
11683: PetscCall(ISDestroy(&zerorows));
11684: }
11685: PetscFunctionReturn(PETSC_SUCCESS);
11686: }
11688: /*@
11689: MatSetOperation - Allows user to set a matrix operation for any matrix type
11691: Logically Collective
11693: Input Parameters:
11694: + mat - the matrix
11695: . op - the name of the operation
11696: - f - the function that provides the operation
11698: Level: developer
11700: Example Usage:
11701: .vb
11702: extern PetscErrorCode usermult(Mat, Vec, Vec);
11704: PetscCall(MatCreateXXX(comm, ..., &A));
11705: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11706: .ve
11708: Notes:
11709: See the file `include/petscmat.h` for a complete list of matrix
11710: operations, which all have the form MATOP_<OPERATION>, where
11711: <OPERATION> is the name (in all capital letters) of the
11712: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11714: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11715: sequence as the usual matrix interface routines, since they
11716: are intended to be accessed via the usual matrix interface
11717: routines, e.g.,
11718: .vb
11719: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11720: .ve
11722: In particular each function MUST return `PETSC_SUCCESS` on success and
11723: nonzero on failure.
11725: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11727: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11728: @*/
11729: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11730: {
11731: PetscFunctionBegin;
11734: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11735: #if !PetscDefined(USE_COMPLEX)
11736: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11737: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11738: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11739: #endif
11740: ((PetscErrorCodeFn **)mat->ops)[op] = f;
11741: PetscFunctionReturn(PETSC_SUCCESS);
11742: }
11744: /*@
11745: MatGetOperation - Gets a matrix operation for any matrix type.
11747: Not Collective
11749: Input Parameters:
11750: + mat - the matrix
11751: - op - the name of the operation
11753: Output Parameter:
11754: . f - the function that provides the operation
11756: Level: developer
11758: Example Usage:
11759: .vb
11760: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11762: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11763: .ve
11765: Notes:
11766: See the file `include/petscmat.h` for a complete list of matrix
11767: operations, which all have the form MATOP_<OPERATION>, where
11768: <OPERATION> is the name (in all capital letters) of the
11769: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11771: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11773: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11774: @*/
11775: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11776: {
11777: PetscFunctionBegin;
11779: PetscAssertPointer(f, 3);
11780: #if !PetscDefined(USE_COMPLEX)
11781: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11782: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11783: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11784: #endif
11785: *f = ((PetscErrorCodeFn **)mat->ops)[op];
11786: PetscFunctionReturn(PETSC_SUCCESS);
11787: }
11789: /*@
11790: MatHasOperation - Determines whether the given matrix supports the particular operation.
11792: Not Collective
11794: Input Parameters:
11795: + mat - the matrix
11796: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11798: Output Parameter:
11799: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11801: Level: advanced
11803: Note:
11804: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11806: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11807: @*/
11808: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11809: {
11810: PetscFunctionBegin;
11812: PetscAssertPointer(has, 3);
11813: #if !PetscDefined(USE_COMPLEX)
11814: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11815: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11816: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11817: #endif
11818: if (op == MATOP_ADOT || op == MATOP_ANORM) {
11819: /* MatADot() and MatANorm() fall back to MatMult() when the type has no method */
11820: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11821: else PetscCall(MatHasOperation(mat, MATOP_MULT, has));
11822: PetscFunctionReturn(PETSC_SUCCESS);
11823: }
11824: if (mat->ops->hasoperation) {
11825: PetscUseTypeMethod(mat, hasoperation, op, has);
11826: } else {
11827: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11828: else {
11829: *has = PETSC_FALSE;
11830: if (op == MATOP_CREATE_SUBMATRIX) {
11831: PetscMPIInt size;
11833: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11834: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11835: }
11836: }
11837: }
11838: PetscFunctionReturn(PETSC_SUCCESS);
11839: }
11841: /*@
11842: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11844: Collective
11846: Input Parameter:
11847: . mat - the matrix
11849: Output Parameter:
11850: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11852: Level: beginner
11854: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11855: @*/
11856: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11857: {
11858: PetscFunctionBegin;
11861: PetscAssertPointer(cong, 2);
11862: if (!mat->rmap || !mat->cmap) {
11863: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11864: PetscFunctionReturn(PETSC_SUCCESS);
11865: }
11866: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11867: PetscCall(PetscLayoutSetUp(mat->rmap));
11868: PetscCall(PetscLayoutSetUp(mat->cmap));
11869: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11870: if (*cong) mat->congruentlayouts = 1;
11871: else mat->congruentlayouts = 0;
11872: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11873: PetscFunctionReturn(PETSC_SUCCESS);
11874: }
11876: /*@
11877: MatFlag - set infinity into the local part of the matrix on any subset of MPI processes
11879: Logically Collective
11881: Input Parameters:
11882: + A - the matrix, can be `NULL` but only if on all processes
11883: - flg - indicates if this processes portion of the matrix should be set to infinity
11885: Level: developer
11887: Notes:
11888: This is used to flag a block of solutions that a linear solver failed to compute, as `VecFlag()` does for a single solution.
11890: The state of `A` is increased on all processes, whether or not their portion is flagged, so an outer solver that tracks it detects the failure even when the entries were already infinite.
11892: Only the dense types (`MATSEQDENSE`, `MATMPIDENSE`, and their device variants) currently implement this operation.
11894: .seealso: [](ch_matrices), `Mat`, `VecFlag()`, `MatZeroEntries()`, `MatSetValues()`
11895: @*/
11896: PetscErrorCode MatFlag(Mat A, PetscInt flg)
11897: {
11898: PetscFunctionBegin;
11899: if (!A) PetscFunctionReturn(PETSC_SUCCESS);
11902: MatCheckPreallocated(A, 1);
11903: PetscCall(PetscObjectStateIncrease((PetscObject)A));
11904: if (flg) PetscUseTypeMethod(A, setinf);
11905: PetscFunctionReturn(PETSC_SUCCESS);
11906: }
11908: /*@
11909: 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
11910: and possibly removes small values from the graph structure.
11912: Collective
11914: Input Parameters:
11915: + A - the matrix
11916: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11917: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11918: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with $|entries| \le filter$
11919: . num_idx - size of `index` array
11920: - index - array of block indices to use for graph strength of connection weight
11922: Output Parameter:
11923: . graph - the resulting graph
11925: Level: advanced
11927: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11928: @*/
11929: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11930: {
11931: PetscFunctionBegin;
11935: PetscAssertPointer(graph, 7);
11936: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11937: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11938: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11939: PetscFunctionReturn(PETSC_SUCCESS);
11940: }
11942: /*@
11943: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11944: meaning the same memory is used for the matrix, and no new memory is allocated.
11946: Collective
11948: Input Parameters:
11949: + A - the matrix
11950: - 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
11952: Level: intermediate
11954: Developer Note:
11955: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11956: of the arrays in the data structure may be no longer needed to represent the matrix.
11958: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11959: @*/
11960: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11961: {
11962: PetscFunctionBegin;
11964: PetscUseTypeMethod(A, eliminatezeros, keep);
11965: PetscFunctionReturn(PETSC_SUCCESS);
11966: }
11968: /*@
11969: MatGetCurrentMemType - Get the memory location of the matrix
11971: Not Collective, but the result will be the same on all MPI processes
11973: Input Parameter:
11974: . A - the matrix whose memory type we are checking
11976: Output Parameter:
11977: . m - the memory type, see `PetscMemType`
11979: Level: intermediate
11981: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11982: @*/
11983: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11984: {
11985: PetscFunctionBegin;
11987: PetscAssertPointer(m, 2);
11988: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11989: else *m = PETSC_MEMTYPE_HOST;
11990: PetscFunctionReturn(PETSC_SUCCESS);
11991: }