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: PetscFunctionReturn(PETSC_SUCCESS);
495: }
497: /*@C
498: MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix
500: Collective
502: Input Parameter:
503: . mat - the matrix
505: Output Parameters:
506: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
507: - ghosts - the global indices of the ghost points
509: Level: advanced
511: Note:
512: `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`
514: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
515: @*/
516: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
517: {
518: PetscFunctionBegin;
521: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
522: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
523: if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
524: else {
525: if (nghosts) *nghosts = 0;
526: if (ghosts) *ghosts = NULL;
527: }
528: PetscFunctionReturn(PETSC_SUCCESS);
529: }
531: /*@
532: MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part
534: Logically Collective
536: Input Parameter:
537: . mat - the matrix
539: Level: advanced
541: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
542: @*/
543: PetscErrorCode MatImaginaryPart(Mat mat)
544: {
545: PetscFunctionBegin;
548: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
549: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
550: MatCheckPreallocated(mat, 1);
551: PetscUseTypeMethod(mat, imaginarypart);
552: PetscFunctionReturn(PETSC_SUCCESS);
553: }
555: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
556: /*@C
557: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
558: for each row that you get to ensure that your application does
559: not bleed memory.
561: Not Collective
563: Input Parameters:
564: + mat - the matrix
565: - row - the row to get
567: Output Parameters:
568: + ncols - if not `NULL`, the number of nonzeros in `row`
569: . cols - if not `NULL`, the column numbers
570: - vals - if not `NULL`, the numerical values
572: Level: advanced
574: Notes:
575: This routine is provided for people who need to have direct access
576: to the structure of a matrix. We hope that we provide enough
577: high-level matrix routines that few users will need it.
579: `MatGetRow()` always returns 0-based column indices, regardless of
580: whether the internal representation is 0-based (default) or 1-based.
582: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
583: not wish to extract these quantities.
585: The user can only examine the values extracted with `MatGetRow()`;
586: the values CANNOT be altered. To change the matrix entries, one
587: must use `MatSetValues()`.
589: You can only have one call to `MatGetRow()` outstanding for a particular
590: matrix at a time, per processor. `MatGetRow()` can only obtain rows
591: associated with the given processor, it cannot get rows from the
592: other processors; for that we suggest using `MatCreateSubMatrices()`, then
593: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
594: is in the global number of rows.
596: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
598: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
600: Fortran Note:
601: .vb
602: PetscInt, pointer :: cols(:)
603: PetscScalar, pointer :: vals(:)
604: .ve
606: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
607: @*/
608: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
609: {
610: PetscInt incols;
612: PetscFunctionBegin;
615: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
616: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
617: MatCheckPreallocated(mat, 1);
618: 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);
619: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
620: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
621: if (ncols) *ncols = incols;
622: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
623: PetscFunctionReturn(PETSC_SUCCESS);
624: }
626: /*@
627: MatConjugate - replaces the matrix values with their complex conjugates
629: Logically Collective
631: Input Parameter:
632: . mat - the matrix
634: Level: advanced
636: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
637: @*/
638: PetscErrorCode MatConjugate(Mat mat)
639: {
640: PetscFunctionBegin;
642: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
643: if (PetscDefined(USE_COMPLEX) && !(mat->symmetric == PETSC_BOOL3_TRUE && mat->hermitian == PETSC_BOOL3_TRUE)) {
644: PetscUseTypeMethod(mat, conjugate);
645: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
646: }
647: PetscFunctionReturn(PETSC_SUCCESS);
648: }
650: /*@C
651: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
653: Not Collective
655: Input Parameters:
656: + mat - the matrix
657: . row - the row to get
658: . ncols - the number of nonzeros
659: . cols - the columns of the nonzeros
660: - vals - if nonzero the column values
662: Level: advanced
664: Notes:
665: This routine should be called after you have finished examining the entries.
667: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
668: us of the array after it has been restored. If you pass `NULL`, it will
669: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
671: Fortran Note:
672: .vb
673: PetscInt, pointer :: cols(:)
674: PetscScalar, pointer :: vals(:)
675: .ve
677: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
678: @*/
679: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
680: {
681: PetscFunctionBegin;
683: if (ncols) PetscAssertPointer(ncols, 3);
684: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
685: PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
686: if (ncols) *ncols = 0;
687: if (cols) *cols = NULL;
688: if (vals) *vals = NULL;
689: PetscFunctionReturn(PETSC_SUCCESS);
690: }
692: /*@
693: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
694: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
696: Not Collective
698: Input Parameter:
699: . mat - the matrix
701: Level: advanced
703: Note:
704: 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.
706: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
707: @*/
708: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
709: {
710: PetscFunctionBegin;
713: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
714: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
715: MatCheckPreallocated(mat, 1);
716: PetscTryTypeMethod(mat, getrowuppertriangular);
717: PetscFunctionReturn(PETSC_SUCCESS);
718: }
720: /*@
721: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
723: Not Collective
725: Input Parameter:
726: . mat - the matrix
728: Level: advanced
730: Note:
731: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
733: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
734: @*/
735: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
736: {
737: PetscFunctionBegin;
740: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
741: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
742: MatCheckPreallocated(mat, 1);
743: PetscTryTypeMethod(mat, restorerowuppertriangular);
744: PetscFunctionReturn(PETSC_SUCCESS);
745: }
747: /*@
748: MatSetOptionsPrefix - Sets the prefix used for searching for all
749: `Mat` options in the database.
751: Logically Collective
753: Input Parameters:
754: + A - the matrix
755: - prefix - the prefix to prepend to all option names
757: Level: advanced
759: Notes:
760: A hyphen (-) must NOT be given at the beginning of the prefix name.
761: The first character of all runtime options is AUTOMATICALLY the hyphen.
763: This is NOT used for options for the factorization of the matrix. Normally the
764: prefix is automatically passed in from the PC calling the factorization. To set
765: it directly use `MatSetOptionsPrefixFactor()`
767: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
768: @*/
769: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
770: {
771: PetscFunctionBegin;
773: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
774: PetscTryMethod(A, "MatSetOptionsPrefix_C", (Mat, const char[]), (A, prefix));
775: PetscFunctionReturn(PETSC_SUCCESS);
776: }
778: /*@
779: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
780: for matrices created with `MatGetFactor()`
782: Logically Collective
784: Input Parameters:
785: + A - the matrix
786: - prefix - the prefix to prepend to all option names for the factored matrix
788: Level: developer
790: Notes:
791: A hyphen (-) must NOT be given at the beginning of the prefix name.
792: The first character of all runtime options is AUTOMATICALLY the hyphen.
794: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
795: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
797: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
798: @*/
799: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
800: {
801: PetscFunctionBegin;
803: if (prefix) {
804: PetscAssertPointer(prefix, 2);
805: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
806: if (prefix != A->factorprefix) {
807: PetscCall(PetscFree(A->factorprefix));
808: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
809: }
810: } else PetscCall(PetscFree(A->factorprefix));
811: PetscFunctionReturn(PETSC_SUCCESS);
812: }
814: /*@
815: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
816: for matrices created with `MatGetFactor()`
818: Logically Collective
820: Input Parameters:
821: + A - the matrix
822: - prefix - the prefix to prepend to all option names for the factored matrix
824: Level: developer
826: Notes:
827: A hyphen (-) must NOT be given at the beginning of the prefix name.
828: The first character of all runtime options is AUTOMATICALLY the hyphen.
830: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
831: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
833: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
834: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
835: `MatSetOptionsPrefix()`
836: @*/
837: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
838: {
839: size_t len1, len2, new_len;
841: PetscFunctionBegin;
843: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
844: if (!A->factorprefix) {
845: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
846: PetscFunctionReturn(PETSC_SUCCESS);
847: }
848: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
850: PetscCall(PetscStrlen(A->factorprefix, &len1));
851: PetscCall(PetscStrlen(prefix, &len2));
852: new_len = len1 + len2 + 1;
853: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
854: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
855: PetscFunctionReturn(PETSC_SUCCESS);
856: }
858: /*@
859: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
860: matrix options in the database.
862: Logically Collective
864: Input Parameters:
865: + A - the matrix
866: - prefix - the prefix to prepend to all option names
868: Level: advanced
870: Note:
871: A hyphen (-) must NOT be given at the beginning of the prefix name.
872: The first character of all runtime options is AUTOMATICALLY the hyphen.
874: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
875: @*/
876: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
877: {
878: PetscFunctionBegin;
880: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
881: PetscTryMethod(A, "MatAppendOptionsPrefix_C", (Mat, const char[]), (A, prefix));
882: PetscFunctionReturn(PETSC_SUCCESS);
883: }
885: /*@
886: MatGetOptionsPrefix - Gets the prefix used for searching for all
887: matrix options in the database.
889: Not Collective
891: Input Parameter:
892: . A - the matrix
894: Output Parameter:
895: . prefix - pointer to the prefix string used
897: Level: advanced
899: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
900: @*/
901: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
902: {
903: PetscFunctionBegin;
905: PetscAssertPointer(prefix, 2);
906: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
907: PetscFunctionReturn(PETSC_SUCCESS);
908: }
910: /*@
911: MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`
913: Not Collective
915: Input Parameter:
916: . A - the matrix
918: Output Parameter:
919: . state - the object state
921: Level: advanced
923: Note:
924: Object state is an integer which gets increased every time
925: the object is changed. By saving and later querying the object state
926: one can determine whether information about the object is still current.
928: See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.
930: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
931: @*/
932: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
933: {
934: PetscFunctionBegin;
936: PetscAssertPointer(state, 2);
937: PetscCall(PetscObjectStateGet((PetscObject)A, state));
938: PetscFunctionReturn(PETSC_SUCCESS);
939: }
941: /*@
942: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
944: Collective
946: Input Parameter:
947: . A - the matrix
949: Level: beginner
951: Notes:
952: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
953: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
954: makes all of the preallocation space available
956: Current values in the matrix are lost in this call
958: Currently only supported for `MATAIJ` matrices.
960: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
961: @*/
962: PetscErrorCode MatResetPreallocation(Mat A)
963: {
964: PetscFunctionBegin;
967: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
968: PetscFunctionReturn(PETSC_SUCCESS);
969: }
971: /*@
972: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
974: Collective
976: Input Parameter:
977: . A - the matrix
979: Level: intermediate
981: Notes:
982: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
984: Currently only supported for `MATAIJ` matrices.
986: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
987: @*/
988: PetscErrorCode MatResetHash(Mat A)
989: {
990: PetscFunctionBegin;
993: 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()");
994: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
995: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
996: /* These flags are used to determine whether certain setups occur */
997: A->was_assembled = PETSC_FALSE;
998: A->assembled = PETSC_FALSE;
999: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
1000: PetscCall(PetscObjectStateIncrease((PetscObject)A));
1001: PetscFunctionReturn(PETSC_SUCCESS);
1002: }
1004: /*@
1005: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
1007: Collective
1009: Input Parameter:
1010: . A - the matrix
1012: Level: advanced
1014: Notes:
1015: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
1016: setting values in the matrix.
1018: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
1020: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1021: @*/
1022: PetscErrorCode MatSetUp(Mat A)
1023: {
1024: PetscFunctionBegin;
1026: if (!((PetscObject)A)->type_name) {
1027: PetscMPIInt size;
1029: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1030: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1031: }
1032: if (!A->preallocated) PetscTryTypeMethod(A, setup);
1033: PetscCall(PetscLayoutSetUp(A->rmap));
1034: PetscCall(PetscLayoutSetUp(A->cmap));
1035: A->preallocated = PETSC_TRUE;
1036: PetscFunctionReturn(PETSC_SUCCESS);
1037: }
1039: #if PetscDefined(HAVE_SAWS)
1040: #include <petscviewersaws.h>
1041: #endif
1043: /*
1044: If threadsafety is on extraneous matrices may be printed
1046: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1047: */
1048: #if !PetscDefined(HAVE_THREADSAFETY)
1049: static PetscInt insidematview = 0;
1050: #endif
1052: /*@
1053: MatViewFromOptions - View properties of the matrix based on options set in the options database
1055: Collective
1057: Input Parameters:
1058: + A - the matrix
1059: . obj - optional additional object that provides the options prefix to use
1060: - name - command line option
1062: Options Database Key:
1063: . -name [viewertype][:...] - option name and values. See `PetscObjectViewFromOptions()` for the possible arguments
1065: Level: intermediate
1067: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1068: @*/
1069: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1070: {
1071: PetscFunctionBegin;
1073: #if !PetscDefined(HAVE_THREADSAFETY)
1074: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1075: #endif
1076: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1077: PetscFunctionReturn(PETSC_SUCCESS);
1078: }
1080: /*@
1081: MatView - display information about a matrix in a variety ways
1083: Collective on viewer
1085: Input Parameters:
1086: + mat - the matrix
1087: - viewer - visualization context
1089: Options Database Keys:
1090: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1091: . -mat_view ::ascii_info_detail - Prints more detailed info
1092: . -mat_view - Prints matrix in ASCII format
1093: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1094: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1095: . -display name - Sets display name (default is host)
1096: . -draw_pause sec - Sets number of seconds to pause after display
1097: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1098: . -viewer_socket_machine machine - -
1099: . -viewer_socket_port port - -
1100: . -mat_view binary - save matrix to file in binary format
1101: - -viewer_binary_filename name - -
1103: Level: beginner
1105: Notes:
1106: The available visualization contexts include
1107: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1108: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1109: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1110: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1112: The user can open alternative visualization contexts with
1113: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1114: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1115: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1116: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1118: The user can call `PetscViewerPushFormat()` to specify the output
1119: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1120: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1121: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1122: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1123: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1124: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1125: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1126: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1127: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1129: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1130: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1132: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1134: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1135: viewer is used.
1137: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1138: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1140: One can use `-mat_view draw -draw_pause -1` to pause the graphical display of matrix nonzero structure,
1141: and then use the following mouse functions.
1142: .vb
1143: left mouse: zoom in
1144: middle mouse: zoom out
1145: right mouse: continue with the simulation
1146: .ve
1148: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1149: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1150: @*/
1151: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1152: {
1153: PetscInt rows, cols, rbs, cbs;
1154: PetscBool isascii, isstring, issaws;
1155: PetscViewerFormat format;
1156: PetscMPIInt size;
1158: PetscFunctionBegin;
1161: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1164: PetscCall(PetscViewerGetFormat(viewer, &format));
1165: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1166: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1168: #if !PetscDefined(HAVE_THREADSAFETY)
1169: insidematview++;
1170: #endif
1171: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1172: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1173: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1174: 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");
1176: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1177: if (isascii) {
1178: if (!mat->preallocated) {
1179: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1180: #if !PetscDefined(HAVE_THREADSAFETY)
1181: insidematview--;
1182: #endif
1183: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1184: PetscFunctionReturn(PETSC_SUCCESS);
1185: }
1186: if (!mat->assembled) {
1187: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1188: #if !PetscDefined(HAVE_THREADSAFETY)
1189: insidematview--;
1190: #endif
1191: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1192: PetscFunctionReturn(PETSC_SUCCESS);
1193: }
1194: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1195: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1196: MatNullSpace nullsp, transnullsp;
1197: PetscBool nz_factor = PETSC_TRUE;
1199: PetscCall(PetscViewerASCIIPushTab(viewer));
1200: PetscCall(MatGetSize(mat, &rows, &cols));
1201: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1202: if (rbs != 1 || cbs != 1) {
1203: 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" : ""));
1204: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1205: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1206: if (mat->factortype) {
1207: MatSolverType solver;
1209: PetscCall(MatFactorGetSolverType(mat, &solver));
1210: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1211: PetscCall(PetscStrcmpAny(solver, &nz_factor, MATSOLVERUMFPACK, MATSOLVERCHOLMOD, MATSOLVERSUPERLU, MATSOLVERSUPERLU_DIST, MATSOLVERSTRUMPACK, MATSOLVERHTOOL, ""));
1212: nz_factor = !nz_factor;
1213: }
1214: if (mat->ops->getinfo) {
1215: PetscBool is_constant_or_diagonal;
1217: // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1218: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1219: if (!is_constant_or_diagonal && nz_factor) {
1220: MatInfo info;
1222: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1223: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1224: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1225: }
1226: }
1227: PetscCall(MatGetNullSpace(mat, &nullsp));
1228: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1229: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1230: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1231: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1232: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1233: PetscCall(PetscViewerASCIIPushTab(viewer));
1234: PetscCall(MatProductView(mat, viewer));
1235: PetscCall(PetscViewerASCIIPopTab(viewer));
1236: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1237: IS tmp;
1239: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1240: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1241: PetscCall(PetscViewerASCIIPushTab(viewer));
1242: PetscCall(ISView(tmp, viewer));
1243: PetscCall(PetscViewerASCIIPopTab(viewer));
1244: PetscCall(ISDestroy(&tmp));
1245: }
1246: }
1247: } else if (issaws) {
1248: #if PetscDefined(HAVE_SAWS)
1249: PetscMPIInt rank;
1251: PetscCall(PetscObjectName((PetscObject)mat));
1252: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1253: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1254: #endif
1255: } else if (isstring) {
1256: const char *type;
1257: PetscCall(MatGetType(mat, &type));
1258: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1259: PetscTryTypeMethod(mat, view, viewer);
1260: }
1261: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1262: PetscCall(PetscViewerASCIIPushTab(viewer));
1263: PetscUseTypeMethod(mat, viewnative, viewer);
1264: PetscCall(PetscViewerASCIIPopTab(viewer));
1265: } else if (mat->ops->view) {
1266: PetscCall(PetscViewerASCIIPushTab(viewer));
1267: PetscUseTypeMethod(mat, view, viewer);
1268: PetscCall(PetscViewerASCIIPopTab(viewer));
1269: }
1270: if (isascii) {
1271: PetscCall(PetscViewerGetFormat(viewer, &format));
1272: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1273: }
1274: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1275: #if !PetscDefined(HAVE_THREADSAFETY)
1276: insidematview--;
1277: #endif
1278: PetscFunctionReturn(PETSC_SUCCESS);
1279: }
1281: #if PetscDefined(USE_DEBUG)
1282: #include <../src/sys/totalview/tv_data_display.h>
1283: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1284: {
1285: TV_add_row("Local rows", "int", &mat->rmap->n);
1286: TV_add_row("Local columns", "int", &mat->cmap->n);
1287: TV_add_row("Global rows", "int", &mat->rmap->N);
1288: TV_add_row("Global columns", "int", &mat->cmap->N);
1289: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1290: return TV_format_OK;
1291: }
1292: #endif
1294: /*@
1295: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1296: with `MatView()`. The matrix format is determined from the options database.
1297: Generates a parallel MPI matrix if the communicator has more than one
1298: processor. The default matrix type is `MATAIJ`.
1300: Collective
1302: Input Parameters:
1303: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1304: or some related function before a call to `MatLoad()`
1305: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1307: Options Database Key:
1308: . -matload_block_size bs - set block size
1310: Level: beginner
1312: Notes:
1313: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1314: `Mat` before calling this routine if you wish to set it from the options database.
1316: `MatLoad()` automatically loads into the options database any options
1317: given in the file filename.info where filename is the name of the file
1318: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1319: file will be ignored if you use the `-viewer_binary_skip_info` option.
1321: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1322: sets the default matrix type AIJ and sets the local and global sizes.
1323: If type and/or size is already set, then the same are used.
1325: In parallel, each processor can load a subset of rows (or the
1326: entire matrix). This routine is especially useful when a large
1327: matrix is stored on disk and only part of it is desired on each
1328: processor. For example, a parallel solver may access only some of
1329: the rows from each processor. The algorithm used here reads
1330: relatively small blocks of data rather than reading the entire
1331: matrix and then subsetting it.
1333: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1334: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1335: or the sequence like
1336: .vb
1337: PetscViewer v;
1338: PetscViewerCreate(PETSC_COMM_WORLD, &v);
1339: PetscViewerSetType(v, PETSCVIEWERBINARY);
1340: PetscViewerSetFromOptions(v);
1341: PetscViewerFileSetMode(v, FILE_MODE_READ);
1342: PetscViewerFileSetName(v, "datafile");
1343: .ve
1344: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1345: .vb
1346: -viewer_type (binary|hdf5)
1347: .ve
1349: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1350: and src/mat/tutorials/ex10.c with the second approach.
1352: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1353: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1354: Multiple objects, both matrices and vectors, can be stored within the same file.
1355: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1357: Most users should not need to know the details of the binary storage
1358: format, since `MatLoad()` and `MatView()` completely hide these details.
1359: But for anyone who is interested, the standard binary matrix storage
1360: format is
1362: .vb
1363: PetscInt MAT_FILE_CLASSID
1364: PetscInt number of rows
1365: PetscInt number of columns
1366: PetscInt total number of nonzeros
1367: PetscInt *number nonzeros in each row
1368: PetscInt *column indices of all nonzeros (starting index is zero)
1369: PetscScalar *values of all nonzeros
1370: .ve
1371: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1372: 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
1373: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1375: PETSc automatically does the byte swapping for
1376: machines that store the bytes reversed. Thus if you write your own binary
1377: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1378: and `PetscBinaryWrite()` to see how this may be done.
1380: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1381: Each processor's chunk is loaded independently by its owning MPI process.
1382: Multiple objects, both matrices and vectors, can be stored within the same file.
1383: They are looked up by their PetscObject name.
1385: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1386: by default the same structure and naming of the AIJ arrays and column count
1387: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1388: .vb
1389: save example.mat A b -v7.3
1390: .ve
1391: can be directly read by this routine (see Reference 1 for details).
1393: Depending on your MATLAB version, this format might be a default,
1394: otherwise you can set it as default in Preferences.
1396: Unless `-nocompression` flag is used to save the file in MATLAB,
1397: PETSc must be configured with ZLIB package.
1399: See also examples `src/mat/tutorials/ex10.c` and `src/ksp/ksp/tutorials/ex27.c`
1401: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE`, and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1403: Corresponding `MatView()` is not yet implemented.
1405: The loaded matrix is actually a transpose of the original one in MATLAB,
1406: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1407: With this format, matrix is automatically transposed by PETSc,
1408: unless the matrix is marked as SPD or symmetric
1409: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1411: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1413: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1414: @*/
1415: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1416: {
1417: PetscBool flg;
1419: PetscFunctionBegin;
1423: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1425: flg = PETSC_FALSE;
1426: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1427: if (flg) {
1428: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1429: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1430: }
1431: flg = PETSC_FALSE;
1432: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1433: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1435: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1436: PetscUseTypeMethod(mat, load, viewer);
1437: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1438: PetscFunctionReturn(PETSC_SUCCESS);
1439: }
1441: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1442: {
1443: Mat_Redundant *redund = *redundant;
1445: PetscFunctionBegin;
1446: if (redund) {
1447: if (redund->matseq) { /* via MatCreateSubMatrices() */
1448: PetscCall(ISDestroy(&redund->isrow));
1449: PetscCall(ISDestroy(&redund->iscol));
1450: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1451: } else {
1452: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1453: PetscCall(PetscFree(redund->sbuf_j));
1454: PetscCall(PetscFree(redund->sbuf_a));
1455: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1456: PetscCall(PetscFree(redund->rbuf_j[i]));
1457: PetscCall(PetscFree(redund->rbuf_a[i]));
1458: }
1459: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1460: }
1462: PetscCall(PetscCommDestroy(&redund->subcomm));
1463: PetscCall(PetscFree(redund));
1464: }
1465: PetscFunctionReturn(PETSC_SUCCESS);
1466: }
1468: /*@
1469: MatDestroy - Frees space taken by a matrix.
1471: Collective
1473: Input Parameter:
1474: . A - the matrix
1476: Level: beginner
1478: Developer Note:
1479: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1480: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1481: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1482: if changes are needed here.
1484: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1485: @*/
1486: PetscErrorCode MatDestroy(Mat *A)
1487: {
1488: PetscFunctionBegin;
1489: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1491: if (--((PetscObject)*A)->refct > 0) {
1492: *A = NULL;
1493: PetscFunctionReturn(PETSC_SUCCESS);
1494: }
1496: /* if memory was published with SAWs then destroy it */
1497: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1498: PetscTryTypeMethod(*A, destroy);
1500: PetscCall(PetscFree((*A)->factorprefix));
1501: PetscCall(PetscFree((*A)->defaultvectype));
1502: PetscCall(PetscFree((*A)->defaultrandtype));
1503: PetscCall(PetscFree((*A)->bsizes));
1504: PetscCall(PetscFree((*A)->solvertype));
1505: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1506: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1507: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1508: PetscCall(MatProductClear(*A));
1509: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1510: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1511: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1512: PetscCall(MatDestroy(&(*A)->schur));
1513: PetscCall(VecDestroy(&(*A)->dot_vec));
1514: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1515: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1516: PetscCall(PetscHeaderDestroy(A));
1517: PetscFunctionReturn(PETSC_SUCCESS);
1518: }
1520: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1521: /*@
1522: MatSetValues - Inserts or adds a block of values into a matrix.
1523: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1524: MUST be called after all calls to `MatSetValues()` have been completed.
1526: Not Collective
1528: Input Parameters:
1529: + mat - the matrix
1530: . m - the number of rows
1531: . idxm - the global indices of the rows
1532: . n - the number of columns
1533: . idxn - the global indices of the columns
1534: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1535: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1536: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1538: Level: beginner
1540: Notes:
1541: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1542: options cannot be mixed without intervening calls to the assembly
1543: routines.
1545: `MatSetValues()` uses 0-based row and column numbers in Fortran
1546: as well as in C.
1548: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are simply ignored. This allows easily inserting element stiffness matrices
1549: with homogeneous Dirichlet boundary conditions that you don't want represented
1550: in the matrix.
1552: Efficiency Alert:
1553: The routine `MatSetValuesBlocked()` may offer much better efficiency
1554: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1556: Fortran Notes:
1557: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1558: .vb
1559: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1560: .ve
1562: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1563: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1565: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1566: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1567: @*/
1568: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1569: {
1570: PetscFunctionBeginHot;
1573: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1574: PetscAssertPointer(idxm, 3);
1575: PetscAssertPointer(idxn, 5);
1576: MatCheckPreallocated(mat, 1);
1578: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1579: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1581: if (PetscDefined(USE_DEBUG)) {
1582: PetscInt i, j;
1584: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1585: if (v) {
1586: for (i = 0; i < m; i++) {
1587: for (j = 0; j < n; j++) {
1588: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1589: #if PetscDefined(USE_COMPLEX)
1590: 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]);
1591: #else
1592: 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]);
1593: #endif
1594: }
1595: }
1596: }
1597: 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);
1598: 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);
1599: }
1601: if (mat->assembled) {
1602: mat->was_assembled = PETSC_TRUE;
1603: mat->assembled = PETSC_FALSE;
1604: }
1605: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1606: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1607: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1608: PetscFunctionReturn(PETSC_SUCCESS);
1609: }
1611: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1612: /*@
1613: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1614: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1615: MUST be called after all calls to `MatSetValues()` have been completed.
1617: Not Collective
1619: Input Parameters:
1620: + mat - the matrix
1621: . ism - the rows to provide
1622: . isn - the columns to provide
1623: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1624: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1625: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1627: Level: beginner
1629: Notes:
1630: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1632: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1633: options cannot be mixed without intervening calls to the assembly
1634: routines.
1636: `MatSetValues()` uses 0-based row and column numbers in Fortran
1637: as well as in C.
1639: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1640: simply ignored. This allows easily inserting element stiffness matrices
1641: with homogeneous Dirichlet boundary conditions that you don't want represented
1642: in the matrix.
1644: Fortran Note:
1645: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1646: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1648: Efficiency Alert:
1649: The routine `MatSetValuesBlocked()` may offer much better efficiency
1650: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1652: This is currently not optimized for any particular `ISType`
1654: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1655: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1656: @*/
1657: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1658: {
1659: PetscInt m, n;
1660: const PetscInt *rows, *cols;
1662: PetscFunctionBeginHot;
1664: PetscCall(ISGetIndices(ism, &rows));
1665: PetscCall(ISGetIndices(isn, &cols));
1666: PetscCall(ISGetLocalSize(ism, &m));
1667: PetscCall(ISGetLocalSize(isn, &n));
1668: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1669: PetscCall(ISRestoreIndices(ism, &rows));
1670: PetscCall(ISRestoreIndices(isn, &cols));
1671: PetscFunctionReturn(PETSC_SUCCESS);
1672: }
1674: /*@
1675: MatSetValuesRowLocal - Inserts a row of nonzero values into a matrix
1677: Not Collective
1679: Input Parameters:
1680: + mat - the matrix
1681: . row - the row to set
1682: - v - a one-dimensional array that contains the values
1684: Level: intermediate
1686: Notes:
1687: Currently only supported for `MATAIJ`.
1689: All the nonzero values in `row` must be provided
1691: The matrix must have previously had its column indices set, likely by having been assembled.
1693: `row` must belong to this MPI process
1695: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1696: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`, `MATAIJ`
1697: @*/
1698: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1699: {
1700: PetscInt globalrow;
1702: PetscFunctionBegin;
1705: PetscAssertPointer(v, 3);
1706: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1707: PetscCall(MatSetValuesRow(mat, globalrow, v));
1708: PetscFunctionReturn(PETSC_SUCCESS);
1709: }
1711: /*@
1712: MatSetValuesRow - Inserts a row of nonzero values into a matrix
1714: Not Collective
1716: Input Parameters:
1717: + mat - the matrix
1718: . row - the row to set
1719: - v - a one dimensional array of values
1721: Level: advanced
1723: Notes:
1724: Currently only supported for `MATAIJ`.
1726: All the nonzeros in `row` must be provided
1728: The matrix must have previously had its column indices set, likely by having been assembled.
1730: `row` must belong to this process
1732: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1733: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MATAIJ`
1734: @*/
1735: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1736: {
1737: PetscFunctionBeginHot;
1740: MatCheckPreallocated(mat, 1);
1741: PetscAssertPointer(v, 3);
1742: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1743: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1744: mat->insertmode = INSERT_VALUES;
1746: if (mat->assembled) {
1747: mat->was_assembled = PETSC_TRUE;
1748: mat->assembled = PETSC_FALSE;
1749: }
1750: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1751: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1752: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1753: PetscFunctionReturn(PETSC_SUCCESS);
1754: }
1756: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1757: /*@
1758: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1759: Using structured grid indexing
1761: Not Collective
1763: Input Parameters:
1764: + mat - the matrix
1765: . m - number of rows being entered
1766: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1767: . n - number of columns being entered
1768: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1769: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1770: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1771: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1773: Level: beginner
1775: Notes:
1776: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1778: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1779: options cannot be mixed without intervening calls to the assembly
1780: routines.
1782: The grid coordinates are across the entire grid, not just the local portion
1784: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1785: as well as in C.
1787: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1789: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1790: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1792: The columns and rows in the stencil passed in MUST be contained within the
1793: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1794: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1795: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1796: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1798: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1799: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1800: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1801: `DM_BOUNDARY_PERIODIC` boundary type.
1803: For indices that don't mean anything for your case (like the k index when working in 2d) or the c index when you have
1804: a single value per point) you can skip filling those indices.
1806: Inspired by the structured grid interface to the HYPRE package
1807: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1809: Fortran Notes:
1810: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1811: .vb
1812: call MatSetValuesStencil(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1813: .ve
1815: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1816: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1818: Efficiency Alert:
1819: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1820: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1822: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1823: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1824: @*/
1825: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1826: {
1827: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1828: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1829: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1831: PetscFunctionBegin;
1832: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1835: PetscAssertPointer(idxm, 3);
1836: PetscAssertPointer(idxn, 5);
1838: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1839: jdxm = buf;
1840: jdxn = buf + m;
1841: } else {
1842: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1843: jdxm = bufm;
1844: jdxn = bufn;
1845: }
1846: for (i = 0; i < m; i++) {
1847: for (j = 0; j < 3 - sdim; j++) dxm++;
1848: tmp = *dxm++ - starts[0];
1849: for (j = 0; j < dim - 1; j++) {
1850: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1851: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1852: }
1853: if (mat->stencil.noc) dxm++;
1854: jdxm[i] = tmp;
1855: }
1856: for (i = 0; i < n; i++) {
1857: for (j = 0; j < 3 - sdim; j++) dxn++;
1858: tmp = *dxn++ - starts[0];
1859: for (j = 0; j < dim - 1; j++) {
1860: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1861: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1862: }
1863: if (mat->stencil.noc) dxn++;
1864: jdxn[i] = tmp;
1865: }
1866: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1867: PetscCall(PetscFree2(bufm, bufn));
1868: PetscFunctionReturn(PETSC_SUCCESS);
1869: }
1871: /*@
1872: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1873: Using structured grid indexing
1875: Not Collective
1877: Input Parameters:
1878: + mat - the matrix
1879: . m - number of rows being entered
1880: . idxm - grid coordinates for matrix rows being entered
1881: . n - number of columns being entered
1882: . idxn - grid coordinates for matrix columns being entered
1883: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1884: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1885: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1887: Level: beginner
1889: Notes:
1890: By default the values, `v`, are row-oriented and unsorted.
1891: See `MatSetOption()` for other options.
1893: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1894: options cannot be mixed without intervening calls to the assembly
1895: routines.
1897: The grid coordinates are across the entire grid, not just the local portion
1899: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1900: as well as in C.
1902: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1904: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1905: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1907: The columns and rows in the stencil passed in MUST be contained within the
1908: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1909: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1910: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1911: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1913: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1914: simply ignored. This allows easily inserting element stiffness matrices
1915: with homogeneous Dirichlet boundary conditions that you don't want represented
1916: in the matrix.
1918: Inspired by the structured grid interface to the HYPRE package
1919: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1921: Fortran Notes:
1922: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1923: .vb
1924: call MatSetValuesBlockedStencil(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1925: .ve
1927: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1928: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1930: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1931: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1932: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1933: @*/
1934: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1935: {
1936: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1937: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1938: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1940: PetscFunctionBegin;
1941: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1944: PetscAssertPointer(idxm, 3);
1945: PetscAssertPointer(idxn, 5);
1946: PetscAssertPointer(v, 6);
1948: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1949: jdxm = buf;
1950: jdxn = buf + m;
1951: } else {
1952: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1953: jdxm = bufm;
1954: jdxn = bufn;
1955: }
1956: for (i = 0; i < m; i++) {
1957: for (j = 0; j < 3 - sdim; j++) dxm++;
1958: tmp = *dxm++ - starts[0];
1959: for (j = 0; j < sdim - 1; j++) {
1960: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1961: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1962: }
1963: dxm++;
1964: jdxm[i] = tmp;
1965: }
1966: for (i = 0; i < n; i++) {
1967: for (j = 0; j < 3 - sdim; j++) dxn++;
1968: tmp = *dxn++ - starts[0];
1969: for (j = 0; j < sdim - 1; j++) {
1970: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1971: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1972: }
1973: dxn++;
1974: jdxn[i] = tmp;
1975: }
1976: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1977: PetscCall(PetscFree2(bufm, bufn));
1978: PetscFunctionReturn(PETSC_SUCCESS);
1979: }
1981: /*@
1982: MatSetStencil - Sets the grid information for setting values into a matrix via
1983: `MatSetValuesStencil()`
1985: Not Collective
1987: Input Parameters:
1988: + mat - the matrix
1989: . dim - dimension of the grid 1, 2, or 3
1990: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1991: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1992: - dof - number of degrees of freedom per node
1994: Level: beginner
1996: Notes:
1997: Inspired by the structured grid interface to the HYPRE package
1998: (www.llnl.gov/CASC/hyper)
2000: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
2001: user.
2003: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2004: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
2005: @*/
2006: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
2007: {
2008: PetscFunctionBegin;
2010: PetscAssertPointer(dims, 3);
2011: PetscAssertPointer(starts, 4);
2013: mat->stencil.dim = dim + (dof > 1);
2014: for (PetscInt i = 0; i < dim; i++) {
2015: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
2016: mat->stencil.starts[i] = starts[dim - i - 1];
2017: }
2018: mat->stencil.dims[dim] = dof;
2019: mat->stencil.starts[dim] = 0;
2020: mat->stencil.noc = (PetscBool)(dof == 1);
2021: PetscFunctionReturn(PETSC_SUCCESS);
2022: }
2024: /*@
2025: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
2027: Not Collective
2029: Input Parameters:
2030: + mat - the matrix
2031: . m - the number of block rows
2032: . idxm - the global block indices
2033: . n - the number of block columns
2034: . idxn - the global block indices
2035: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2036: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2037: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
2039: Level: intermediate
2041: Notes:
2042: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2043: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2045: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2046: NOT the total number of rows/columns; for example, if the block size is 2 and
2047: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2048: The values in `idxm` would be 1 2; that is the first index for each block divided by
2049: the block size.
2051: You must call `MatSetBlockSize()` when constructing this matrix (before
2052: preallocating it).
2054: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2056: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2057: options cannot be mixed without intervening calls to the assembly
2058: routines.
2060: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2061: as well as in C.
2063: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2064: simply ignored. This allows easily inserting element stiffness matrices
2065: with homogeneous Dirichlet boundary conditions that you don't want represented
2066: in the matrix.
2068: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2069: internal searching must be done to determine where to place the
2070: data in the matrix storage space. By instead inserting blocks of
2071: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2072: reduced.
2074: Example:
2075: .vb
2076: Suppose m=n=2 and block size(bs) = 2 The array is
2078: 1 2 | 3 4
2079: 5 6 | 7 8
2080: - - - | - - -
2081: 9 10 | 11 12
2082: 13 14 | 15 16
2084: v[] should be passed in like
2085: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2087: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2088: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2089: .ve
2091: Fortran Notes:
2092: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2093: .vb
2094: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2095: .ve
2097: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2098: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2100: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2101: @*/
2102: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2103: {
2104: PetscFunctionBeginHot;
2107: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2108: PetscAssertPointer(idxm, 3);
2109: PetscAssertPointer(idxn, 5);
2110: MatCheckPreallocated(mat, 1);
2111: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2112: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2113: if (PetscDefined(USE_DEBUG)) {
2114: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2115: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2116: }
2117: if (PetscDefined(USE_DEBUG)) {
2118: PetscInt rbs, cbs, M, N, i;
2119: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2120: PetscCall(MatGetSize(mat, &M, &N));
2121: 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);
2122: for (i = 0; i < n; i++)
2123: 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);
2124: }
2125: if (mat->assembled) {
2126: mat->was_assembled = PETSC_TRUE;
2127: mat->assembled = PETSC_FALSE;
2128: }
2129: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2130: if (mat->ops->setvaluesblocked) PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2131: else {
2132: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2133: PetscInt i, j, bs, cbs;
2135: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2136: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2137: iidxm = buf;
2138: iidxn = buf + m * bs;
2139: } else {
2140: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2141: iidxm = bufr;
2142: iidxn = bufc;
2143: }
2144: for (i = 0; i < m; i++) {
2145: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2146: }
2147: if (m != n || bs != cbs || idxm != idxn) {
2148: for (i = 0; i < n; i++) {
2149: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2150: }
2151: } else iidxn = iidxm;
2152: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2153: PetscCall(PetscFree2(bufr, bufc));
2154: }
2155: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2156: PetscFunctionReturn(PETSC_SUCCESS);
2157: }
2159: /*@
2160: MatGetValues - Gets a block of local values from a matrix.
2162: Not Collective; can only return values that are owned by the give process
2164: Input Parameters:
2165: + mat - the matrix
2166: . v - a logically two-dimensional array for storing the values
2167: . m - the number of rows
2168: . idxm - the global indices of the rows
2169: . n - the number of columns
2170: - idxn - the global indices of the columns
2172: Level: advanced
2174: Notes:
2175: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2177: The values, `v`, are returned in a row-oriented format, analogous to that used by default in `MatSetValues()`,
2178: unless `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE)` is called in which case they are returned column oriented.
2180: `MatGetValues()` uses 0-based row and column numbers in
2181: Fortran as well as in C.
2183: For `MATSBAIJ` matrices only the block upper triangular entries will be set.
2185: `MatGetValues()` requires that the matrix has been assembled
2186: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2187: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2188: without intermediate matrix assembly.
2190: Negative row or column indices will be ignored and those locations in `v` will be
2191: left unchanged.
2193: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2194: That is, rows with global index greater than or equal to `rstart` and less than `rend` where `rstart` and `rend` are obtainable
2195: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2197: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2198: @*/
2199: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2200: {
2201: PetscFunctionBegin;
2204: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2205: PetscAssertPointer(idxm, 3);
2206: PetscAssertPointer(idxn, 5);
2207: PetscAssertPointer(v, 6);
2208: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2209: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2210: MatCheckPreallocated(mat, 1);
2212: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2213: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2214: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2215: PetscFunctionReturn(PETSC_SUCCESS);
2216: }
2218: /*@
2219: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2220: defined previously by `MatSetLocalToGlobalMapping()`
2222: Not Collective
2224: Input Parameters:
2225: + mat - the matrix
2226: . nrow - number of rows
2227: . irow - the row local indices
2228: . ncol - number of columns
2229: - icol - the column local indices
2231: Output Parameter:
2232: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2233: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2235: Level: advanced
2237: Notes:
2238: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2240: 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,
2241: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2242: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2243: with `MatSetLocalToGlobalMapping()`.
2245: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2246: `MatSetValuesLocal()`, `MatGetValues()`
2247: @*/
2248: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2249: {
2250: PetscFunctionBeginHot;
2253: MatCheckPreallocated(mat, 1);
2254: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2255: PetscAssertPointer(irow, 3);
2256: PetscAssertPointer(icol, 5);
2257: if (PetscDefined(USE_DEBUG)) {
2258: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2259: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2260: }
2261: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2262: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2263: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2264: else {
2265: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2266: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2267: irowm = buf;
2268: icolm = buf + nrow;
2269: } else {
2270: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2271: irowm = bufr;
2272: icolm = bufc;
2273: }
2274: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2275: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2276: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2277: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2278: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2279: PetscCall(PetscFree2(bufr, bufc));
2280: }
2281: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2282: PetscFunctionReturn(PETSC_SUCCESS);
2283: }
2285: /*@
2286: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2287: the same size. Currently, this can only be called once and creates the given matrix.
2289: Not Collective
2291: Input Parameters:
2292: + mat - the matrix
2293: . nb - the number of blocks
2294: . bs - the number of rows (and columns) in each block
2295: . rows - a concatenation of the rows for each block
2296: - v - a concatenation of logically two-dimensional arrays of values
2298: Level: advanced
2300: Notes:
2301: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2303: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2305: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2306: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2307: @*/
2308: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2309: {
2310: PetscFunctionBegin;
2313: PetscAssertPointer(rows, 4);
2314: PetscAssertPointer(v, 5);
2315: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2317: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2318: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2319: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2320: PetscFunctionReturn(PETSC_SUCCESS);
2321: }
2323: /*@
2324: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2325: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2326: using a local (per-processor) numbering.
2328: Not Collective
2330: Input Parameters:
2331: + x - the matrix
2332: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2333: - cmapping - column mapping
2335: Level: intermediate
2337: Note:
2338: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2340: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2341: @*/
2342: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2343: {
2344: PetscFunctionBegin;
2349: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2350: else {
2351: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2352: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2353: }
2354: PetscFunctionReturn(PETSC_SUCCESS);
2355: }
2357: /*@
2358: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2360: Not Collective
2362: Input Parameter:
2363: . A - the matrix
2365: Output Parameters:
2366: + rmapping - row mapping
2367: - cmapping - column mapping
2369: Level: advanced
2371: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2372: @*/
2373: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2374: {
2375: PetscFunctionBegin;
2378: if (rmapping) {
2379: PetscAssertPointer(rmapping, 2);
2380: *rmapping = A->rmap->mapping;
2381: }
2382: if (cmapping) {
2383: PetscAssertPointer(cmapping, 3);
2384: *cmapping = A->cmap->mapping;
2385: }
2386: PetscFunctionReturn(PETSC_SUCCESS);
2387: }
2389: /*@
2390: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2392: Logically Collective
2394: Input Parameters:
2395: + A - the matrix
2396: . rmap - row layout
2397: - cmap - column layout
2399: Level: advanced
2401: Note:
2402: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2404: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2405: @*/
2406: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2407: {
2408: PetscFunctionBegin;
2410: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2411: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2412: PetscFunctionReturn(PETSC_SUCCESS);
2413: }
2415: /*@
2416: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2418: Not Collective
2420: Input Parameter:
2421: . A - the matrix
2423: Output Parameters:
2424: + rmap - row layout
2425: - cmap - column layout
2427: Level: advanced
2429: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2430: @*/
2431: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2432: {
2433: PetscFunctionBegin;
2436: if (rmap) {
2437: PetscAssertPointer(rmap, 2);
2438: *rmap = A->rmap;
2439: }
2440: if (cmap) {
2441: PetscAssertPointer(cmap, 3);
2442: *cmap = A->cmap;
2443: }
2444: PetscFunctionReturn(PETSC_SUCCESS);
2445: }
2447: /*@
2448: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2449: using a local numbering of the rows and columns.
2451: Not Collective
2453: Input Parameters:
2454: + mat - the matrix
2455: . nrow - number of rows
2456: . irow - the row local indices
2457: . ncol - number of columns
2458: . icol - the column local indices
2459: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2460: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2461: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2463: Level: intermediate
2465: Notes:
2466: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2468: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2469: options cannot be mixed without intervening calls to the assembly
2470: routines.
2472: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2473: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2475: Fortran Notes:
2476: If any of `irow`, `icol`, and `v` are scalars pass them using, for example,
2477: .vb
2478: call MatSetValuesLocal(mat, one, [irow], one, [icol], [v], INSERT_VALUES, ierr)
2479: .ve
2481: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2482: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2484: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2485: `MatGetValuesLocal()`
2486: @*/
2487: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar v[], InsertMode addv)
2488: {
2489: PetscFunctionBeginHot;
2492: MatCheckPreallocated(mat, 1);
2493: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2494: PetscAssertPointer(irow, 3);
2495: PetscAssertPointer(icol, 5);
2496: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2497: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2498: if (PetscDefined(USE_DEBUG)) {
2499: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2500: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2501: }
2503: if (mat->assembled) {
2504: mat->was_assembled = PETSC_TRUE;
2505: mat->assembled = PETSC_FALSE;
2506: }
2507: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2508: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, v, addv);
2509: else {
2510: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2511: const PetscInt *irowm, *icolm;
2513: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2514: bufr = buf;
2515: bufc = buf + nrow;
2516: irowm = bufr;
2517: icolm = bufc;
2518: } else {
2519: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2520: irowm = bufr;
2521: icolm = bufc;
2522: }
2523: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2524: else irowm = irow;
2525: if (mat->cmap->mapping) {
2526: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2527: else icolm = irowm;
2528: } else icolm = icol;
2529: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, v, addv));
2530: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2531: }
2532: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2533: PetscFunctionReturn(PETSC_SUCCESS);
2534: }
2536: /*@
2537: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2538: using a local ordering of the nodes a block at a time.
2540: Not Collective
2542: Input Parameters:
2543: + mat - the matrix
2544: . nrow - number of rows
2545: . irow - the row local indices
2546: . ncol - number of columns
2547: . icol - the column local indices
2548: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2549: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2550: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2552: Level: intermediate
2554: Notes:
2555: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2556: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2558: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2559: options cannot be mixed without intervening calls to the assembly
2560: routines.
2562: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2563: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2565: Fortran Notes:
2566: If any of `irow`, `icol`, and `v` are scalars pass them using, for example,
2567: .vb
2568: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [v], INSERT_VALUES, ierr)
2569: .ve
2571: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2572: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2574: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2575: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2576: @*/
2577: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar v[], InsertMode addv)
2578: {
2579: PetscFunctionBeginHot;
2582: MatCheckPreallocated(mat, 1);
2583: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2584: PetscAssertPointer(irow, 3);
2585: PetscAssertPointer(icol, 5);
2586: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2587: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2588: if (PetscDefined(USE_DEBUG)) {
2589: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2590: 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);
2591: }
2593: if (mat->assembled) {
2594: mat->was_assembled = PETSC_TRUE;
2595: mat->assembled = PETSC_FALSE;
2596: }
2597: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2598: PetscInt irbs, rbs;
2599: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2600: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2601: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2602: }
2603: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2604: PetscInt icbs, cbs;
2605: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2606: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2607: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2608: }
2609: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2610: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, v, addv);
2611: else {
2612: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2613: const PetscInt *irowm, *icolm;
2615: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2616: bufr = buf;
2617: bufc = buf + nrow;
2618: irowm = bufr;
2619: icolm = bufc;
2620: } else {
2621: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2622: irowm = bufr;
2623: icolm = bufc;
2624: }
2625: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2626: else irowm = irow;
2627: if (mat->cmap->mapping) {
2628: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2629: else icolm = irowm;
2630: } else icolm = icol;
2631: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, v, addv));
2632: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2633: }
2634: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2635: PetscFunctionReturn(PETSC_SUCCESS);
2636: }
2638: /*@
2639: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2641: Collective
2643: Input Parameters:
2644: + mat - the matrix
2645: - x - the vector to be multiplied
2647: Output Parameter:
2648: . y - the result
2650: Level: developer
2652: Note:
2653: The vectors `x` and `y` cannot be the same. I.e., one cannot
2654: call `MatMultDiagonalBlock`(A,y,y).
2656: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2657: @*/
2658: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2659: {
2660: PetscFunctionBegin;
2666: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2667: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2668: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2669: MatCheckPreallocated(mat, 1);
2671: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2672: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2673: PetscFunctionReturn(PETSC_SUCCESS);
2674: }
2676: /*@
2677: MatMult - Computes the matrix-vector product, $y = Ax$.
2679: Neighbor-wise Collective
2681: Input Parameters:
2682: + mat - the matrix
2683: - x - the vector to be multiplied
2685: Output Parameter:
2686: . y - the result
2688: Level: beginner
2690: Note:
2691: The vectors `x` and `y` cannot be the same. I.e., one cannot
2692: call `MatMult`(A,y,y).
2694: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2695: @*/
2696: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2697: {
2698: PetscFunctionBegin;
2702: VecCheckAssembled(x);
2704: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2705: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2706: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2707: 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);
2708: 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);
2709: 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);
2710: 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);
2711: PetscCall(VecSetErrorIfLocked(y, 3));
2712: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2713: MatCheckPreallocated(mat, 1);
2715: PetscCall(VecLockReadPush(x));
2716: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2717: PetscUseTypeMethod(mat, mult, x, y);
2718: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2719: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2720: PetscCall(VecLockReadPop(x));
2721: PetscFunctionReturn(PETSC_SUCCESS);
2722: }
2724: /*@
2725: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2727: Neighbor-wise Collective
2729: Input Parameters:
2730: + mat - the matrix
2731: - x - the vector to be multiplied
2733: Output Parameter:
2734: . y - the result
2736: Level: beginner
2738: Notes:
2739: The vectors `x` and `y` cannot be the same. I.e., one cannot
2740: call `MatMultTranspose`(A,y,y).
2742: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2743: use `MatMultHermitianTranspose()`
2745: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2746: @*/
2747: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2748: {
2749: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2751: PetscFunctionBegin;
2755: VecCheckAssembled(x);
2758: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2759: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2760: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2761: 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);
2762: 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);
2763: 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);
2764: 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);
2765: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2766: MatCheckPreallocated(mat, 1);
2768: if (!mat->ops->multtranspose) {
2769: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2770: 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);
2771: } else op = mat->ops->multtranspose;
2772: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2773: PetscCall(VecLockReadPush(x));
2774: PetscCall((*op)(mat, x, y));
2775: PetscCall(VecLockReadPop(x));
2776: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2777: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2778: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2779: PetscFunctionReturn(PETSC_SUCCESS);
2780: }
2782: /*@
2783: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2785: Neighbor-wise Collective
2787: Input Parameters:
2788: + mat - the matrix
2789: - x - the vector to be multiplied
2791: Output Parameter:
2792: . y - the result
2794: Level: beginner
2796: Notes:
2797: The vectors `x` and `y` cannot be the same. I.e., one cannot
2798: call `MatMultHermitianTranspose`(A,y,y).
2800: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2802: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2804: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2805: @*/
2806: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2807: {
2808: PetscFunctionBegin;
2814: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2815: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2816: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2817: 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);
2818: 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);
2819: 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);
2820: 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);
2821: MatCheckPreallocated(mat, 1);
2823: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2824: if (PetscDefined(USE_COMPLEX)) {
2825: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2826: PetscCall(VecLockReadPush(x));
2827: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2828: else PetscUseTypeMethod(mat, mult, x, y);
2829: PetscCall(VecLockReadPop(x));
2830: } else {
2831: Vec w;
2832: PetscCall(VecDuplicate(x, &w));
2833: PetscCall(VecCopy(x, w));
2834: PetscCall(VecConjugate(w));
2835: PetscCall(MatMultTranspose(mat, w, y));
2836: PetscCall(VecDestroy(&w));
2837: PetscCall(VecConjugate(y));
2838: }
2839: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2840: } else PetscCall(MatMultTranspose(mat, x, y));
2841: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2842: PetscFunctionReturn(PETSC_SUCCESS);
2843: }
2845: /*@
2846: MatMultAdd - Computes $v3 = v2 + A * v1$.
2848: Neighbor-wise Collective
2850: Input Parameters:
2851: + mat - the matrix
2852: . v1 - the vector to be multiplied by `mat`
2853: - v2 - the vector to be added to the result
2855: Output Parameter:
2856: . v3 - the result
2858: Level: beginner
2860: Note:
2861: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2862: call `MatMultAdd`(A,v1,v2,v1).
2864: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2865: @*/
2866: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2867: {
2868: PetscFunctionBegin;
2875: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2876: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2877: PetscCheck(mat->cmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v1->map->N);
2878: /* PetscCheck(mat->rmap->N == v2->map->N,PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT,mat->rmap->N,v2->map->N);
2879: PetscCheck(mat->rmap->N == v3->map->N,PETSC_COMM_SELF,PETSC_ERR_ARG_SIZ,"Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT,mat->rmap->N,v3->map->N); */
2880: PetscCheck(mat->rmap->n == v3->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, v3->map->n);
2881: PetscCheck(mat->rmap->n == v2->map->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: local dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->n, v2->map->n);
2882: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2883: MatCheckPreallocated(mat, 1);
2885: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2886: PetscCall(VecLockReadPush(v1));
2887: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2888: PetscCall(VecLockReadPop(v1));
2889: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2890: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2891: PetscFunctionReturn(PETSC_SUCCESS);
2892: }
2894: /*@
2895: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2897: Neighbor-wise Collective
2899: Input Parameters:
2900: + mat - the matrix
2901: . v1 - the vector to be multiplied by the transpose of the matrix
2902: - v2 - the vector to be added to the result
2904: Output Parameter:
2905: . v3 - the result
2907: Level: beginner
2909: Note:
2910: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2911: call `MatMultTransposeAdd`(A,v1,v2,v1).
2913: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2914: @*/
2915: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2916: {
2917: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2919: PetscFunctionBegin;
2926: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2927: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2928: PetscCheck(mat->rmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, v1->map->N);
2929: PetscCheck(mat->cmap->N == v2->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v2->map->N);
2930: PetscCheck(mat->cmap->N == v3->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v3->map->N);
2931: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2932: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2933: MatCheckPreallocated(mat, 1);
2935: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2936: PetscCall(VecLockReadPush(v1));
2937: PetscCall((*op)(mat, v1, v2, v3));
2938: PetscCall(VecLockReadPop(v1));
2939: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2940: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2941: PetscFunctionReturn(PETSC_SUCCESS);
2942: }
2944: /*@
2945: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2947: Neighbor-wise Collective
2949: Input Parameters:
2950: + mat - the matrix
2951: . v1 - the vector to be multiplied by the Hermitian transpose
2952: - v2 - the vector to be added to the result
2954: Output Parameter:
2955: . v3 - the result
2957: Level: beginner
2959: Note:
2960: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2961: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2963: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2964: @*/
2965: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2966: {
2967: PetscFunctionBegin;
2974: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2975: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2976: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2977: PetscCheck(mat->rmap->N == v1->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v1: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->rmap->N, v1->map->N);
2978: PetscCheck(mat->cmap->N == v2->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v2: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v2->map->N);
2979: PetscCheck(mat->cmap->N == v3->map->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_SIZ, "Mat mat,Vec v3: global dim %" PetscInt_FMT " %" PetscInt_FMT, mat->cmap->N, v3->map->N);
2980: MatCheckPreallocated(mat, 1);
2982: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2983: PetscCall(VecLockReadPush(v1));
2984: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2985: else {
2986: Vec w, z;
2987: PetscCall(VecDuplicate(v1, &w));
2988: PetscCall(VecCopy(v1, w));
2989: PetscCall(VecConjugate(w));
2990: PetscCall(VecDuplicate(v3, &z));
2991: PetscCall(MatMultTranspose(mat, w, z));
2992: PetscCall(VecDestroy(&w));
2993: PetscCall(VecConjugate(z));
2994: if (v2 != v3) PetscCall(VecWAXPY(v3, 1.0, v2, z));
2995: else PetscCall(VecAXPY(v3, 1.0, z));
2996: PetscCall(VecDestroy(&z));
2997: }
2998: PetscCall(VecLockReadPop(v1));
2999: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
3000: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
3001: PetscFunctionReturn(PETSC_SUCCESS);
3002: }
3004: PetscErrorCode MatADot_Default(Mat mat, Vec x, Vec y, PetscScalar *val)
3005: {
3006: PetscFunctionBegin;
3007: if (!mat->dot_vec) PetscCall(MatCreateVecs(mat, &mat->dot_vec, NULL));
3008: PetscCall(MatMult(mat, x, mat->dot_vec));
3009: PetscCall(VecDot(mat->dot_vec, y, val));
3010: PetscFunctionReturn(PETSC_SUCCESS);
3011: }
3013: PetscErrorCode MatANorm_Default(Mat mat, Vec x, PetscReal *val)
3014: {
3015: PetscScalar sval;
3017: PetscFunctionBegin;
3018: PetscCall(MatADot_Default(mat, x, x, &sval));
3019: PetscCheck(PetscRealPart(sval) >= 0.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not positive definite");
3020: PetscCheck(PetscAbsReal(PetscImaginaryPart(sval)) < 100 * PETSC_MACHINE_EPSILON, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not Hermitian");
3021: *val = PetscSqrtReal(PetscRealPart(sval));
3022: PetscFunctionReturn(PETSC_SUCCESS);
3023: }
3025: /*@
3026: 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)
3027: positive definite.
3029: Collective
3031: Input Parameters:
3032: + mat - matrix used to define the inner product
3033: . x - first vector
3034: - y - second vector
3036: Output Parameter:
3037: . val - the dot product with respect to `A`
3039: Level: intermediate
3041: Note:
3042: For complex vectors, `MatADot()` computes
3043: $$
3044: val = (x,y)_A = y^H A x,
3045: $$
3046: where $y^H$ denotes the conjugate transpose of `y`. Note that this corresponds to the "mathematicians" complex
3047: inner product where the SECOND argument gets the complex conjugate.
3049: .seealso: [](ch_matrices), `Mat`, `MatANorm()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3050: @*/
3051: PetscErrorCode MatADot(Mat mat, Vec x, Vec y, PetscScalar *val)
3052: {
3053: PetscFunctionBegin;
3057: VecCheckAssembled(x);
3059: VecCheckAssembled(y);
3062: PetscAssertPointer(val, 4);
3063: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3064: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3065: 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);
3066: 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);
3067: 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);
3068: 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);
3069: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3070: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_TRUE));
3071: MatCheckPreallocated(mat, 1);
3073: PetscCall(VecLockReadPush(x));
3074: PetscCall(VecLockReadPush(y));
3075: PetscCall(PetscLogEventBegin(MAT_ADot, mat, x, y, 0));
3076: PetscUseTypeMethod(mat, adot, x, y, val);
3077: PetscCall(PetscLogEventEnd(MAT_ADot, mat, x, y, 0));
3078: PetscCall(VecLockReadPop(y));
3079: PetscCall(VecLockReadPop(x));
3080: PetscFunctionReturn(PETSC_SUCCESS);
3081: }
3083: /*@
3084: 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)
3085: positive definite.
3087: Collective
3089: Input Parameters:
3090: + mat - matrix used to define norm
3091: - x - the vector to compute the norm of
3093: Output Parameter:
3094: . val - the norm with respect to `A`
3096: Level: intermediate
3098: Note:
3099: For complex vectors, `MatANorm()` computes
3100: $$
3101: val = (x,x)_A^{1/2} = (x^H A x)^{1/2},
3102: $$
3103: where $x^H$ denotes the conjugate transpose of `x`.
3105: .seealso: [](ch_matrices), `Mat`, `MatADot()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3106: @*/
3107: PetscErrorCode MatANorm(Mat mat, Vec x, PetscReal *val)
3108: {
3109: PetscFunctionBegin;
3113: VecCheckAssembled(x);
3115: PetscAssertPointer(val, 3);
3116: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3117: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3118: 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);
3119: 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);
3120: 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);
3121: 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);
3122: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3123: MatCheckPreallocated(mat, 1);
3125: PetscCall(VecLockReadPush(x));
3126: PetscCall(PetscLogEventBegin(MAT_ANorm, mat, x, 0, 0));
3127: PetscUseTypeMethod(mat, anorm, x, val);
3128: PetscCall(PetscLogEventEnd(MAT_ANorm, mat, x, 0, 0));
3129: PetscCall(VecLockReadPop(x));
3130: PetscFunctionReturn(PETSC_SUCCESS);
3131: }
3133: /*@
3134: MatGetFactorType - gets the type of factorization a matrix is
3136: Not Collective
3138: Input Parameter:
3139: . mat - the matrix
3141: Output Parameter:
3142: . 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`
3144: Level: intermediate
3146: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3147: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3148: @*/
3149: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3150: {
3151: PetscFunctionBegin;
3154: PetscAssertPointer(t, 2);
3155: *t = mat->factortype;
3156: PetscFunctionReturn(PETSC_SUCCESS);
3157: }
3159: /*@
3160: MatSetFactorType - sets the type of factorization a matrix is
3162: Logically Collective
3164: Input Parameters:
3165: + mat - the matrix
3166: - 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`
3168: Level: intermediate
3170: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3171: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3172: @*/
3173: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3174: {
3175: PetscFunctionBegin;
3178: mat->factortype = t;
3179: PetscFunctionReturn(PETSC_SUCCESS);
3180: }
3182: /*@
3183: MatGetInfo - Returns information about matrix storage (number of
3184: nonzeros, memory, etc.).
3186: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3188: Input Parameters:
3189: + mat - the matrix
3190: - flag - flag indicating the type of parameters to be returned (`MAT_LOCAL` - local matrix, `MAT_GLOBAL_MAX` - maximum over all processors, `MAT_GLOBAL_SUM` - sum over all processors)
3192: Output Parameter:
3193: . info - matrix information context
3195: Options Database Key:
3196: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3198: Level: intermediate
3200: Notes:
3201: The `MatInfo` context contains a variety of matrix data, including
3202: number of nonzeros allocated and used, number of mallocs during
3203: matrix assembly, etc. Additional information for factored matrices
3204: is provided (such as the fill ratio, number of mallocs during
3205: factorization, etc.).
3207: Example:
3208: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3209: data within the `MatInfo` context. For example,
3210: .vb
3211: MatInfo info;
3212: Mat A;
3213: double mal, nz_a, nz_u;
3215: MatGetInfo(A, MAT_LOCAL, &info);
3216: mal = info.mallocs;
3217: nz_a = info.nz_allocated;
3218: .ve
3220: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3221: @*/
3222: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3223: {
3224: PetscFunctionBegin;
3227: PetscAssertPointer(info, 3);
3228: MatCheckPreallocated(mat, 1);
3229: PetscUseTypeMethod(mat, getinfo, flag, info);
3230: PetscFunctionReturn(PETSC_SUCCESS);
3231: }
3233: /*
3234: This is used by external packages where it is not easy to get the info from the actual
3235: matrix factorization.
3236: */
3237: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3238: {
3239: PetscFunctionBegin;
3240: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3241: PetscFunctionReturn(PETSC_SUCCESS);
3242: }
3244: /*@
3245: MatLUFactor - Performs in-place LU factorization of matrix.
3247: Collective
3249: Input Parameters:
3250: + mat - the matrix
3251: . row - row permutation
3252: . col - column permutation
3253: - info - options for factorization, includes
3254: .vb
3255: fill - expected fill as ratio of original fill.
3256: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3257: Run with the option -info to determine an optimal value to use
3258: .ve
3260: Level: developer
3262: Notes:
3263: Most users should employ the `KSP` interface for linear solvers
3264: instead of working directly with matrix algebra routines such as this.
3265: See, e.g., `KSPCreate()`.
3267: This changes the state of the matrix to a factored matrix; it cannot be used
3268: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3270: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3271: when not using `KSP`.
3273: Fortran Note:
3274: A valid (non-null) `info` argument must be provided
3276: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3277: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3278: @*/
3279: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3280: {
3281: MatFactorInfo tinfo;
3283: PetscFunctionBegin;
3287: if (info) PetscAssertPointer(info, 4);
3289: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3290: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3291: MatCheckPreallocated(mat, 1);
3292: if (!info) {
3293: PetscCall(MatFactorInfoInitialize(&tinfo));
3294: info = &tinfo;
3295: }
3297: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3298: PetscUseTypeMethod(mat, lufactor, row, col, info);
3299: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3300: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3301: PetscFunctionReturn(PETSC_SUCCESS);
3302: }
3304: /*@
3305: MatILUFactor - Performs in-place ILU factorization of matrix.
3307: Collective
3309: Input Parameters:
3310: + mat - the matrix
3311: . row - row permutation
3312: . col - column permutation
3313: - info - structure containing
3314: .vb
3315: levels - number of levels of fill.
3316: expected fill - as ratio of original fill.
3317: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3318: missing diagonal entries)
3319: .ve
3321: Level: developer
3323: Notes:
3324: Most users should employ the `KSP` interface for linear solvers
3325: instead of working directly with matrix algebra routines such as this.
3326: See, e.g., `KSPCreate()`.
3328: Probably really in-place only when level of fill is zero, otherwise allocates
3329: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatLUFactorNumeric()`
3330: when not using `KSP`.
3332: Fortran Note:
3333: A valid (non-null) `info` argument must be provided
3335: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3336: @*/
3337: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3338: {
3339: PetscFunctionBegin;
3343: PetscAssertPointer(info, 4);
3345: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
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);
3350: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3351: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3352: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3353: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3354: PetscFunctionReturn(PETSC_SUCCESS);
3355: }
3357: /*@
3358: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3359: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3361: Collective
3363: Input Parameters:
3364: + fact - the factor matrix obtained with `MatGetFactor()`
3365: . mat - the matrix
3366: . row - the row permutation
3367: . col - the column permutation
3368: - info - options for factorization, includes
3369: .vb
3370: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3371: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3372: .ve
3374: Level: developer
3376: Notes:
3377: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3379: Most users should employ the simplified `KSP` interface for linear solvers
3380: instead of working directly with matrix algebra routines such as this.
3381: See, e.g., `KSPCreate()`.
3383: Fortran Note:
3384: A valid (non-null) `info` argument must be provided
3386: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3387: @*/
3388: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3389: {
3390: MatFactorInfo tinfo;
3392: PetscFunctionBegin;
3397: if (info) PetscAssertPointer(info, 5);
3400: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3401: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3402: MatCheckPreallocated(mat, 2);
3403: if (!info) {
3404: PetscCall(MatFactorInfoInitialize(&tinfo));
3405: info = &tinfo;
3406: }
3408: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3409: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3410: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3411: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3412: PetscFunctionReturn(PETSC_SUCCESS);
3413: }
3415: /*@
3416: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3417: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3419: Collective
3421: Input Parameters:
3422: + fact - the factor matrix obtained with `MatGetFactor()`
3423: . mat - the matrix
3424: - info - options for factorization
3426: Level: developer
3428: Notes:
3429: See `MatLUFactor()` for in-place factorization. See
3430: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3432: Most users should employ the `KSP` interface for linear solvers
3433: instead of working directly with matrix algebra routines such as this.
3434: See, e.g., `KSPCreate()`.
3436: Fortran Note:
3437: A valid (non-null) `info` argument must be provided
3439: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3440: @*/
3441: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3442: {
3443: MatFactorInfo tinfo;
3445: PetscFunctionBegin;
3450: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3451: 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,
3452: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3454: MatCheckPreallocated(mat, 2);
3455: if (!info) {
3456: PetscCall(MatFactorInfoInitialize(&tinfo));
3457: info = &tinfo;
3458: }
3460: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3461: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3462: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3463: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3464: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3465: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3466: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3467: PetscFunctionReturn(PETSC_SUCCESS);
3468: }
3470: /*@
3471: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3472: symmetric matrix.
3474: Collective
3476: Input Parameters:
3477: + mat - the matrix
3478: . perm - row and column permutations
3479: - info - expected fill as ratio of original fill
3481: Level: developer
3483: Notes:
3484: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3485: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3487: Most users should employ the `KSP` interface for linear solvers
3488: instead of working directly with matrix algebra routines such as this.
3489: See, e.g., `KSPCreate()`.
3491: Fortran Note:
3492: A valid (non-null) `info` argument must be provided
3494: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`,
3495: `MatGetOrdering()`
3496: @*/
3497: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3498: {
3499: MatFactorInfo tinfo;
3501: PetscFunctionBegin;
3504: if (info) PetscAssertPointer(info, 3);
3506: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3507: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3508: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3509: MatCheckPreallocated(mat, 1);
3510: if (!info) {
3511: PetscCall(MatFactorInfoInitialize(&tinfo));
3512: info = &tinfo;
3513: }
3515: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3516: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3517: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3518: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3519: PetscFunctionReturn(PETSC_SUCCESS);
3520: }
3522: /*@
3523: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3524: of a symmetric matrix.
3526: Collective
3528: Input Parameters:
3529: + fact - the factor matrix obtained with `MatGetFactor()`
3530: . mat - the matrix
3531: . perm - row and column permutations
3532: - info - options for factorization, includes
3533: .vb
3534: fill - expected fill as ratio of original fill.
3535: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3536: Run with the option -info to determine an optimal value to use
3537: .ve
3539: Level: developer
3541: Notes:
3542: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3543: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3545: Most users should employ the `KSP` interface for linear solvers
3546: instead of working directly with matrix algebra routines such as this.
3547: See, e.g., `KSPCreate()`.
3549: Fortran Note:
3550: A valid (non-null) `info` argument must be provided
3552: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`,
3553: `MatGetOrdering()`
3554: @*/
3555: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3556: {
3557: MatFactorInfo tinfo;
3559: PetscFunctionBegin;
3563: if (info) PetscAssertPointer(info, 4);
3566: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3567: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3568: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3569: MatCheckPreallocated(mat, 2);
3570: if (!info) {
3571: PetscCall(MatFactorInfoInitialize(&tinfo));
3572: info = &tinfo;
3573: }
3575: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3576: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3577: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3578: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3579: PetscFunctionReturn(PETSC_SUCCESS);
3580: }
3582: /*@
3583: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3584: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3585: `MatCholeskyFactorSymbolic()`.
3587: Collective
3589: Input Parameters:
3590: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3591: . mat - the initial matrix that is to be factored
3592: - info - options for factorization
3594: Level: developer
3596: Note:
3597: Most users should employ the `KSP` interface for linear solvers
3598: instead of working directly with matrix algebra routines such as this.
3599: See, e.g., `KSPCreate()`.
3601: Fortran Note:
3602: A valid (non-null) `info` argument must be provided
3604: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3605: @*/
3606: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3607: {
3608: MatFactorInfo tinfo;
3610: PetscFunctionBegin;
3615: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3616: 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,
3617: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3618: MatCheckPreallocated(mat, 2);
3619: if (!info) {
3620: PetscCall(MatFactorInfoInitialize(&tinfo));
3621: info = &tinfo;
3622: }
3624: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3625: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3626: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3627: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3628: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3629: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3630: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3631: PetscFunctionReturn(PETSC_SUCCESS);
3632: }
3634: /*@
3635: MatQRFactor - Performs in-place QR factorization of matrix.
3637: Collective
3639: Input Parameters:
3640: + mat - the matrix
3641: . col - column permutation
3642: - info - options for factorization, includes
3643: .vb
3644: fill - expected fill as ratio of original fill.
3645: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3646: Run with the option -info to determine an optimal value to use
3647: .ve
3649: Level: developer
3651: Notes:
3652: Most users should employ the `KSP` interface for linear solvers
3653: instead of working directly with matrix algebra routines such as this.
3654: See, e.g., `KSPCreate()`.
3656: This changes the state of the matrix to a factored matrix; it cannot be used
3657: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3659: Fortran Note:
3660: A valid (non-null) `info` argument must be provided
3662: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3663: `MatSetUnfactored()`
3664: @*/
3665: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3666: {
3667: PetscFunctionBegin;
3670: if (info) PetscAssertPointer(info, 3);
3672: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3673: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3674: MatCheckPreallocated(mat, 1);
3675: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3676: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3677: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3678: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3679: PetscFunctionReturn(PETSC_SUCCESS);
3680: }
3682: /*@
3683: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3684: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3686: Collective
3688: Input Parameters:
3689: + fact - the factor matrix obtained with `MatGetFactor()`
3690: . mat - the matrix
3691: . col - column permutation
3692: - info - options for factorization, includes
3693: .vb
3694: fill - expected fill as ratio of original fill.
3695: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3696: Run with the option -info to determine an optimal value to use
3697: .ve
3699: Level: developer
3701: Note:
3702: Most users should employ the `KSP` interface for linear solvers
3703: instead of working directly with matrix algebra routines such as this.
3704: See, e.g., `KSPCreate()`.
3706: Fortran Note:
3707: A valid (non-null) `info` argument must be provided
3709: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3710: @*/
3711: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3712: {
3713: MatFactorInfo tinfo;
3715: PetscFunctionBegin;
3719: if (info) PetscAssertPointer(info, 4);
3722: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3723: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3724: MatCheckPreallocated(mat, 2);
3725: if (!info) {
3726: PetscCall(MatFactorInfoInitialize(&tinfo));
3727: info = &tinfo;
3728: }
3730: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3731: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3732: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3733: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3734: PetscFunctionReturn(PETSC_SUCCESS);
3735: }
3737: /*@
3738: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3739: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3741: Collective
3743: Input Parameters:
3744: + fact - the factor matrix obtained with `MatGetFactor()`
3745: . mat - the matrix
3746: - info - options for factorization
3748: Level: developer
3750: Notes:
3751: See `MatQRFactor()` for in-place factorization.
3753: Most users should employ the `KSP` interface for linear solvers
3754: instead of working directly with matrix algebra routines such as this.
3755: See, e.g., `KSPCreate()`.
3757: Fortran Note:
3758: A valid (non-null) `info` argument must be provided
3760: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3761: @*/
3762: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3763: {
3764: MatFactorInfo tinfo;
3766: PetscFunctionBegin;
3771: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3772: 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,
3773: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3775: MatCheckPreallocated(mat, 2);
3776: if (!info) {
3777: PetscCall(MatFactorInfoInitialize(&tinfo));
3778: info = &tinfo;
3779: }
3781: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3782: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3783: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3784: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3785: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3786: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3787: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3788: PetscFunctionReturn(PETSC_SUCCESS);
3789: }
3791: /*@
3792: MatSolve - Solves $A x = b$, given a factored matrix.
3794: Neighbor-wise Collective
3796: Input Parameters:
3797: + mat - the factored matrix
3798: - b - the right-hand-side vector
3800: Output Parameter:
3801: . x - the result vector
3803: Level: developer
3805: Notes:
3806: The vectors `b` and `x` cannot be the same. I.e., one cannot
3807: call `MatSolve`(A,x,x).
3809: Most users should employ the `KSP` interface for linear solvers
3810: instead of working directly with matrix algebra routines such as this.
3811: See, e.g., `KSPCreate()`.
3813: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3814: @*/
3815: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3816: {
3817: PetscFunctionBegin;
3822: PetscCheckSameComm(mat, 1, b, 2);
3823: PetscCheckSameComm(mat, 1, x, 3);
3824: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3825: 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);
3826: 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);
3827: 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);
3828: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3829: MatCheckPreallocated(mat, 1);
3831: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3832: PetscCall(VecFlag(x, mat->factorerrortype));
3833: if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3834: else PetscUseTypeMethod(mat, solve, b, x);
3835: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3836: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3837: PetscFunctionReturn(PETSC_SUCCESS);
3838: }
3840: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3841: {
3842: Vec b, x;
3843: PetscInt N;
3844: PetscErrorCode (*f)(Mat, Vec, Vec);
3845: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3847: PetscFunctionBegin;
3848: if (A->factorerrortype) {
3849: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3850: PetscCall(MatSetInf(X));
3851: PetscFunctionReturn(PETSC_SUCCESS);
3852: }
3853: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3854: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3855: PetscCall(MatBoundToCPU(A, &Abound));
3856: if (!Abound) {
3857: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3858: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3859: }
3860: #if PetscDefined(HAVE_CUDA)
3861: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3862: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3863: #elif PetscDefined(HAVE_HIP)
3864: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3865: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3866: #endif
3867: PetscCall(MatGetSize(B, NULL, &N));
3868: for (PetscInt i = 0; i < N; i++) {
3869: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3870: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3871: PetscCall((*f)(A, b, x));
3872: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3873: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3874: }
3875: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3876: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3877: PetscFunctionReturn(PETSC_SUCCESS);
3878: }
3880: /*@
3881: MatMatSolve - Solves $A X = B$, given a factored matrix.
3883: Neighbor-wise Collective
3885: Input Parameters:
3886: + A - the factored matrix
3887: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3889: Output Parameter:
3890: . X - the result matrix (dense matrix)
3892: Level: developer
3894: Note:
3895: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3896: otherwise, `B` and `X` cannot be the same.
3898: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3899: @*/
3900: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3901: {
3902: PetscFunctionBegin;
3907: PetscCheckSameComm(A, 1, B, 2);
3908: PetscCheckSameComm(A, 1, X, 3);
3909: PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
3910: PetscCheck(A->rmap->N == B->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N);
3911: PetscCheck(X->cmap->N == B->cmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as rhs matrix");
3912: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3913: MatCheckPreallocated(A, 1);
3915: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3916: if (!A->ops->matsolve) {
3917: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3918: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3919: } else PetscUseTypeMethod(A, matsolve, B, X);
3920: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3921: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3922: PetscFunctionReturn(PETSC_SUCCESS);
3923: }
3925: /*@
3926: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3928: Neighbor-wise Collective
3930: Input Parameters:
3931: + A - the factored matrix
3932: - B - the right-hand-side matrix (`MATDENSE` matrix)
3934: Output Parameter:
3935: . X - the result matrix (dense matrix)
3937: Level: developer
3939: Note:
3940: The matrices `B` and `X` cannot be the same. I.e., one cannot
3941: call `MatMatSolveTranspose`(A,X,X).
3943: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3944: @*/
3945: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3946: {
3947: PetscFunctionBegin;
3952: PetscCheckSameComm(A, 1, B, 2);
3953: PetscCheckSameComm(A, 1, X, 3);
3954: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3955: 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);
3956: 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);
3957: 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);
3958: 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");
3959: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3960: MatCheckPreallocated(A, 1);
3962: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3963: if (!A->ops->matsolvetranspose) {
3964: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3965: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3966: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3967: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3968: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3969: PetscFunctionReturn(PETSC_SUCCESS);
3970: }
3972: /*@
3973: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3975: Neighbor-wise Collective
3977: Input Parameters:
3978: + A - the factored matrix
3979: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3981: Output Parameter:
3982: . X - the result matrix (dense matrix)
3984: Level: developer
3986: Note:
3987: For MUMPS, it only supports centralized sparse compressed column format on the host processor for right-hand side matrix. User must create `Bt` in sparse compressed row
3988: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3990: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3991: @*/
3992: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3993: {
3994: PetscFunctionBegin;
3999: PetscCheckSameComm(A, 1, Bt, 2);
4000: PetscCheckSameComm(A, 1, X, 3);
4002: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
4003: 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);
4004: 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);
4005: 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");
4006: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4007: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
4008: MatCheckPreallocated(A, 1);
4010: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
4011: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
4012: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
4013: PetscCall(PetscObjectStateIncrease((PetscObject)X));
4014: PetscFunctionReturn(PETSC_SUCCESS);
4015: }
4017: /*@
4018: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
4019: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4021: Neighbor-wise Collective
4023: Input Parameters:
4024: + mat - the factored matrix
4025: - b - the right-hand-side vector
4027: Output Parameter:
4028: . x - the result vector
4030: Level: developer
4032: Notes:
4033: `MatSolve()` should be used for most applications, as it performs
4034: a forward solve followed by a backward solve.
4036: The vectors `b` and `x` cannot be the same, i.e., one cannot
4037: call `MatForwardSolve`(A,x,x).
4039: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4040: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4041: `MatForwardSolve()` solves $U^T*D y = b$, and
4042: `MatBackwardSolve()` solves $U x = y$.
4043: Thus they do not provide a symmetric preconditioner.
4045: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
4046: @*/
4047: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
4048: {
4049: PetscFunctionBegin;
4054: PetscCheckSameComm(mat, 1, b, 2);
4055: PetscCheckSameComm(mat, 1, x, 3);
4056: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4057: 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);
4058: 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);
4059: 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);
4060: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4061: MatCheckPreallocated(mat, 1);
4063: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
4064: PetscUseTypeMethod(mat, forwardsolve, b, x);
4065: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
4066: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4067: PetscFunctionReturn(PETSC_SUCCESS);
4068: }
4070: /*@
4071: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
4072: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4074: Neighbor-wise Collective
4076: Input Parameters:
4077: + mat - the factored matrix
4078: - b - the right-hand-side vector
4080: Output Parameter:
4081: . x - the result vector
4083: Level: developer
4085: Notes:
4086: `MatSolve()` should be used for most applications, as it performs
4087: a forward solve followed by a backward solve.
4089: The vectors `b` and `x` cannot be the same. I.e., one cannot
4090: call `MatBackwardSolve`(A,x,x).
4092: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4093: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4094: `MatForwardSolve()` solves $U^T*D y = b$, and
4095: `MatBackwardSolve()` solves $U x = y$.
4096: Thus they do not provide a symmetric preconditioner.
4098: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
4099: @*/
4100: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
4101: {
4102: PetscFunctionBegin;
4107: PetscCheckSameComm(mat, 1, b, 2);
4108: PetscCheckSameComm(mat, 1, x, 3);
4109: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4110: 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);
4111: 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);
4112: 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);
4113: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4114: MatCheckPreallocated(mat, 1);
4116: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
4117: PetscUseTypeMethod(mat, backwardsolve, b, x);
4118: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
4119: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4120: PetscFunctionReturn(PETSC_SUCCESS);
4121: }
4123: /*@
4124: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
4126: Neighbor-wise Collective
4128: Input Parameters:
4129: + mat - the factored matrix
4130: . b - the right-hand-side vector
4131: - y - the vector to be added to
4133: Output Parameter:
4134: . x - the result vector
4136: Level: developer
4138: Note:
4139: The vectors `b` and `x` cannot be the same. I.e., one cannot
4140: call `MatSolveAdd`(A,x,y,x).
4142: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4143: @*/
4144: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4145: {
4146: PetscScalar one = 1.0;
4147: Vec tmp;
4149: PetscFunctionBegin;
4155: PetscCheckSameComm(mat, 1, b, 2);
4156: PetscCheckSameComm(mat, 1, y, 3);
4157: PetscCheckSameComm(mat, 1, x, 4);
4158: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4159: 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);
4160: 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);
4161: 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);
4162: 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);
4163: 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);
4164: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4165: MatCheckPreallocated(mat, 1);
4167: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4168: PetscCall(VecFlag(x, mat->factorerrortype));
4169: if (mat->factorerrortype) {
4170: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4171: } else if (mat->ops->solveadd) {
4172: PetscUseTypeMethod(mat, solveadd, b, y, x);
4173: } else {
4174: /* do the solve then the add manually */
4175: if (x != y) {
4176: PetscCall(MatSolve(mat, b, x));
4177: PetscCall(VecAXPY(x, one, y));
4178: } else {
4179: PetscCall(VecDuplicate(x, &tmp));
4180: PetscCall(VecCopy(x, tmp));
4181: PetscCall(MatSolve(mat, b, x));
4182: PetscCall(VecAXPY(x, one, tmp));
4183: PetscCall(VecDestroy(&tmp));
4184: }
4185: }
4186: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4187: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4188: PetscFunctionReturn(PETSC_SUCCESS);
4189: }
4191: /*@
4192: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4194: Neighbor-wise Collective
4196: Input Parameters:
4197: + mat - the factored matrix
4198: - b - the right-hand-side vector
4200: Output Parameter:
4201: . x - the result vector
4203: Level: developer
4205: Notes:
4206: The vectors `b` and `x` cannot be the same. I.e., one cannot
4207: call `MatSolveTranspose`(A,x,x).
4209: Most users should employ the `KSP` interface for linear solvers
4210: instead of working directly with matrix algebra routines such as this.
4211: See, e.g., `KSPCreate()`.
4213: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4214: @*/
4215: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4216: {
4217: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4219: PetscFunctionBegin;
4224: PetscCheckSameComm(mat, 1, b, 2);
4225: PetscCheckSameComm(mat, 1, x, 3);
4226: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4227: 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);
4228: 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);
4229: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4230: MatCheckPreallocated(mat, 1);
4231: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4232: PetscCall(VecFlag(x, mat->factorerrortype));
4233: if (mat->factorerrortype) {
4234: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4235: } else {
4236: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4237: PetscCall((*f)(mat, b, x));
4238: }
4239: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4240: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4241: PetscFunctionReturn(PETSC_SUCCESS);
4242: }
4244: /*@
4245: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4246: factored matrix.
4248: Neighbor-wise Collective
4250: Input Parameters:
4251: + mat - the factored matrix
4252: . b - the right-hand-side vector
4253: - y - the vector to be added to
4255: Output Parameter:
4256: . x - the result vector
4258: Level: developer
4260: Note:
4261: The vectors `b` and `x` cannot be the same. I.e., one cannot
4262: call `MatSolveTransposeAdd`(A,x,y,x).
4264: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4265: @*/
4266: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4267: {
4268: PetscScalar one = 1.0;
4269: Vec tmp;
4270: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4272: PetscFunctionBegin;
4278: PetscCheckSameComm(mat, 1, b, 2);
4279: PetscCheckSameComm(mat, 1, y, 3);
4280: PetscCheckSameComm(mat, 1, x, 4);
4281: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4282: 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);
4283: 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);
4284: 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);
4285: 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);
4286: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4287: MatCheckPreallocated(mat, 1);
4289: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4290: PetscCall(VecFlag(x, mat->factorerrortype));
4291: if (mat->factorerrortype) {
4292: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4293: } else if (f) {
4294: PetscCall((*f)(mat, b, y, x));
4295: } else {
4296: /* do the solve then the add manually */
4297: if (x != y) {
4298: PetscCall(MatSolveTranspose(mat, b, x));
4299: PetscCall(VecAXPY(x, one, y));
4300: } else {
4301: PetscCall(VecDuplicate(x, &tmp));
4302: PetscCall(VecCopy(x, tmp));
4303: PetscCall(MatSolveTranspose(mat, b, x));
4304: PetscCall(VecAXPY(x, one, tmp));
4305: PetscCall(VecDestroy(&tmp));
4306: }
4307: }
4308: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4309: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4310: PetscFunctionReturn(PETSC_SUCCESS);
4311: }
4313: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4314: /*@
4315: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4317: Neighbor-wise Collective
4319: Input Parameters:
4320: + mat - the matrix
4321: . b - the right-hand side
4322: . omega - the relaxation factor
4323: . flag - flag indicating the type of SOR (see below)
4324: . shift - diagonal shift
4325: . its - the number of iterations
4326: - lits - the number of local iterations
4328: Output Parameter:
4329: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4331: SOR Flags:
4332: + `SOR_FORWARD_SWEEP` - forward SOR
4333: . `SOR_BACKWARD_SWEEP` - backward SOR
4334: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4335: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4336: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4337: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4338: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4339: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies upper/lower triangular part of matrix to vector (with `omega`)
4340: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4342: Level: developer
4344: Notes:
4345: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4346: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4347: on each processor.
4349: Application programmers will not generally use `MatSOR()` directly,
4350: but instead will employ `PCSOR` or `PCEISENSTAT`
4352: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with inodes, this does a block SOR smoothing, otherwise it does a pointwise smoothing.
4353: For `MATAIJ` matrices with inodes, the block sizes are determined by the inode sizes, not the block size set with `MatSetBlockSize()`
4355: Vectors `x` and `b` CANNOT be the same
4357: The flags are implemented as bitwise inclusive or operations.
4358: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4359: to specify a zero initial guess for SSOR.
4361: Developer Note:
4362: We should add block SOR support for `MATAIJ` matrices with block size set to greater than one and no inodes
4364: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4365: @*/
4366: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4367: {
4368: PetscFunctionBegin;
4373: PetscCheckSameComm(mat, 1, b, 2);
4374: PetscCheckSameComm(mat, 1, x, 8);
4375: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4376: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4377: 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);
4378: 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);
4379: 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);
4380: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4381: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4382: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4384: MatCheckPreallocated(mat, 1);
4385: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4386: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4387: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4388: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4389: PetscFunctionReturn(PETSC_SUCCESS);
4390: }
4392: /*
4393: Default matrix copy routine.
4394: */
4395: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4396: {
4397: PetscInt i, rstart = 0, rend = 0, nz;
4398: const PetscInt *cwork;
4399: const PetscScalar *vwork;
4401: PetscFunctionBegin;
4402: if (B->assembled) PetscCall(MatZeroEntries(B));
4403: if (str == SAME_NONZERO_PATTERN) {
4404: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4405: for (i = rstart; i < rend; i++) {
4406: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4407: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4408: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4409: }
4410: } else {
4411: PetscCall(MatAYPX(B, 0.0, A, str));
4412: }
4413: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4414: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4415: PetscFunctionReturn(PETSC_SUCCESS);
4416: }
4418: /*@
4419: MatCopy - Copies a matrix to another matrix.
4421: Collective
4423: Input Parameters:
4424: + A - the matrix
4425: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4427: Output Parameter:
4428: . B - where the copy is put
4430: Level: intermediate
4432: Notes:
4433: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4435: `MatCopy()` copies the matrix entries of a matrix to another existing
4436: matrix (after first zeroing the second matrix). A related routine is
4437: `MatConvert()`, which first creates a new matrix and then copies the data.
4439: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4440: @*/
4441: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4442: {
4443: PetscInt i;
4445: PetscFunctionBegin;
4450: PetscCheckSameComm(A, 1, B, 2);
4451: MatCheckPreallocated(B, 2);
4452: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4453: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4454: 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,
4455: A->cmap->N, B->cmap->N);
4456: MatCheckPreallocated(A, 1);
4457: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4459: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4460: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4461: else PetscCall(MatCopy_Basic(A, B, str));
4463: B->stencil.dim = A->stencil.dim;
4464: B->stencil.noc = A->stencil.noc;
4465: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4466: B->stencil.dims[i] = A->stencil.dims[i];
4467: B->stencil.starts[i] = A->stencil.starts[i];
4468: }
4470: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4471: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4472: PetscFunctionReturn(PETSC_SUCCESS);
4473: }
4475: /*@
4476: MatConvert - Converts a matrix to another matrix, either of the same
4477: or different type.
4479: Collective
4481: Input Parameters:
4482: + mat - the matrix
4483: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4484: same type as the original matrix.
4485: - reuse - denotes if the destination matrix is to be created or reused.
4486: 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
4487: `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).
4489: Output Parameter:
4490: . M - pointer to place new matrix
4492: Level: intermediate
4494: Notes:
4495: `MatConvert()` first creates a new matrix and then copies the data from
4496: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4497: entries of one matrix to another already existing matrix context.
4499: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4500: the MPI communicator of the generated matrix is always the same as the communicator
4501: of the input matrix.
4503: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4504: @*/
4505: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4506: {
4507: PetscBool sametype, issame, flg;
4508: PetscBool3 issymmetric, ishermitian, isspd;
4509: char convname[256], mtype[256];
4510: Mat B;
4512: PetscFunctionBegin;
4515: PetscAssertPointer(M, 4);
4516: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4517: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4518: MatCheckPreallocated(mat, 1);
4520: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4521: if (flg) newtype = mtype;
4523: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4524: PetscCall(PetscStrcmp(newtype, "same", &issame));
4525: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4526: if (reuse == MAT_REUSE_MATRIX) {
4528: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4529: }
4531: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4532: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4533: PetscFunctionReturn(PETSC_SUCCESS);
4534: }
4536: /* Cache Mat options because some converters use MatHeaderReplace() */
4537: issymmetric = mat->symmetric;
4538: ishermitian = mat->hermitian;
4539: isspd = mat->spd;
4541: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4542: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4543: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4544: } else {
4545: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4546: const char *prefix[3] = {"seq", "mpi", ""};
4547: PetscInt i;
4548: /*
4549: Order of precedence:
4550: 0) See if newtype is a superclass of the current matrix.
4551: 1) See if a specialized converter is known to the current matrix.
4552: 2) See if a specialized converter is known to the desired matrix class.
4553: 3) See if a good general converter is registered for the desired class
4554: (as of 6/27/03 only MATMPIADJ falls into this category).
4555: 4) See if a good general converter is known for the current matrix.
4556: 5) Use a really basic converter.
4557: */
4559: /* 0) See if newtype is a superclass of the current matrix.
4560: i.e mat is mpiaij and newtype is aij */
4561: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4562: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4563: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4564: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4565: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4566: if (flg) {
4567: if (reuse == MAT_INPLACE_MATRIX) {
4568: PetscCall(PetscInfo(mat, "Early return\n"));
4569: PetscFunctionReturn(PETSC_SUCCESS);
4570: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4571: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4572: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4573: PetscFunctionReturn(PETSC_SUCCESS);
4574: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4575: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4576: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4577: PetscFunctionReturn(PETSC_SUCCESS);
4578: }
4579: }
4580: }
4581: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4582: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4583: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4584: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4585: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4586: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4587: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4588: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4589: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4590: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4591: if (conv) goto foundconv;
4592: }
4594: /* 2) See if a specialized converter is known to the desired matrix class. */
4595: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4596: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4597: PetscCall(MatSetType(B, newtype));
4598: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4599: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4600: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4601: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4602: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4603: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4604: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4605: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4606: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4607: if (conv) {
4608: PetscCall(MatDestroy(&B));
4609: goto foundconv;
4610: }
4611: }
4613: /* 3) See if a good general converter is registered for the desired class */
4614: conv = B->ops->convertfrom;
4615: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4616: PetscCall(MatDestroy(&B));
4617: if (conv) goto foundconv;
4619: /* 4) See if a good general converter is known for the current matrix */
4620: if (mat->ops->convert) conv = mat->ops->convert;
4621: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4622: if (conv) goto foundconv;
4624: /* 5) Use a really basic converter. */
4625: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4626: conv = MatConvert_Basic;
4628: foundconv:
4629: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4630: PetscCall((*conv)(mat, newtype, reuse, M));
4631: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4632: /* the block sizes must be same if the mappings are copied over */
4633: (*M)->rmap->bs = mat->rmap->bs;
4634: (*M)->cmap->bs = mat->cmap->bs;
4635: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4636: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4637: (*M)->rmap->mapping = mat->rmap->mapping;
4638: (*M)->cmap->mapping = mat->cmap->mapping;
4639: }
4640: (*M)->stencil.dim = mat->stencil.dim;
4641: (*M)->stencil.noc = mat->stencil.noc;
4642: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4643: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4644: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4645: }
4646: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4647: }
4648: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4650: /* Reset Mat options */
4651: if (issymmetric != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PetscBool3ToBool(issymmetric)));
4652: if (ishermitian != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PetscBool3ToBool(ishermitian)));
4653: if (isspd != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SPD, PetscBool3ToBool(isspd)));
4654: PetscFunctionReturn(PETSC_SUCCESS);
4655: }
4657: /*@
4658: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4660: Not Collective
4662: Input Parameter:
4663: . mat - the matrix, must be a factored matrix
4665: Output Parameter:
4666: . type - the string name of the package (do not free this string)
4668: Level: intermediate
4670: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4671: @*/
4672: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4673: {
4674: PetscErrorCode (*conv)(Mat, MatSolverType *);
4676: PetscFunctionBegin;
4679: PetscAssertPointer(type, 2);
4680: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4681: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4682: if (conv) PetscCall((*conv)(mat, type));
4683: else *type = MATSOLVERPETSC;
4684: PetscFunctionReturn(PETSC_SUCCESS);
4685: }
4687: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4688: struct _MatSolverTypeForSpecifcType {
4689: MatType mtype;
4690: /* no entry for MAT_FACTOR_NONE */
4691: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4692: MatSolverTypeForSpecifcType next;
4693: };
4695: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4696: struct _MatSolverTypeHolder {
4697: char *name;
4698: MatSolverTypeForSpecifcType handlers;
4699: MatSolverTypeHolder next;
4700: };
4702: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4704: /*@C
4705: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4707: Logically Collective, No Fortran Support
4709: Input Parameters:
4710: + package - name of the package, for example `petsc` or `superlu`
4711: . mtype - the matrix type that works with this package
4712: . ftype - the type of factorization supported by the package
4713: - createfactor - routine that will create the factored matrix ready to be used
4715: Level: developer
4717: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4718: `MatGetFactor()`
4719: @*/
4720: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4721: {
4722: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4723: PetscBool flg;
4724: MatSolverTypeForSpecifcType inext, iprev = NULL;
4726: PetscFunctionBegin;
4727: PetscCall(MatInitializePackage());
4728: if (!next) {
4729: PetscCall(PetscNew(&MatSolverTypeHolders));
4730: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4731: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4732: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4733: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4734: PetscFunctionReturn(PETSC_SUCCESS);
4735: }
4736: while (next) {
4737: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4738: if (flg) {
4739: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4740: inext = next->handlers;
4741: while (inext) {
4742: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4743: if (flg) {
4744: inext->createfactor[(int)ftype - 1] = createfactor;
4745: PetscFunctionReturn(PETSC_SUCCESS);
4746: }
4747: iprev = inext;
4748: inext = inext->next;
4749: }
4750: PetscCall(PetscNew(&iprev->next));
4751: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4752: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4753: PetscFunctionReturn(PETSC_SUCCESS);
4754: }
4755: prev = next;
4756: next = next->next;
4757: }
4758: PetscCall(PetscNew(&prev->next));
4759: PetscCall(PetscStrallocpy(package, &prev->next->name));
4760: PetscCall(PetscNew(&prev->next->handlers));
4761: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4762: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4763: PetscFunctionReturn(PETSC_SUCCESS);
4764: }
4766: /*@C
4767: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4769: Input Parameters:
4770: + 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
4771: . ftype - the type of factorization supported by the type
4772: - mtype - the matrix type that works with this type
4774: Output Parameters:
4775: + foundtype - `PETSC_TRUE` if the type was registered
4776: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4777: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4779: Calling sequence of `createfactor`:
4780: + A - the matrix providing the factor matrix
4781: . ftype - the `MatFactorType` of the factor requested
4782: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4784: Level: developer
4786: Note:
4787: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4788: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4789: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4791: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4792: `MatInitializePackage()`
4793: @*/
4794: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4795: {
4796: MatSolverTypeHolder next = MatSolverTypeHolders;
4797: PetscBool flg;
4798: MatSolverTypeForSpecifcType inext;
4800: PetscFunctionBegin;
4801: if (foundtype) *foundtype = PETSC_FALSE;
4802: if (foundmtype) *foundmtype = PETSC_FALSE;
4803: if (createfactor) *createfactor = NULL;
4805: if (type) {
4806: while (next) {
4807: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4808: if (flg) {
4809: if (foundtype) *foundtype = PETSC_TRUE;
4810: inext = next->handlers;
4811: while (inext) {
4812: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4813: if (flg) {
4814: if (foundmtype) *foundmtype = PETSC_TRUE;
4815: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4816: PetscFunctionReturn(PETSC_SUCCESS);
4817: }
4818: inext = inext->next;
4819: }
4820: }
4821: next = next->next;
4822: }
4823: } else {
4824: while (next) {
4825: inext = next->handlers;
4826: while (inext) {
4827: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4828: if (flg && inext->createfactor[(int)ftype - 1]) {
4829: if (foundtype) *foundtype = PETSC_TRUE;
4830: if (foundmtype) *foundmtype = PETSC_TRUE;
4831: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4832: PetscFunctionReturn(PETSC_SUCCESS);
4833: }
4834: inext = inext->next;
4835: }
4836: next = next->next;
4837: }
4838: /* try with base classes inext->mtype */
4839: next = MatSolverTypeHolders;
4840: while (next) {
4841: inext = next->handlers;
4842: while (inext) {
4843: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4844: if (flg && inext->createfactor[(int)ftype - 1]) {
4845: if (foundtype) *foundtype = PETSC_TRUE;
4846: if (foundmtype) *foundmtype = PETSC_TRUE;
4847: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4848: PetscFunctionReturn(PETSC_SUCCESS);
4849: }
4850: inext = inext->next;
4851: }
4852: next = next->next;
4853: }
4854: }
4855: PetscFunctionReturn(PETSC_SUCCESS);
4856: }
4858: PetscErrorCode MatSolverTypeDestroy(void)
4859: {
4860: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4861: MatSolverTypeForSpecifcType inext, iprev;
4863: PetscFunctionBegin;
4864: while (next) {
4865: PetscCall(PetscFree(next->name));
4866: inext = next->handlers;
4867: while (inext) {
4868: PetscCall(PetscFree(inext->mtype));
4869: iprev = inext;
4870: inext = inext->next;
4871: PetscCall(PetscFree(iprev));
4872: }
4873: prev = next;
4874: next = next->next;
4875: PetscCall(PetscFree(prev));
4876: }
4877: MatSolverTypeHolders = NULL;
4878: PetscFunctionReturn(PETSC_SUCCESS);
4879: }
4881: static PetscErrorCode MatGetFactor_Private(Mat mat, MatFactorType ftype, PetscBool exact, PetscBool *found, Mat *f)
4882: {
4883: MatSolverTypeHolder next = MatSolverTypeHolders;
4884: MatSolverTypeForSpecifcType inext;
4885: PetscBool flg, same;
4887: PetscFunctionBegin;
4888: *found = PETSC_FALSE;
4889: *f = NULL;
4890: /* When no solver type is requested, MatGetFactor() must honor registration order, but a registered
4891: MatSolverType may only be able to reject a particular MatType at runtime by returning NULL in *f.
4892: Keep walking the registry until a matching backend actually creates a factor. */
4893: while (next) {
4894: inext = next->handlers;
4895: while (inext) {
4896: PetscCall(PetscStrcmp(((PetscObject)mat)->type_name, inext->mtype, &same));
4897: if (exact) flg = same;
4898: else {
4899: /* Do the base-type pass separately from the exact pass so exact registrations for the MatType
4900: are all tried before broader registrations such as implementation base classes. */
4901: PetscCall(PetscStrbeginswith(((PetscObject)mat)->type_name, inext->mtype, &flg));
4902: flg = (PetscBool)(flg && !same);
4903: }
4904: if (flg && inext->createfactor[(int)ftype - 1]) {
4905: *found = PETSC_TRUE;
4906: PetscCall((*inext->createfactor[(int)ftype - 1])(mat, ftype, f));
4907: if (*f) PetscFunctionReturn(PETSC_SUCCESS);
4908: }
4909: inext = inext->next;
4910: }
4911: next = next->next;
4912: }
4913: PetscFunctionReturn(PETSC_SUCCESS);
4914: }
4916: /*@
4917: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4919: Logically Collective
4921: Input Parameter:
4922: . mat - the matrix
4924: Output Parameter:
4925: . flg - `PETSC_TRUE` if uses the ordering
4927: Level: developer
4929: Note:
4930: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4931: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4933: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4934: @*/
4935: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4936: {
4937: PetscFunctionBegin;
4938: *flg = mat->canuseordering;
4939: PetscFunctionReturn(PETSC_SUCCESS);
4940: }
4942: /*@
4943: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4945: Logically Collective
4947: Input Parameters:
4948: + mat - the matrix obtained with `MatGetFactor()`
4949: - ftype - the factorization type to be used
4951: Output Parameter:
4952: . otype - the preferred ordering type
4954: Level: developer
4956: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4957: @*/
4958: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4959: {
4960: PetscFunctionBegin;
4961: *otype = mat->preferredordering[ftype];
4962: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4963: PetscFunctionReturn(PETSC_SUCCESS);
4964: }
4966: /*@
4967: MatGetFactor - Returns a matrix suitable to calls to routines such as `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
4968: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, and `MatCholeskyFactorNumeric()`
4970: Collective
4972: Input Parameters:
4973: + mat - the matrix
4974: . 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
4975: the other criteria is returned
4976: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4978: Output Parameter:
4979: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4981: Options Database Keys:
4982: + -pc_factor_mat_solver_type type - choose the type at run time. When using `KSP` solvers
4983: . -pc_factor_mat_factor_on_host (true|false) - do matrix factorization on host (with device matrices). Default is doing it on device
4984: - -pc_factor_mat_solve_on_host (true|false) - do matrix solve on host (with device matrices). Default is doing it on device
4986: Level: intermediate
4988: Notes:
4989: Some of the packages, such as MUMPS, have options for controlling the factorization, these are in the form `-prefix_mat_packagename_packageoption`
4990: (for example, `-mat_mumps_icntl_6 1`) where `prefix` is normally set automatically from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly,
4991: without using a `PC`, one can set the prefix by
4992: calling `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4994: Some PETSc matrix formats have alternative solvers available that are provided by alternative packages
4995: such as PaStiX, SuperLU_DIST, MUMPS etc. PETSc must have been configured to use the external solver,
4996: using the corresponding `./configure` option such as `--download-package` or `--with-package-dir`.
4998: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4999: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
5000: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
5002: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
5003: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
5005: Developer Note:
5006: This should actually be called `MatCreateFactor()` since it creates a new factor object
5008: The `MatGetFactor()` implementations should not be accessing the PETSc options database or making other decisions about solver options,
5009: that should be delayed until the later operations. This is to ensure the correct options prefix has been set in the factor matrix.
5011: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
5012: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`,
5013: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`,
5014: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
5015: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactorNumeric()`
5016: @*/
5017: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
5018: {
5019: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
5020: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
5022: PetscFunctionBegin;
5026: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5027: MatCheckPreallocated(mat, 1);
5029: PetscCall(MatIsShell(mat, &shell));
5030: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
5031: if (hasop) {
5032: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
5033: PetscFunctionReturn(PETSC_SUCCESS);
5034: }
5036: if (!type) {
5037: PetscBool foundbase;
5039: /* First try exact MatType registrations in solver registration order. If all matching backends
5040: decline this matrix instance by returning NULL, then try base-type registrations. */
5041: PetscCall(MatGetFactor_Private(mat, ftype, PETSC_TRUE, &foundtype, f));
5042: if (!*f) {
5043: PetscCall(MatGetFactor_Private(mat, ftype, PETSC_FALSE, &foundbase, f));
5044: foundtype = (PetscBool)(foundtype || foundbase);
5045: }
5046: 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);
5047: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
5048: PetscFunctionReturn(PETSC_SUCCESS);
5049: }
5051: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
5052: 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],
5053: ((PetscObject)mat)->type_name, type ? " Perhaps you must ./configure with --download-" : "", type ? type : "");
5054: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
5055: 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);
5057: PetscCall((*conv)(mat, ftype, f));
5058: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
5059: PetscFunctionReturn(PETSC_SUCCESS);
5060: }
5062: /*@
5063: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
5065: Not Collective
5067: Input Parameters:
5068: + mat - the matrix
5069: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
5070: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
5072: Output Parameter:
5073: . flg - PETSC_TRUE if the factorization is available
5075: Level: intermediate
5077: Notes:
5078: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
5079: such as pastix, superlu, mumps etc.
5081: PETSc must have been ./configure to use the external solver, using the option --download-package
5083: Developer Note:
5084: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
5086: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
5087: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
5088: @*/
5089: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
5090: {
5091: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
5093: PetscFunctionBegin;
5095: PetscAssertPointer(flg, 4);
5097: *flg = PETSC_FALSE;
5098: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
5100: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5101: MatCheckPreallocated(mat, 1);
5103: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
5104: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
5105: PetscFunctionReturn(PETSC_SUCCESS);
5106: }
5108: /*@
5109: MatDuplicate - Duplicates a matrix including the non-zero structure.
5111: Collective
5113: Input Parameters:
5114: + mat - the matrix
5115: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
5116: See the manual page for `MatDuplicateOption()` for an explanation of these options.
5118: Output Parameter:
5119: . M - pointer to place new matrix
5121: Level: intermediate
5123: Notes:
5124: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
5126: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
5128: 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.
5130: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
5131: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
5132: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
5134: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
5135: @*/
5136: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
5137: {
5138: Mat B;
5139: VecType vtype;
5140: PetscInt i;
5141: PetscObject dm, container_h, container_d;
5142: PetscErrorCodeFn *viewf;
5144: PetscFunctionBegin;
5147: PetscAssertPointer(M, 3);
5148: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
5149: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5150: MatCheckPreallocated(mat, 1);
5152: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
5153: PetscUseTypeMethod(mat, duplicate, op, M);
5154: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
5155: B = *M;
5157: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
5158: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
5159: PetscCall(MatGetVecType(mat, &vtype));
5160: PetscCall(MatSetVecType(B, vtype));
5162: B->stencil.dim = mat->stencil.dim;
5163: B->stencil.noc = mat->stencil.noc;
5164: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
5165: B->stencil.dims[i] = mat->stencil.dims[i];
5166: B->stencil.starts[i] = mat->stencil.starts[i];
5167: }
5169: B->nooffproczerorows = mat->nooffproczerorows;
5170: B->nooffprocentries = mat->nooffprocentries;
5172: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5173: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5174: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5175: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5176: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5177: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5178: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5179: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5180: PetscFunctionReturn(PETSC_SUCCESS);
5181: }
5183: /*@
5184: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5186: Logically Collective
5188: Input Parameter:
5189: . mat - the matrix
5191: Output Parameter:
5192: . v - the diagonal of the matrix
5194: Level: intermediate
5196: Note:
5197: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5198: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5199: is larger than `ndiag`, the values of the remaining entries are unspecified.
5201: Currently only correct in parallel for square matrices.
5203: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5204: @*/
5205: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5206: {
5207: PetscFunctionBegin;
5211: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5212: MatCheckPreallocated(mat, 1);
5213: if (PetscDefined(USE_DEBUG)) {
5214: PetscInt nv, row, col, ndiag;
5216: PetscCall(VecGetLocalSize(v, &nv));
5217: PetscCall(MatGetLocalSize(mat, &row, &col));
5218: ndiag = PetscMin(row, col);
5219: 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);
5220: }
5222: PetscUseTypeMethod(mat, getdiagonal, v);
5223: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5224: PetscFunctionReturn(PETSC_SUCCESS);
5225: }
5227: /*@
5228: MatGetRowMin - Gets the minimum value (of the real part) of each
5229: row of the matrix
5231: Logically Collective
5233: Input Parameter:
5234: . mat - the matrix
5236: Output Parameters:
5237: + v - the vector for storing the maximums
5238: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5240: Level: intermediate
5242: Note:
5243: The result of this call are the same as if one converted the matrix to dense format
5244: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5246: This code is only implemented for a couple of matrix formats.
5248: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5249: `MatGetRowMax()`
5250: @*/
5251: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5252: {
5253: PetscFunctionBegin;
5257: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5259: if (!mat->cmap->N) {
5260: PetscCall(VecSet(v, PETSC_MAX_REAL));
5261: if (idx) {
5262: PetscInt i, m = mat->rmap->n;
5263: for (i = 0; i < m; i++) idx[i] = -1;
5264: }
5265: } else {
5266: MatCheckPreallocated(mat, 1);
5267: }
5268: PetscUseTypeMethod(mat, getrowmin, v, idx);
5269: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5270: PetscFunctionReturn(PETSC_SUCCESS);
5271: }
5273: /*@
5274: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5275: row of the matrix
5277: Logically Collective
5279: Input Parameter:
5280: . mat - the matrix
5282: Output Parameters:
5283: + v - the vector for storing the minimums
5284: - idx - the indices of the column found for each row (or `NULL` if not needed)
5286: Level: intermediate
5288: Notes:
5289: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5290: row is 0 (the first column).
5292: This code is only implemented for a couple of matrix formats.
5294: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5295: @*/
5296: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5297: {
5298: PetscFunctionBegin;
5302: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5303: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5305: if (!mat->cmap->N) {
5306: PetscCall(VecSet(v, 0.0));
5307: if (idx) {
5308: PetscInt i, m = mat->rmap->n;
5309: for (i = 0; i < m; i++) idx[i] = -1;
5310: }
5311: } else {
5312: MatCheckPreallocated(mat, 1);
5313: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5314: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5315: }
5316: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5317: PetscFunctionReturn(PETSC_SUCCESS);
5318: }
5320: /*@
5321: MatGetRowMax - Gets the maximum value (of the real part) of each
5322: row of the matrix
5324: Logically Collective
5326: Input Parameter:
5327: . mat - the matrix
5329: Output Parameters:
5330: + v - the vector for storing the maximums
5331: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5333: Level: intermediate
5335: Notes:
5336: The result of this call are the same as if one converted the matrix to dense format
5337: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5339: This code is only implemented for a couple of matrix formats.
5341: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5342: @*/
5343: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5344: {
5345: PetscFunctionBegin;
5349: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5351: if (!mat->cmap->N) {
5352: PetscCall(VecSet(v, PETSC_MIN_REAL));
5353: if (idx) {
5354: PetscInt i, m = mat->rmap->n;
5355: for (i = 0; i < m; i++) idx[i] = -1;
5356: }
5357: } else {
5358: MatCheckPreallocated(mat, 1);
5359: PetscUseTypeMethod(mat, getrowmax, v, idx);
5360: }
5361: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5362: PetscFunctionReturn(PETSC_SUCCESS);
5363: }
5365: /*@
5366: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5367: row of the matrix
5369: Logically Collective
5371: Input Parameter:
5372: . mat - the matrix
5374: Output Parameters:
5375: + v - the vector for storing the maximums
5376: - idx - the indices of the column found for each row (or `NULL` if not needed)
5378: Level: intermediate
5380: Notes:
5381: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5382: row is 0 (the first column).
5384: This code is only implemented for a couple of matrix formats.
5386: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5387: @*/
5388: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5389: {
5390: PetscFunctionBegin;
5394: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5396: if (!mat->cmap->N) {
5397: PetscCall(VecSet(v, 0.0));
5398: if (idx) {
5399: PetscInt i, m = mat->rmap->n;
5400: for (i = 0; i < m; i++) idx[i] = -1;
5401: }
5402: } else {
5403: MatCheckPreallocated(mat, 1);
5404: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5405: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5406: }
5407: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5408: PetscFunctionReturn(PETSC_SUCCESS);
5409: }
5411: /*@
5412: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5414: Logically Collective
5416: Input Parameter:
5417: . mat - the matrix
5419: Output Parameter:
5420: . v - the vector for storing the sum
5422: Level: intermediate
5424: This code is only implemented for a couple of matrix formats.
5426: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5427: @*/
5428: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5429: {
5430: PetscFunctionBegin;
5434: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5436: if (!mat->cmap->N) PetscCall(VecSet(v, 0.0));
5437: else {
5438: MatCheckPreallocated(mat, 1);
5439: PetscUseTypeMethod(mat, getrowsumabs, v);
5440: }
5441: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5442: PetscFunctionReturn(PETSC_SUCCESS);
5443: }
5445: /*@
5446: MatGetRowSum - Gets the sum of each row of the matrix
5448: Logically or Neighborhood Collective
5450: Input Parameter:
5451: . mat - the matrix
5453: Output Parameter:
5454: . v - the vector for storing the sum of rows
5456: Level: intermediate
5458: Note:
5459: This code is slow since it is not currently specialized for different formats
5461: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5462: @*/
5463: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5464: {
5465: Vec ones;
5467: PetscFunctionBegin;
5471: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5472: MatCheckPreallocated(mat, 1);
5473: PetscCall(MatCreateVecs(mat, &ones, NULL));
5474: PetscCall(VecSet(ones, 1.));
5475: PetscCall(MatMult(mat, ones, v));
5476: PetscCall(VecDestroy(&ones));
5477: PetscFunctionReturn(PETSC_SUCCESS);
5478: }
5480: /*@
5481: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5482: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5484: Collective
5486: Input Parameter:
5487: . mat - the matrix to provide the transpose
5489: Output Parameter:
5490: . 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
5492: Level: advanced
5494: Note:
5495: 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
5496: routine allows bypassing that call.
5498: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5499: @*/
5500: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5501: {
5502: MatParentState *rb = NULL;
5504: PetscFunctionBegin;
5505: PetscCall(PetscNew(&rb));
5506: rb->id = ((PetscObject)mat)->id;
5507: rb->state = 0;
5508: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5509: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5510: PetscFunctionReturn(PETSC_SUCCESS);
5511: }
5513: static PetscErrorCode MatTranspose_Private(Mat mat, MatReuse reuse, Mat *B, PetscBool conjugate)
5514: {
5515: PetscContainer rB = NULL;
5516: MatParentState *rb = NULL;
5517: PetscErrorCode (*f)(Mat, MatReuse, Mat *) = NULL;
5519: PetscFunctionBegin;
5522: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5523: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5524: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5525: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5526: MatCheckPreallocated(mat, 1);
5527: if (reuse == MAT_REUSE_MATRIX) {
5528: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5529: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5530: PetscCall(PetscContainerGetPointer(rB, &rb));
5531: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5532: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5533: }
5535: if (conjugate) {
5536: f = mat->ops->hermitiantranspose;
5537: if (f) PetscCall((*f)(mat, reuse, B));
5538: }
5539: if (!f && !(reuse == MAT_INPLACE_MATRIX && mat->hermitian == PETSC_BOOL3_TRUE && conjugate)) {
5540: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5541: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5542: PetscUseTypeMethod(mat, transpose, reuse, B);
5543: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5544: }
5545: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5546: if (conjugate) PetscCall(MatConjugate(*B));
5547: }
5549: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5550: if (reuse != MAT_INPLACE_MATRIX) {
5551: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5552: PetscCall(PetscContainerGetPointer(rB, &rb));
5553: rb->state = ((PetscObject)mat)->state;
5554: rb->nonzerostate = mat->nonzerostate;
5555: }
5556: PetscFunctionReturn(PETSC_SUCCESS);
5557: }
5559: /*@
5560: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5562: Collective
5564: Input Parameters:
5565: + mat - the matrix to transpose
5566: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5568: Output Parameter:
5569: . B - the transpose of the matrix
5571: Level: intermediate
5573: Notes:
5574: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5576: `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
5577: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5579: 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.
5581: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5582: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5584: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5586: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5588: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5589: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5590: @*/
5591: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5592: {
5593: PetscFunctionBegin;
5594: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_FALSE));
5595: PetscFunctionReturn(PETSC_SUCCESS);
5596: }
5598: /*@
5599: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5601: Collective
5603: Input Parameter:
5604: . A - the matrix to transpose
5606: Output Parameter:
5607: . 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
5608: numerical portion.
5610: Level: intermediate
5612: Note:
5613: This is not supported for many matrix types, use `MatTranspose()` in those cases
5615: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5616: @*/
5617: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5618: {
5619: PetscFunctionBegin;
5622: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5623: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5624: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5625: PetscUseTypeMethod(A, transposesymbolic, B);
5626: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5628: PetscCall(MatTransposeSetPrecursor(A, *B));
5629: PetscFunctionReturn(PETSC_SUCCESS);
5630: }
5632: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5633: {
5634: PetscContainer rB;
5635: MatParentState *rb;
5637: PetscFunctionBegin;
5640: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5641: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5642: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5643: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5644: PetscCall(PetscContainerGetPointer(rB, &rb));
5645: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5646: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5647: PetscFunctionReturn(PETSC_SUCCESS);
5648: }
5650: /*@
5651: MatIsTranspose - Test whether a matrix is another one's transpose,
5652: or its own, in which case it tests symmetry.
5654: Collective
5656: Input Parameters:
5657: + A - the matrix to test
5658: . B - the matrix to test against, this can equal the first parameter
5659: - tol - tolerance, differences between entries smaller than this are counted as zero
5661: Output Parameter:
5662: . flg - the result
5664: Level: intermediate
5666: Notes:
5667: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5668: test involves parallel copies of the block off-diagonal parts of the matrix.
5670: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5671: @*/
5672: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5673: {
5674: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5676: PetscFunctionBegin;
5679: PetscAssertPointer(flg, 4);
5680: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5681: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5682: *flg = PETSC_FALSE;
5683: if (f && g) {
5684: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5685: PetscCall((*f)(A, B, tol, flg));
5686: } else {
5687: MatType mattype;
5689: PetscCall(MatGetType(f ? B : A, &mattype));
5690: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5691: }
5692: PetscFunctionReturn(PETSC_SUCCESS);
5693: }
5695: /*@
5696: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5698: Collective
5700: Input Parameters:
5701: + mat - the matrix to transpose and complex conjugate
5702: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5704: Output Parameter:
5705: . B - the Hermitian transpose
5707: Level: intermediate
5709: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5710: @*/
5711: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5712: {
5713: PetscFunctionBegin;
5714: PetscCall(MatTranspose_Private(mat, reuse, B, PetscDefined(USE_COMPLEX) ? PETSC_TRUE : PETSC_FALSE));
5715: PetscFunctionReturn(PETSC_SUCCESS);
5716: }
5718: /*@
5719: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5721: Collective
5723: Input Parameters:
5724: + A - the matrix to test
5725: . B - the matrix to test against, this can equal the first parameter
5726: - tol - tolerance, differences between entries smaller than this are counted as zero
5728: Output Parameter:
5729: . flg - the result
5731: Level: intermediate
5733: Notes:
5734: Only available for `MATAIJ` matrices.
5736: The sequential algorithm
5737: has a running time of the order of the number of nonzeros; the parallel
5738: test involves parallel copies of the block off-diagonal parts of the matrix.
5740: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5741: @*/
5742: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5743: {
5744: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5746: PetscFunctionBegin;
5749: PetscAssertPointer(flg, 4);
5750: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5751: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5752: if (f && g) {
5753: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5754: PetscCall((*f)(A, B, tol, flg));
5755: } else {
5756: MatType mattype;
5758: PetscCall(MatGetType(f ? B : A, &mattype));
5759: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for Hermitian transpose", mattype);
5760: }
5761: PetscFunctionReturn(PETSC_SUCCESS);
5762: }
5764: /*@
5765: MatPermute - Creates a new matrix with rows and columns permuted from the
5766: original.
5768: Collective
5770: Input Parameters:
5771: + mat - the matrix to permute
5772: . row - row permutation, each processor supplies only the permutation for its rows
5773: - col - column permutation, each processor supplies only the permutation for its columns
5775: Output Parameter:
5776: . B - the permuted matrix
5778: Level: advanced
5780: Note:
5781: The index sets map from row/col of permuted matrix to row/col of original matrix.
5782: The index sets should be on the same communicator as mat and have the same local sizes.
5784: Developer Note:
5785: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5786: exploit the fact that row and col are permutations, consider implementing the
5787: more general `MatCreateSubMatrix()` instead.
5789: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5790: @*/
5791: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5792: {
5793: PetscFunctionBegin;
5798: PetscAssertPointer(B, 4);
5799: PetscCheckSameComm(mat, 1, row, 2);
5800: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5801: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5802: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5803: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5804: MatCheckPreallocated(mat, 1);
5806: if (mat->ops->permute) {
5807: PetscUseTypeMethod(mat, permute, row, col, B);
5808: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5809: } else {
5810: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5811: }
5812: PetscFunctionReturn(PETSC_SUCCESS);
5813: }
5815: /*@
5816: MatEqual - Compares two matrices.
5818: Collective
5820: Input Parameters:
5821: + A - the first matrix
5822: - B - the second matrix
5824: Output Parameter:
5825: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5827: Level: intermediate
5829: Note:
5830: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5831: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5833: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5834: @*/
5835: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5836: {
5837: PetscFunctionBegin;
5842: PetscAssertPointer(flg, 3);
5843: PetscCheckSameComm(A, 1, B, 2);
5844: MatCheckPreallocated(A, 1);
5845: MatCheckPreallocated(B, 2);
5846: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5847: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5848: 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,
5849: B->cmap->N);
5850: if (A->ops->equal && A->ops->equal == B->ops->equal) PetscUseTypeMethod(A, equal, B, flg);
5851: else PetscCall(MatMultEqual(A, B, 10, flg));
5852: PetscFunctionReturn(PETSC_SUCCESS);
5853: }
5855: /*@
5856: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5857: matrices that are stored as vectors. Either of the two scaling
5858: matrices can be `NULL`.
5860: Collective
5862: Input Parameters:
5863: + mat - the matrix to be scaled
5864: . l - the left scaling vector (or `NULL`)
5865: - r - the right scaling vector (or `NULL`)
5867: Level: intermediate
5869: Note:
5870: `MatDiagonalScale()` computes $A = LAR$, where
5871: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5872: The L scales the rows of the matrix, the R scales the columns of the matrix.
5874: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5875: @*/
5876: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5877: {
5878: PetscBool flg = PETSC_FALSE;
5880: PetscFunctionBegin;
5883: if (l) {
5885: PetscCheckSameComm(mat, 1, l, 2);
5886: }
5887: if (r) {
5889: PetscCheckSameComm(mat, 1, r, 3);
5890: }
5891: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5892: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5893: MatCheckPreallocated(mat, 1);
5894: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5896: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5897: PetscUseTypeMethod(mat, diagonalscale, l, r);
5898: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5899: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5900: if (l != r && (PetscBool3ToBool(mat->symmetric) || PetscBool3ToBool(mat->hermitian))) {
5901: if (!PetscDefined(USE_COMPLEX) || PetscBool3ToBool(mat->symmetric)) {
5902: if (l && r) PetscCall(VecEqual(l, r, &flg));
5903: if (!flg) {
5904: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5905: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format, left and right scaling vectors must be the same");
5906: mat->symmetric = mat->spd = PETSC_BOOL3_FALSE;
5907: if (!PetscDefined(USE_COMPLEX)) mat->hermitian = PETSC_BOOL3_FALSE;
5908: else mat->hermitian = PETSC_BOOL3_UNKNOWN;
5909: }
5910: }
5911: if (PetscDefined(USE_COMPLEX) && PetscBool3ToBool(mat->hermitian)) {
5912: flg = PETSC_FALSE;
5913: if (l && r) {
5914: Vec conjugate;
5916: PetscCall(VecDuplicate(l, &conjugate));
5917: PetscCall(VecCopy(l, conjugate));
5918: PetscCall(VecConjugate(conjugate));
5919: PetscCall(VecEqual(conjugate, r, &flg));
5920: PetscCall(VecDestroy(&conjugate));
5921: }
5922: if (!flg) {
5923: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5924: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format and Hermitian matrix, left and right scaling vectors must be conjugate one of the other");
5925: mat->hermitian = PETSC_BOOL3_FALSE;
5926: mat->symmetric = mat->spd = PETSC_BOOL3_UNKNOWN;
5927: }
5928: }
5929: }
5930: PetscFunctionReturn(PETSC_SUCCESS);
5931: }
5933: /*@
5934: MatScale - Scales all elements of a matrix by a given number.
5936: Logically Collective
5938: Input Parameters:
5939: + mat - the matrix to be scaled
5940: - a - the scaling value
5942: Level: intermediate
5944: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5945: @*/
5946: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5947: {
5948: PetscFunctionBegin;
5951: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5952: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5954: MatCheckPreallocated(mat, 1);
5956: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5957: if (a != (PetscScalar)1.0) {
5958: PetscUseTypeMethod(mat, scale, a);
5959: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5960: }
5961: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5962: PetscFunctionReturn(PETSC_SUCCESS);
5963: }
5965: /*@
5966: MatNorm - Calculates various norms of a matrix.
5968: Collective
5970: Input Parameters:
5971: + mat - the matrix
5972: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5974: Output Parameter:
5975: . nrm - the resulting norm
5977: Level: intermediate
5979: .seealso: [](ch_matrices), `Mat`, `MatNormApproximate()`
5980: @*/
5981: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5982: {
5983: PetscFunctionBegin;
5987: PetscAssertPointer(nrm, 3);
5989: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5990: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5991: MatCheckPreallocated(mat, 1);
5993: PetscUseTypeMethod(mat, norm, type, nrm);
5994: PetscFunctionReturn(PETSC_SUCCESS);
5995: }
5997: static PetscErrorCode VecSetFinalNormApp_Private(Vec x)
5998: {
5999: PetscScalar *ax, nm1;
6000: PetscInt st, en, n;
6002: PetscFunctionBegin;
6003: PetscCall(VecGetSize(x, &n));
6004: if (n < 2) PetscFunctionReturn(PETSC_SUCCESS);
6005: nm1 = n - 1;
6006: PetscCall(VecGetOwnershipRange(x, &st, &en));
6007: PetscCall(VecGetArrayWrite(x, &ax));
6008: for (PetscInt i = st; i < en; i++) {
6009: const PetscInt ii = i - st;
6010: const PetscScalar s = i % 2 ? -1.0 : 1.0;
6012: ax[ii] = s * (1.0 + i / nm1);
6013: }
6014: PetscCall(VecRestoreArrayWrite(x, &ax));
6015: PetscFunctionReturn(PETSC_SUCCESS);
6016: }
6018: static PetscErrorCode MatNormApproximateForwardOnly_Private(Mat A, NormType normtype, PetscInt maxit, PetscBool boundtocpu, PetscReal *n)
6019: {
6020: Vec x, y;
6021: PetscReal normx, normy;
6022: PetscInt i, N;
6023: PetscRandom rnd;
6025: PetscFunctionBegin;
6026: if (maxit < 0) maxit = 1;
6027: PetscCall(PetscRandomCreate(PetscObjectComm((PetscObject)A), &rnd));
6028: PetscCall(PetscRandomSetFromOptions(rnd));
6029: PetscCall(MatCreateVecs(A, &x, &y));
6030: PetscCall(VecBindToCPU(x, boundtocpu));
6031: PetscCall(VecBindToCPU(y, boundtocpu));
6032: PetscCall(VecGetSize(x, &N));
6033: *n = 0.0;
6034: for (i = 0; i < maxit; i++) {
6035: PetscCall(VecSetRandom(x, rnd));
6036: switch (normtype) {
6037: case NORM_1:
6038: PetscCall(VecNorm(x, NORM_1, &normx));
6039: if (normx > 0.0) PetscCall(VecScale(x, 1.0 / normx));
6040: break;
6041: case NORM_INFINITY:
6042: PetscCall(VecShift(x, -0.5));
6043: PetscCall(VecPointwiseSign(x, x, VEC_SIGN_ZERO_TO_SIGNED_UNIT));
6044: break;
6045: case NORM_2:
6046: PetscCall(VecNormalize(x, NULL));
6047: break;
6048: default:
6049: PetscUnreachable();
6050: }
6051: PetscCall(MatMult(A, x, y));
6052: PetscCall(VecNorm(y, normtype, &normy));
6053: *n = PetscMax(*n, normy);
6054: PetscCall(PetscInfo(A, "%s norm forward-only sample %" PetscInt_FMT " -> %g\n", NormTypes[normtype], i, (double)normy));
6055: }
6056: PetscCall(VecDestroy(&x));
6057: PetscCall(VecDestroy(&y));
6058: PetscCall(PetscRandomDestroy(&rnd));
6059: PetscFunctionReturn(PETSC_SUCCESS);
6060: }
6062: /*@
6063: MatNormApproximate - Approximate the norm of a matrix.
6065: Collective
6067: Input Parameters:
6068: + A - the matrix
6069: . normtype - the `NormType`
6070: - maxit - maximum number of iterations to use
6072: Output Parameter:
6073: . n - the norm estimate
6075: Level: intermediate
6077: Notes:
6078: 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`.
6080: If `maxit` is negative, a default number of iterations (10 for `NORM_1` and `NORM_INFINITY` and 20 for `NORM_2`) is performed.
6082: .seealso: [](ch_matrices), `Mat`, `MatNorm()`
6083: @*/
6084: PetscErrorCode MatNormApproximate(Mat A, NormType normtype, PetscInt maxit, PetscReal *n)
6085: {
6086: Vec x, y, w, z;
6087: PetscReal normz, adot;
6088: PetscScalar dot;
6089: PetscInt i, j, N, jold = -1;
6090: PetscBool boundtocpu = PETSC_TRUE, setherm, isherm, hasop;
6092: PetscFunctionBegin;
6097: PetscAssertPointer(n, 4);
6098: #if PetscDefined(HAVE_DEVICE)
6099: boundtocpu = A->boundtocpu;
6100: #endif
6101: PetscCall(MatHasOperation(A, MATOP_MULT_HERMITIAN_TRANSPOSE, &hasop));
6102: switch (normtype) {
6103: case NORM_INFINITY:
6104: case NORM_1:
6105: if (!hasop) {
6106: PetscCall(MatNormApproximateForwardOnly_Private(A, normtype, maxit, boundtocpu, n));
6107: i = maxit;
6108: break;
6109: } else {
6110: PetscCall(MatIsHermitianKnown(A, &setherm, &isherm));
6111: if ((setherm && isherm) || normtype == NORM_1) PetscCall(PetscObjectReference((PetscObject)A));
6112: else {
6113: Mat B;
6115: PetscCall(MatCreateHermitianTranspose(A, &B));
6116: A = B;
6117: }
6118: }
6119: if (maxit < 0) maxit = 10; /* pure guess */
6120: PetscCall(MatCreateVecs(A, &x, &y));
6121: PetscCall(MatCreateVecs(A, &z, &w));
6122: PetscCall(VecBindToCPU(x, boundtocpu));
6123: PetscCall(VecBindToCPU(y, boundtocpu));
6124: PetscCall(VecBindToCPU(z, boundtocpu));
6125: PetscCall(VecBindToCPU(w, boundtocpu));
6126: PetscCall(VecGetSize(x, &N));
6127: PetscCall(VecSet(x, 1. / N));
6128: *n = 0.0;
6129: for (i = 0; i < maxit; i++) {
6130: PetscCall(MatMult(A, x, y));
6131: PetscCall(VecNorm(y, NORM_1, n));
6132: if (PetscDefined(USE_COMPLEX)) {
6133: PetscCall(VecCopy(y, w));
6134: PetscCall(VecAbs(w));
6135: PetscCall(VecPointwiseDivide(w, y, w));
6136: } else PetscCall(VecPointwiseSign(w, y, VEC_SIGN_ZERO_TO_SIGNED_UNIT));
6137: PetscCall(MatMultHermitianTranspose(A, w, z));
6138: PetscCall(VecRealPart(z));
6139: PetscCall(VecNorm(z, NORM_INFINITY, &normz));
6140: PetscCall(VecDot(x, z, &dot));
6141: adot = PetscAbsScalar(dot);
6142: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> %g (%g %g)\n", NormTypes[normtype], i, (double)*n, (double)normz, (double)adot));
6143: if (normz <= adot && i > 0) {
6144: PetscCall(PetscInfo(A, "%s norm converged\n", NormTypes[normtype]));
6145: break;
6146: }
6147: PetscCall(VecAbs(z));
6148: PetscCall(VecMax(z, &j, &normz));
6149: if (j == jold) {
6150: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> breakdown (j==jold)\n", NormTypes[normtype], i));
6151: break;
6152: }
6153: jold = j;
6154: if (i < maxit - 1) PetscCall(VecSetStdBasis(x, j));
6155: }
6156: /* last check */
6157: if (N > 1) {
6158: PetscReal ny;
6160: PetscCall(VecSetFinalNormApp_Private(x));
6161: PetscCall(MatMult(A, x, y));
6162: PetscCall(VecNorm(y, NORM_1, &ny));
6163: ny = 2 * ny / (3 * N);
6164: PetscCall(PetscInfo(A, "%s norm final check: current %g test %g\n", NormTypes[normtype], (double)*n, (double)ny));
6165: *n = PetscMax(*n, ny);
6166: }
6167: PetscCall(MatDestroy(&A));
6168: PetscCall(VecDestroy(&x));
6169: PetscCall(VecDestroy(&w));
6170: PetscCall(VecDestroy(&y));
6171: PetscCall(VecDestroy(&z));
6172: break;
6173: case NORM_2:
6174: if (!hasop) {
6175: PetscCall(MatNormApproximateForwardOnly_Private(A, normtype, maxit, boundtocpu, n));
6176: i = maxit;
6177: break;
6178: }
6179: if (maxit < 0) maxit = 20; /* pure guess */
6180: PetscCall(MatCreateVecs(A, &x, &y));
6181: PetscCall(MatCreateVecs(A, &z, NULL));
6182: PetscCall(VecBindToCPU(x, boundtocpu));
6183: PetscCall(VecBindToCPU(y, boundtocpu));
6184: PetscCall(VecBindToCPU(z, boundtocpu));
6185: PetscCall(VecSetRandom(x, NULL));
6186: PetscCall(VecNormalize(x, NULL));
6187: *n = 0.0;
6188: for (i = 0; i < maxit; i++) {
6189: PetscCall(MatMult(A, x, y));
6190: PetscCall(VecNormalize(y, n));
6191: PetscCall(MatMultHermitianTranspose(A, y, z));
6192: PetscCall(VecNorm(z, NORM_2, &normz));
6193: PetscCall(VecDot(x, z, &dot));
6194: adot = PetscAbsScalar(dot);
6195: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> %g (%g %g)\n", NormTypes[normtype], i, (double)*n, (double)normz, (double)adot));
6196: if (normz <= adot) {
6197: PetscCall(PetscInfo(A, "%s norm converged\n", NormTypes[normtype]));
6198: break;
6199: }
6200: if (i < maxit - 1) {
6201: Vec t;
6203: PetscCall(VecNormalize(z, NULL));
6204: t = x;
6205: x = z;
6206: z = t;
6207: }
6208: }
6209: PetscCall(VecDestroy(&x));
6210: PetscCall(VecDestroy(&y));
6211: PetscCall(VecDestroy(&z));
6212: break;
6213: default:
6214: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "%s norm not supported", NormTypes[normtype]);
6215: }
6216: PetscCall(PetscInfo(A, "%s norm %g computed in %" PetscInt_FMT " iterations\n", NormTypes[normtype], (double)*n, i));
6217: PetscFunctionReturn(PETSC_SUCCESS);
6218: }
6220: /*
6221: This variable is used to prevent counting of MatAssemblyBegin() that
6222: are called from within a MatAssemblyEnd().
6223: */
6224: static PetscInt MatAssemblyEnd_InUse = 0;
6225: /*@
6226: MatAssemblyBegin - Begins assembling the matrix. This routine should
6227: be called after completing all calls to `MatSetValues()`.
6229: Collective
6231: Input Parameters:
6232: + mat - the matrix
6233: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
6235: Level: beginner
6237: Notes:
6238: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
6239: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
6241: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
6242: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
6243: using the matrix.
6245: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
6246: 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
6247: a global collective operation requiring all processes that share the matrix.
6249: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
6250: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
6251: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
6253: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
6254: @*/
6255: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
6256: {
6257: PetscFunctionBegin;
6260: MatCheckPreallocated(mat, 1);
6261: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
6262: if (mat->assembled) {
6263: mat->was_assembled = PETSC_TRUE;
6264: mat->assembled = PETSC_FALSE;
6265: }
6267: if (!MatAssemblyEnd_InUse) {
6268: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
6269: PetscTryTypeMethod(mat, assemblybegin, type);
6270: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
6271: } else PetscTryTypeMethod(mat, assemblybegin, type);
6272: PetscFunctionReturn(PETSC_SUCCESS);
6273: }
6275: /*@
6276: MatAssembled - Indicates if a matrix has been assembled and is ready for
6277: use; for example, in matrix-vector product.
6279: Not Collective
6281: Input Parameter:
6282: . mat - the matrix
6284: Output Parameter:
6285: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
6287: Level: advanced
6289: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
6290: @*/
6291: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
6292: {
6293: PetscFunctionBegin;
6295: PetscAssertPointer(assembled, 2);
6296: *assembled = mat->assembled;
6297: PetscFunctionReturn(PETSC_SUCCESS);
6298: }
6300: /*@
6301: MatAssemblyEnd - Completes assembling the matrix. This routine should
6302: be called after `MatAssemblyBegin()`.
6304: Collective
6306: Input Parameters:
6307: + mat - the matrix
6308: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
6310: Options Database Key:
6311: . -mat_view [viewertype][:...] - option name and values. See `MatViewFromOptions()`/`PetscObjectViewFromOptions()` for the possible arguments
6313: Level: beginner
6315: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`,
6316: `MatViewFromOptions()`, `PetscObjectViewFromOptions()`
6317: @*/
6318: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
6319: {
6320: static PetscInt inassm = 0;
6321: PetscBool flg = PETSC_FALSE;
6323: PetscFunctionBegin;
6327: inassm++;
6328: MatAssemblyEnd_InUse++;
6329: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
6330: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
6331: PetscTryTypeMethod(mat, assemblyend, type);
6332: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
6333: } else PetscTryTypeMethod(mat, assemblyend, type);
6335: /* Flush assembly is not a true assembly */
6336: if (type != MAT_FLUSH_ASSEMBLY) {
6337: if (mat->num_ass) {
6338: if (!mat->symmetry_eternal) {
6339: mat->symmetric = PETSC_BOOL3_UNKNOWN;
6340: mat->hermitian = PETSC_BOOL3_UNKNOWN;
6341: }
6342: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
6343: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
6344: }
6345: mat->num_ass++;
6346: mat->assembled = PETSC_TRUE;
6347: mat->ass_nonzerostate = mat->nonzerostate;
6348: }
6350: mat->insertmode = NOT_SET_VALUES;
6351: MatAssemblyEnd_InUse--;
6352: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6353: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
6354: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6356: if (mat->checksymmetryonassembly) {
6357: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
6358: if (flg) {
6359: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6360: } else {
6361: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6362: }
6363: }
6364: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
6365: }
6366: inassm--;
6367: PetscFunctionReturn(PETSC_SUCCESS);
6368: }
6370: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
6371: /*@
6372: MatSetOption - Sets a parameter option for a matrix. Some options
6373: may be specific to certain storage formats. Some options
6374: determine how values will be inserted (or added). Sorted,
6375: row-oriented input will generally assemble the fastest. The default
6376: is row-oriented.
6378: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
6380: Input Parameters:
6381: + mat - the matrix
6382: . op - the option, one of those listed below (and possibly others),
6383: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6385: Options Describing Matrix Structure:
6386: + `MAT_SPD` - symmetric positive definite
6387: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
6388: . `MAT_HERMITIAN` - transpose is the complex conjugation
6389: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
6390: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
6391: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
6392: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
6394: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
6395: do not need to be computed (usually at a high cost)
6397: Options For Use with `MatSetValues()` and `MatGetValues()`:
6398: Insert a logically dense subblock, which can be
6399: . `MAT_ROW_ORIENTED` - row-oriented (default)
6401: These options reflect the data you pass in with `MatSetValues()` or receive with `MatGetValues()`; it has
6402: nothing to do with how the data is stored internally in the matrix
6403: data structure.
6405: When (re)assembling a matrix, we can restrict the input for
6406: efficiency/debugging purposes. These options include
6407: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
6408: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
6409: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
6410: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
6411: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
6412: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
6413: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
6414: performance for very large process counts.
6415: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
6416: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
6417: functions, instead sending only neighbor messages.
6419: Level: intermediate
6421: Notes:
6422: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
6424: Some options are relevant only for particular matrix types and
6425: are thus ignored by others. Other options are not supported by
6426: certain matrix types and will generate an error message if set.
6428: If using Fortran to compute a matrix, one may need to
6429: use the column-oriented option (or convert to the row-oriented
6430: format).
6432: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6433: that would generate a new entry in the nonzero structure is instead
6434: ignored. Thus, if memory has not already been allocated for this particular
6435: data, then the insertion is ignored. For dense matrices, in which
6436: the entire array is allocated, no entries are ever ignored.
6437: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6439: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6440: that would generate a new entry in the nonzero structure instead produces
6441: 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
6443: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6444: that would generate a new entry that has not been preallocated will
6445: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6446: only.) This is a useful flag when debugging matrix memory preallocation.
6447: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6449: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6450: other processors should be dropped, rather than stashed.
6451: This is useful if you know that the "owning" processor is also
6452: always generating the correct matrix entries, so that PETSc need
6453: not transfer duplicate entries generated on another processor.
6455: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6456: searches during matrix assembly. When this flag is set, the hash table
6457: is created during the first matrix assembly. This hash table is
6458: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6459: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6460: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6461: supported by `MATMPIBAIJ` format only.
6463: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6464: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6466: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6467: a zero location in the matrix
6469: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6471: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6472: zero row routines and thus improves performance for very large process counts.
6474: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6475: part of the matrix (since they should match the upper triangular part).
6477: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6478: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6479: with finite difference schemes with non-periodic boundary conditions.
6481: Developer Note:
6482: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6483: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6484: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6485: not changed.
6487: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6488: @*/
6489: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6490: {
6491: PetscFunctionBegin;
6493: if (op > 0) {
6496: }
6498: 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);
6500: switch (op) {
6501: case MAT_FORCE_DIAGONAL_ENTRIES:
6502: mat->force_diagonals = flg;
6503: PetscFunctionReturn(PETSC_SUCCESS);
6504: case MAT_NO_OFF_PROC_ENTRIES:
6505: mat->nooffprocentries = flg;
6506: PetscFunctionReturn(PETSC_SUCCESS);
6507: case MAT_SUBSET_OFF_PROC_ENTRIES:
6508: mat->assembly_subset = flg;
6509: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6510: #if !PetscDefined(HAVE_MPIUNI)
6511: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6512: #endif
6513: mat->stash.first_assembly_done = PETSC_FALSE;
6514: }
6515: PetscFunctionReturn(PETSC_SUCCESS);
6516: case MAT_NO_OFF_PROC_ZERO_ROWS:
6517: mat->nooffproczerorows = flg;
6518: PetscFunctionReturn(PETSC_SUCCESS);
6519: case MAT_SPD:
6520: if (flg) {
6521: mat->spd = PETSC_BOOL3_TRUE;
6522: mat->symmetric = PETSC_BOOL3_TRUE;
6523: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6524: #if !PetscDefined(USE_COMPLEX)
6525: mat->hermitian = PETSC_BOOL3_TRUE;
6526: #endif
6527: } else {
6528: mat->spd = PETSC_BOOL3_FALSE;
6529: }
6530: break;
6531: case MAT_SYMMETRIC:
6532: mat->symmetric = PetscBoolToBool3(flg);
6533: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6534: #if !PetscDefined(USE_COMPLEX)
6535: mat->hermitian = PetscBoolToBool3(flg);
6536: #endif
6537: break;
6538: case MAT_HERMITIAN:
6539: mat->hermitian = PetscBoolToBool3(flg);
6540: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6541: #if !PetscDefined(USE_COMPLEX)
6542: mat->symmetric = PetscBoolToBool3(flg);
6543: #endif
6544: break;
6545: case MAT_STRUCTURALLY_SYMMETRIC:
6546: mat->structurally_symmetric = PetscBoolToBool3(flg);
6547: break;
6548: case MAT_SYMMETRY_ETERNAL:
6549: 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");
6550: mat->symmetry_eternal = flg;
6551: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6552: break;
6553: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6554: 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");
6555: mat->structural_symmetry_eternal = flg;
6556: break;
6557: case MAT_SPD_ETERNAL:
6558: 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");
6559: mat->spd_eternal = flg;
6560: if (flg) {
6561: mat->structural_symmetry_eternal = PETSC_TRUE;
6562: mat->symmetry_eternal = PETSC_TRUE;
6563: }
6564: break;
6565: case MAT_STRUCTURE_ONLY:
6566: mat->structure_only = flg;
6567: break;
6568: case MAT_SORTED_FULL:
6569: mat->sortedfull = flg;
6570: break;
6571: default:
6572: break;
6573: }
6574: 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");
6575: PetscTryTypeMethod(mat, setoption, op, flg);
6576: PetscFunctionReturn(PETSC_SUCCESS);
6577: }
6579: /*@
6580: MatGetOption - Gets a parameter option that has been set for a matrix.
6582: Logically Collective
6584: Input Parameters:
6585: + mat - the matrix
6586: - op - the option, this only responds to certain options, check the code for which ones
6588: Output Parameter:
6589: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6591: Level: intermediate
6593: Notes:
6594: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6596: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6597: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6599: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6600: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6601: @*/
6602: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6603: {
6604: PetscFunctionBegin;
6608: 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);
6609: 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()");
6611: switch (op) {
6612: case MAT_NO_OFF_PROC_ENTRIES:
6613: *flg = mat->nooffprocentries;
6614: break;
6615: case MAT_NO_OFF_PROC_ZERO_ROWS:
6616: *flg = mat->nooffproczerorows;
6617: break;
6618: case MAT_SYMMETRIC:
6619: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6620: break;
6621: case MAT_HERMITIAN:
6622: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6623: break;
6624: case MAT_STRUCTURALLY_SYMMETRIC:
6625: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6626: break;
6627: case MAT_SPD:
6628: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6629: break;
6630: case MAT_SYMMETRY_ETERNAL:
6631: *flg = mat->symmetry_eternal;
6632: break;
6633: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6634: *flg = mat->symmetry_eternal;
6635: break;
6636: default:
6637: break;
6638: }
6639: PetscFunctionReturn(PETSC_SUCCESS);
6640: }
6642: /*@
6643: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6644: this routine retains the old nonzero structure.
6646: Logically Collective
6648: Input Parameter:
6649: . mat - the matrix
6651: Level: intermediate
6653: Note:
6654: 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.
6655: See the Performance chapter of the users manual for information on preallocating matrices.
6657: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6658: @*/
6659: PetscErrorCode MatZeroEntries(Mat mat)
6660: {
6661: PetscFunctionBegin;
6664: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6665: 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");
6666: MatCheckPreallocated(mat, 1);
6668: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6669: PetscUseTypeMethod(mat, zeroentries);
6670: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6671: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6672: PetscFunctionReturn(PETSC_SUCCESS);
6673: }
6675: /*@
6676: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6677: of a set of rows and columns of a matrix.
6679: Collective
6681: Input Parameters:
6682: + mat - the matrix
6683: . numRows - the number of rows/columns to zero
6684: . rows - the global row indices
6685: . diag - value put in the diagonal of the eliminated rows
6686: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6687: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6689: Level: intermediate
6691: Notes:
6692: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6694: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6695: 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
6697: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6698: Krylov method to take advantage of the known solution on the zeroed rows.
6700: For the parallel case, all processes that share the matrix (i.e.,
6701: those in the communicator used for matrix creation) MUST call this
6702: routine, regardless of whether any rows being zeroed are owned by
6703: them.
6705: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6706: 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
6707: missing.
6709: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6710: list only rows local to itself).
6712: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6714: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6715: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6716: @*/
6717: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6718: {
6719: PetscFunctionBegin;
6722: if (numRows) PetscAssertPointer(rows, 3);
6723: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6724: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6725: MatCheckPreallocated(mat, 1);
6727: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6728: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6729: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6730: PetscFunctionReturn(PETSC_SUCCESS);
6731: }
6733: /*@
6734: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6735: of a set of rows and columns of a matrix.
6737: Collective
6739: Input Parameters:
6740: + mat - the matrix
6741: . is - the rows to zero
6742: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6743: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6744: - b - optional vector of right-hand side, that will be adjusted by provided solution
6746: Level: intermediate
6748: Note:
6749: See `MatZeroRowsColumns()` for details on how this routine operates.
6751: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6752: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6753: @*/
6754: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6755: {
6756: PetscInt numRows;
6757: const PetscInt *rows;
6759: PetscFunctionBegin;
6764: PetscCall(ISGetLocalSize(is, &numRows));
6765: PetscCall(ISGetIndices(is, &rows));
6766: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6767: PetscCall(ISRestoreIndices(is, &rows));
6768: PetscFunctionReturn(PETSC_SUCCESS);
6769: }
6771: /*@
6772: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6773: of a set of rows of a matrix.
6775: Collective
6777: Input Parameters:
6778: + mat - the matrix
6779: . numRows - the number of rows to zero
6780: . rows - the global row indices
6781: . diag - value put in the diagonal of the zeroed rows
6782: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6783: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6785: Level: intermediate
6787: Notes:
6788: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6790: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6792: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6793: Krylov method to take advantage of the known solution on the zeroed rows.
6795: 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)
6796: from the matrix.
6798: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6799: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6800: formats this does not alter the nonzero structure.
6802: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6803: of the matrix is not changed the values are
6804: merely zeroed.
6806: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6807: formats can optionally remove the main diagonal entry from the
6808: nonzero structure as well, by passing 0.0 as the final argument).
6810: For the parallel case, all processes that share the matrix (i.e.,
6811: those in the communicator used for matrix creation) MUST call this
6812: routine, regardless of whether any rows being zeroed are owned by
6813: them.
6815: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6816: list only rows local to itself).
6818: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6819: owns that are to be zeroed. This saves a global synchronization in the implementation.
6821: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6822: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6823: @*/
6824: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6825: {
6826: PetscFunctionBegin;
6829: if (numRows) PetscAssertPointer(rows, 3);
6830: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6831: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6832: MatCheckPreallocated(mat, 1);
6834: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6835: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6836: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6837: PetscFunctionReturn(PETSC_SUCCESS);
6838: }
6840: /*@
6841: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6842: of a set of rows of a matrix indicated by an `IS`
6844: Collective
6846: Input Parameters:
6847: + mat - the matrix
6848: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6849: . diag - value put in all diagonals of eliminated rows
6850: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6851: - b - optional vector of right-hand side, that will be adjusted by provided solution
6853: Level: intermediate
6855: Note:
6856: See `MatZeroRows()` for details on how this routine operates.
6858: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6859: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6860: @*/
6861: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6862: {
6863: PetscInt numRows = 0;
6864: const PetscInt *rows = NULL;
6866: PetscFunctionBegin;
6869: if (is) {
6871: PetscCall(ISGetLocalSize(is, &numRows));
6872: PetscCall(ISGetIndices(is, &rows));
6873: }
6874: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6875: if (is) PetscCall(ISRestoreIndices(is, &rows));
6876: PetscFunctionReturn(PETSC_SUCCESS);
6877: }
6879: /*@
6880: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6881: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6883: Collective
6885: Input Parameters:
6886: + mat - the matrix
6887: . numRows - the number of rows to remove
6888: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6889: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6890: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6891: - b - optional vector of right-hand side, that will be adjusted by provided solution
6893: Level: intermediate
6895: Notes:
6896: See `MatZeroRows()` for details on how this routine operates.
6898: The grid coordinates are across the entire grid, not just the local portion
6900: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6901: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6902: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6903: `DM_BOUNDARY_PERIODIC` boundary type.
6905: 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
6906: a single value per point) you can skip filling those indices.
6908: Fortran Note:
6909: `idxm` and `idxn` should be declared as
6910: .vb
6911: MatStencil idxm(4, m)
6912: .ve
6913: and the values inserted using
6914: .vb
6915: idxm(MatStencil_i, 1) = i
6916: idxm(MatStencil_j, 1) = j
6917: idxm(MatStencil_k, 1) = k
6918: idxm(MatStencil_c, 1) = c
6919: etc
6920: .ve
6922: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6923: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6924: @*/
6925: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6926: {
6927: PetscInt dim = mat->stencil.dim;
6928: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6929: PetscInt *dims = mat->stencil.dims + 1;
6930: PetscInt *starts = mat->stencil.starts;
6931: PetscInt *dxm = (PetscInt *)rows;
6932: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6934: PetscFunctionBegin;
6937: if (numRows) PetscAssertPointer(rows, 3);
6939: PetscCall(PetscMalloc1(numRows, &jdxm));
6940: for (i = 0; i < numRows; ++i) {
6941: /* Skip unused dimensions (they are ordered k, j, i, c) */
6942: for (j = 0; j < 3 - sdim; ++j) dxm++;
6943: /* Local index in X dir */
6944: tmp = *dxm++ - starts[0];
6945: /* Loop over remaining dimensions */
6946: for (j = 0; j < dim - 1; ++j) {
6947: /* If nonlocal, set index to be negative */
6948: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6949: /* Update local index */
6950: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6951: }
6952: /* Skip component slot if necessary */
6953: if (mat->stencil.noc) dxm++;
6954: /* Local row number */
6955: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6956: }
6957: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6958: PetscCall(PetscFree(jdxm));
6959: PetscFunctionReturn(PETSC_SUCCESS);
6960: }
6962: /*@
6963: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6964: of a set of rows and columns of a matrix.
6966: Collective
6968: Input Parameters:
6969: + mat - the matrix
6970: . numRows - the number of rows/columns to remove
6971: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6972: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6973: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6974: - b - optional vector of right-hand side, that will be adjusted by provided solution
6976: Level: intermediate
6978: Notes:
6979: See `MatZeroRowsColumns()` for details on how this routine operates.
6981: The grid coordinates are across the entire grid, not just the local portion
6983: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6984: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6985: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6986: `DM_BOUNDARY_PERIODIC` boundary type.
6988: 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
6989: a single value per point) you can skip filling those indices.
6991: Fortran Note:
6992: `idxm` and `idxn` should be declared as
6993: .vb
6994: MatStencil idxm(4, m)
6995: .ve
6996: and the values inserted using
6997: .vb
6998: idxm(MatStencil_i, 1) = i
6999: idxm(MatStencil_j, 1) = j
7000: idxm(MatStencil_k, 1) = k
7001: idxm(MatStencil_c, 1) = c
7002: etc
7003: .ve
7005: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7006: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
7007: @*/
7008: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
7009: {
7010: PetscInt dim = mat->stencil.dim;
7011: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
7012: PetscInt *dims = mat->stencil.dims + 1;
7013: PetscInt *starts = mat->stencil.starts;
7014: PetscInt *dxm = (PetscInt *)rows;
7015: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
7017: PetscFunctionBegin;
7020: if (numRows) PetscAssertPointer(rows, 3);
7022: PetscCall(PetscMalloc1(numRows, &jdxm));
7023: for (i = 0; i < numRows; ++i) {
7024: /* Skip unused dimensions (they are ordered k, j, i, c) */
7025: for (j = 0; j < 3 - sdim; ++j) dxm++;
7026: /* Local index in X dir */
7027: tmp = *dxm++ - starts[0];
7028: /* Loop over remaining dimensions */
7029: for (j = 0; j < dim - 1; ++j) {
7030: /* If nonlocal, set index to be negative */
7031: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
7032: /* Update local index */
7033: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
7034: }
7035: /* Skip component slot if necessary */
7036: if (mat->stencil.noc) dxm++;
7037: /* Local row number */
7038: if (tmp >= 0) jdxm[numNewRows++] = tmp;
7039: }
7040: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
7041: PetscCall(PetscFree(jdxm));
7042: PetscFunctionReturn(PETSC_SUCCESS);
7043: }
7045: /*@
7046: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
7047: of a set of rows of a matrix; using local numbering of rows.
7049: Collective
7051: Input Parameters:
7052: + mat - the matrix
7053: . numRows - the number of rows to remove
7054: . rows - the local row indices
7055: . diag - value put in all diagonals of eliminated rows
7056: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7057: - b - optional vector of right-hand side, that will be adjusted by provided solution
7059: Level: intermediate
7061: Notes:
7062: Before calling `MatZeroRowsLocal()`, the user must first set the
7063: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
7065: See `MatZeroRows()` for details on how this routine operates.
7067: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
7068: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7069: @*/
7070: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
7071: {
7072: PetscFunctionBegin;
7075: if (numRows) PetscAssertPointer(rows, 3);
7076: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7077: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7078: MatCheckPreallocated(mat, 1);
7080: if (mat->ops->zerorowslocal) {
7081: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
7082: } else {
7083: IS is, newis;
7084: PetscInt *newRows, nl = 0;
7086: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
7087: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
7088: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
7089: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
7090: for (PetscInt i = 0; i < numRows; i++)
7091: if (newRows[i] > -1) newRows[nl++] = newRows[i];
7092: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
7093: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
7094: PetscCall(ISDestroy(&newis));
7095: PetscCall(ISDestroy(&is));
7096: }
7097: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
7098: PetscFunctionReturn(PETSC_SUCCESS);
7099: }
7101: /*@
7102: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
7103: of a set of rows of a matrix; using local numbering of rows.
7105: Collective
7107: Input Parameters:
7108: + mat - the matrix
7109: . is - index set of rows to remove
7110: . diag - value put in all diagonals of eliminated rows
7111: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7112: - b - optional vector of right-hand side, that will be adjusted by provided solution
7114: Level: intermediate
7116: Notes:
7117: Before calling `MatZeroRowsLocalIS()`, the user must first set the
7118: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7120: See `MatZeroRows()` for details on how this routine operates.
7122: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7123: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7124: @*/
7125: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
7126: {
7127: PetscInt numRows;
7128: const PetscInt *rows;
7130: PetscFunctionBegin;
7134: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7135: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7136: MatCheckPreallocated(mat, 1);
7138: PetscCall(ISGetLocalSize(is, &numRows));
7139: PetscCall(ISGetIndices(is, &rows));
7140: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
7141: PetscCall(ISRestoreIndices(is, &rows));
7142: PetscFunctionReturn(PETSC_SUCCESS);
7143: }
7145: /*@
7146: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
7147: of a set of rows and columns of a matrix; using local numbering of rows.
7149: Collective
7151: Input Parameters:
7152: + mat - the matrix
7153: . numRows - the number of rows to remove
7154: . rows - the global row indices
7155: . diag - value put in all diagonals of eliminated rows
7156: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7157: - b - optional vector of right-hand side, that will be adjusted by provided solution
7159: Level: intermediate
7161: Notes:
7162: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
7163: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7165: See `MatZeroRowsColumns()` for details on how this routine operates.
7167: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7168: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7169: @*/
7170: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
7171: {
7172: PetscFunctionBegin;
7175: if (numRows) PetscAssertPointer(rows, 3);
7176: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7177: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7178: MatCheckPreallocated(mat, 1);
7180: if (mat->ops->zerorowscolumnslocal) {
7181: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
7182: } else {
7183: IS is, newis;
7184: PetscInt *newRows, nl = 0;
7186: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
7187: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
7188: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
7189: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
7190: for (PetscInt i = 0; i < numRows; i++)
7191: if (newRows[i] > -1) newRows[nl++] = newRows[i];
7192: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
7193: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
7194: PetscCall(ISDestroy(&newis));
7195: PetscCall(ISDestroy(&is));
7196: }
7197: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
7198: PetscFunctionReturn(PETSC_SUCCESS);
7199: }
7201: /*@
7202: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
7203: of a set of rows and columns of a matrix; using local numbering of rows.
7205: Collective
7207: Input Parameters:
7208: + mat - the matrix
7209: . is - index set of rows to remove
7210: . diag - value put in all diagonals of eliminated rows
7211: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7212: - b - optional vector of right-hand side, that will be adjusted by provided solution
7214: Level: intermediate
7216: Notes:
7217: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
7218: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7220: See `MatZeroRowsColumns()` for details on how this routine operates.
7222: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7223: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7224: @*/
7225: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
7226: {
7227: PetscInt numRows;
7228: const PetscInt *rows;
7230: PetscFunctionBegin;
7234: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7235: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7236: MatCheckPreallocated(mat, 1);
7238: PetscCall(ISGetLocalSize(is, &numRows));
7239: PetscCall(ISGetIndices(is, &rows));
7240: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
7241: PetscCall(ISRestoreIndices(is, &rows));
7242: PetscFunctionReturn(PETSC_SUCCESS);
7243: }
7245: /*@
7246: MatGetSize - Returns the numbers of rows and columns in a matrix.
7248: Not Collective
7250: Input Parameter:
7251: . mat - the matrix
7253: Output Parameters:
7254: + m - the number of global rows
7255: - n - the number of global columns
7257: Level: beginner
7259: Note:
7260: Both output parameters can be `NULL` on input.
7262: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
7263: @*/
7264: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
7265: {
7266: PetscFunctionBegin;
7268: if (m) *m = mat->rmap->N;
7269: if (n) *n = mat->cmap->N;
7270: PetscFunctionReturn(PETSC_SUCCESS);
7271: }
7273: /*@
7274: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
7275: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
7277: Not Collective
7279: Input Parameter:
7280: . mat - the matrix
7282: Output Parameters:
7283: + m - the number of local rows, use `NULL` to not obtain this value
7284: - n - the number of local columns, use `NULL` to not obtain this value
7286: Level: beginner
7288: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
7289: @*/
7290: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
7291: {
7292: PetscFunctionBegin;
7294: if (m) PetscAssertPointer(m, 2);
7295: if (n) PetscAssertPointer(n, 3);
7296: if (m) *m = mat->rmap->n;
7297: if (n) *n = mat->cmap->n;
7298: PetscFunctionReturn(PETSC_SUCCESS);
7299: }
7301: /*@
7302: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
7303: vector one multiplies this matrix by that are owned by this processor.
7305: Not Collective, unless matrix has not been allocated, then collective
7307: Input Parameter:
7308: . mat - the matrix
7310: Output Parameters:
7311: + m - the global index of the first local column, use `NULL` to not obtain this value
7312: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
7314: Level: developer
7316: Notes:
7317: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7319: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7320: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7322: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7323: the local values in the matrix.
7325: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
7326: Layouts](sec_matlayout) for details on matrix layouts.
7328: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7329: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7330: @*/
7331: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
7332: {
7333: PetscFunctionBegin;
7336: if (m) PetscAssertPointer(m, 2);
7337: if (n) PetscAssertPointer(n, 3);
7338: MatCheckPreallocated(mat, 1);
7339: if (m) *m = mat->cmap->rstart;
7340: if (n) *n = mat->cmap->rend;
7341: PetscFunctionReturn(PETSC_SUCCESS);
7342: }
7344: /*@
7345: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
7346: this MPI process.
7348: Not Collective
7350: Input Parameter:
7351: . mat - the matrix
7353: Output Parameters:
7354: + m - the global index of the first local row, use `NULL` to not obtain this value
7355: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
7357: Level: beginner
7359: Notes:
7360: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7362: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7363: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7365: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7366: the local values in the matrix.
7368: The high argument is one more than the last element stored locally.
7370: For all matrices it returns the range of matrix rows associated with rows of a vector that
7371: would contain the result of a matrix vector product with this matrix. See [Matrix
7372: Layouts](sec_matlayout) for details on matrix layouts.
7374: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
7375: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7376: @*/
7377: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
7378: {
7379: PetscFunctionBegin;
7382: if (m) PetscAssertPointer(m, 2);
7383: if (n) PetscAssertPointer(n, 3);
7384: MatCheckPreallocated(mat, 1);
7385: if (m) *m = mat->rmap->rstart;
7386: if (n) *n = mat->rmap->rend;
7387: PetscFunctionReturn(PETSC_SUCCESS);
7388: }
7390: /*@C
7391: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
7392: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
7394: Not Collective, unless matrix has not been allocated
7396: Input Parameter:
7397: . mat - the matrix
7399: Output Parameter:
7400: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
7401: where `size` is the number of MPI processes used by `mat`
7403: Level: beginner
7405: Notes:
7406: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7408: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7409: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7411: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7412: the local values in the matrix.
7414: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
7415: would contain the result of a matrix vector product with this matrix. See [Matrix
7416: Layouts](sec_matlayout) for details on matrix layouts.
7418: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7419: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
7420: `DMDAGetGhostCorners()`, `DM`
7421: @*/
7422: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
7423: {
7424: PetscFunctionBegin;
7427: MatCheckPreallocated(mat, 1);
7428: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
7429: PetscFunctionReturn(PETSC_SUCCESS);
7430: }
7432: /*@C
7433: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
7434: vector one multiplies this vector by that are owned by each processor.
7436: Not Collective, unless matrix has not been allocated
7438: Input Parameter:
7439: . mat - the matrix
7441: Output Parameter:
7442: . ranges - start of each processors portion plus one more than the total length at the end
7444: Level: beginner
7446: Notes:
7447: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7449: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7450: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7452: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7453: the local values in the matrix.
7455: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7456: Layouts](sec_matlayout) for details on matrix layouts.
7458: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7459: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7460: `DMDAGetGhostCorners()`, `DM`
7461: @*/
7462: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7463: {
7464: PetscFunctionBegin;
7467: MatCheckPreallocated(mat, 1);
7468: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7469: PetscFunctionReturn(PETSC_SUCCESS);
7470: }
7472: /*@
7473: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7475: Not Collective
7477: Input Parameter:
7478: . A - matrix
7480: Output Parameters:
7481: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7482: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7484: Level: intermediate
7486: Note:
7487: You should call `ISDestroy()` on the returned `IS`
7489: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7490: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7491: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7492: details on matrix layouts.
7494: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7495: @*/
7496: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7497: {
7498: PetscErrorCode (*f)(Mat, IS *, IS *);
7500: PetscFunctionBegin;
7503: MatCheckPreallocated(A, 1);
7504: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7505: if (f) {
7506: PetscCall((*f)(A, rows, cols));
7507: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7508: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7509: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7510: }
7511: PetscFunctionReturn(PETSC_SUCCESS);
7512: }
7514: /*@
7515: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7516: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7517: to complete the factorization.
7519: Collective
7521: Input Parameters:
7522: + fact - the factorized matrix obtained with `MatGetFactor()`
7523: . mat - the matrix
7524: . row - row permutation
7525: . col - column permutation
7526: - info - structure containing
7527: .vb
7528: levels - number of levels of fill.
7529: expected fill - as ratio of original fill.
7530: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7531: missing diagonal entries)
7532: .ve
7534: Level: developer
7536: Notes:
7537: See [Matrix Factorization](sec_matfactor) for additional information.
7539: Most users should employ the `KSP` interface for linear solvers
7540: instead of working directly with matrix algebra routines such as this.
7541: See, e.g., `KSPCreate()`.
7543: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7545: Fortran Note:
7546: A valid (non-null) `info` argument must be provided
7548: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
7549: `MatGetOrdering()`, `MatFactorInfo`
7550: @*/
7551: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7552: {
7553: PetscFunctionBegin;
7558: PetscAssertPointer(info, 5);
7559: PetscAssertPointer(fact, 1);
7560: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7561: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7562: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7563: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7564: MatCheckPreallocated(mat, 2);
7566: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7567: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7568: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7569: PetscFunctionReturn(PETSC_SUCCESS);
7570: }
7572: /*@
7573: MatICCFactorSymbolic - Performs symbolic incomplete
7574: Cholesky factorization for a symmetric matrix. Use
7575: `MatCholeskyFactorNumeric()` to complete the factorization.
7577: Collective
7579: Input Parameters:
7580: + fact - the factorized matrix obtained with `MatGetFactor()`
7581: . mat - the matrix to be factored
7582: . perm - row and column permutation
7583: - info - structure containing
7584: .vb
7585: levels - number of levels of fill.
7586: expected fill - as ratio of original fill.
7587: .ve
7589: Level: developer
7591: Notes:
7592: Most users should employ the `KSP` interface for linear solvers
7593: instead of working directly with matrix algebra routines such as this.
7594: See, e.g., `KSPCreate()`.
7596: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7598: Fortran Note:
7599: A valid (non-null) `info` argument must be provided
7601: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7602: @*/
7603: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7604: {
7605: PetscFunctionBegin;
7609: PetscAssertPointer(info, 4);
7610: PetscAssertPointer(fact, 1);
7611: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7612: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7613: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7614: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7615: MatCheckPreallocated(mat, 2);
7617: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7618: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7619: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7620: PetscFunctionReturn(PETSC_SUCCESS);
7621: }
7623: /*@C
7624: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7625: points to an array of valid matrices, they may be reused to store the new
7626: submatrices.
7628: Collective
7630: Input Parameters:
7631: + mat - the matrix
7632: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7633: . irow - index set of rows to extract
7634: . icol - index set of columns to extract
7635: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7637: Output Parameter:
7638: . submat - the array of submatrices
7640: Level: advanced
7642: Notes:
7643: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7644: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7645: to extract a parallel submatrix.
7647: Some matrix types place restrictions on the row and column
7648: indices, such as that they be sorted or that they be equal to each other.
7650: The index sets may not have duplicate entries.
7652: When extracting submatrices from a parallel matrix, each processor can
7653: form a different submatrix by setting the rows and columns of its
7654: individual index sets according to the local submatrix desired.
7656: When finished using the submatrices, the user should destroy
7657: them with `MatDestroySubMatrices()`.
7659: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7660: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7662: This routine creates the matrices in submat; you should NOT create them before
7663: calling it. It also allocates the array of matrix pointers submat.
7665: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7666: request one row/column in a block, they must request all rows/columns that are in
7667: that block. For example, if the block size is 2 you cannot request just row 0 and
7668: column 0.
7670: Fortran Note:
7671: .vb
7672: Mat, pointer :: submat(:)
7673: .ve
7675: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7676: @*/
7677: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7678: {
7679: PetscInt i;
7680: PetscBool eq;
7682: PetscFunctionBegin;
7685: if (n) {
7686: PetscAssertPointer(irow, 3);
7688: PetscAssertPointer(icol, 4);
7690: }
7691: PetscAssertPointer(submat, 6);
7692: if (n && scall == MAT_REUSE_MATRIX) {
7693: PetscAssertPointer(*submat, 6);
7695: }
7696: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7697: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7698: MatCheckPreallocated(mat, 1);
7699: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7700: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7701: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7702: for (i = 0; i < n; i++) {
7703: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7704: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7705: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7706: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
7707: if (mat->boundtocpu && mat->bindingpropagates) {
7708: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7709: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7710: }
7711: #endif
7712: }
7713: PetscFunctionReturn(PETSC_SUCCESS);
7714: }
7716: /*@C
7717: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7719: Collective
7721: Input Parameters:
7722: + mat - the matrix
7723: . n - the number of submatrixes to be extracted
7724: . irow - index set of rows to extract
7725: . icol - index set of columns to extract
7726: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7728: Output Parameter:
7729: . submat - the array of submatrices
7731: Level: advanced
7733: Note:
7734: This is used by `PCGASM`
7736: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7737: @*/
7738: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7739: {
7740: PetscInt i;
7741: PetscBool eq;
7743: PetscFunctionBegin;
7746: if (n) {
7747: PetscAssertPointer(irow, 3);
7749: PetscAssertPointer(icol, 4);
7751: }
7752: PetscAssertPointer(submat, 6);
7753: if (n && scall == MAT_REUSE_MATRIX) {
7754: PetscAssertPointer(*submat, 6);
7756: }
7757: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7758: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7759: MatCheckPreallocated(mat, 1);
7761: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7762: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7763: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7764: for (i = 0; i < n; i++) {
7765: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7766: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7767: }
7768: PetscFunctionReturn(PETSC_SUCCESS);
7769: }
7771: /*@C
7772: MatDestroyMatrices - Destroys an array of matrices
7774: Collective
7776: Input Parameters:
7777: + n - the number of local matrices
7778: - mat - the matrices (this is a pointer to the array of matrices)
7780: Level: advanced
7782: Notes:
7783: Frees not only the matrices, but also the array that contains the matrices
7785: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7787: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7788: @*/
7789: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7790: {
7791: PetscInt i;
7793: PetscFunctionBegin;
7794: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7795: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7796: PetscAssertPointer(mat, 2);
7798: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7800: /* memory is allocated even if n = 0 */
7801: PetscCall(PetscFree(*mat));
7802: PetscFunctionReturn(PETSC_SUCCESS);
7803: }
7805: /*@C
7806: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7808: Collective
7810: Input Parameters:
7811: + n - the number of local matrices
7812: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7814: Level: advanced
7816: Note:
7817: Frees not only the matrices, but also the array that contains the matrices
7819: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7820: @*/
7821: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7822: {
7823: Mat mat0;
7825: PetscFunctionBegin;
7826: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7827: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7828: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7829: PetscAssertPointer(mat, 2);
7831: mat0 = (*mat)[0];
7832: if (mat0 && mat0->ops->destroysubmatrices) {
7833: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7834: } else {
7835: PetscCall(MatDestroyMatrices(n, mat));
7836: }
7837: PetscFunctionReturn(PETSC_SUCCESS);
7838: }
7840: /*@
7841: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7843: Collective
7845: Input Parameter:
7846: . mat - the matrix
7848: Output Parameter:
7849: . matstruct - the sequential matrix with the nonzero structure of `mat`
7851: Level: developer
7853: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7854: @*/
7855: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7856: {
7857: PetscFunctionBegin;
7859: PetscAssertPointer(matstruct, 2);
7862: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7863: MatCheckPreallocated(mat, 1);
7865: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7866: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7867: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7868: PetscFunctionReturn(PETSC_SUCCESS);
7869: }
7871: /*@C
7872: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7874: Collective
7876: Input Parameter:
7877: . mat - the matrix
7879: Level: advanced
7881: Note:
7882: This is not needed, one can just call `MatDestroy()`
7884: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7885: @*/
7886: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7887: {
7888: PetscFunctionBegin;
7889: PetscAssertPointer(mat, 1);
7890: PetscCall(MatDestroy(mat));
7891: PetscFunctionReturn(PETSC_SUCCESS);
7892: }
7894: /*@
7895: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7896: replaces the index sets by larger ones that represent submatrices with
7897: additional overlap.
7899: Collective
7901: Input Parameters:
7902: + mat - the matrix
7903: . n - the number of index sets
7904: . is - the array of index sets (these index sets will changed during the call)
7905: - ov - the additional overlap requested
7907: Options Database Key:
7908: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7910: Level: developer
7912: Note:
7913: The computed overlap preserves the matrix block sizes when the blocks are square.
7914: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7915: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7917: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7918: @*/
7919: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7920: {
7921: PetscInt i, bs, cbs;
7923: PetscFunctionBegin;
7927: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7928: if (n) {
7929: PetscAssertPointer(is, 3);
7931: }
7932: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7933: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7934: MatCheckPreallocated(mat, 1);
7936: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7937: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7938: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7939: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7940: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7941: if (bs == cbs) {
7942: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7943: }
7944: PetscFunctionReturn(PETSC_SUCCESS);
7945: }
7947: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7949: /*@
7950: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7951: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7952: additional overlap.
7954: Collective
7956: Input Parameters:
7957: + mat - the matrix
7958: . n - the number of index sets
7959: . is - the array of index sets (these index sets will changed during the call)
7960: - ov - the additional overlap requested
7962: ` Options Database Key:
7963: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7965: Level: developer
7967: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7968: @*/
7969: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7970: {
7971: PetscInt i;
7973: PetscFunctionBegin;
7976: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7977: if (n) {
7978: PetscAssertPointer(is, 3);
7980: }
7981: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7982: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7983: MatCheckPreallocated(mat, 1);
7984: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7985: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7986: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7987: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7988: PetscFunctionReturn(PETSC_SUCCESS);
7989: }
7991: /*@
7992: MatGetBlockSize - Returns the matrix block size.
7994: Not Collective
7996: Input Parameter:
7997: . mat - the matrix
7999: Output Parameter:
8000: . bs - block size
8002: Level: intermediate
8004: Notes:
8005: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
8007: If the block size has not been set yet this routine returns 1.
8009: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
8010: @*/
8011: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
8012: {
8013: PetscFunctionBegin;
8015: PetscAssertPointer(bs, 2);
8016: *bs = mat->rmap->bs;
8017: PetscFunctionReturn(PETSC_SUCCESS);
8018: }
8020: /*@
8021: MatGetBlockSizes - Returns the matrix block row and column sizes.
8023: Not Collective
8025: Input Parameter:
8026: . mat - the matrix
8028: Output Parameters:
8029: + rbs - row block size
8030: - cbs - column block size
8032: Level: intermediate
8034: Notes:
8035: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
8036: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8038: If a block size has not been set yet this routine returns 1.
8040: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
8041: @*/
8042: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
8043: {
8044: PetscFunctionBegin;
8046: if (rbs) PetscAssertPointer(rbs, 2);
8047: if (cbs) PetscAssertPointer(cbs, 3);
8048: if (rbs) *rbs = mat->rmap->bs;
8049: if (cbs) *cbs = mat->cmap->bs;
8050: PetscFunctionReturn(PETSC_SUCCESS);
8051: }
8053: /*@
8054: MatSetBlockSize - Sets the matrix block size.
8056: Logically Collective
8058: Input Parameters:
8059: + mat - the matrix
8060: - bs - block size
8062: Level: intermediate
8064: Notes:
8065: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
8066: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8068: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
8069: is compatible with the matrix local sizes.
8071: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
8072: @*/
8073: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
8074: {
8075: PetscFunctionBegin;
8078: PetscCall(MatSetBlockSizes(mat, bs, bs));
8079: PetscFunctionReturn(PETSC_SUCCESS);
8080: }
8082: typedef struct {
8083: PetscInt n;
8084: IS *is;
8085: Mat *mat;
8086: PetscObjectState nonzerostate;
8087: Mat C;
8088: } EnvelopeData;
8090: static PetscErrorCode EnvelopeDataDestroy(PetscCtxRt ptr)
8091: {
8092: EnvelopeData *edata = *(EnvelopeData **)ptr;
8094: PetscFunctionBegin;
8095: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
8096: PetscCall(PetscFree(edata->is));
8097: PetscCall(PetscFree(edata));
8098: PetscFunctionReturn(PETSC_SUCCESS);
8099: }
8101: /*@
8102: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
8103: the sizes of these blocks in the matrix. An individual block may lie over several processes.
8105: Collective
8107: Input Parameter:
8108: . mat - the matrix
8110: Level: intermediate
8112: Notes:
8113: There can be zeros within the blocks
8115: The blocks can overlap between processes, including laying on more than two processes
8117: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
8118: @*/
8119: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
8120: {
8121: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
8122: PetscInt *diag, *odiag, sc;
8123: VecScatter scatter;
8124: PetscScalar *seqv;
8125: const PetscScalar *parv;
8126: const PetscInt *ia, *ja;
8127: PetscBool set, flag, done;
8128: Mat AA = mat, A;
8129: MPI_Comm comm;
8130: PetscMPIInt rank, size, tag;
8131: MPI_Status status;
8132: PetscContainer container;
8133: EnvelopeData *edata;
8134: Vec seq, par;
8135: IS isglobal;
8137: PetscFunctionBegin;
8139: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
8140: if (!set || !flag) {
8141: /* TODO: only needs nonzero structure of transpose */
8142: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
8143: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
8144: }
8145: PetscCall(MatAIJGetLocalMat(AA, &A));
8146: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
8147: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
8149: PetscCall(MatGetLocalSize(mat, &n, NULL));
8150: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
8151: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
8152: PetscCallMPI(MPI_Comm_size(comm, &size));
8153: PetscCallMPI(MPI_Comm_rank(comm, &rank));
8155: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
8157: if (rank > 0) {
8158: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
8159: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
8160: }
8161: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
8162: for (i = 0; i < n; i++) {
8163: env = PetscMax(env, ja[ia[i + 1] - 1]);
8164: II = rstart + i;
8165: if (env == II) {
8166: starts[lblocks] = tbs;
8167: sizes[lblocks++] = 1 + II - tbs;
8168: tbs = 1 + II;
8169: }
8170: }
8171: if (rank < size - 1) {
8172: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
8173: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
8174: }
8176: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
8177: if (!set || !flag) PetscCall(MatDestroy(&AA));
8178: PetscCall(MatDestroy(&A));
8180: PetscCall(PetscNew(&edata));
8181: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
8182: edata->n = lblocks;
8183: /* create IS needed for extracting blocks from the original matrix */
8184: PetscCall(PetscMalloc1(lblocks, &edata->is));
8185: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
8187: /* Create the resulting inverse matrix nonzero structure with preallocation information */
8188: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
8189: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
8190: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
8191: PetscCall(MatSetType(edata->C, MATAIJ));
8193: /* Communicate the start and end of each row, from each block to the correct rank */
8194: /* TODO: Use PetscSF instead of VecScatter */
8195: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
8196: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
8197: PetscCall(VecGetArrayWrite(seq, &seqv));
8198: for (PetscInt i = 0; i < lblocks; i++) {
8199: for (PetscInt j = 0; j < sizes[i]; j++) {
8200: seqv[cnt] = starts[i];
8201: seqv[cnt + 1] = starts[i] + sizes[i];
8202: cnt += 2;
8203: }
8204: }
8205: PetscCall(VecRestoreArrayWrite(seq, &seqv));
8206: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
8207: sc -= cnt;
8208: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
8209: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
8210: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
8211: PetscCall(ISDestroy(&isglobal));
8212: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
8213: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
8214: PetscCall(VecScatterDestroy(&scatter));
8215: PetscCall(VecDestroy(&seq));
8216: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
8217: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
8218: PetscCall(VecGetArrayRead(par, &parv));
8219: cnt = 0;
8220: PetscCall(MatGetSize(mat, NULL, &n));
8221: for (PetscInt i = 0; i < mat->rmap->n; i++) {
8222: PetscInt start, end, d = 0, od = 0;
8224: start = (PetscInt)PetscRealPart(parv[cnt]);
8225: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
8226: cnt += 2;
8228: if (start < cstart) {
8229: od += cstart - start + n - cend;
8230: d += cend - cstart;
8231: } else if (start < cend) {
8232: od += n - cend;
8233: d += cend - start;
8234: } else od += n - start;
8235: if (end <= cstart) {
8236: od -= cstart - end + n - cend;
8237: d -= cend - cstart;
8238: } else if (end < cend) {
8239: od -= n - cend;
8240: d -= cend - end;
8241: } else od -= n - end;
8243: odiag[i] = od;
8244: diag[i] = d;
8245: }
8246: PetscCall(VecRestoreArrayRead(par, &parv));
8247: PetscCall(VecDestroy(&par));
8248: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
8249: PetscCall(PetscFree2(diag, odiag));
8250: PetscCall(PetscFree2(sizes, starts));
8252: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
8253: PetscCall(PetscContainerSetPointer(container, edata));
8254: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
8255: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
8256: PetscCall(PetscObjectDereference((PetscObject)container));
8257: PetscFunctionReturn(PETSC_SUCCESS);
8258: }
8260: /*@
8261: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
8263: Collective
8265: Input Parameters:
8266: + A - the matrix
8267: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
8269: Output Parameter:
8270: . C - matrix with inverted block diagonal of `A`
8272: Level: advanced
8274: Note:
8275: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
8277: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
8278: @*/
8279: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
8280: {
8281: PetscContainer container;
8282: EnvelopeData *edata;
8283: PetscObjectState nonzerostate;
8285: PetscFunctionBegin;
8286: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
8287: if (!container) {
8288: PetscCall(MatComputeVariableBlockEnvelope(A));
8289: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
8290: }
8291: PetscCall(PetscContainerGetPointer(container, &edata));
8292: PetscCall(MatGetNonzeroState(A, &nonzerostate));
8293: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
8294: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
8296: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
8297: *C = edata->C;
8299: for (PetscInt i = 0; i < edata->n; i++) {
8300: Mat D;
8301: PetscScalar *dvalues;
8303: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
8304: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
8305: PetscCall(MatSeqDenseInvert(D));
8306: PetscCall(MatDenseGetArray(D, &dvalues));
8307: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
8308: PetscCall(MatDestroy(&D));
8309: }
8310: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
8311: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
8312: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
8313: PetscFunctionReturn(PETSC_SUCCESS);
8314: }
8316: /*@
8317: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
8319: Not Collective
8321: Input Parameters:
8322: + mat - the matrix
8323: . nblocks - the number of blocks on this process, each block can only exist on a single process
8324: - bsizes - the block sizes
8326: Level: intermediate
8328: Notes:
8329: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
8331: Each variable point-block set of degrees of freedom must live on a single MPI process. That is a point block cannot straddle two MPI processes.
8333: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
8334: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
8335: @*/
8336: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
8337: {
8338: PetscInt ncnt = 0, nlocal;
8340: PetscFunctionBegin;
8342: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
8343: 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);
8344: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
8345: 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);
8346: PetscCall(PetscFree(mat->bsizes));
8347: mat->nblocks = nblocks;
8348: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
8349: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
8350: PetscFunctionReturn(PETSC_SUCCESS);
8351: }
8353: /*@C
8354: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
8356: Not Collective; No Fortran Support
8358: Input Parameter:
8359: . mat - the matrix
8361: Output Parameters:
8362: + nblocks - the number of blocks on this process
8363: - bsizes - the block sizes
8365: Level: intermediate
8367: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8368: @*/
8369: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
8370: {
8371: PetscFunctionBegin;
8373: if (nblocks) *nblocks = mat->nblocks;
8374: if (bsizes) *bsizes = mat->bsizes;
8375: PetscFunctionReturn(PETSC_SUCCESS);
8376: }
8378: /*@
8379: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
8381: Not Collective
8383: Input Parameter:
8384: + subA - the submatrix
8385: . A - the original matrix
8386: - isrow - The `IS` of selected rows for the submatrix, must be sorted
8388: Level: developer
8390: Notes:
8391: If the index set is not sorted or contains off-process entries, this function will do nothing.
8393: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8394: @*/
8395: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
8396: {
8397: const PetscInt *rows;
8398: PetscInt n, rStart, rEnd, Nb = 0;
8399: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
8401: PetscFunctionBegin;
8402: // The code for block size extraction does not support an unsorted IS
8403: if (flg) PetscCall(ISSorted(isrow, &flg));
8404: // We don't support originally off-diagonal blocks
8405: if (flg) {
8406: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
8407: PetscCall(ISGetLocalSize(isrow, &n));
8408: PetscCall(ISGetIndices(isrow, &rows));
8409: for (PetscInt i = 0; i < n && flg; ++i) {
8410: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
8411: }
8412: PetscCall(ISRestoreIndices(isrow, &rows));
8413: }
8414: // quiet return if we can't extract block size
8415: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
8416: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
8418: // extract block sizes
8419: PetscCall(ISGetIndices(isrow, &rows));
8420: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8421: PetscBool occupied = PETSC_FALSE;
8423: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8424: const PetscInt row = gr + br;
8426: if (i == n) break;
8427: if (rows[i] == row) {
8428: occupied = PETSC_TRUE;
8429: ++i;
8430: }
8431: while (i < n && rows[i] < row) ++i;
8432: }
8433: gr += A->bsizes[b];
8434: if (occupied) ++Nb;
8435: }
8436: subA->nblocks = Nb;
8437: PetscCall(PetscFree(subA->bsizes));
8438: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
8439: PetscInt sb = 0;
8440: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8441: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
8442: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8443: const PetscInt row = gr + br;
8445: if (i == n) break;
8446: if (rows[i] == row) {
8447: ++subA->bsizes[sb];
8448: ++i;
8449: }
8450: while (i < n && rows[i] < row) ++i;
8451: }
8452: gr += A->bsizes[b];
8453: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8454: }
8455: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8456: PetscInt nlocal, ncnt = 0;
8457: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8458: 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);
8459: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8460: 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);
8461: PetscCall(ISRestoreIndices(isrow, &rows));
8462: PetscFunctionReturn(PETSC_SUCCESS);
8463: }
8465: /*@
8466: MatSetBlockSizes - Sets the matrix block row and column sizes.
8468: Logically Collective
8470: Input Parameters:
8471: + mat - the matrix
8472: . rbs - row block size
8473: - cbs - column block size
8475: Level: intermediate
8477: Notes:
8478: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8479: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8480: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8482: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8483: are compatible with the matrix local sizes.
8485: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8487: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8488: @*/
8489: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8490: {
8491: PetscFunctionBegin;
8495: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8496: if (mat->rmap->refcnt) {
8497: ISLocalToGlobalMapping l2g = NULL;
8498: PetscLayout nmap = NULL;
8500: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8501: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8502: PetscCall(PetscLayoutDestroy(&mat->rmap));
8503: mat->rmap = nmap;
8504: mat->rmap->mapping = l2g;
8505: }
8506: if (mat->cmap->refcnt) {
8507: ISLocalToGlobalMapping l2g = NULL;
8508: PetscLayout nmap = NULL;
8510: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8511: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8512: PetscCall(PetscLayoutDestroy(&mat->cmap));
8513: mat->cmap = nmap;
8514: mat->cmap->mapping = l2g;
8515: }
8516: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8517: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8518: PetscFunctionReturn(PETSC_SUCCESS);
8519: }
8521: /*@
8522: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8524: Logically Collective
8526: Input Parameters:
8527: + mat - the matrix
8528: . fromRow - matrix from which to copy row block size
8529: - fromCol - matrix from which to copy column block size (can be same as `fromRow`)
8531: Level: developer
8533: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8534: @*/
8535: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8536: {
8537: PetscFunctionBegin;
8541: PetscTryTypeMethod(mat, setblocksizes, fromRow->rmap->bs, fromCol->cmap->bs);
8542: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8543: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8544: PetscFunctionReturn(PETSC_SUCCESS);
8545: }
8547: /*@
8548: MatResidual - Default routine to calculate the residual r = b - Ax
8550: Collective
8552: Input Parameters:
8553: + mat - the matrix
8554: . b - the right-hand-side
8555: - x - the approximate solution
8557: Output Parameter:
8558: . r - location to store the residual
8560: Level: developer
8562: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8563: @*/
8564: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8565: {
8566: PetscFunctionBegin;
8572: MatCheckPreallocated(mat, 1);
8573: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8574: if (!mat->ops->residual) {
8575: PetscCall(MatMult(mat, x, r));
8576: PetscCall(VecAYPX(r, -1.0, b));
8577: } else {
8578: PetscUseTypeMethod(mat, residual, b, x, r);
8579: }
8580: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8581: PetscFunctionReturn(PETSC_SUCCESS);
8582: }
8584: /*@C
8585: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8587: Collective
8589: Input Parameters:
8590: + mat - the matrix
8591: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8592: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8593: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8594: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8595: always used.
8597: Output Parameters:
8598: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8599: . 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
8600: . ja - the column indices, use `NULL` if not needed
8601: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8602: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8604: Level: developer
8606: Notes:
8607: You CANNOT change any of the ia[] or ja[] values.
8609: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8611: Fortran Notes:
8612: Use
8613: .vb
8614: PetscInt, pointer :: ia(:),ja(:)
8615: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8616: ! Access the ith and jth entries via ia(i) and ja(j)
8617: .ve
8619: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8620: @*/
8621: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8622: {
8623: PetscFunctionBegin;
8626: if (n) PetscAssertPointer(n, 5);
8627: if (ia) PetscAssertPointer(ia, 6);
8628: if (ja) PetscAssertPointer(ja, 7);
8629: if (done) PetscAssertPointer(done, 8);
8630: MatCheckPreallocated(mat, 1);
8631: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8632: else {
8633: if (done) *done = PETSC_TRUE;
8634: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8635: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8636: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8637: }
8638: PetscFunctionReturn(PETSC_SUCCESS);
8639: }
8641: /*@C
8642: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8644: Collective
8646: Input Parameters:
8647: + mat - the matrix
8648: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8649: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8650: symmetrized
8651: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8652: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8653: always used.
8655: Output Parameters:
8656: + n - number of columns in the (possibly compressed) matrix
8657: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8658: . ja - the row indices
8659: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8661: Level: developer
8663: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8664: @*/
8665: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8666: {
8667: PetscFunctionBegin;
8670: PetscAssertPointer(n, 5);
8671: if (ia) PetscAssertPointer(ia, 6);
8672: if (ja) PetscAssertPointer(ja, 7);
8673: PetscAssertPointer(done, 8);
8674: MatCheckPreallocated(mat, 1);
8675: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8676: else {
8677: *done = PETSC_TRUE;
8678: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8679: }
8680: PetscFunctionReturn(PETSC_SUCCESS);
8681: }
8683: /*@C
8684: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8686: Collective
8688: Input Parameters:
8689: + mat - the matrix
8690: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8691: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8692: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8693: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8694: always used.
8695: . n - size of (possibly compressed) matrix
8696: . ia - the row pointers
8697: - ja - the column indices
8699: Output Parameter:
8700: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8702: Level: developer
8704: Note:
8705: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8706: us of the array after it has been restored. If you pass `NULL`, it will
8707: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8709: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8710: @*/
8711: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8712: {
8713: PetscFunctionBegin;
8716: if (ia) PetscAssertPointer(ia, 6);
8717: if (ja) PetscAssertPointer(ja, 7);
8718: if (done) PetscAssertPointer(done, 8);
8719: MatCheckPreallocated(mat, 1);
8721: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8722: else {
8723: if (done) *done = PETSC_TRUE;
8724: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8725: if (n) *n = 0;
8726: if (ia) *ia = NULL;
8727: if (ja) *ja = NULL;
8728: }
8729: PetscFunctionReturn(PETSC_SUCCESS);
8730: }
8732: /*@C
8733: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8735: Collective
8737: Input Parameters:
8738: + mat - the matrix
8739: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8740: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8741: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8742: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8743: always used.
8745: Output Parameters:
8746: + n - size of (possibly compressed) matrix
8747: . ia - the column pointers
8748: . ja - the row indices
8749: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8751: Level: developer
8753: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8754: @*/
8755: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8756: {
8757: PetscFunctionBegin;
8760: if (ia) PetscAssertPointer(ia, 6);
8761: if (ja) PetscAssertPointer(ja, 7);
8762: PetscAssertPointer(done, 8);
8763: MatCheckPreallocated(mat, 1);
8765: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8766: else {
8767: *done = PETSC_TRUE;
8768: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8769: if (n) *n = 0;
8770: if (ia) *ia = NULL;
8771: if (ja) *ja = NULL;
8772: }
8773: PetscFunctionReturn(PETSC_SUCCESS);
8774: }
8776: /*@
8777: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8778: `MatGetColumnIJ()`.
8780: Collective
8782: Input Parameters:
8783: + mat - the matrix
8784: . ncolors - maximum color value
8785: . n - number of entries in colorarray
8786: - colorarray - array indicating color for each column
8788: Output Parameter:
8789: . iscoloring - coloring generated using colorarray information
8791: Level: developer
8793: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8794: @*/
8795: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8796: {
8797: PetscFunctionBegin;
8800: PetscAssertPointer(colorarray, 4);
8801: PetscAssertPointer(iscoloring, 5);
8802: MatCheckPreallocated(mat, 1);
8804: if (!mat->ops->coloringpatch) {
8805: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8806: } else {
8807: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8808: }
8809: PetscFunctionReturn(PETSC_SUCCESS);
8810: }
8812: /*@
8813: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8815: Logically Collective
8817: Input Parameter:
8818: . mat - the factored matrix to be reset
8820: Level: developer
8822: Notes:
8823: This routine should be used only with factored matrices formed by in-place
8824: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8825: format). This option can save memory, for example, when solving nonlinear
8826: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8827: ILU(0) preconditioner.
8829: One can specify in-place ILU(0) factorization by calling
8830: .vb
8831: PCType(pc,PCILU);
8832: PCFactorSeUseInPlace(pc);
8833: .ve
8834: or by using the options -pc_type ilu -pc_factor_in_place
8836: In-place factorization ILU(0) can also be used as a local
8837: solver for the blocks within the block Jacobi or additive Schwarz
8838: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8839: for details on setting local solver options.
8841: Most users should employ the `KSP` interface for linear solvers
8842: instead of working directly with matrix algebra routines such as this.
8843: See, e.g., `KSPCreate()`.
8845: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8846: @*/
8847: PetscErrorCode MatSetUnfactored(Mat mat)
8848: {
8849: PetscFunctionBegin;
8852: MatCheckPreallocated(mat, 1);
8853: mat->factortype = MAT_FACTOR_NONE;
8854: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8855: PetscUseTypeMethod(mat, setunfactored);
8856: PetscFunctionReturn(PETSC_SUCCESS);
8857: }
8859: /*@
8860: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8861: as the original matrix.
8863: Collective
8865: Input Parameters:
8866: + mat - the original matrix
8867: . isrow - parallel `IS` containing the rows this processor should obtain
8868: . 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.
8869: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8871: Output Parameter:
8872: . newmat - the new submatrix, of the same type as the original matrix
8874: Level: advanced
8876: Notes:
8877: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8879: Some matrix types place restrictions on the row and column indices, such
8880: 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;
8881: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8883: The index sets may not have duplicate entries.
8885: The first time this is called you should use a `cll` of `MAT_INITIAL_MATRIX`,
8886: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8887: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8888: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8889: you are finished using it.
8891: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8892: the input matrix.
8894: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8896: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8897: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8899: Example usage:
8900: Consider the following 8x8 matrix with 34 non-zero values, that is
8901: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8902: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8903: as follows
8904: .vb
8905: 1 2 0 | 0 3 0 | 0 4
8906: Proc0 0 5 6 | 7 0 0 | 8 0
8907: 9 0 10 | 11 0 0 | 12 0
8908: -------------------------------------
8909: 13 0 14 | 15 16 17 | 0 0
8910: Proc1 0 18 0 | 19 20 21 | 0 0
8911: 0 0 0 | 22 23 0 | 24 0
8912: -------------------------------------
8913: Proc2 25 26 27 | 0 0 28 | 29 0
8914: 30 0 0 | 31 32 33 | 0 34
8915: .ve
8917: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8919: .vb
8920: 2 0 | 0 3 0 | 0
8921: Proc0 5 6 | 7 0 0 | 8
8922: -------------------------------
8923: Proc1 18 0 | 19 20 21 | 0
8924: -------------------------------
8925: Proc2 26 27 | 0 0 28 | 29
8926: 0 0 | 31 32 33 | 0
8927: .ve
8929: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8930: @*/
8931: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8932: {
8933: PetscMPIInt size;
8934: Mat *local;
8935: IS iscoltmp;
8936: PetscBool flg;
8938: PetscFunctionBegin;
8942: PetscAssertPointer(newmat, 5);
8945: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8946: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8947: PetscCheck(cll != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_INPLACE_MATRIX");
8949: MatCheckPreallocated(mat, 1);
8950: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8952: if (!iscol || isrow == iscol) {
8953: PetscBool stride;
8954: PetscMPIInt grab = 0;
8955: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8956: if (stride) {
8957: PetscInt first, step, n, rstart, rend;
8958: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8959: if (step == 1) {
8960: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8961: if (rstart == first) {
8962: PetscCall(ISGetLocalSize(isrow, &n));
8963: if (n == rend - rstart) grab = 1;
8964: }
8965: }
8966: }
8967: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8968: if (grab) {
8969: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8970: if (cll == MAT_INITIAL_MATRIX) {
8971: *newmat = mat;
8972: PetscCall(PetscObjectReference((PetscObject)mat));
8973: }
8974: PetscFunctionReturn(PETSC_SUCCESS);
8975: }
8976: }
8978: if (!iscol) {
8979: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8980: } else {
8981: iscoltmp = iscol;
8982: }
8984: /* if original matrix is on just one processor then use submatrix generated */
8985: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8986: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8987: goto setproperties;
8988: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8989: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8990: *newmat = *local;
8991: PetscCall(PetscFree(local));
8992: goto setproperties;
8993: } else if (!mat->ops->createsubmatrix) {
8994: /* Create a new matrix type that implements the operation using the full matrix */
8995: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8996: switch (cll) {
8997: case MAT_INITIAL_MATRIX:
8998: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8999: break;
9000: case MAT_REUSE_MATRIX:
9001: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
9002: break;
9003: default:
9004: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
9005: }
9006: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
9007: goto setproperties;
9008: }
9010: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
9011: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
9012: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
9014: setproperties:
9015: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
9016: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
9017: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
9018: }
9019: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
9020: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
9021: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
9022: PetscFunctionReturn(PETSC_SUCCESS);
9023: }
9025: /*@
9026: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
9028: Not Collective
9030: Input Parameters:
9031: + A - the matrix we wish to propagate options from
9032: - B - the matrix we wish to propagate options to
9034: Level: beginner
9036: Note:
9037: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
9039: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
9040: @*/
9041: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
9042: {
9043: PetscFunctionBegin;
9046: B->symmetry_eternal = A->symmetry_eternal;
9047: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
9048: B->symmetric = A->symmetric;
9049: B->structurally_symmetric = A->structurally_symmetric;
9050: B->spd = A->spd;
9051: B->hermitian = A->hermitian;
9052: PetscFunctionReturn(PETSC_SUCCESS);
9053: }
9055: /*@
9056: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
9057: used during the assembly process to store values that belong to
9058: other processors.
9060: Not Collective
9062: Input Parameters:
9063: + mat - the matrix
9064: . size - the initial size of the stash.
9065: - bsize - the initial size of the block-stash(if used).
9067: Options Database Keys:
9068: + -matstash_initial_size size or size0,size1,...,sizep-1 - set initial size
9069: - -matstash_block_initial_size bsize or bsize0,bsize1,...,bsizep-1 - set initial block size
9071: Level: intermediate
9073: Notes:
9074: The block-stash is used for values set with `MatSetValuesBlocked()` while
9075: the stash is used for values set with `MatSetValues()`
9077: Run with the option -info and look for output of the form
9078: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
9079: to determine the appropriate value, MM, to use for size and
9080: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
9081: to determine the value, BMM to use for bsize
9083: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
9084: @*/
9085: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
9086: {
9087: PetscFunctionBegin;
9090: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
9091: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
9092: PetscFunctionReturn(PETSC_SUCCESS);
9093: }
9095: /*@
9096: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
9097: the matrix
9099: Neighbor-wise Collective
9101: Input Parameters:
9102: + A - the matrix
9103: . x - the vector to be multiplied by the interpolation operator
9104: - y - the vector to be added to the result
9106: Output Parameter:
9107: . w - the resulting vector
9109: Level: intermediate
9111: Notes:
9112: `w` may be the same vector as `y`.
9114: This allows one to use either the restriction or interpolation (its transpose)
9115: matrix to do the interpolation
9117: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
9118: @*/
9119: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
9120: {
9121: PetscInt M, N, Ny;
9123: PetscFunctionBegin;
9128: PetscCall(MatGetSize(A, &M, &N));
9129: PetscCall(VecGetSize(y, &Ny));
9130: if (M == Ny) PetscCall(MatMultAdd(A, x, y, w));
9131: else PetscCall(MatMultTransposeAdd(A, x, y, w));
9132: PetscFunctionReturn(PETSC_SUCCESS);
9133: }
9135: /*@
9136: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
9137: the matrix
9139: Neighbor-wise Collective
9141: Input Parameters:
9142: + A - the matrix
9143: - x - the vector to be interpolated
9145: Output Parameter:
9146: . y - the resulting vector
9148: Level: intermediate
9150: Note:
9151: This allows one to use either the restriction or interpolation (its transpose)
9152: matrix to do the interpolation
9154: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
9155: @*/
9156: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
9157: {
9158: PetscInt M, N, Ny;
9160: PetscFunctionBegin;
9164: PetscCall(MatGetSize(A, &M, &N));
9165: PetscCall(VecGetSize(y, &Ny));
9166: if (M == Ny) PetscCall(MatMult(A, x, y));
9167: else PetscCall(MatMultTranspose(A, x, y));
9168: PetscFunctionReturn(PETSC_SUCCESS);
9169: }
9171: /*@
9172: MatRestrict - $y = A*x$ or $A^T*x$
9174: Neighbor-wise Collective
9176: Input Parameters:
9177: + A - the matrix
9178: - x - the vector to be restricted
9180: Output Parameter:
9181: . y - the resulting vector
9183: Level: intermediate
9185: Note:
9186: This allows one to use either the restriction or interpolation (its transpose)
9187: matrix to do the restriction
9189: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
9190: @*/
9191: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
9192: {
9193: PetscInt M, N, Nx;
9195: PetscFunctionBegin;
9199: PetscCall(MatGetSize(A, &M, &N));
9200: PetscCall(VecGetSize(x, &Nx));
9201: if (M == Nx) PetscCall(MatMultTranspose(A, x, y));
9202: else PetscCall(MatMult(A, x, y));
9203: PetscFunctionReturn(PETSC_SUCCESS);
9204: }
9206: /*@
9207: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
9209: Neighbor-wise Collective
9211: Input Parameters:
9212: + A - the matrix
9213: . x - the input dense matrix to be multiplied
9214: - w - the input dense matrix to be added to the result
9216: Output Parameter:
9217: . y - the output dense matrix
9219: Level: intermediate
9221: Note:
9222: This allows one to use either the restriction or interpolation (its transpose)
9223: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
9224: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9226: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
9227: @*/
9228: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
9229: {
9230: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
9231: PetscBool trans = PETSC_TRUE;
9232: MatReuse reuse = MAT_INITIAL_MATRIX;
9234: PetscFunctionBegin;
9240: PetscCall(MatGetSize(A, &M, &N));
9241: PetscCall(MatGetSize(x, &Mx, &Nx));
9242: if (N == Mx) trans = PETSC_FALSE;
9243: 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);
9244: Mo = trans ? N : M;
9245: if (*y) {
9246: PetscCall(MatGetSize(*y, &My, &Ny));
9247: if (Mo == My && Nx == Ny) reuse = MAT_REUSE_MATRIX;
9248: else {
9249: 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);
9250: PetscCall(MatDestroy(y));
9251: }
9252: }
9254: if (w && *y == w) { /* this is to minimize changes in PCMG */
9255: PetscBool flg;
9257: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
9258: if (w) {
9259: PetscInt My, Ny, Mw, Nw;
9261: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
9262: PetscCall(MatGetSize(*y, &My, &Ny));
9263: PetscCall(MatGetSize(w, &Mw, &Nw));
9264: if (!flg || My != Mw || Ny != Nw) w = NULL;
9265: }
9266: if (!w) {
9267: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
9268: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
9269: PetscCall(PetscObjectDereference((PetscObject)w));
9270: } else PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
9271: }
9272: if (!trans) PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
9273: else PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
9274: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
9275: PetscFunctionReturn(PETSC_SUCCESS);
9276: }
9278: /*@
9279: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
9281: Neighbor-wise Collective
9283: Input Parameters:
9284: + A - the matrix
9285: - x - the input dense matrix
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`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
9298: @*/
9299: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
9300: {
9301: PetscFunctionBegin;
9302: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9303: PetscFunctionReturn(PETSC_SUCCESS);
9304: }
9306: /*@
9307: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
9309: Neighbor-wise Collective
9311: Input Parameters:
9312: + A - the matrix
9313: - x - the input dense matrix
9315: Output Parameter:
9316: . y - the output dense matrix
9318: Level: intermediate
9320: Note:
9321: This allows one to use either the restriction or interpolation (its transpose)
9322: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
9323: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9325: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
9326: @*/
9327: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
9328: {
9329: PetscFunctionBegin;
9330: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9331: PetscFunctionReturn(PETSC_SUCCESS);
9332: }
9334: /*@
9335: MatGetNullSpace - retrieves the null space of a matrix.
9337: Logically Collective
9339: Input Parameters:
9340: + mat - the matrix
9341: - nullsp - the null space object
9343: Level: developer
9345: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
9346: @*/
9347: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
9348: {
9349: PetscFunctionBegin;
9351: PetscAssertPointer(nullsp, 2);
9352: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
9353: PetscFunctionReturn(PETSC_SUCCESS);
9354: }
9356: /*@C
9357: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
9359: Logically Collective
9361: Input Parameters:
9362: + n - the number of matrices
9363: - mat - the array of matrices
9365: Output Parameters:
9366: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
9368: Level: developer
9370: Note:
9371: Call `MatRestoreNullspaces()` to provide these to another array of matrices
9373: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9374: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
9375: @*/
9376: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9377: {
9378: PetscFunctionBegin;
9379: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9380: PetscAssertPointer(mat, 2);
9381: PetscAssertPointer(nullsp, 3);
9383: PetscCall(PetscCalloc1(3 * n, nullsp));
9384: for (PetscInt i = 0; i < n; i++) {
9386: (*nullsp)[i] = mat[i]->nullsp;
9387: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
9388: (*nullsp)[n + i] = mat[i]->nearnullsp;
9389: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
9390: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
9391: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
9392: }
9393: PetscFunctionReturn(PETSC_SUCCESS);
9394: }
9396: /*@C
9397: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
9399: Logically Collective
9401: Input Parameters:
9402: + n - the number of matrices
9403: . mat - the array of matrices
9404: - nullsp - an array of null spaces
9406: Level: developer
9408: Note:
9409: Call `MatGetNullSpaces()` to create `nullsp`
9411: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9412: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
9413: @*/
9414: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9415: {
9416: PetscFunctionBegin;
9417: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9418: PetscAssertPointer(mat, 2);
9419: PetscAssertPointer(nullsp, 3);
9420: PetscAssertPointer(*nullsp, 3);
9422: for (PetscInt i = 0; i < n; i++) {
9424: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
9425: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9426: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9427: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9428: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9429: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9430: }
9431: PetscCall(PetscFree(*nullsp));
9432: PetscFunctionReturn(PETSC_SUCCESS);
9433: }
9435: /*@
9436: MatSetNullSpace - attaches a null space to a matrix.
9438: Logically Collective
9440: Input Parameters:
9441: + mat - the matrix
9442: - nullsp - the null space object
9444: Level: advanced
9446: Notes:
9447: This null space is used by the `KSP` linear solvers to solve singular systems.
9449: 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`
9451: 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
9452: to zero but the linear system will still be solved in a least squares sense.
9454: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9455: 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 $A^T$, $R(A^T)$.
9456: 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
9457: $n(A^T))$ 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
9458: 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 $n(A^T)$.
9459: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9461: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9462: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9463: routine also automatically calls `MatSetTransposeNullSpace()`.
9465: The user should call `MatNullSpaceDestroy()`.
9467: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9468: `KSPSetPCSide()`
9469: @*/
9470: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9471: {
9472: PetscFunctionBegin;
9475: PetscCall(PetscObjectReference((PetscObject)nullsp));
9476: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9477: mat->nullsp = nullsp;
9478: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9479: PetscFunctionReturn(PETSC_SUCCESS);
9480: }
9482: /*@
9483: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
9485: Logically Collective
9487: Input Parameters:
9488: + mat - the matrix
9489: - nullsp - the null space object
9491: Level: developer
9493: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9494: @*/
9495: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9496: {
9497: PetscFunctionBegin;
9500: PetscAssertPointer(nullsp, 2);
9501: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9502: PetscFunctionReturn(PETSC_SUCCESS);
9503: }
9505: /*@
9506: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9508: Logically Collective
9510: Input Parameters:
9511: + mat - the matrix
9512: - nullsp - the null space object
9514: Level: advanced
9516: Notes:
9517: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9519: See `MatSetNullSpace()`
9521: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9522: @*/
9523: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9524: {
9525: PetscFunctionBegin;
9528: PetscCall(PetscObjectReference((PetscObject)nullsp));
9529: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9530: mat->transnullsp = nullsp;
9531: PetscFunctionReturn(PETSC_SUCCESS);
9532: }
9534: /*@
9535: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9536: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9538: Logically Collective
9540: Input Parameters:
9541: + mat - the matrix
9542: - nullsp - the null space object
9544: Level: advanced
9546: Notes:
9547: Overwrites any previous near null space that may have been attached
9549: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9551: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9552: @*/
9553: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9554: {
9555: PetscFunctionBegin;
9559: MatCheckPreallocated(mat, 1);
9560: PetscCall(PetscObjectReference((PetscObject)nullsp));
9561: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9562: mat->nearnullsp = nullsp;
9563: PetscFunctionReturn(PETSC_SUCCESS);
9564: }
9566: /*@
9567: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9569: Not Collective
9571: Input Parameter:
9572: . mat - the matrix
9574: Output Parameter:
9575: . nullsp - the null space object, `NULL` if not set
9577: Level: advanced
9579: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9580: @*/
9581: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9582: {
9583: PetscFunctionBegin;
9586: PetscAssertPointer(nullsp, 2);
9587: MatCheckPreallocated(mat, 1);
9588: *nullsp = mat->nearnullsp;
9589: PetscFunctionReturn(PETSC_SUCCESS);
9590: }
9592: /*@
9593: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9595: Collective
9597: Input Parameters:
9598: + mat - the matrix
9599: . row - row/column permutation
9600: - info - information on desired factorization process
9602: Level: developer
9604: Notes:
9605: Probably really in-place only when level of fill is zero, otherwise allocates
9606: new space to store factored matrix and deletes previous memory.
9608: Most users should employ the `KSP` interface for linear solvers
9609: instead of working directly with matrix algebra routines such as this.
9610: See, e.g., `KSPCreate()`.
9612: Fortran Note:
9613: A valid (non-null) `info` argument must be provided
9615: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9616: @*/
9617: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9618: {
9619: PetscFunctionBegin;
9623: PetscAssertPointer(info, 3);
9624: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9625: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9626: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9627: MatCheckPreallocated(mat, 1);
9628: PetscUseTypeMethod(mat, iccfactor, row, info);
9629: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9630: PetscFunctionReturn(PETSC_SUCCESS);
9631: }
9633: /*@
9634: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9635: ghosted ones.
9637: Not Collective
9639: Input Parameters:
9640: + mat - the matrix
9641: - diag - the diagonal values, including ghost ones
9643: Level: developer
9645: Notes:
9646: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9648: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9650: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9651: @*/
9652: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9653: {
9654: PetscMPIInt size;
9656: PetscFunctionBegin;
9661: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9662: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9663: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9664: if (size == 1) {
9665: PetscInt n, m;
9666: PetscCall(VecGetSize(diag, &n));
9667: PetscCall(MatGetSize(mat, NULL, &m));
9668: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9669: PetscCall(MatDiagonalScale(mat, NULL, diag));
9670: } else PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9671: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9672: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9673: PetscFunctionReturn(PETSC_SUCCESS);
9674: }
9676: /*@
9677: MatGetInertia - Gets the inertia from a factored matrix
9679: Collective
9681: Input Parameter:
9682: . mat - the matrix
9684: Output Parameters:
9685: + nneg - number of negative eigenvalues
9686: . nzero - number of zero eigenvalues
9687: - npos - number of positive eigenvalues
9689: Level: advanced
9691: Note:
9692: Matrix must have been factored by `MatCholeskyFactor()`
9694: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9695: @*/
9696: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9697: {
9698: PetscFunctionBegin;
9701: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9702: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9703: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9704: PetscFunctionReturn(PETSC_SUCCESS);
9705: }
9707: /*@C
9708: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9710: Neighbor-wise Collective
9712: Input Parameters:
9713: + mat - the factored matrix obtained with `MatGetFactor()`
9714: - b - the right-hand-side vectors
9716: Output Parameter:
9717: . x - the result vectors
9719: Level: developer
9721: Note:
9722: The vectors `b` and `x` cannot be the same. I.e., one cannot
9723: call `MatSolves`(A,x,x).
9725: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9726: @*/
9727: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9728: {
9729: PetscFunctionBegin;
9732: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9733: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9734: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9736: MatCheckPreallocated(mat, 1);
9737: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9738: PetscUseTypeMethod(mat, solves, b, x);
9739: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9740: PetscFunctionReturn(PETSC_SUCCESS);
9741: }
9743: /*@
9744: MatIsSymmetric - Test whether a matrix is symmetric
9746: Collective
9748: Input Parameters:
9749: + A - the matrix to test
9750: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9752: Output Parameter:
9753: . flg - the result
9755: Level: intermediate
9757: Notes:
9758: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9760: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9762: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9763: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9765: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9766: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9767: @*/
9768: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9769: {
9770: PetscFunctionBegin;
9772: PetscAssertPointer(flg, 3);
9773: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9774: else {
9775: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9776: else PetscCall(MatIsTranspose(A, A, tol, flg));
9777: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9778: }
9779: PetscFunctionReturn(PETSC_SUCCESS);
9780: }
9782: /*@
9783: MatIsHermitian - Test whether a matrix is Hermitian
9785: Collective
9787: Input Parameters:
9788: + A - the matrix to test
9789: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9791: Output Parameter:
9792: . flg - the result
9794: Level: intermediate
9796: Notes:
9797: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9799: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9801: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9802: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9804: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9805: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9806: @*/
9807: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9808: {
9809: PetscFunctionBegin;
9811: PetscAssertPointer(flg, 3);
9812: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9813: else {
9814: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9815: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9816: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9817: }
9818: PetscFunctionReturn(PETSC_SUCCESS);
9819: }
9821: /*@
9822: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9824: Not Collective
9826: Input Parameter:
9827: . A - the matrix to check
9829: Output Parameters:
9830: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9831: - flg - the result (only valid if set is `PETSC_TRUE`)
9833: Level: advanced
9835: Notes:
9836: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9837: if you want it explicitly checked
9839: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9840: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9842: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9843: @*/
9844: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9845: {
9846: PetscFunctionBegin;
9848: PetscAssertPointer(set, 2);
9849: PetscAssertPointer(flg, 3);
9850: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9851: *set = PETSC_TRUE;
9852: *flg = PetscBool3ToBool(A->symmetric);
9853: } else *set = PETSC_FALSE;
9854: PetscFunctionReturn(PETSC_SUCCESS);
9855: }
9857: /*@
9858: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9860: Not Collective
9862: Input Parameter:
9863: . A - the matrix to check
9865: Output Parameters:
9866: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9867: - flg - the result (only valid if set is `PETSC_TRUE`)
9869: Level: advanced
9871: Notes:
9872: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9874: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9875: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9877: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9878: @*/
9879: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9880: {
9881: PetscFunctionBegin;
9883: PetscAssertPointer(set, 2);
9884: PetscAssertPointer(flg, 3);
9885: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9886: *set = PETSC_TRUE;
9887: *flg = PetscBool3ToBool(A->spd);
9888: } else *set = PETSC_FALSE;
9889: PetscFunctionReturn(PETSC_SUCCESS);
9890: }
9892: /*@
9893: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9895: Not Collective
9897: Input Parameter:
9898: . A - the matrix to check
9900: Output Parameters:
9901: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9902: - flg - the result (only valid if set is `PETSC_TRUE`)
9904: Level: advanced
9906: Notes:
9907: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9908: if you want it explicitly checked
9910: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9911: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9913: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9914: @*/
9915: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9916: {
9917: PetscFunctionBegin;
9919: PetscAssertPointer(set, 2);
9920: PetscAssertPointer(flg, 3);
9921: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9922: *set = PETSC_TRUE;
9923: *flg = PetscBool3ToBool(A->hermitian);
9924: } else *set = PETSC_FALSE;
9925: PetscFunctionReturn(PETSC_SUCCESS);
9926: }
9928: /*@
9929: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9931: Collective
9933: Input Parameter:
9934: . A - the matrix to test
9936: Output Parameter:
9937: . flg - the result
9939: Level: intermediate
9941: Notes:
9942: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9944: 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
9945: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9947: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9948: @*/
9949: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9950: {
9951: PetscFunctionBegin;
9953: PetscAssertPointer(flg, 2);
9954: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) *flg = PetscBool3ToBool(A->structurally_symmetric);
9955: else {
9956: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9957: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9958: }
9959: PetscFunctionReturn(PETSC_SUCCESS);
9960: }
9962: /*@
9963: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9965: Not Collective
9967: Input Parameter:
9968: . A - the matrix to check
9970: Output Parameters:
9971: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9972: - flg - the result (only valid if set is PETSC_TRUE)
9974: Level: advanced
9976: Notes:
9977: 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
9978: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9980: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9982: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9983: @*/
9984: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9985: {
9986: PetscFunctionBegin;
9988: PetscAssertPointer(set, 2);
9989: PetscAssertPointer(flg, 3);
9990: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9991: *set = PETSC_TRUE;
9992: *flg = PetscBool3ToBool(A->structurally_symmetric);
9993: } else *set = PETSC_FALSE;
9994: PetscFunctionReturn(PETSC_SUCCESS);
9995: }
9997: /*@
9998: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9999: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
10001: Not Collective
10003: Input Parameter:
10004: . mat - the matrix
10006: Output Parameters:
10007: + nstash - the size of the stash
10008: . reallocs - the number of additional mallocs incurred.
10009: . bnstash - the size of the block stash
10010: - breallocs - the number of additional mallocs incurred.in the block stash
10012: Level: advanced
10014: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
10015: @*/
10016: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
10017: {
10018: PetscFunctionBegin;
10019: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
10020: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
10021: PetscFunctionReturn(PETSC_SUCCESS);
10022: }
10024: /*@
10025: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
10026: parallel layout, `PetscLayout` for rows and columns
10028: Collective
10030: Input Parameter:
10031: . mat - the matrix
10033: Output Parameters:
10034: + right - (optional) vector that the matrix can be multiplied against
10035: - left - (optional) vector that the matrix vector product can be stored in
10037: Options Database Key:
10038: . -mat_vec_type type - set the `VecType` of the created vectors during `MatSetFromOptions()`
10040: Level: advanced
10042: Notes:
10043: 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()`.
10045: 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`.
10047: These are new vectors which are not owned by the `mat`, they should be destroyed with `VecDestroy()` when no longer needed.
10049: PETSc `Vec` always have all zero entries when created with `MatCreateVecs()` until routines such as `VecSet()` or `VecSetValues()`
10050: are used to change the values. There is no reason to call `VecZeroEntries()` after creation.
10052: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`, `MatSetVecType()`
10053: @*/
10054: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
10055: {
10056: PetscFunctionBegin;
10059: if (mat->ops->getvecs) {
10060: PetscUseTypeMethod(mat, getvecs, right, left);
10061: } else {
10062: if (right) {
10063: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
10064: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
10065: PetscCall(VecSetType(*right, mat->defaultvectype));
10066: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
10067: if (mat->boundtocpu && mat->bindingpropagates) {
10068: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
10069: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
10070: }
10071: #endif
10072: }
10073: if (left) {
10074: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
10075: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
10076: PetscCall(VecSetType(*left, mat->defaultvectype));
10077: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
10078: if (mat->boundtocpu && mat->bindingpropagates) {
10079: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
10080: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
10081: }
10082: #endif
10083: }
10084: }
10085: PetscFunctionReturn(PETSC_SUCCESS);
10086: }
10088: /*@
10089: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
10090: with default values.
10092: Not Collective
10094: Input Parameter:
10095: . info - the `MatFactorInfo` data structure
10097: Level: developer
10099: Notes:
10100: The solvers are generally used through the `KSP` and `PC` objects, for example
10101: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
10103: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
10105: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
10106: @*/
10107: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
10108: {
10109: PetscFunctionBegin;
10110: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
10111: PetscFunctionReturn(PETSC_SUCCESS);
10112: }
10114: /*@
10115: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
10117: Collective
10119: Input Parameters:
10120: + mat - the factored matrix
10121: - is - the index set defining the Schur indices (0-based)
10123: Level: advanced
10125: Notes:
10126: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
10128: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
10130: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
10132: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
10133: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
10134: @*/
10135: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
10136: {
10137: PetscErrorCode (*f)(Mat, IS);
10139: PetscFunctionBegin;
10144: PetscCheckSameComm(mat, 1, is, 2);
10145: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
10146: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
10147: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
10148: PetscCall((*f)(mat, is));
10149: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
10150: PetscFunctionReturn(PETSC_SUCCESS);
10151: }
10153: /*@
10154: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
10156: Logically Collective
10158: Input Parameters:
10159: + F - the factored matrix obtained by calling `MatGetFactor()`
10160: . S - location where to return the Schur complement, can be `NULL`
10161: - status - the status of the Schur complement matrix, can be `NULL`
10163: Level: advanced
10165: Notes:
10166: You must call `MatFactorSetSchurIS()` before calling this routine.
10168: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
10170: The routine provides a copy of the Schur matrix stored within the solver data structures.
10171: The caller must destroy the object when it is no longer needed.
10172: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
10174: 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)
10176: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
10178: Developer Note:
10179: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
10180: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
10182: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
10183: @*/
10184: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
10185: {
10186: PetscFunctionBegin;
10188: if (S) PetscAssertPointer(S, 2);
10189: if (status) PetscAssertPointer(status, 3);
10190: if (S) {
10191: PetscErrorCode (*f)(Mat, Mat *);
10193: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
10194: if (f) PetscCall((*f)(F, S));
10195: else PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
10196: }
10197: if (status) *status = F->schur_status;
10198: PetscFunctionReturn(PETSC_SUCCESS);
10199: }
10201: /*@
10202: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
10204: Logically Collective
10206: Input Parameters:
10207: + F - the factored matrix obtained by calling `MatGetFactor()`
10208: . S - location where to return the Schur complement, can be `NULL`
10209: - status - the status of the Schur complement matrix, can be `NULL`
10211: Level: advanced
10213: Notes:
10214: You must call `MatFactorSetSchurIS()` before calling this routine.
10216: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
10218: The routine returns a the Schur Complement stored within the data structures of the solver.
10220: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
10222: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
10224: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
10226: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
10228: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
10229: @*/
10230: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
10231: {
10232: PetscFunctionBegin;
10234: if (S) {
10235: PetscAssertPointer(S, 2);
10236: *S = F->schur;
10237: }
10238: if (status) {
10239: PetscAssertPointer(status, 3);
10240: *status = F->schur_status;
10241: }
10242: PetscFunctionReturn(PETSC_SUCCESS);
10243: }
10245: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
10246: {
10247: Mat S = F->schur;
10249: PetscFunctionBegin;
10250: switch (F->schur_status) {
10251: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
10252: case MAT_FACTOR_SCHUR_INVERTED:
10253: if (S) {
10254: S->ops->solve = NULL;
10255: S->ops->matsolve = NULL;
10256: S->ops->solvetranspose = NULL;
10257: S->ops->matsolvetranspose = NULL;
10258: S->ops->solveadd = NULL;
10259: S->ops->solvetransposeadd = NULL;
10260: S->factortype = MAT_FACTOR_NONE;
10261: PetscCall(PetscFree(S->solvertype));
10262: }
10263: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
10264: break;
10265: default:
10266: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10267: }
10268: PetscFunctionReturn(PETSC_SUCCESS);
10269: }
10271: /*@
10272: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
10274: Logically Collective
10276: Input Parameters:
10277: + F - the factored matrix obtained by calling `MatGetFactor()`
10278: . S - location where the Schur complement is stored
10279: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
10281: Level: advanced
10283: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
10284: @*/
10285: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
10286: {
10287: PetscFunctionBegin;
10289: if (S) {
10291: *S = NULL;
10292: }
10293: F->schur_status = status;
10294: PetscCall(MatFactorUpdateSchurStatus_Private(F));
10295: PetscFunctionReturn(PETSC_SUCCESS);
10296: }
10298: /*@
10299: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
10301: Logically Collective
10303: Input Parameters:
10304: + F - the factored matrix obtained by calling `MatGetFactor()`
10305: . rhs - location where the right-hand side of the Schur complement system is stored
10306: - sol - location where the solution of the Schur complement system has to be returned
10308: Level: advanced
10310: Notes:
10311: The sizes of the vectors should match the size of the Schur complement
10313: Must be called after `MatFactorSetSchurIS()`
10315: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
10316: @*/
10317: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
10318: {
10319: PetscFunctionBegin;
10326: PetscCheckSameComm(F, 1, rhs, 2);
10327: PetscCheckSameComm(F, 1, sol, 3);
10328: PetscCall(MatFactorFactorizeSchurComplement(F));
10329: switch (F->schur_status) {
10330: case MAT_FACTOR_SCHUR_FACTORED:
10331: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
10332: break;
10333: case MAT_FACTOR_SCHUR_INVERTED:
10334: PetscCall(MatMultTranspose(F->schur, rhs, sol));
10335: break;
10336: default:
10337: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10338: }
10339: PetscFunctionReturn(PETSC_SUCCESS);
10340: }
10342: /*@
10343: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
10345: Logically Collective
10347: Input Parameters:
10348: + F - the factored matrix obtained by calling `MatGetFactor()`
10349: . rhs - location where the right-hand side of the Schur complement system is stored
10350: - sol - location where the solution of the Schur complement system has to be returned
10352: Level: advanced
10354: Notes:
10355: The sizes of the vectors should match the size of the Schur complement
10357: Must be called after `MatFactorSetSchurIS()`
10359: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
10360: @*/
10361: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
10362: {
10363: PetscFunctionBegin;
10370: PetscCheckSameComm(F, 1, rhs, 2);
10371: PetscCheckSameComm(F, 1, sol, 3);
10372: PetscCall(MatFactorFactorizeSchurComplement(F));
10373: switch (F->schur_status) {
10374: case MAT_FACTOR_SCHUR_FACTORED:
10375: PetscCall(MatSolve(F->schur, rhs, sol));
10376: break;
10377: case MAT_FACTOR_SCHUR_INVERTED:
10378: PetscCall(MatMult(F->schur, rhs, sol));
10379: break;
10380: default:
10381: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10382: }
10383: PetscFunctionReturn(PETSC_SUCCESS);
10384: }
10386: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
10387: #if PetscDefined(HAVE_CUDA)
10388: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
10389: #endif
10391: /* Schur status updated in the interface */
10392: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
10393: {
10394: Mat S = F->schur;
10396: PetscFunctionBegin;
10397: if (S) {
10398: PetscMPIInt size;
10399: PetscBool isdense, isdensecuda;
10401: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
10402: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
10403: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
10404: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
10405: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
10406: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
10407: if (isdense) {
10408: PetscCall(MatSeqDenseInvertFactors_Private(S));
10409: } else if (isdensecuda) {
10410: #if PetscDefined(HAVE_CUDA)
10411: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
10412: #endif
10413: }
10414: // HIP??????????????
10415: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
10416: }
10417: PetscFunctionReturn(PETSC_SUCCESS);
10418: }
10420: /*@
10421: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
10423: Logically Collective
10425: Input Parameter:
10426: . F - the factored matrix obtained by calling `MatGetFactor()`
10428: Level: advanced
10430: Notes:
10431: Must be called after `MatFactorSetSchurIS()`.
10433: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
10435: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10436: @*/
10437: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10438: {
10439: PetscFunctionBegin;
10442: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10443: PetscCall(MatFactorFactorizeSchurComplement(F));
10444: PetscCall(MatFactorInvertSchurComplement_Private(F));
10445: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10446: PetscFunctionReturn(PETSC_SUCCESS);
10447: }
10449: /*@
10450: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10452: Logically Collective
10454: Input Parameter:
10455: . F - the factored matrix obtained by calling `MatGetFactor()`
10457: Level: advanced
10459: Note:
10460: Must be called after `MatFactorSetSchurIS()`
10462: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10463: @*/
10464: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10465: {
10466: MatFactorInfo info;
10468: PetscFunctionBegin;
10471: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10472: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10473: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10474: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10475: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10476: } else {
10477: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10478: }
10479: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10480: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10481: PetscFunctionReturn(PETSC_SUCCESS);
10482: }
10484: /*@
10485: MatPtAP - Creates the matrix product $C = P^T * A * P$
10487: Neighbor-wise Collective
10489: Input Parameters:
10490: + A - the matrix
10491: . P - the projection matrix
10492: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10493: - 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
10494: if the result is a dense matrix this is irrelevant
10496: Output Parameter:
10497: . C - the product matrix
10499: Level: intermediate
10501: Notes:
10502: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10504: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_PtAP`
10505: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10507: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10509: Developer Note:
10510: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10512: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10513: @*/
10514: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10515: {
10516: PetscFunctionBegin;
10517: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10518: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10520: if (scall == MAT_INITIAL_MATRIX) {
10521: PetscCall(MatProductCreate(A, P, NULL, C));
10522: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10523: PetscCall(MatProductSetAlgorithm(*C, "default"));
10524: PetscCall(MatProductSetFill(*C, fill));
10526: (*C)->product->api_user = PETSC_TRUE;
10527: PetscCall(MatProductSetFromOptions(*C));
10528: 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);
10529: PetscCall(MatProductSymbolic(*C));
10530: } else { /* scall == MAT_REUSE_MATRIX */
10531: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10532: }
10534: PetscCall(MatProductNumeric(*C));
10535: if (A->symmetric == PETSC_BOOL3_TRUE) {
10536: PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10537: (*C)->spd = A->spd;
10538: }
10539: PetscFunctionReturn(PETSC_SUCCESS);
10540: }
10542: /*@
10543: MatRARt - Creates the matrix product $C = R * A * R^T$
10545: Neighbor-wise Collective
10547: Input Parameters:
10548: + A - the matrix
10549: . R - the projection matrix
10550: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10551: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10552: if the result is a dense matrix this is irrelevant
10554: Output Parameter:
10555: . C - the product matrix
10557: Level: intermediate
10559: Notes:
10560: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10562: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_RARt`
10563: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10565: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10566: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10567: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10568: We recommend using `MatPtAP()` when possible.
10570: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10572: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10573: @*/
10574: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10575: {
10576: PetscFunctionBegin;
10577: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10578: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10580: if (scall == MAT_INITIAL_MATRIX) {
10581: PetscCall(MatProductCreate(A, R, NULL, C));
10582: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10583: PetscCall(MatProductSetAlgorithm(*C, "default"));
10584: PetscCall(MatProductSetFill(*C, fill));
10586: (*C)->product->api_user = PETSC_TRUE;
10587: PetscCall(MatProductSetFromOptions(*C));
10588: 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);
10589: PetscCall(MatProductSymbolic(*C));
10590: } else { /* scall == MAT_REUSE_MATRIX */
10591: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10592: }
10594: PetscCall(MatProductNumeric(*C));
10595: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10596: PetscFunctionReturn(PETSC_SUCCESS);
10597: }
10599: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10600: {
10601: PetscBool flg = PETSC_TRUE;
10603: PetscFunctionBegin;
10604: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10605: if (scall == MAT_INITIAL_MATRIX) {
10606: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10607: PetscCall(MatProductCreate(A, B, NULL, C));
10608: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10609: PetscCall(MatProductSetFill(*C, fill));
10610: } else { /* scall == MAT_REUSE_MATRIX */
10611: Mat_Product *product = (*C)->product;
10613: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10614: if (flg && product && product->type != ptype) {
10615: PetscCall(MatProductClear(*C));
10616: product = NULL;
10617: }
10618: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10619: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10620: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10621: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10622: product = (*C)->product;
10623: product->fill = fill;
10624: product->clear = PETSC_TRUE;
10625: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10626: flg = PETSC_FALSE;
10627: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10628: }
10629: }
10630: if (flg) {
10631: (*C)->product->api_user = PETSC_TRUE;
10632: PetscCall(MatProductSetType(*C, ptype));
10633: PetscCall(MatProductSetFromOptions(*C));
10634: PetscCall(MatProductSymbolic(*C));
10635: }
10636: PetscCall(MatProductNumeric(*C));
10637: PetscFunctionReturn(PETSC_SUCCESS);
10638: }
10640: /*@
10641: MatMatMult - Performs matrix-matrix multiplication $ C=A*B $.
10643: Neighbor-wise Collective
10645: Input Parameters:
10646: + A - the left matrix
10647: . B - the right matrix
10648: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10649: - 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
10650: if the result is a dense matrix this is irrelevant
10652: Output Parameter:
10653: . C - the product matrix
10655: Notes:
10656: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10658: `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
10659: call to this function with `MAT_INITIAL_MATRIX`.
10661: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10663: 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`,
10664: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10666: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10668: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AB`
10669: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10671: Example of Usage:
10672: .vb
10673: MatProductCreate(A,B,NULL,&C);
10674: MatProductSetType(C,MATPRODUCT_AB);
10675: MatProductSymbolic(C);
10676: MatProductNumeric(C); // compute C=A * B
10677: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10678: MatProductNumeric(C);
10679: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10680: MatProductNumeric(C);
10681: .ve
10683: Level: intermediate
10685: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10686: @*/
10687: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10688: {
10689: PetscFunctionBegin;
10690: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10691: PetscFunctionReturn(PETSC_SUCCESS);
10692: }
10694: /*@
10695: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10697: Neighbor-wise Collective
10699: Input Parameters:
10700: + A - the left matrix
10701: . B - the right matrix
10702: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10703: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10705: Output Parameter:
10706: . C - the product matrix
10708: Options Database Key:
10709: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10710: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10711: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10713: Level: intermediate
10715: Notes:
10716: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10718: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10720: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10721: actually needed.
10723: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10724: and for pairs of `MATMPIDENSE` matrices.
10726: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABt`
10727: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10729: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10731: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()`, `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10732: @*/
10733: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10734: {
10735: PetscFunctionBegin;
10736: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10737: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10738: PetscFunctionReturn(PETSC_SUCCESS);
10739: }
10741: /*@
10742: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10744: Neighbor-wise Collective
10746: Input Parameters:
10747: + A - the left matrix
10748: . B - the right matrix
10749: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10750: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10752: Output Parameter:
10753: . C - the product matrix
10755: Level: intermediate
10757: Notes:
10758: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10760: `MAT_REUSE_MATRIX` can only be used if `A` and `B` have the same nonzero pattern as in the previous call.
10762: This is a convenience routine that wraps the use of `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AtB`
10763: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10765: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10766: actually needed.
10768: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10769: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10771: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10773: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10774: @*/
10775: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10776: {
10777: PetscFunctionBegin;
10778: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10779: PetscFunctionReturn(PETSC_SUCCESS);
10780: }
10782: /*@
10783: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10785: Neighbor-wise Collective
10787: Input Parameters:
10788: + A - the left matrix
10789: . B - the middle matrix
10790: . C - the right matrix
10791: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10792: - 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
10793: if the result is a dense matrix this is irrelevant
10795: Output Parameter:
10796: . D - the product matrix
10798: Level: intermediate
10800: Notes:
10801: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10803: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10805: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABC`
10806: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10808: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10809: actually needed.
10811: If you have many matrices with the same non-zero structure to multiply, you
10812: should use `MAT_REUSE_MATRIX` in all calls but the first
10814: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10816: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10817: @*/
10818: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10819: {
10820: PetscFunctionBegin;
10821: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10822: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10824: if (scall == MAT_INITIAL_MATRIX) {
10825: PetscCall(MatProductCreate(A, B, C, D));
10826: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10827: PetscCall(MatProductSetAlgorithm(*D, "default"));
10828: PetscCall(MatProductSetFill(*D, fill));
10830: (*D)->product->api_user = PETSC_TRUE;
10831: PetscCall(MatProductSetFromOptions(*D));
10832: 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,
10833: ((PetscObject)C)->type_name);
10834: PetscCall(MatProductSymbolic(*D));
10835: } else { /* user may change input matrices when REUSE */
10836: PetscCall(MatProductReplaceMats(A, B, C, *D));
10837: }
10838: PetscCall(MatProductNumeric(*D));
10839: PetscFunctionReturn(PETSC_SUCCESS);
10840: }
10842: /*@
10843: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10845: Collective
10847: Input Parameters:
10848: + mat - the matrix
10849: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10850: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10851: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10853: Output Parameter:
10854: . matredundant - redundant matrix
10856: Level: advanced
10858: Notes:
10859: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10860: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10862: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10863: calling it.
10865: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10867: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10868: @*/
10869: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10870: {
10871: MPI_Comm comm;
10872: PetscMPIInt size;
10873: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10874: Mat_Redundant *redund = NULL;
10875: PetscSubcomm psubcomm = NULL;
10876: MPI_Comm subcomm_in = subcomm;
10877: Mat *matseq;
10878: IS isrow, iscol;
10879: PetscBool newsubcomm = PETSC_FALSE;
10881: PetscFunctionBegin;
10883: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10884: PetscAssertPointer(*matredundant, 5);
10886: }
10888: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10889: if (size == 1 || nsubcomm == 1) {
10890: if (reuse == MAT_INITIAL_MATRIX) {
10891: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10892: } else {
10893: 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");
10894: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10895: }
10896: PetscFunctionReturn(PETSC_SUCCESS);
10897: }
10899: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10900: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10901: MatCheckPreallocated(mat, 1);
10903: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10904: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10905: /* create psubcomm, then get subcomm */
10906: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10907: PetscCallMPI(MPI_Comm_size(comm, &size));
10908: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10910: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10911: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10912: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10913: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10914: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10915: newsubcomm = PETSC_TRUE;
10916: PetscCall(PetscSubcommDestroy(&psubcomm));
10917: }
10919: /* get isrow, iscol and a local sequential matrix matseq[0] */
10920: if (reuse == MAT_INITIAL_MATRIX) {
10921: mloc_sub = PETSC_DECIDE;
10922: nloc_sub = PETSC_DECIDE;
10923: if (bs < 1) {
10924: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10925: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10926: } else {
10927: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10928: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10929: }
10930: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10931: rstart = rend - mloc_sub;
10932: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10933: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10934: PetscCall(ISSetIdentity(iscol));
10935: } else { /* reuse == MAT_REUSE_MATRIX */
10936: 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");
10937: /* retrieve subcomm */
10938: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10939: redund = (*matredundant)->redundant;
10940: isrow = redund->isrow;
10941: iscol = redund->iscol;
10942: matseq = redund->matseq;
10943: }
10944: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10946: /* get matredundant over subcomm */
10947: if (reuse == MAT_INITIAL_MATRIX) {
10948: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10950: /* create a supporting struct and attach it to C for reuse */
10951: PetscCall(PetscNew(&redund));
10952: (*matredundant)->redundant = redund;
10953: redund->isrow = isrow;
10954: redund->iscol = iscol;
10955: redund->matseq = matseq;
10956: if (newsubcomm) {
10957: redund->subcomm = subcomm;
10958: } else {
10959: redund->subcomm = MPI_COMM_NULL;
10960: }
10961: } else {
10962: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10963: }
10964: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
10965: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10966: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10967: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10968: }
10969: #endif
10970: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10971: PetscFunctionReturn(PETSC_SUCCESS);
10972: }
10974: /*@C
10975: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10976: a given `Mat`. Each submatrix can span multiple procs.
10978: Collective
10980: Input Parameters:
10981: + mat - the matrix
10982: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10983: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10985: Output Parameter:
10986: . subMat - parallel sub-matrices each spanning a given `subcomm`
10988: Level: advanced
10990: Notes:
10991: The submatrix partition across processors is dictated by `subComm` a
10992: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10993: is not restricted to be grouped with consecutive original MPI processes.
10995: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10996: map directly to the layout of the original matrix [wrt the local
10997: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10998: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10999: the `subMat`. However the offDiagMat looses some columns - and this is
11000: reconstructed with `MatSetValues()`
11002: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
11004: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
11005: @*/
11006: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
11007: {
11008: PetscMPIInt commsize, subCommSize;
11010: PetscFunctionBegin;
11011: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
11012: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
11013: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
11015: 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");
11016: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
11017: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
11018: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
11019: PetscFunctionReturn(PETSC_SUCCESS);
11020: }
11022: /*@
11023: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
11025: Not Collective
11027: Input Parameters:
11028: + mat - matrix to extract local submatrix from
11029: . isrow - local row indices for submatrix
11030: - iscol - local column indices for submatrix
11032: Output Parameter:
11033: . submat - the submatrix
11035: Level: intermediate
11037: Notes:
11038: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
11040: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
11041: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
11043: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
11044: `MatSetValuesBlockedLocal()` will also be implemented.
11046: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
11047: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
11049: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
11050: @*/
11051: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
11052: {
11053: PetscFunctionBegin;
11057: PetscCheckSameComm(isrow, 2, iscol, 3);
11058: PetscAssertPointer(submat, 4);
11059: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
11061: if (mat->ops->getlocalsubmatrix) {
11062: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
11063: } else {
11064: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
11065: }
11066: (*submat)->assembled = mat->assembled;
11067: PetscFunctionReturn(PETSC_SUCCESS);
11068: }
11070: /*@
11071: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
11073: Not Collective
11075: Input Parameters:
11076: + mat - matrix to extract local submatrix from
11077: . isrow - local row indices for submatrix
11078: . iscol - local column indices for submatrix
11079: - submat - the submatrix
11081: Level: intermediate
11083: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
11084: @*/
11085: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
11086: {
11087: PetscFunctionBegin;
11091: PetscCheckSameComm(isrow, 2, iscol, 3);
11092: PetscAssertPointer(submat, 4);
11095: if (mat->ops->restorelocalsubmatrix) {
11096: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
11097: } else {
11098: PetscCall(MatDestroy(submat));
11099: }
11100: *submat = NULL;
11101: PetscFunctionReturn(PETSC_SUCCESS);
11102: }
11104: /*@
11105: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
11107: Collective
11109: Input Parameter:
11110: . mat - the matrix
11112: Output Parameter:
11113: . is - if any rows have zero diagonals this contains the list of them
11115: Level: developer
11117: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
11118: @*/
11119: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
11120: {
11121: PetscFunctionBegin;
11124: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11125: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11127: if (!mat->ops->findzerodiagonals) {
11128: Vec diag;
11129: const PetscScalar *a;
11130: PetscInt *rows;
11131: PetscInt rStart, rEnd, r, nrow = 0;
11133: PetscCall(MatCreateVecs(mat, &diag, NULL));
11134: PetscCall(MatGetDiagonal(mat, diag));
11135: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
11136: PetscCall(VecGetArrayRead(diag, &a));
11137: for (r = 0; r < rEnd - rStart; ++r)
11138: if (a[r] == 0.0) ++nrow;
11139: PetscCall(PetscMalloc1(nrow, &rows));
11140: nrow = 0;
11141: for (r = 0; r < rEnd - rStart; ++r)
11142: if (a[r] == 0.0) rows[nrow++] = r + rStart;
11143: PetscCall(VecRestoreArrayRead(diag, &a));
11144: PetscCall(VecDestroy(&diag));
11145: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
11146: } else {
11147: PetscUseTypeMethod(mat, findzerodiagonals, is);
11148: }
11149: PetscFunctionReturn(PETSC_SUCCESS);
11150: }
11152: /*@
11153: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
11155: Collective
11157: Input Parameter:
11158: . mat - the matrix
11160: Output Parameter:
11161: . is - contains the list of rows with off block diagonal entries
11163: Level: developer
11165: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
11166: @*/
11167: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
11168: {
11169: PetscFunctionBegin;
11172: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11173: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11175: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
11176: PetscFunctionReturn(PETSC_SUCCESS);
11177: }
11179: /*@C
11180: MatInvertBlockDiagonal - Inverts the block diagonal entries.
11182: Collective; No Fortran Support
11184: Input Parameter:
11185: . mat - the matrix
11187: Output Parameter:
11188: . values - the block inverses in column major order (FORTRAN-like)
11190: Level: advanced
11192: Notes:
11193: The size of the blocks is determined by the block size of the matrix.
11195: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
11197: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
11199: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
11200: @*/
11201: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
11202: {
11203: PetscFunctionBegin;
11205: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11206: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11207: PetscUseTypeMethod(mat, invertblockdiagonal, values);
11208: PetscFunctionReturn(PETSC_SUCCESS);
11209: }
11211: /*@
11212: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
11214: Collective; No Fortran Support
11216: Input Parameters:
11217: + mat - the matrix
11218: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
11219: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
11221: Output Parameter:
11222: . values - the block inverses in column major order (FORTRAN-like)
11224: Level: advanced
11226: Notes:
11227: Use `MatInvertBlockDiagonal()` if all blocks have the same size
11229: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
11231: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
11232: @*/
11233: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
11234: {
11235: PetscFunctionBegin;
11237: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11238: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11239: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
11240: PetscFunctionReturn(PETSC_SUCCESS);
11241: }
11243: /*@
11244: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
11246: Collective
11248: Input Parameters:
11249: + A - the matrix
11250: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
11252: Level: advanced
11254: Note:
11255: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
11257: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
11258: @*/
11259: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
11260: {
11261: const PetscScalar *vals;
11262: PetscInt *dnnz;
11263: PetscInt m, rstart, rend, bs, i, j;
11265: PetscFunctionBegin;
11266: PetscCall(MatInvertBlockDiagonal(A, &vals));
11267: PetscCall(MatGetBlockSize(A, &bs));
11268: PetscCall(MatGetLocalSize(A, &m, NULL));
11269: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
11270: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
11271: PetscCall(PetscMalloc1(m / bs, &dnnz));
11272: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
11273: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
11274: PetscCall(PetscFree(dnnz));
11275: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
11276: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
11277: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
11278: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
11279: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
11280: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
11281: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
11282: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
11283: PetscFunctionReturn(PETSC_SUCCESS);
11284: }
11286: /*@
11287: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
11288: via `MatTransposeColoringCreate()`.
11290: Collective
11292: Input Parameter:
11293: . c - coloring context
11295: Level: intermediate
11297: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
11298: @*/
11299: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
11300: {
11301: MatTransposeColoring matcolor = *c;
11303: PetscFunctionBegin;
11304: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
11305: if (--((PetscObject)matcolor)->refct > 0) {
11306: matcolor = NULL;
11307: PetscFunctionReturn(PETSC_SUCCESS);
11308: }
11310: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
11311: PetscCall(PetscFree(matcolor->rows));
11312: PetscCall(PetscFree(matcolor->den2sp));
11313: PetscCall(PetscFree(matcolor->colorforcol));
11314: PetscCall(PetscFree(matcolor->columns));
11315: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
11316: PetscCall(PetscHeaderDestroy(c));
11317: PetscFunctionReturn(PETSC_SUCCESS);
11318: }
11320: /*@
11321: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
11322: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
11323: `MatTransposeColoring` to sparse `B`.
11325: Collective
11327: Input Parameters:
11328: + coloring - coloring context created with `MatTransposeColoringCreate()`
11329: - B - sparse matrix
11331: Output Parameter:
11332: . Btdense - dense matrix $B^T$
11334: Level: developer
11336: Note:
11337: These are used internally for some implementations of `MatRARt()`
11339: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
11340: @*/
11341: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
11342: {
11343: PetscFunctionBegin;
11348: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
11349: PetscFunctionReturn(PETSC_SUCCESS);
11350: }
11352: /*@
11353: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
11354: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
11355: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
11356: $C_{sp}$ from $C_{den}$.
11358: Collective
11360: Input Parameters:
11361: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
11362: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
11364: Output Parameter:
11365: . Csp - sparse matrix
11367: Level: developer
11369: Note:
11370: These are used internally for some implementations of `MatRARt()`
11372: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
11373: @*/
11374: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
11375: {
11376: PetscFunctionBegin;
11381: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
11382: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
11383: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
11384: PetscFunctionReturn(PETSC_SUCCESS);
11385: }
11387: /*@
11388: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
11390: Collective
11392: Input Parameters:
11393: + mat - the matrix product C
11394: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
11396: Output Parameter:
11397: . color - the new coloring context
11399: Level: intermediate
11401: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
11402: `MatTransColoringApplyDenToSp()`
11403: @*/
11404: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
11405: {
11406: MatTransposeColoring c;
11407: MPI_Comm comm;
11409: PetscFunctionBegin;
11410: PetscAssertPointer(color, 3);
11412: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11413: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
11414: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
11415: c->ctype = iscoloring->ctype;
11416: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
11417: *color = c;
11418: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11419: PetscFunctionReturn(PETSC_SUCCESS);
11420: }
11422: /*@
11423: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
11424: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
11426: Not Collective
11428: Input Parameter:
11429: . mat - the matrix
11431: Output Parameter:
11432: . state - the current state
11434: Level: intermediate
11436: Notes:
11437: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11438: different matrices
11440: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
11442: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
11444: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11445: @*/
11446: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11447: {
11448: PetscFunctionBegin;
11450: *state = mat->nonzerostate;
11451: PetscFunctionReturn(PETSC_SUCCESS);
11452: }
11454: /*@
11455: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11456: matrices from each processor
11458: Collective
11460: Input Parameters:
11461: + comm - the communicators the parallel matrix will live on
11462: . seqmat - the input sequential matrices
11463: . n - number of local columns (or `PETSC_DECIDE`)
11464: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11466: Output Parameter:
11467: . mpimat - the parallel matrix generated
11469: Level: developer
11471: Note:
11472: The number of columns of the matrix in EACH processor MUST be the same.
11474: .seealso: [](ch_matrices), `Mat`
11475: @*/
11476: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11477: {
11478: PetscMPIInt size;
11480: PetscFunctionBegin;
11481: PetscCallMPI(MPI_Comm_size(comm, &size));
11482: if (size == 1) {
11483: if (reuse == MAT_INITIAL_MATRIX) {
11484: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11485: } else {
11486: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11487: }
11488: PetscFunctionReturn(PETSC_SUCCESS);
11489: }
11491: 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");
11493: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11494: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11495: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11496: PetscFunctionReturn(PETSC_SUCCESS);
11497: }
11499: /*@
11500: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11502: Collective
11504: Input Parameters:
11505: + A - the matrix to create subdomains from
11506: - N - requested number of subdomains
11508: Output Parameters:
11509: + n - number of subdomains resulting on this MPI process
11510: - iss - `IS` list with indices of subdomains on this MPI process
11512: Level: advanced
11514: Note:
11515: The number of subdomains must be smaller than the communicator size
11517: .seealso: [](ch_matrices), `Mat`, `IS`
11518: @*/
11519: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11520: {
11521: MPI_Comm comm, subcomm;
11522: PetscMPIInt size, rank, color;
11523: PetscInt rstart, rend, k;
11525: PetscFunctionBegin;
11526: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11527: PetscCallMPI(MPI_Comm_size(comm, &size));
11528: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11529: 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);
11530: *n = 1;
11531: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11532: color = rank / k;
11533: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11534: PetscCall(PetscMalloc1(1, iss));
11535: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11536: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11537: PetscCallMPI(MPI_Comm_free(&subcomm));
11538: PetscFunctionReturn(PETSC_SUCCESS);
11539: }
11541: /*@
11542: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11544: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11545: If they are not the same, uses `MatMatMatMult()`.
11547: Once the coarse grid problem is constructed, correct for interpolation operators
11548: that are not of full rank, which can legitimately happen in the case of non-nested
11549: geometric multigrid.
11551: Input Parameters:
11552: + restrct - restriction operator
11553: . dA - fine grid matrix
11554: . interpolate - interpolation operator
11555: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11556: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11558: Output Parameter:
11559: . A - the Galerkin coarse matrix
11561: Options Database Key:
11562: . -pc_mg_galerkin (both|pmat|mat|none) - for what matrices the Galerkin process should be used
11564: Level: developer
11566: Note:
11567: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11569: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11570: @*/
11571: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11572: {
11573: IS zerorows;
11574: Vec diag;
11576: PetscFunctionBegin;
11577: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11578: /* Construct the coarse grid matrix */
11579: if (interpolate == restrct) {
11580: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11581: } else {
11582: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11583: }
11585: /* If the interpolation matrix is not of full rank, A will have zero rows.
11586: This can legitimately happen in the case of non-nested geometric multigrid.
11587: In that event, we set the rows of the matrix to the rows of the identity,
11588: ignoring the equations (as the RHS will also be zero). */
11590: PetscCall(MatFindZeroRows(*A, &zerorows));
11592: if (zerorows != NULL) { /* if there are any zero rows */
11593: PetscCall(MatCreateVecs(*A, &diag, NULL));
11594: PetscCall(MatGetDiagonal(*A, diag));
11595: PetscCall(VecISSet(diag, zerorows, 1.0));
11596: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11597: PetscCall(VecDestroy(&diag));
11598: PetscCall(ISDestroy(&zerorows));
11599: }
11600: PetscFunctionReturn(PETSC_SUCCESS);
11601: }
11603: /*@C
11604: MatSetOperation - Allows user to set a matrix operation for any matrix type
11606: Logically Collective
11608: Input Parameters:
11609: + mat - the matrix
11610: . op - the name of the operation
11611: - f - the function that provides the operation
11613: Level: developer
11615: Example Usage:
11616: .vb
11617: extern PetscErrorCode usermult(Mat, Vec, Vec);
11619: PetscCall(MatCreateXXX(comm, ..., &A));
11620: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11621: .ve
11623: Notes:
11624: See the file `include/petscmat.h` for a complete list of matrix
11625: operations, which all have the form MATOP_<OPERATION>, where
11626: <OPERATION> is the name (in all capital letters) of the
11627: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11629: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11630: sequence as the usual matrix interface routines, since they
11631: are intended to be accessed via the usual matrix interface
11632: routines, e.g.,
11633: .vb
11634: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11635: .ve
11637: In particular each function MUST return `PETSC_SUCCESS` on success and
11638: nonzero on failure.
11640: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11642: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11643: @*/
11644: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11645: {
11646: PetscFunctionBegin;
11649: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11650: #if !PetscDefined(USE_COMPLEX)
11651: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11652: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11653: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11654: #endif
11655: (((PetscErrorCodeFn **)mat->ops)[op]) = f;
11656: PetscFunctionReturn(PETSC_SUCCESS);
11657: }
11659: /*@C
11660: MatGetOperation - Gets a matrix operation for any matrix type.
11662: Not Collective
11664: Input Parameters:
11665: + mat - the matrix
11666: - op - the name of the operation
11668: Output Parameter:
11669: . f - the function that provides the operation
11671: Level: developer
11673: Example Usage:
11674: .vb
11675: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11677: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11678: .ve
11680: Notes:
11681: See the file `include/petscmat.h` for a complete list of matrix
11682: operations, which all have the form MATOP_<OPERATION>, where
11683: <OPERATION> is the name (in all capital letters) of the
11684: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11686: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11688: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11689: @*/
11690: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11691: {
11692: PetscFunctionBegin;
11694: PetscAssertPointer(f, 3);
11695: #if !PetscDefined(USE_COMPLEX)
11696: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11697: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11698: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11699: #endif
11700: *f = (((PetscErrorCodeFn **)mat->ops)[op]);
11701: PetscFunctionReturn(PETSC_SUCCESS);
11702: }
11704: /*@
11705: MatHasOperation - Determines whether the given matrix supports the particular operation.
11707: Not Collective
11709: Input Parameters:
11710: + mat - the matrix
11711: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11713: Output Parameter:
11714: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11716: Level: advanced
11718: Note:
11719: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11721: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11722: @*/
11723: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11724: {
11725: PetscFunctionBegin;
11727: PetscAssertPointer(has, 3);
11728: #if !PetscDefined(USE_COMPLEX)
11729: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11730: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11731: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11732: #endif
11733: if (mat->ops->hasoperation) {
11734: PetscUseTypeMethod(mat, hasoperation, op, has);
11735: } else {
11736: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11737: else {
11738: *has = PETSC_FALSE;
11739: if (op == MATOP_CREATE_SUBMATRIX) {
11740: PetscMPIInt size;
11742: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11743: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11744: }
11745: }
11746: }
11747: PetscFunctionReturn(PETSC_SUCCESS);
11748: }
11750: /*@
11751: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11753: Collective
11755: Input Parameter:
11756: . mat - the matrix
11758: Output Parameter:
11759: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11761: Level: beginner
11763: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11764: @*/
11765: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11766: {
11767: PetscFunctionBegin;
11770: PetscAssertPointer(cong, 2);
11771: if (!mat->rmap || !mat->cmap) {
11772: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11773: PetscFunctionReturn(PETSC_SUCCESS);
11774: }
11775: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11776: PetscCall(PetscLayoutSetUp(mat->rmap));
11777: PetscCall(PetscLayoutSetUp(mat->cmap));
11778: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11779: if (*cong) mat->congruentlayouts = 1;
11780: else mat->congruentlayouts = 0;
11781: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11782: PetscFunctionReturn(PETSC_SUCCESS);
11783: }
11785: /*@
11786: MatSetInf - Set every entry (of a given nonzero pattern) of a matrix to positive infinity.
11788: Logically Collective
11790: Input Parameter:
11791: . A - the matrix
11793: Level: developer
11795: .seealso: `Mat`, `MatZeroEntries()`, `MatSetValues()`
11796: @*/
11797: PetscErrorCode MatSetInf(Mat A)
11798: {
11799: PetscFunctionBegin;
11800: PetscUseTypeMethod(A, setinf);
11801: PetscFunctionReturn(PETSC_SUCCESS);
11802: }
11804: /*@
11805: 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
11806: and possibly removes small values from the graph structure.
11808: Collective
11810: Input Parameters:
11811: + A - the matrix
11812: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11813: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11814: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11815: . num_idx - size of `index` array
11816: - index - array of block indices to use for graph strength of connection weight
11818: Output Parameter:
11819: . graph - the resulting graph
11821: Level: advanced
11823: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11824: @*/
11825: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11826: {
11827: PetscFunctionBegin;
11831: PetscAssertPointer(graph, 7);
11832: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11833: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11834: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11835: PetscFunctionReturn(PETSC_SUCCESS);
11836: }
11838: /*@
11839: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11840: meaning the same memory is used for the matrix, and no new memory is allocated.
11842: Collective
11844: Input Parameters:
11845: + A - the matrix
11846: - 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
11848: Level: intermediate
11850: Developer Note:
11851: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11852: of the arrays in the data structure are unneeded.
11854: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11855: @*/
11856: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11857: {
11858: PetscFunctionBegin;
11860: PetscUseTypeMethod(A, eliminatezeros, keep);
11861: PetscFunctionReturn(PETSC_SUCCESS);
11862: }
11864: /*@C
11865: MatGetCurrentMemType - Get the memory location of the matrix
11867: Not Collective, but the result will be the same on all MPI processes
11869: Input Parameter:
11870: . A - the matrix whose memory type we are checking
11872: Output Parameter:
11873: . m - the memory type
11875: Level: intermediate
11877: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11878: @*/
11879: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11880: {
11881: PetscFunctionBegin;
11883: PetscAssertPointer(m, 2);
11884: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11885: else *m = PETSC_MEMTYPE_HOST;
11886: PetscFunctionReturn(PETSC_SUCCESS);
11887: }