Actual source code: matrix.c
1: /*
2: This is where the abstract matrix operations are defined
3: Portions of this code are under:
4: Copyright (c) 2022 Advanced Micro Devices, Inc. All rights reserved.
5: */
7: #include <petsc/private/matimpl.h>
8: #include <petsc/private/isimpl.h>
9: #include <petsc/private/vecimpl.h>
11: /* Logging support */
12: PetscClassId MAT_CLASSID;
13: PetscClassId MAT_COLORING_CLASSID;
14: PetscClassId MAT_FDCOLORING_CLASSID;
15: PetscClassId MAT_TRANSPOSECOLORING_CLASSID;
17: PetscLogEvent MAT_Mult, MAT_MultAdd, MAT_MultTranspose;
18: PetscLogEvent MAT_ADot, MAT_ANorm;
19: PetscLogEvent MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve, MAT_MatTrSolve;
20: PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
21: PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
22: PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
23: PetscLogEvent MAT_QRFactorNumeric, MAT_QRFactorSymbolic, MAT_QRFactor;
24: PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
25: PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_PartitioningND, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
26: PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply, MAT_Transpose, MAT_FDColoringFunction, MAT_CreateSubMat;
27: PetscLogEvent MAT_TransposeColoringCreate;
28: PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
29: PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric, MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
30: PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
31: PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
32: PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
33: PetscLogEvent MAT_MultHermitianTranspose, MAT_MultHermitianTransposeAdd;
34: PetscLogEvent MAT_Getsymtransreduced, MAT_GetBrowsOfAcols;
35: PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
36: PetscLogEvent MAT_GetMultiProcBlock;
37: PetscLogEvent MAT_CUSPARSECopyToGPU, MAT_CUSPARSECopyFromGPU, MAT_CUSPARSEGenerateTranspose, MAT_CUSPARSESolveAnalysis;
38: PetscLogEvent MAT_HIPSPARSECopyToGPU, MAT_HIPSPARSECopyFromGPU, MAT_HIPSPARSEGenerateTranspose, MAT_HIPSPARSESolveAnalysis;
39: PetscLogEvent MAT_PreallCOO, MAT_SetVCOO;
40: PetscLogEvent MAT_CreateGraph;
41: PetscLogEvent MAT_SetValuesBatch;
42: PetscLogEvent MAT_ViennaCLCopyToGPU;
43: PetscLogEvent MAT_CUDACopyToGPU, MAT_HIPCopyToGPU;
44: PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
45: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
46: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
47: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
48: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;
50: const char *const MatFactorTypes[] = {"NONE", "LU", "CHOLESKY", "ILU", "ICC", "ILUDT", "QR", "MatFactorType", "MAT_FACTOR_", NULL};
52: /*@
53: MatSetRandom - Sets all components of a matrix to random numbers.
55: Logically Collective
57: Input Parameters:
58: + x - the matrix
59: - rctx - the `PetscRandom` object, formed by `PetscRandomCreate()`, or `NULL` and
60: it will create one internally.
62: Example:
63: .vb
64: PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
65: MatSetRandom(x,rctx);
66: PetscRandomDestroy(rctx);
67: .ve
69: Level: intermediate
71: Notes:
72: For sparse matrices that have been preallocated but not been assembled, it randomly selects appropriate locations,
74: for sparse matrices that already have nonzero locations, it fills the locations with random numbers.
76: It generates an error if used on unassembled sparse matrices that have not been preallocated.
78: .seealso: [](ch_matrices), `Mat`, `PetscRandom`, `PetscRandomCreate()`, `MatZeroEntries()`, `MatSetValues()`, `PetscRandomDestroy()`
79: @*/
80: PetscErrorCode MatSetRandom(Mat x, PetscRandom rctx)
81: {
82: PetscRandom randObj = NULL;
84: PetscFunctionBegin;
88: MatCheckPreallocated(x, 1);
90: if (!rctx) {
91: MPI_Comm comm;
92: PetscCall(PetscObjectGetComm((PetscObject)x, &comm));
93: PetscCall(PetscRandomCreate(comm, &randObj));
94: PetscCall(PetscRandomSetType(randObj, x->defaultrandtype));
95: PetscCall(PetscRandomSetFromOptions(randObj));
96: rctx = randObj;
97: }
98: PetscCall(PetscLogEventBegin(MAT_SetRandom, x, rctx, 0, 0));
99: PetscUseTypeMethod(x, setrandom, rctx);
100: PetscCall(PetscLogEventEnd(MAT_SetRandom, x, rctx, 0, 0));
102: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
103: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
104: PetscCall(PetscRandomDestroy(&randObj));
105: PetscFunctionReturn(PETSC_SUCCESS);
106: }
108: /*@
109: MatCopyHashToXAIJ - copy hash table entries into an XAIJ matrix type
111: Logically Collective
113: Input Parameter:
114: . A - A matrix in unassembled, hash table form
116: Output Parameter:
117: . B - The XAIJ matrix. This can either be `A` or some matrix of equivalent size, e.g. obtained from `A` via `MatDuplicate()`
119: Example:
120: .vb
121: PetscCall(MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, &B));
122: PetscCall(MatCopyHashToXAIJ(A, B));
123: .ve
125: Level: advanced
127: Notes:
128: If `B` is `A`, then the hash table data structure will be destroyed. `B` is assembled
130: .seealso: [](ch_matrices), `Mat`, `MAT_USE_HASH_TABLE`
131: @*/
132: PetscErrorCode MatCopyHashToXAIJ(Mat A, Mat B)
133: {
134: PetscFunctionBegin;
136: PetscUseTypeMethod(A, copyhashtoxaij, B);
137: PetscFunctionReturn(PETSC_SUCCESS);
138: }
140: /*@
141: MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in
143: Logically Collective
145: Input Parameter:
146: . mat - the factored matrix
148: Output Parameters:
149: + pivot - the pivot value computed
150: - row - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
151: the share the matrix
153: Level: advanced
155: Notes:
156: This routine does not work for factorizations done with external packages.
158: This routine should only be called if `MatGetFactorError()` returns a value of `MAT_FACTOR_NUMERIC_ZEROPIVOT`
160: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
162: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
163: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
164: `MAT_FACTOR_NUMERIC_ZEROPIVOT`
165: @*/
166: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
167: {
168: PetscFunctionBegin;
170: PetscAssertPointer(pivot, 2);
171: PetscAssertPointer(row, 3);
172: *pivot = mat->factorerror_zeropivot_value;
173: *row = mat->factorerror_zeropivot_row;
174: PetscFunctionReturn(PETSC_SUCCESS);
175: }
177: /*@
178: MatFactorGetError - gets the error code from a factorization
180: Logically Collective
182: Input Parameter:
183: . mat - the factored matrix
185: Output Parameter:
186: . err - the error code
188: Level: advanced
190: Note:
191: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
193: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
194: `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
195: @*/
196: PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
197: {
198: PetscFunctionBegin;
200: PetscAssertPointer(err, 2);
201: *err = mat->factorerrortype;
202: PetscFunctionReturn(PETSC_SUCCESS);
203: }
205: /*@
206: MatFactorClearError - clears the error code in a factorization
208: Logically Collective
210: Input Parameter:
211: . mat - the factored matrix
213: Level: developer
215: Note:
216: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
218: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
219: `MatGetErrorCode()`, `MatFactorError`
220: @*/
221: PetscErrorCode MatFactorClearError(Mat mat)
222: {
223: PetscFunctionBegin;
225: mat->factorerrortype = MAT_FACTOR_NOERROR;
226: mat->factorerror_zeropivot_value = 0.0;
227: mat->factorerror_zeropivot_row = 0;
228: PetscFunctionReturn(PETSC_SUCCESS);
229: }
231: PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
232: {
233: Vec r, l;
234: const PetscScalar *al;
235: PetscInt i, nz, gnz, N, n, st;
237: PetscFunctionBegin;
238: PetscCall(MatCreateVecs(mat, &r, &l));
239: if (!cols) { /* nonzero rows */
240: PetscCall(MatGetOwnershipRange(mat, &st, NULL));
241: PetscCall(MatGetSize(mat, &N, NULL));
242: PetscCall(MatGetLocalSize(mat, &n, NULL));
243: PetscCall(VecSetRandom(r, NULL));
244: PetscCall(MatMult(mat, r, l));
245: PetscCall(VecGetArrayRead(l, &al));
246: } else { /* nonzero columns */
247: PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
248: PetscCall(MatGetSize(mat, NULL, &N));
249: PetscCall(MatGetLocalSize(mat, NULL, &n));
250: PetscCall(VecSet(r, 0.0));
251: PetscCall(VecSetRandom(l, NULL));
252: PetscCall(MatMultTranspose(mat, l, r));
253: PetscCall(VecGetArrayRead(r, &al));
254: }
255: if (tol <= 0.0) {
256: for (i = 0, nz = 0; i < n; i++)
257: if (al[i] != 0.0) nz++;
258: } else {
259: for (i = 0, nz = 0; i < n; i++)
260: if (PetscAbsScalar(al[i]) > tol) nz++;
261: }
262: PetscCallMPI(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
263: if (gnz != N) {
264: PetscInt *nzr;
265: PetscCall(PetscMalloc1(nz, &nzr));
266: if (nz) {
267: if (tol < 0) {
268: for (i = 0, nz = 0; i < n; i++)
269: if (al[i] != 0.0) nzr[nz++] = i + st;
270: } else {
271: for (i = 0, nz = 0; i < n; i++)
272: if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
273: }
274: }
275: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
276: } else *nonzero = NULL;
277: if (!cols) { /* nonzero rows */
278: PetscCall(VecRestoreArrayRead(l, &al));
279: } else {
280: PetscCall(VecRestoreArrayRead(r, &al));
281: }
282: PetscCall(VecDestroy(&l));
283: PetscCall(VecDestroy(&r));
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: /*@
288: MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix
290: Input Parameter:
291: . mat - the matrix
293: Output Parameter:
294: . keptrows - the rows that are not completely zero
296: Level: intermediate
298: Note:
299: `keptrows` is set to `NULL` if all rows are nonzero.
301: Developer Note:
302: If `keptrows` is not `NULL`, it must be sorted.
304: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
305: @*/
306: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
307: {
308: PetscFunctionBegin;
311: PetscAssertPointer(keptrows, 2);
312: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
313: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
314: if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
315: else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
316: if (keptrows && *keptrows) PetscCall(ISSetInfo(*keptrows, IS_SORTED, IS_GLOBAL, PETSC_FALSE, PETSC_TRUE));
317: PetscFunctionReturn(PETSC_SUCCESS);
318: }
320: /*@
321: MatFindZeroRows - Locate all rows that are completely zero in the matrix
323: Input Parameter:
324: . mat - the matrix
326: Output Parameter:
327: . zerorows - the rows that are completely zero
329: Level: intermediate
331: Note:
332: `zerorows` is set to `NULL` if no rows are zero.
334: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
335: @*/
336: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
337: {
338: IS keptrows;
339: PetscInt m, n;
341: PetscFunctionBegin;
344: PetscAssertPointer(zerorows, 2);
345: PetscCall(MatFindNonzeroRows(mat, &keptrows));
346: /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
347: In keeping with this convention, we set zerorows to NULL if there are no zero
348: rows. */
349: if (keptrows == NULL) {
350: *zerorows = NULL;
351: } else {
352: PetscCall(MatGetOwnershipRange(mat, &m, &n));
353: PetscCall(ISComplement(keptrows, m, n, zerorows));
354: PetscCall(ISDestroy(&keptrows));
355: }
356: PetscFunctionReturn(PETSC_SUCCESS);
357: }
359: /*@
360: MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling
362: Not Collective
364: Input Parameter:
365: . A - the matrix
367: Output Parameter:
368: . a - the diagonal part (which is a SEQUENTIAL matrix)
370: Level: advanced
372: Notes:
373: See `MatCreateAIJ()` for more information on the "diagonal part" of the matrix.
375: Use caution, as the reference count on the returned matrix is not incremented and it is used as part of `A`'s normal operation.
377: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
378: @*/
379: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
380: {
381: PetscFunctionBegin;
384: PetscAssertPointer(a, 2);
385: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
386: if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
387: else {
388: PetscMPIInt size;
390: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
391: PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
392: *a = A;
393: }
394: PetscFunctionReturn(PETSC_SUCCESS);
395: }
397: /*@
398: MatGetMultPetscSF - Returns the `PetscSF` that communicates to each MPI process the values held on other MPI processes that are coupled to it by the `Mat`
400: Not Collective
402: Input Parameter:
403: . A - the matrix
405: Output Parameter:
406: . sf - the `PetscSF`.
408: Level: advanced
410: Notes:
411: The returned `PetscSF` is owned by the matrix; do not destroy it.
413: It is only valid if this function is called after the matrix has been assembled
414: (and for `MATMPIDENSE` after a `MatMult()` has been additionally called).
416: This is only implemented for the matrix types listed below; calling it on a sequential matrix or a type that does not
417: build such a `PetscSF` raises an error.
419: For `MATMPIAIJ`, `MATMPIBAIJ`, `MATMPIDENSE`, and `MATMPISELL`, this `PetscSF` is used within
420: `MatMult()` to provide the contribution of vector entries that are not local to each MPI process to the matrix-vector product.
421: For `MATMPISBAIJ` the returned `PetscSF` is instead the off-process column gather used by operations such as
422: `MatDiagonalScale()`; `MatMult_MPISBAIJ()` uses a separate, augmented scatter context.
423: In all cases the `PetscSF` maps the global vector layout (the matrix column layout) onto the off-process columns that the local rows couple to,
424: so it can be reused to communicate any per-column data, for example with `PetscSFBcastBegin()`.
426: For `MATMPIDENSE` this `PetscSF` is an allgather: every process gathers all columns, including its own, in the natural global
427: order rather than a sparse `garray` order. The leaf set and ordering therefore differ across matrix types, so callers should use
428: `PetscSFGetGraph()` rather than assume a particular leaf layout.
430: .seealso: [](ch_matrices), `Mat`, `PetscSF`, `MatGetDiagonalBlock()`, `MatMPIAIJGetSeqAIJ()`, `PetscSFBcastBegin()`, `MatMult()`
431: @*/
432: PetscErrorCode MatGetMultPetscSF(Mat A, PetscSF *sf)
433: {
434: PetscFunctionBegin;
437: PetscAssertPointer(sf, 2);
438: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
439: *sf = NULL;
440: PetscUseMethod(A, "MatGetMultPetscSF_C", (Mat, PetscSF *), (A, sf));
441: PetscFunctionReturn(PETSC_SUCCESS);
442: }
444: /*@
445: MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.
447: Collective
449: Input Parameter:
450: . mat - the matrix
452: Output Parameter:
453: . trace - the sum of the diagonal entries
455: Level: advanced
457: .seealso: [](ch_matrices), `Mat`
458: @*/
459: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
460: {
461: Vec diag;
463: PetscFunctionBegin;
465: PetscAssertPointer(trace, 2);
466: PetscCall(MatCreateVecs(mat, &diag, NULL));
467: PetscCall(MatGetDiagonal(mat, diag));
468: PetscCall(VecSum(diag, trace));
469: PetscCall(VecDestroy(&diag));
470: PetscFunctionReturn(PETSC_SUCCESS);
471: }
473: /*@
474: MatRealPart - Zeros out the imaginary part of the matrix
476: Logically Collective
478: Input Parameter:
479: . mat - the matrix
481: Level: advanced
483: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
484: @*/
485: PetscErrorCode MatRealPart(Mat mat)
486: {
487: PetscFunctionBegin;
490: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
491: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
492: MatCheckPreallocated(mat, 1);
493: PetscUseTypeMethod(mat, realpart);
494: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
495: PetscFunctionReturn(PETSC_SUCCESS);
496: }
498: /*@
499: MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix
501: Collective
503: Input Parameter:
504: . mat - the matrix
506: Output Parameters:
507: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
508: - ghosts - the global indices of the ghost points
510: Level: advanced
512: Note:
513: `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`
515: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
516: @*/
517: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
518: {
519: PetscFunctionBegin;
522: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
523: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
524: if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
525: else {
526: if (nghosts) *nghosts = 0;
527: if (ghosts) *ghosts = NULL;
528: }
529: PetscFunctionReturn(PETSC_SUCCESS);
530: }
532: /*@
533: MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part
535: Logically Collective
537: Input Parameter:
538: . mat - the matrix
540: Level: advanced
542: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
543: @*/
544: PetscErrorCode MatImaginaryPart(Mat mat)
545: {
546: PetscFunctionBegin;
549: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
550: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
551: MatCheckPreallocated(mat, 1);
552: PetscUseTypeMethod(mat, imaginarypart);
553: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
554: PetscFunctionReturn(PETSC_SUCCESS);
555: }
557: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
558: /*@
559: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
560: for each row that you get to ensure that your application does
561: not bleed memory.
563: Not Collective
565: Input Parameters:
566: + mat - the matrix
567: - row - the row to get
569: Output Parameters:
570: + ncols - if not `NULL`, the number of nonzeros in `row`
571: . cols - if not `NULL`, the column numbers
572: - vals - if not `NULL`, the numerical values
574: Level: advanced
576: Notes:
577: This routine is provided for people who need to have direct access
578: to the structure of a matrix. We hope that we provide enough
579: high-level matrix routines that few users will need it.
581: `MatGetRow()` always returns 0-based column indices, regardless of
582: whether the internal representation is 0-based (default) or 1-based.
584: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
585: not wish to extract these quantities.
587: The user can only examine the values extracted with `MatGetRow()`;
588: the values CANNOT be altered. To change the matrix entries, one
589: must use `MatSetValues()`.
591: You can only have one call to `MatGetRow()` outstanding for a particular
592: matrix at a time, per process. `MatGetRow()` can only obtain rows
593: associated with the given process, it cannot get rows from the
594: other processes; for that we suggest using `MatCreateSubMatrices()`, then
595: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
596: is in the global number of rows.
598: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
600: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
602: Fortran Note:
603: .vb
604: PetscInt, pointer :: cols(:)
605: PetscScalar, pointer :: vals(:)
606: .ve
608: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
609: @*/
610: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
611: {
612: PetscInt incols;
614: PetscFunctionBegin;
617: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
618: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
619: MatCheckPreallocated(mat, 1);
620: 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);
621: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
622: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
623: if (ncols) *ncols = incols;
624: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
625: PetscFunctionReturn(PETSC_SUCCESS);
626: }
628: /*@
629: MatConjugate - replaces the matrix values with their complex conjugates
631: Logically Collective
633: Input Parameter:
634: . mat - the matrix
636: Level: advanced
638: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
639: @*/
640: PetscErrorCode MatConjugate(Mat mat)
641: {
642: PetscFunctionBegin;
644: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
645: if (PetscDefined(USE_COMPLEX) && !(mat->symmetric == PETSC_BOOL3_TRUE && mat->hermitian == PETSC_BOOL3_TRUE)) {
646: PetscUseTypeMethod(mat, conjugate);
647: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
648: }
649: PetscFunctionReturn(PETSC_SUCCESS);
650: }
652: /*@
653: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
655: Not Collective
657: Input Parameters:
658: + mat - the matrix
659: . row - the row to get
660: . ncols - the number of nonzeros
661: . cols - the columns of the nonzeros
662: - vals - if nonzero the column values
664: Level: advanced
666: Notes:
667: This routine should be called after you have finished examining the entries.
669: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
670: us of the array after it has been restored. If you pass `NULL`, it will
671: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
673: Fortran Note:
674: .vb
675: PetscInt, pointer :: cols(:)
676: PetscScalar, pointer :: vals(:)
677: .ve
679: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
680: @*/
681: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
682: {
683: PetscFunctionBegin;
685: if (ncols) PetscAssertPointer(ncols, 3);
686: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
687: PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
688: if (ncols) *ncols = 0;
689: if (cols) *cols = NULL;
690: if (vals) *vals = NULL;
691: PetscFunctionReturn(PETSC_SUCCESS);
692: }
694: /*@
695: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
696: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
698: Not Collective
700: Input Parameter:
701: . mat - the matrix
703: Level: advanced
705: Note:
706: 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.
708: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
709: @*/
710: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
711: {
712: PetscFunctionBegin;
715: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
716: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
717: MatCheckPreallocated(mat, 1);
718: PetscTryTypeMethod(mat, getrowuppertriangular);
719: PetscFunctionReturn(PETSC_SUCCESS);
720: }
722: /*@
723: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
725: Not Collective
727: Input Parameter:
728: . mat - the matrix
730: Level: advanced
732: Note:
733: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
735: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
736: @*/
737: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
738: {
739: PetscFunctionBegin;
742: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
743: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
744: MatCheckPreallocated(mat, 1);
745: PetscTryTypeMethod(mat, restorerowuppertriangular);
746: PetscFunctionReturn(PETSC_SUCCESS);
747: }
749: /*@
750: MatSetOptionsPrefix - Sets the prefix used for searching for all
751: `Mat` options in the database.
753: Logically Collective
755: Input Parameters:
756: + A - the matrix
757: - prefix - the prefix to prepend to all option names
759: Level: advanced
761: Notes:
762: A hyphen (-) must NOT be given at the beginning of the prefix name.
763: The first character of all runtime options is AUTOMATICALLY the hyphen.
765: This is NOT used for options for the factorization of the matrix. Normally the
766: prefix is automatically passed in from the PC calling the factorization. To set
767: it directly use `MatSetOptionsPrefixFactor()`
769: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
770: @*/
771: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
772: {
773: PetscFunctionBegin;
775: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
776: PetscTryMethod(A, "MatSetOptionsPrefix_C", (Mat, const char[]), (A, prefix));
777: PetscFunctionReturn(PETSC_SUCCESS);
778: }
780: /*@
781: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
782: for matrices created with `MatGetFactor()`
784: Logically Collective
786: Input Parameters:
787: + A - the matrix
788: - prefix - the prefix to prepend to all option names for the factored matrix
790: Level: developer
792: Notes:
793: A hyphen (-) must NOT be given at the beginning of the prefix name.
794: The first character of all runtime options is AUTOMATICALLY the hyphen.
796: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
797: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
799: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
800: @*/
801: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
802: {
803: PetscFunctionBegin;
805: if (prefix) {
806: PetscAssertPointer(prefix, 2);
807: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
808: if (prefix != A->factorprefix) {
809: PetscCall(PetscFree(A->factorprefix));
810: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
811: }
812: } else PetscCall(PetscFree(A->factorprefix));
813: PetscFunctionReturn(PETSC_SUCCESS);
814: }
816: /*@
817: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
818: for matrices created with `MatGetFactor()`
820: Logically Collective
822: Input Parameters:
823: + A - the matrix
824: - prefix - the prefix to prepend to all option names for the factored matrix
826: Level: developer
828: Notes:
829: A hyphen (-) must NOT be given at the beginning of the prefix name.
830: The first character of all runtime options is AUTOMATICALLY the hyphen.
832: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
833: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
835: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
836: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
837: `MatSetOptionsPrefix()`
838: @*/
839: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
840: {
841: size_t len1, len2, new_len;
843: PetscFunctionBegin;
845: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
846: if (!A->factorprefix) {
847: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
848: PetscFunctionReturn(PETSC_SUCCESS);
849: }
850: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
852: PetscCall(PetscStrlen(A->factorprefix, &len1));
853: PetscCall(PetscStrlen(prefix, &len2));
854: new_len = len1 + len2 + 1;
855: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
856: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
857: PetscFunctionReturn(PETSC_SUCCESS);
858: }
860: /*@
861: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
862: matrix options in the database.
864: Logically Collective
866: Input Parameters:
867: + A - the matrix
868: - prefix - the prefix to prepend to all option names
870: Level: advanced
872: Note:
873: A hyphen (-) must NOT be given at the beginning of the prefix name.
874: The first character of all runtime options is AUTOMATICALLY the hyphen.
876: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
877: @*/
878: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
879: {
880: PetscFunctionBegin;
882: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
883: PetscTryMethod(A, "MatAppendOptionsPrefix_C", (Mat, const char[]), (A, prefix));
884: PetscFunctionReturn(PETSC_SUCCESS);
885: }
887: /*@
888: MatGetOptionsPrefix - Gets the prefix used for searching for all
889: matrix options in the database.
891: Not Collective
893: Input Parameter:
894: . A - the matrix
896: Output Parameter:
897: . prefix - pointer to the prefix string used
899: Level: advanced
901: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
902: @*/
903: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
904: {
905: PetscFunctionBegin;
907: PetscAssertPointer(prefix, 2);
908: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
909: PetscFunctionReturn(PETSC_SUCCESS);
910: }
912: /*@
913: MatGetState - Gets a snapshot of the state of a `Mat`
915: Not Collective, No Fortran Support
917: Input Parameter:
918: . A - the matrix
920: Output Parameter:
921: . state - the matrix state
923: Level: developer
925: Notes:
926: The snapshot includes the matrix identity, object state, and nonzero state. Use `MatStateCompare()` to determine whether two snapshots are the same, or `MatStateCompareUpdate()` to compare and update a saved snapshot.
928: .seealso: [](ch_matrices), `Mat`, `MatState`, `MatStateCompare()`, `MatStateCompareUpdate()`, `MatStateInvalidate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
929: @*/
930: PetscErrorCode MatGetState(Mat A, MatState *state)
931: {
932: PetscFunctionBegin;
934: PetscAssertPointer(state, 2);
935: state->id = ((PetscObject)A)->id;
936: state->state = ((PetscObject)A)->state;
937: state->nonzerostate = A->nonzerostate;
938: PetscFunctionReturn(PETSC_SUCCESS);
939: }
941: /*@
942: MatStateCompare - Compares two matrix state snapshots
944: Not Collective, No Fortran Support
946: Input Parameters:
947: + state1 - the first matrix state
948: - state2 - the second matrix state
950: Output Parameter:
951: . same - `PETSC_TRUE` if the matrix identity, object state, and nonzero state are the same, `PETSC_FALSE` otherwise
953: Level: developer
955: .seealso: [](ch_matrices), `Mat`, `MatState`, `MatGetState()`, `MatStateCompareUpdate()`, `MatStateInvalidate()`
956: @*/
957: PetscErrorCode MatStateCompare(MatState state1, MatState state2, PetscBool *same)
958: {
959: PetscFunctionBegin;
960: PetscAssertPointer(same, 3);
961: *same = (PetscBool)(state1.id == state2.id && state1.state == state2.state && state1.nonzerostate == state2.nonzerostate);
962: PetscFunctionReturn(PETSC_SUCCESS);
963: }
965: /*@
966: MatStateCompareUpdate - Compares a matrix with a state snapshot, then updates the snapshot
968: Not Collective, No Fortran Support
970: Input Parameter:
971: . A - the matrix
973: Input/Output Parameter:
974: . state - the matrix state snapshot to compare with and update
976: Output Parameter:
977: . same - `PETSC_TRUE` if the matrix state matched the snapshot before it was updated, `PETSC_FALSE` otherwise
979: Level: developer
981: .seealso: [](ch_matrices), `Mat`, `MatState`, `MatGetState()`, `MatStateCompare()`, `MatStateInvalidate()`
982: @*/
983: PetscErrorCode MatStateCompareUpdate(Mat A, MatState *state, PetscBool *same)
984: {
985: MatState current;
987: PetscFunctionBegin;
989: PetscAssertPointer(state, 2);
990: PetscAssertPointer(same, 3);
991: PetscCall(MatGetState(A, ¤t));
992: PetscCall(MatStateCompare(current, *state, same));
993: *state = current;
994: PetscFunctionReturn(PETSC_SUCCESS);
995: }
997: /*@
998: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
1000: Collective
1002: Input Parameter:
1003: . A - the matrix
1005: Level: beginner
1007: Notes:
1008: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
1009: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
1010: makes all of the preallocation space available
1012: Current values in the matrix are lost in this call
1014: Currently only supported for `MATAIJ` matrices.
1016: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
1017: @*/
1018: PetscErrorCode MatResetPreallocation(Mat A)
1019: {
1020: PetscFunctionBegin;
1023: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
1024: PetscFunctionReturn(PETSC_SUCCESS);
1025: }
1027: /*@
1028: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
1030: Collective
1032: Input Parameter:
1033: . A - the matrix
1035: Level: intermediate
1037: Notes:
1038: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
1040: Currently only supported for `MATAIJ` matrices.
1042: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
1043: @*/
1044: PetscErrorCode MatResetHash(Mat A)
1045: {
1046: PetscFunctionBegin;
1049: 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()");
1050: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
1051: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
1052: /* These flags are used to determine whether certain setups occur */
1053: A->was_assembled = PETSC_FALSE;
1054: A->assembled = PETSC_FALSE;
1055: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
1056: PetscCall(PetscObjectStateIncrease((PetscObject)A));
1057: PetscFunctionReturn(PETSC_SUCCESS);
1058: }
1060: /*@
1061: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
1063: Collective
1065: Input Parameter:
1066: . A - the matrix
1068: Level: advanced
1070: Notes:
1071: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
1072: setting values in the matrix.
1074: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
1076: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1077: @*/
1078: PetscErrorCode MatSetUp(Mat A)
1079: {
1080: PetscFunctionBegin;
1082: if (!((PetscObject)A)->type_name) {
1083: PetscMPIInt size;
1085: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1086: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1087: }
1088: if (!A->preallocated) PetscTryTypeMethod(A, setup);
1089: PetscCall(PetscLayoutSetUp(A->rmap));
1090: PetscCall(PetscLayoutSetUp(A->cmap));
1091: A->preallocated = PETSC_TRUE;
1092: PetscFunctionReturn(PETSC_SUCCESS);
1093: }
1095: #if PetscDefined(HAVE_SAWS)
1096: #include <petscviewersaws.h>
1097: #endif
1099: /*
1100: If threadsafety is on extraneous matrices may be printed
1102: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1103: */
1104: #if !PetscDefined(HAVE_THREADSAFETY)
1105: static PetscInt insidematview = 0;
1106: #endif
1108: /*@
1109: MatViewFromOptions - View properties of the matrix based on options set in the options database
1111: Collective
1113: Input Parameters:
1114: + A - the matrix
1115: . obj - optional additional object that provides the options prefix to use
1116: - name - command line option
1118: Options Database Key:
1119: . -name viewer_specification - See `PetscOptionsCreateViewer()` for the values of `viewer_specification`
1121: Level: intermediate
1123: Note:
1124: This checks the options database, creates the viewer on-the-fly, uses it and then destroys it. Hence it should not be called in heavily used routines,
1125: rather `PetscOptionsCreateViewer()` should be used to construct the viewer once which can then be utilized in the heavily used routine.
1127: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`, `PetscOptionsCreateViewer()`
1128: @*/
1129: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1130: {
1131: PetscFunctionBegin;
1133: #if !PetscDefined(HAVE_THREADSAFETY)
1134: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1135: #endif
1136: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1137: PetscFunctionReturn(PETSC_SUCCESS);
1138: }
1140: /*@
1141: MatView - display information about a matrix in a variety ways
1143: Collective on viewer
1145: Input Parameters:
1146: + mat - the matrix
1147: - viewer - visualization context
1149: Options Database Key:
1150: . -mat_view viewer_specification - Call `MatView()` at the conclusion of `MatAssemblyEnd()` or other routines that have changed the matrix values.
1151: See `PetscOptionsCreateViewer()` for the values of `viewer_specification`.
1153: Level: beginner
1155: Notes:
1156: The available visualization contexts include
1157: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1158: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1159: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1160: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1162: The user can open alternative visualization contexts with
1163: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1164: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1165: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1166: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1168: The user can call `PetscViewerPushFormat()` to specify the output
1169: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1170: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1171: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1172: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1173: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1174: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1175: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1176: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1177: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1179: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1180: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1182: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1184: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1185: viewer is used.
1187: `MatViewFromOptions()` provides an alternative to this routine that only views the matrix if the requested value
1188: is provided in the options database.
1190: See `share/petsc/matlab/PetscBinaryRead.m` for a MATLAB code that can read in the binary file when the binary
1191: viewer is used and `lib/petsc/bin/PetscBinaryIO.py` for loading them into Python.
1193: One can use `-mat_view draw -draw_pause -1` to pause the graphical display of matrix nonzero structure,
1194: and then use the following mouse functions.
1195: .vb
1196: left mouse: zoom in
1197: middle mouse: zoom out
1198: right mouse: continue with the simulation
1199: .ve
1201: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1202: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`, `PetscOptionsCreateViewer()`
1203: @*/
1204: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1205: {
1206: PetscInt rows, cols, rbs, cbs;
1207: PetscBool isascii, isstring, issaws;
1208: PetscViewerFormat format;
1209: PetscMPIInt size;
1211: PetscFunctionBegin;
1214: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1217: PetscCall(PetscViewerGetFormat(viewer, &format));
1218: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1219: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1221: #if !PetscDefined(HAVE_THREADSAFETY)
1222: insidematview++;
1223: #endif
1224: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1225: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1226: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1227: 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");
1229: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1230: if (isascii) {
1231: if (!mat->preallocated) {
1232: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1233: #if !PetscDefined(HAVE_THREADSAFETY)
1234: insidematview--;
1235: #endif
1236: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1237: PetscFunctionReturn(PETSC_SUCCESS);
1238: }
1239: if (!mat->assembled) {
1240: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1241: #if !PetscDefined(HAVE_THREADSAFETY)
1242: insidematview--;
1243: #endif
1244: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1245: PetscFunctionReturn(PETSC_SUCCESS);
1246: }
1247: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1248: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1249: MatNullSpace nullsp, transnullsp;
1250: PetscBool nz_factor = PETSC_TRUE;
1252: PetscCall(PetscViewerASCIIPushTab(viewer));
1253: PetscCall(MatGetSize(mat, &rows, &cols));
1254: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1255: if (rbs != 1 || cbs != 1) {
1256: 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" : ""));
1257: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1258: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1259: if (mat->factortype) {
1260: MatSolverType solver;
1262: PetscCall(MatFactorGetSolverType(mat, &solver));
1263: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1264: PetscCall(PetscStrcmpAny(solver, &nz_factor, MATSOLVERUMFPACK, MATSOLVERCHOLMOD, MATSOLVERSUPERLU, MATSOLVERSUPERLU_DIST, MATSOLVERSTRUMPACK, MATSOLVERHTOOL, ""));
1265: nz_factor = !nz_factor;
1266: }
1267: if (mat->ops->getinfo) {
1268: PetscBool is_constant_or_diagonal;
1270: // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1271: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1272: if (!is_constant_or_diagonal && nz_factor) {
1273: MatInfo info;
1275: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1276: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1277: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1278: }
1279: }
1280: PetscCall(MatGetNullSpace(mat, &nullsp));
1281: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1282: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1283: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1284: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1285: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1286: PetscCall(PetscViewerASCIIPushTab(viewer));
1287: PetscCall(MatProductView(mat, viewer));
1288: PetscCall(PetscViewerASCIIPopTab(viewer));
1289: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1290: IS tmp;
1292: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1293: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1294: PetscCall(PetscViewerASCIIPushTab(viewer));
1295: PetscCall(ISView(tmp, viewer));
1296: PetscCall(PetscViewerASCIIPopTab(viewer));
1297: PetscCall(ISDestroy(&tmp));
1298: }
1299: }
1300: } else if (issaws) {
1301: #if PetscDefined(HAVE_SAWS)
1302: PetscMPIInt rank;
1304: PetscCall(PetscObjectName((PetscObject)mat));
1305: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1306: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1307: #endif
1308: } else if (isstring) {
1309: const char *type;
1310: PetscCall(MatGetType(mat, &type));
1311: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1312: PetscTryTypeMethod(mat, view, viewer);
1313: }
1314: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1315: PetscCall(PetscViewerASCIIPushTab(viewer));
1316: PetscUseTypeMethod(mat, viewnative, viewer);
1317: PetscCall(PetscViewerASCIIPopTab(viewer));
1318: } else if (mat->ops->view) {
1319: PetscCall(PetscViewerASCIIPushTab(viewer));
1320: PetscUseTypeMethod(mat, view, viewer);
1321: PetscCall(PetscViewerASCIIPopTab(viewer));
1322: }
1323: if (isascii) {
1324: PetscCall(PetscViewerGetFormat(viewer, &format));
1325: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1326: }
1327: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1328: #if !PetscDefined(HAVE_THREADSAFETY)
1329: insidematview--;
1330: #endif
1331: PetscFunctionReturn(PETSC_SUCCESS);
1332: }
1334: #if PetscDefined(USE_DEBUG)
1335: #include <../src/sys/totalview/tv_data_display.h>
1336: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1337: {
1338: TV_add_row("Local rows", "int", &mat->rmap->n);
1339: TV_add_row("Local columns", "int", &mat->cmap->n);
1340: TV_add_row("Global rows", "int", &mat->rmap->N);
1341: TV_add_row("Global columns", "int", &mat->cmap->N);
1342: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1343: return TV_format_OK;
1344: }
1345: #endif
1347: /*@
1348: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1349: with `MatView()`. The matrix format is determined from the options database.
1350: Generates a parallel MPI matrix if the communicator has more than one
1351: process. The default matrix type is `MATAIJ`.
1353: Collective
1355: Input Parameters:
1356: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1357: or some related function before a call to `MatLoad()`
1358: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1360: Options Database Key:
1361: . -matload_block_size bs - set block size
1363: Level: beginner
1365: Notes:
1366: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1367: `Mat` before calling this routine if you wish to set it from the options database.
1369: `MatLoad()` automatically loads into the options database any options
1370: given in the file filename.info where filename is the name of the file
1371: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1372: file will be ignored if you use the `-viewer_binary_skip_info` option.
1374: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1375: sets the default matrix type AIJ and sets the local and global sizes.
1376: If type and/or size is already set, then the same are used.
1378: In parallel, each process can load a subset of rows (or the
1379: entire matrix). This routine is especially useful when a large
1380: matrix is stored on disk and only part of it is desired on each
1381: process. For example, a parallel solver may access only some of
1382: the rows from each process. The algorithm used here reads
1383: relatively small blocks of data rather than reading the entire
1384: matrix and then subsetting it.
1386: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1387: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1388: or the sequence like
1389: .vb
1390: PetscViewer v;
1391: PetscViewerCreate(PETSC_COMM_WORLD, &v);
1392: PetscViewerSetType(v, PETSCVIEWERBINARY);
1393: PetscViewerSetFromOptions(v);
1394: PetscViewerFileSetMode(v, FILE_MODE_READ);
1395: PetscViewerFileSetName(v, "datafile");
1396: .ve
1397: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1398: .vb
1399: -viewer_type (binary|hdf5)
1400: .ve
1402: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1403: and src/mat/tutorials/ex10.c with the second approach.
1405: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1406: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1407: Multiple objects, both matrices and vectors, can be stored within the same file.
1408: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1410: Most users should not need to know the details of the binary storage
1411: format, since `MatLoad()` and `MatView()` completely hide these details.
1412: But for anyone who is interested, the standard binary matrix storage
1413: format is
1415: .vb
1416: PetscInt MAT_FILE_CLASSID
1417: PetscInt number of rows
1418: PetscInt number of columns
1419: PetscInt total number of nonzeros
1420: PetscInt *number nonzeros in each row
1421: PetscInt *column indices of all nonzeros (starting index is zero)
1422: PetscScalar *values of all nonzeros
1423: .ve
1424: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1425: 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
1426: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1428: PETSc automatically does the byte swapping for
1429: machines that store the bytes reversed. Thus if you write your own binary
1430: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1431: and `PetscBinaryWrite()` to see how this may be done.
1433: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1434: Each process's chunk is loaded independently by its owning MPI process.
1435: Multiple objects, both matrices and vectors, can be stored within the same file.
1436: They are looked up by their PetscObject name.
1438: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1439: by default the same structure and naming of the AIJ arrays and column count
1440: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1441: .vb
1442: save example.mat A b -v7.3
1443: .ve
1444: can be directly read by this routine (see Reference 1 for details).
1446: Depending on your MATLAB version, this format might be a default,
1447: otherwise you can set it as default in Preferences.
1449: Unless `-nocompression` flag is used to save the file in MATLAB,
1450: PETSc must be configured with ZLIB package.
1452: See also examples `src/mat/tutorials/ex10.c` and `src/ksp/ksp/tutorials/ex27.c`
1454: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE`, and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1456: Corresponding `MatView()` is not yet implemented.
1458: The loaded matrix is actually a transpose of the original one in MATLAB,
1459: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1460: With this format, matrix is automatically transposed by PETSc,
1461: unless the matrix is marked as SPD or symmetric
1462: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1464: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1466: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1467: @*/
1468: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1469: {
1470: PetscBool flg;
1472: PetscFunctionBegin;
1476: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1478: flg = PETSC_FALSE;
1479: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1480: if (flg) {
1481: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1482: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1483: }
1484: flg = PETSC_FALSE;
1485: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1486: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1488: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1489: PetscUseTypeMethod(mat, load, viewer);
1490: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1491: PetscFunctionReturn(PETSC_SUCCESS);
1492: }
1494: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1495: {
1496: Mat_Redundant *redund = *redundant;
1498: PetscFunctionBegin;
1499: if (redund) {
1500: if (redund->matseq) { /* via MatCreateSubMatrices() */
1501: PetscCall(ISDestroy(&redund->isrow));
1502: PetscCall(ISDestroy(&redund->iscol));
1503: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1504: } else {
1505: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1506: PetscCall(PetscFree(redund->sbuf_j));
1507: PetscCall(PetscFree(redund->sbuf_a));
1508: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1509: PetscCall(PetscFree(redund->rbuf_j[i]));
1510: PetscCall(PetscFree(redund->rbuf_a[i]));
1511: }
1512: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1513: }
1515: PetscCall(PetscCommDestroy(&redund->subcomm));
1516: PetscCall(PetscFree(redund));
1517: }
1518: PetscFunctionReturn(PETSC_SUCCESS);
1519: }
1521: /*@
1522: MatDestroy - Frees space taken by a matrix.
1524: Collective
1526: Input Parameter:
1527: . A - the matrix
1529: Level: beginner
1531: Developer Note:
1532: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1533: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1534: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1535: if changes are needed here.
1537: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1538: @*/
1539: PetscErrorCode MatDestroy(Mat *A)
1540: {
1541: PetscFunctionBegin;
1542: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1544: if (--((PetscObject)*A)->refct > 0) {
1545: *A = NULL;
1546: PetscFunctionReturn(PETSC_SUCCESS);
1547: }
1549: /* if memory was published with SAWs then destroy it */
1550: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1551: PetscTryTypeMethod(*A, destroy);
1553: PetscCall(PetscFree((*A)->factorprefix));
1554: PetscCall(PetscFree((*A)->defaultvectype));
1555: PetscCall(PetscFree((*A)->defaultrandtype));
1556: PetscCall(PetscFree((*A)->bsizes));
1557: PetscCall(PetscFree((*A)->solvertype));
1558: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1559: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1560: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1561: PetscCall(MatProductClear(*A));
1562: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1563: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1564: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1565: PetscCall(MatDestroy(&(*A)->schur));
1566: PetscCall(VecDestroy(&(*A)->dot_vec));
1567: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1568: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1569: PetscCall(PetscHeaderDestroy(A));
1570: PetscFunctionReturn(PETSC_SUCCESS);
1571: }
1573: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1574: /*@
1575: MatSetValues - Inserts or adds a block of values into a matrix.
1576: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1577: MUST be called after all calls to `MatSetValues()` have been completed.
1579: Not Collective
1581: Input Parameters:
1582: + mat - the matrix
1583: . m - the number of rows
1584: . idxm - the global indices of the rows
1585: . n - the number of columns
1586: . idxn - the global indices of the columns
1587: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1588: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1589: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1591: Level: beginner
1593: Notes:
1594: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1595: options cannot be mixed without intervening calls to the assembly
1596: routines.
1598: `MatSetValues()` uses 0-based row and column numbers in Fortran
1599: as well as in C.
1601: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are simply ignored. This allows easily inserting element stiffness matrices
1602: with homogeneous Dirichlet boundary conditions that you don't want represented
1603: in the matrix.
1605: Efficiency Alert:
1606: The routine `MatSetValuesBlocked()` may offer much better efficiency
1607: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1609: Fortran Notes:
1610: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1611: .vb
1612: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1613: .ve
1615: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1616: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1618: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1619: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1620: @*/
1621: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1622: {
1623: PetscFunctionBeginHot;
1626: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1627: PetscAssertPointer(idxm, 3);
1628: PetscAssertPointer(idxn, 5);
1629: MatCheckPreallocated(mat, 1);
1631: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1632: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1634: if (PetscDefined(USE_DEBUG)) {
1635: PetscInt i, j;
1637: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1638: if (v) {
1639: for (i = 0; i < m; i++) {
1640: for (j = 0; j < n; j++) {
1641: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1642: #if PetscDefined(USE_COMPLEX)
1643: 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]);
1644: #else
1645: 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]);
1646: #endif
1647: }
1648: }
1649: }
1650: 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);
1651: 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);
1652: }
1654: if (mat->assembled) {
1655: mat->was_assembled = PETSC_TRUE;
1656: mat->assembled = PETSC_FALSE;
1657: }
1658: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1659: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1660: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1661: PetscFunctionReturn(PETSC_SUCCESS);
1662: }
1664: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1665: /*@
1666: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1667: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1668: MUST be called after all calls to `MatSetValues()` have been completed.
1670: Not Collective
1672: Input Parameters:
1673: + mat - the matrix
1674: . ism - the rows to provide
1675: . isn - the columns to provide
1676: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1677: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1678: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1680: Level: beginner
1682: Notes:
1683: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1685: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1686: options cannot be mixed without intervening calls to the assembly
1687: routines.
1689: `MatSetValues()` uses 0-based row and column numbers in Fortran
1690: as well as in C.
1692: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1693: simply ignored. This allows easily inserting element stiffness matrices
1694: with homogeneous Dirichlet boundary conditions that you don't want represented
1695: in the matrix.
1697: Fortran Note:
1698: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1699: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1701: Efficiency Alert:
1702: The routine `MatSetValuesBlocked()` may offer much better efficiency
1703: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1705: This is currently not optimized for any particular `ISType`
1707: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1708: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1709: @*/
1710: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1711: {
1712: PetscInt m, n;
1713: const PetscInt *rows, *cols;
1715: PetscFunctionBeginHot;
1717: PetscCall(ISGetIndices(ism, &rows));
1718: PetscCall(ISGetIndices(isn, &cols));
1719: PetscCall(ISGetLocalSize(ism, &m));
1720: PetscCall(ISGetLocalSize(isn, &n));
1721: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1722: PetscCall(ISRestoreIndices(ism, &rows));
1723: PetscCall(ISRestoreIndices(isn, &cols));
1724: PetscFunctionReturn(PETSC_SUCCESS);
1725: }
1727: /*@
1728: MatSetValuesRowLocal - Inserts a row of nonzero values into a matrix
1730: Not Collective
1732: Input Parameters:
1733: + mat - the matrix
1734: . row - the row to set
1735: - v - a one-dimensional array that contains the values
1737: Level: intermediate
1739: Notes:
1740: Currently only supported for `MATAIJ`.
1742: All the nonzero values in `row` must be provided
1744: The matrix must have previously had its column indices set, likely by having been assembled.
1746: `row` must belong to this MPI process
1748: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1749: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`, `MATAIJ`
1750: @*/
1751: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1752: {
1753: PetscInt globalrow;
1755: PetscFunctionBegin;
1758: PetscAssertPointer(v, 3);
1759: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1760: PetscCall(MatSetValuesRow(mat, globalrow, v));
1761: PetscFunctionReturn(PETSC_SUCCESS);
1762: }
1764: /*@
1765: MatSetValuesRow - Inserts a row of nonzero values into a matrix
1767: Not Collective
1769: Input Parameters:
1770: + mat - the matrix
1771: . row - the row to set
1772: - v - a one dimensional array of values
1774: Level: advanced
1776: Notes:
1777: Currently only supported for `MATAIJ`.
1779: All the nonzeros in `row` must be provided
1781: The matrix must have previously had its column indices set, likely by having been assembled.
1783: `row` must belong to this process
1785: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1786: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MATAIJ`
1787: @*/
1788: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1789: {
1790: PetscFunctionBeginHot;
1793: MatCheckPreallocated(mat, 1);
1794: PetscAssertPointer(v, 3);
1795: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1796: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1797: mat->insertmode = INSERT_VALUES;
1799: if (mat->assembled) {
1800: mat->was_assembled = PETSC_TRUE;
1801: mat->assembled = PETSC_FALSE;
1802: }
1803: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1804: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1805: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1806: PetscFunctionReturn(PETSC_SUCCESS);
1807: }
1809: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1810: /*@
1811: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1812: Using structured grid indexing
1814: Not Collective
1816: Input Parameters:
1817: + mat - the matrix
1818: . m - number of rows being entered
1819: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1820: . n - number of columns being entered
1821: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1822: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1823: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1824: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1826: Level: beginner
1828: Notes:
1829: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1831: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1832: options cannot be mixed without intervening calls to the assembly
1833: routines.
1835: The grid coordinates are across the entire grid, not just the local portion
1837: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1838: as well as in C.
1840: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1842: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1843: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1845: The columns and rows in the stencil passed in MUST be contained within the
1846: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1847: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1848: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1849: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1851: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1852: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1853: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1854: `DM_BOUNDARY_PERIODIC` boundary type.
1856: 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
1857: a single value per point) you can skip filling those indices.
1859: Inspired by the structured grid interface to the HYPRE package
1860: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1862: Fortran Notes:
1863: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1864: .vb
1865: call MatSetValuesStencil(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1866: .ve
1868: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1869: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1871: Efficiency Alert:
1872: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1873: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1875: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1876: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1877: @*/
1878: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1879: {
1880: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1881: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1882: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1884: PetscFunctionBegin;
1885: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1888: PetscAssertPointer(idxm, 3);
1889: PetscAssertPointer(idxn, 5);
1891: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1892: jdxm = buf;
1893: jdxn = buf + m;
1894: } else {
1895: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1896: jdxm = bufm;
1897: jdxn = bufn;
1898: }
1899: for (i = 0; i < m; i++) {
1900: for (j = 0; j < 3 - sdim; j++) dxm++;
1901: tmp = *dxm++ - starts[0];
1902: for (j = 0; j < dim - 1; j++) {
1903: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1904: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1905: }
1906: if (mat->stencil.noc) dxm++;
1907: jdxm[i] = tmp;
1908: }
1909: for (i = 0; i < n; i++) {
1910: for (j = 0; j < 3 - sdim; j++) dxn++;
1911: tmp = *dxn++ - starts[0];
1912: for (j = 0; j < dim - 1; j++) {
1913: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1914: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1915: }
1916: if (mat->stencil.noc) dxn++;
1917: jdxn[i] = tmp;
1918: }
1919: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1920: PetscCall(PetscFree2(bufm, bufn));
1921: PetscFunctionReturn(PETSC_SUCCESS);
1922: }
1924: /*@
1925: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1926: Using structured grid indexing
1928: Not Collective
1930: Input Parameters:
1931: + mat - the matrix
1932: . m - number of rows being entered
1933: . idxm - grid coordinates for matrix rows being entered
1934: . n - number of columns being entered
1935: . idxn - grid coordinates for matrix columns being entered
1936: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1937: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1938: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1940: Level: beginner
1942: Notes:
1943: By default the values, `v`, are row-oriented and unsorted.
1944: See `MatSetOption()` for other options.
1946: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1947: options cannot be mixed without intervening calls to the assembly
1948: routines.
1950: The grid coordinates are across the entire grid, not just the local portion
1952: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1953: as well as in C.
1955: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1957: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1958: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1960: The columns and rows in the stencil passed in MUST be contained within the
1961: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1962: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1963: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1964: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1966: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1967: simply ignored. This allows easily inserting element stiffness matrices
1968: with homogeneous Dirichlet boundary conditions that you don't want represented
1969: in the matrix.
1971: Inspired by the structured grid interface to the HYPRE package
1972: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1974: Fortran Notes:
1975: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1976: .vb
1977: call MatSetValuesBlockedStencil(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1978: .ve
1980: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
1981: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
1983: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1984: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1985: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1986: @*/
1987: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1988: {
1989: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1990: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1991: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1993: PetscFunctionBegin;
1994: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1997: PetscAssertPointer(idxm, 3);
1998: PetscAssertPointer(idxn, 5);
1999: PetscAssertPointer(v, 6);
2001: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2002: jdxm = buf;
2003: jdxn = buf + m;
2004: } else {
2005: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
2006: jdxm = bufm;
2007: jdxn = bufn;
2008: }
2009: for (i = 0; i < m; i++) {
2010: for (j = 0; j < 3 - sdim; j++) dxm++;
2011: tmp = *dxm++ - starts[0];
2012: for (j = 0; j < sdim - 1; j++) {
2013: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
2014: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
2015: }
2016: dxm++;
2017: jdxm[i] = tmp;
2018: }
2019: for (i = 0; i < n; i++) {
2020: for (j = 0; j < 3 - sdim; j++) dxn++;
2021: tmp = *dxn++ - starts[0];
2022: for (j = 0; j < sdim - 1; j++) {
2023: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
2024: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
2025: }
2026: dxn++;
2027: jdxn[i] = tmp;
2028: }
2029: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
2030: PetscCall(PetscFree2(bufm, bufn));
2031: PetscFunctionReturn(PETSC_SUCCESS);
2032: }
2034: /*@
2035: MatSetStencil - Sets the grid information for setting values into a matrix via
2036: `MatSetValuesStencil()`
2038: Not Collective
2040: Input Parameters:
2041: + mat - the matrix
2042: . dim - dimension of the grid 1, 2, or 3
2043: . dims - number of grid points in x, y, and z direction, including ghost points on your process
2044: . starts - starting point of ghost nodes on your process in x, y, and z direction
2045: - dof - number of degrees of freedom per node
2047: Level: beginner
2049: Notes:
2050: Inspired by the structured grid interface to the HYPRE package
2051: (www.llnl.gov/CASC/hyper)
2053: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
2054: user.
2056: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2057: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
2058: @*/
2059: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
2060: {
2061: PetscFunctionBegin;
2063: PetscAssertPointer(dims, 3);
2064: PetscAssertPointer(starts, 4);
2066: mat->stencil.dim = dim + (dof > 1);
2067: for (PetscInt i = 0; i < dim; i++) {
2068: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
2069: mat->stencil.starts[i] = starts[dim - i - 1];
2070: }
2071: mat->stencil.dims[dim] = dof;
2072: mat->stencil.starts[dim] = 0;
2073: mat->stencil.noc = (PetscBool)(dof == 1);
2074: PetscFunctionReturn(PETSC_SUCCESS);
2075: }
2077: /*@
2078: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
2080: Not Collective
2082: Input Parameters:
2083: + mat - the matrix
2084: . m - the number of block rows
2085: . idxm - the global block indices
2086: . n - the number of block columns
2087: . idxn - the global block indices
2088: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2089: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2090: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
2092: Level: intermediate
2094: Notes:
2095: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2096: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2098: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2099: NOT the total number of rows/columns; for example, if the block size is 2 and
2100: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2101: The values in `idxm` would be 1 2; that is the first index for each block divided by
2102: the block size.
2104: You must call `MatSetBlockSize()` when constructing this matrix (before
2105: preallocating it).
2107: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2109: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2110: options cannot be mixed without intervening calls to the assembly
2111: routines.
2113: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2114: as well as in C.
2116: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2117: simply ignored. This allows easily inserting element stiffness matrices
2118: with homogeneous Dirichlet boundary conditions that you don't want represented
2119: in the matrix.
2121: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2122: internal searching must be done to determine where to place the
2123: data in the matrix storage space. By instead inserting blocks of
2124: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2125: reduced.
2127: Example:
2128: .vb
2129: Suppose m=n=2 and block size(bs) = 2 The array is
2131: 1 2 | 3 4
2132: 5 6 | 7 8
2133: - - - | - - -
2134: 9 10 | 11 12
2135: 13 14 | 15 16
2137: v[] should be passed in like
2138: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2140: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2141: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2142: .ve
2144: Fortran Notes:
2145: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2146: .vb
2147: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2148: .ve
2150: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2151: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2153: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2154: @*/
2155: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2156: {
2157: PetscFunctionBeginHot;
2160: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2161: PetscAssertPointer(idxm, 3);
2162: PetscAssertPointer(idxn, 5);
2163: MatCheckPreallocated(mat, 1);
2164: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2165: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2166: if (PetscDefined(USE_DEBUG)) {
2167: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2168: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2169: }
2170: if (PetscDefined(USE_DEBUG)) {
2171: PetscInt rbs, cbs, M, N, i;
2172: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2173: PetscCall(MatGetSize(mat, &M, &N));
2174: 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);
2175: for (i = 0; i < n; i++)
2176: 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);
2177: }
2178: if (mat->assembled) {
2179: mat->was_assembled = PETSC_TRUE;
2180: mat->assembled = PETSC_FALSE;
2181: }
2182: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2183: if (mat->ops->setvaluesblocked) PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2184: else {
2185: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2186: PetscInt i, j, bs, cbs;
2188: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2189: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2190: iidxm = buf;
2191: iidxn = buf + m * bs;
2192: } else {
2193: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2194: iidxm = bufr;
2195: iidxn = bufc;
2196: }
2197: for (i = 0; i < m; i++) {
2198: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2199: }
2200: if (m != n || bs != cbs || idxm != idxn) {
2201: for (i = 0; i < n; i++) {
2202: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2203: }
2204: } else iidxn = iidxm;
2205: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2206: PetscCall(PetscFree2(bufr, bufc));
2207: }
2208: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2209: PetscFunctionReturn(PETSC_SUCCESS);
2210: }
2212: /*@
2213: MatGetValues - Gets a block of local values from a matrix.
2215: Not Collective; can only return values that are owned by the give process
2217: Input Parameters:
2218: + mat - the matrix
2219: . v - a logically two-dimensional array for storing the values
2220: . m - the number of rows
2221: . idxm - the global indices of the rows
2222: . n - the number of columns
2223: - idxn - the global indices of the columns
2225: Level: advanced
2227: Notes:
2228: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2230: The values, `v`, are returned in a row-oriented format, analogous to that used by default in `MatSetValues()`,
2231: unless `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE)` is called in which case they are returned column oriented.
2233: `MatGetValues()` uses 0-based row and column numbers in
2234: Fortran as well as in C.
2236: For `MATSBAIJ` matrices only the block upper triangular entries will be set.
2238: `MatGetValues()` requires that the matrix has been assembled
2239: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2240: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2241: without intermediate matrix assembly.
2243: Negative row or column indices will be ignored and those locations in `v` will be
2244: left unchanged.
2246: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2247: That is, rows with global index greater than or equal to `rstart` and less than `rend` where `rstart` and `rend` are obtainable
2248: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2250: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2251: @*/
2252: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2253: {
2254: PetscFunctionBegin;
2257: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2258: PetscAssertPointer(idxm, 3);
2259: PetscAssertPointer(idxn, 5);
2260: PetscAssertPointer(v, 6);
2261: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2262: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2263: MatCheckPreallocated(mat, 1);
2265: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2266: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2267: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2268: PetscFunctionReturn(PETSC_SUCCESS);
2269: }
2271: /*@
2272: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2273: defined previously by `MatSetLocalToGlobalMapping()`
2275: Not Collective
2277: Input Parameters:
2278: + mat - the matrix
2279: . nrow - number of rows
2280: . irow - the row local indices
2281: . ncol - number of columns
2282: - icol - the column local indices
2284: Output Parameter:
2285: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2286: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2288: Level: advanced
2290: Notes:
2291: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2293: 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,
2294: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2295: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2296: with `MatSetLocalToGlobalMapping()`.
2298: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2299: `MatSetValuesLocal()`, `MatGetValues()`
2300: @*/
2301: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2302: {
2303: PetscFunctionBeginHot;
2306: MatCheckPreallocated(mat, 1);
2307: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2308: PetscAssertPointer(irow, 3);
2309: PetscAssertPointer(icol, 5);
2310: if (PetscDefined(USE_DEBUG)) {
2311: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2312: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2313: }
2314: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2315: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2316: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2317: else {
2318: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2319: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2320: irowm = buf;
2321: icolm = buf + nrow;
2322: } else {
2323: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2324: irowm = bufr;
2325: icolm = bufc;
2326: }
2327: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2328: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2329: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2330: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2331: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2332: PetscCall(PetscFree2(bufr, bufc));
2333: }
2334: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2335: PetscFunctionReturn(PETSC_SUCCESS);
2336: }
2338: /*@
2339: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2340: the same size. Currently, this can only be called once and creates the given matrix.
2342: Not Collective
2344: Input Parameters:
2345: + mat - the matrix
2346: . nb - the number of blocks
2347: . bs - the number of rows (and columns) in each block
2348: . rows - a concatenation of the rows for each block
2349: - v - a concatenation of logically two-dimensional arrays of values
2351: Level: advanced
2353: Notes:
2354: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2356: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2358: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2359: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2360: @*/
2361: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2362: {
2363: PetscFunctionBegin;
2366: PetscAssertPointer(rows, 4);
2367: PetscAssertPointer(v, 5);
2368: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2370: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2371: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2372: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2373: PetscFunctionReturn(PETSC_SUCCESS);
2374: }
2376: /*@
2377: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2378: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2379: using a local (per-process) numbering.
2381: Not Collective
2383: Input Parameters:
2384: + x - the matrix
2385: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2386: - cmapping - column mapping
2388: Level: intermediate
2390: Note:
2391: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2393: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2394: @*/
2395: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2396: {
2397: PetscFunctionBegin;
2402: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2403: else {
2404: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2405: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2406: }
2407: PetscFunctionReturn(PETSC_SUCCESS);
2408: }
2410: /*@
2411: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2413: Not Collective
2415: Input Parameter:
2416: . A - the matrix
2418: Output Parameters:
2419: + rmapping - row mapping
2420: - cmapping - column mapping
2422: Level: advanced
2424: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2425: @*/
2426: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2427: {
2428: PetscFunctionBegin;
2431: if (rmapping) {
2432: PetscAssertPointer(rmapping, 2);
2433: *rmapping = A->rmap->mapping;
2434: }
2435: if (cmapping) {
2436: PetscAssertPointer(cmapping, 3);
2437: *cmapping = A->cmap->mapping;
2438: }
2439: PetscFunctionReturn(PETSC_SUCCESS);
2440: }
2442: /*@
2443: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2445: Logically Collective
2447: Input Parameters:
2448: + A - the matrix
2449: . rmap - row layout
2450: - cmap - column layout
2452: Level: advanced
2454: Note:
2455: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2457: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2458: @*/
2459: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2460: {
2461: PetscFunctionBegin;
2463: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2464: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2465: PetscFunctionReturn(PETSC_SUCCESS);
2466: }
2468: /*@
2469: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2471: Not Collective
2473: Input Parameter:
2474: . A - the matrix
2476: Output Parameters:
2477: + rmap - row layout
2478: - cmap - column layout
2480: Level: advanced
2482: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2483: @*/
2484: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2485: {
2486: PetscFunctionBegin;
2489: if (rmap) {
2490: PetscAssertPointer(rmap, 2);
2491: *rmap = A->rmap;
2492: }
2493: if (cmap) {
2494: PetscAssertPointer(cmap, 3);
2495: *cmap = A->cmap;
2496: }
2497: PetscFunctionReturn(PETSC_SUCCESS);
2498: }
2500: /*@
2501: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2502: using a local numbering of the rows and columns.
2504: Not Collective
2506: Input Parameters:
2507: + mat - the matrix
2508: . nrow - number of rows
2509: . irow - the row local indices
2510: . ncol - number of columns
2511: . icol - the column local indices
2512: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2513: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2514: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2516: Level: intermediate
2518: Notes:
2519: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2521: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2522: options cannot be mixed without intervening calls to the assembly
2523: routines.
2525: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2526: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2528: Fortran Notes:
2529: If any of `irow`, `icol`, and `v` are scalars pass them using, for example,
2530: .vb
2531: call MatSetValuesLocal(mat, one, [irow], one, [icol], [v], INSERT_VALUES, ierr)
2532: .ve
2534: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2535: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2537: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2538: `MatGetValuesLocal()`
2539: @*/
2540: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar v[], InsertMode addv)
2541: {
2542: PetscFunctionBeginHot;
2545: MatCheckPreallocated(mat, 1);
2546: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2547: PetscAssertPointer(irow, 3);
2548: PetscAssertPointer(icol, 5);
2549: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2550: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2551: if (PetscDefined(USE_DEBUG)) {
2552: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2553: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2554: }
2556: if (mat->assembled) {
2557: mat->was_assembled = PETSC_TRUE;
2558: mat->assembled = PETSC_FALSE;
2559: }
2560: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2561: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, v, addv);
2562: else {
2563: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2564: const PetscInt *irowm, *icolm;
2566: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2567: bufr = buf;
2568: bufc = buf + nrow;
2569: irowm = bufr;
2570: icolm = bufc;
2571: } else {
2572: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2573: irowm = bufr;
2574: icolm = bufc;
2575: }
2576: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2577: else irowm = irow;
2578: if (mat->cmap->mapping) {
2579: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2580: else icolm = irowm;
2581: } else icolm = icol;
2582: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, v, addv));
2583: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2584: }
2585: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2586: PetscFunctionReturn(PETSC_SUCCESS);
2587: }
2589: /*@
2590: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2591: using a local ordering of the nodes a block at a time.
2593: Not Collective
2595: Input Parameters:
2596: + mat - the matrix
2597: . nrow - number of rows
2598: . irow - the row local indices
2599: . ncol - number of columns
2600: . icol - the column local indices
2601: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2602: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2603: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2605: Level: intermediate
2607: Notes:
2608: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2609: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2611: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2612: options cannot be mixed without intervening calls to the assembly
2613: routines.
2615: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2616: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2618: Fortran Notes:
2619: If any of `irow`, `icol`, and `v` are scalars pass them using, for example,
2620: .vb
2621: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [v], INSERT_VALUES, ierr)
2622: .ve
2624: If `v` is a two-dimensional array make sure to first call `MatSetOption(mat, MAT_ROW_ORIENTED, PETSC_FALSE, ierr)` before using this function,
2625: otherwise the transpose of `v` will seemingly be inserted in the matrix, since Fortran passes two-dimensional arrays with column orientation.
2627: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2628: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2629: @*/
2630: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar v[], InsertMode addv)
2631: {
2632: PetscFunctionBeginHot;
2635: MatCheckPreallocated(mat, 1);
2636: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2637: PetscAssertPointer(irow, 3);
2638: PetscAssertPointer(icol, 5);
2639: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2640: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2641: if (PetscDefined(USE_DEBUG)) {
2642: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2643: 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);
2644: }
2646: if (mat->assembled) {
2647: mat->was_assembled = PETSC_TRUE;
2648: mat->assembled = PETSC_FALSE;
2649: }
2650: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2651: PetscInt irbs, rbs;
2652: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2653: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2654: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2655: }
2656: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2657: PetscInt icbs, cbs;
2658: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2659: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2660: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2661: }
2662: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2663: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, v, addv);
2664: else {
2665: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2666: const PetscInt *irowm, *icolm;
2668: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2669: bufr = buf;
2670: bufc = buf + nrow;
2671: irowm = bufr;
2672: icolm = bufc;
2673: } else {
2674: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2675: irowm = bufr;
2676: icolm = bufc;
2677: }
2678: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2679: else irowm = irow;
2680: if (mat->cmap->mapping) {
2681: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2682: else icolm = irowm;
2683: } else icolm = icol;
2684: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, v, addv));
2685: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2686: }
2687: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2688: PetscFunctionReturn(PETSC_SUCCESS);
2689: }
2691: /*@
2692: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2694: Collective
2696: Input Parameters:
2697: + mat - the matrix
2698: - x - the vector to be multiplied
2700: Output Parameter:
2701: . y - the result
2703: Level: developer
2705: Note:
2706: The vectors `x` and `y` cannot be the same. I.e., one cannot
2707: call `MatMultDiagonalBlock`(A,y,y).
2709: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2710: @*/
2711: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2712: {
2713: PetscFunctionBegin;
2719: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2720: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2721: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2722: MatCheckPreallocated(mat, 1);
2724: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2725: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2726: PetscFunctionReturn(PETSC_SUCCESS);
2727: }
2729: /*@
2730: MatMult - Computes the matrix-vector product, $y = Ax$.
2732: Neighbor-wise Collective
2734: Input Parameters:
2735: + mat - the matrix
2736: - x - the vector to be multiplied
2738: Output Parameter:
2739: . y - the result
2741: Level: beginner
2743: Note:
2744: The vectors `x` and `y` cannot be the same. I.e., one cannot
2745: call `MatMult`(A,y,y).
2747: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2748: @*/
2749: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2750: {
2751: PetscFunctionBegin;
2755: VecCheckAssembled(x);
2757: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2758: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2759: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2760: 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);
2761: 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);
2762: 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);
2763: 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);
2764: PetscCall(VecSetErrorIfLocked(y, 3));
2765: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2766: MatCheckPreallocated(mat, 1);
2768: PetscCall(VecLockReadPush(x));
2769: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2770: PetscUseTypeMethod(mat, mult, x, y);
2771: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2772: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2773: PetscCall(VecLockReadPop(x));
2774: PetscFunctionReturn(PETSC_SUCCESS);
2775: }
2777: /*@
2778: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2780: Neighbor-wise Collective
2782: Input Parameters:
2783: + mat - the matrix
2784: - x - the vector to be multiplied
2786: Output Parameter:
2787: . y - the result
2789: Level: beginner
2791: Notes:
2792: The vectors `x` and `y` cannot be the same. I.e., one cannot
2793: call `MatMultTranspose`(A,y,y).
2795: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2796: use `MatMultHermitianTranspose()`
2798: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2799: @*/
2800: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2801: {
2802: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2804: PetscFunctionBegin;
2808: VecCheckAssembled(x);
2811: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2812: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2813: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2814: 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);
2815: 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);
2816: 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);
2817: 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);
2818: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2819: MatCheckPreallocated(mat, 1);
2821: if (!mat->ops->multtranspose) {
2822: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2823: 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);
2824: } else op = mat->ops->multtranspose;
2825: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2826: PetscCall(VecLockReadPush(x));
2827: PetscCall((*op)(mat, x, y));
2828: PetscCall(VecLockReadPop(x));
2829: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2830: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2831: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2832: PetscFunctionReturn(PETSC_SUCCESS);
2833: }
2835: /*@
2836: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2838: Neighbor-wise Collective
2840: Input Parameters:
2841: + mat - the matrix
2842: - x - the vector to be multiplied
2844: Output Parameter:
2845: . y - the result
2847: Level: beginner
2849: Notes:
2850: The vectors `x` and `y` cannot be the same. I.e., one cannot
2851: call `MatMultHermitianTranspose`(A,y,y).
2853: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2855: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2857: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2858: @*/
2859: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2860: {
2861: PetscFunctionBegin;
2867: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2868: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2869: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2870: 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);
2871: 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);
2872: 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);
2873: 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);
2874: MatCheckPreallocated(mat, 1);
2876: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2877: if (PetscDefined(USE_COMPLEX)) {
2878: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2879: PetscCall(VecLockReadPush(x));
2880: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2881: else PetscUseTypeMethod(mat, mult, x, y);
2882: PetscCall(VecLockReadPop(x));
2883: } else {
2884: Vec w;
2885: PetscCall(VecDuplicate(x, &w));
2886: PetscCall(VecCopy(x, w));
2887: PetscCall(VecConjugate(w));
2888: PetscCall(MatMultTranspose(mat, w, y));
2889: PetscCall(VecDestroy(&w));
2890: PetscCall(VecConjugate(y));
2891: }
2892: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2893: } else PetscCall(MatMultTranspose(mat, x, y));
2894: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2895: PetscFunctionReturn(PETSC_SUCCESS);
2896: }
2898: /*@
2899: MatMultAdd - Computes $v3 = v2 + A * v1$.
2901: Neighbor-wise Collective
2903: Input Parameters:
2904: + mat - the matrix
2905: . v1 - the vector to be multiplied by `mat`
2906: - v2 - the vector to be added to the result
2908: Output Parameter:
2909: . v3 - the result
2911: Level: beginner
2913: Note:
2914: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2915: call `MatMultAdd`(A,v1,v2,v1).
2917: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2918: @*/
2919: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2920: {
2921: PetscFunctionBegin;
2928: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2929: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2930: PetscCheck(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);
2931: /* 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);
2932: 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); */
2933: 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);
2934: 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);
2935: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2936: MatCheckPreallocated(mat, 1);
2938: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2939: PetscCall(VecLockReadPush(v1));
2940: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2941: PetscCall(VecLockReadPop(v1));
2942: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2943: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2944: PetscFunctionReturn(PETSC_SUCCESS);
2945: }
2947: /*@
2948: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2950: Neighbor-wise Collective
2952: Input Parameters:
2953: + mat - the matrix
2954: . v1 - the vector to be multiplied by the transpose of the matrix
2955: - v2 - the vector to be added to the result
2957: Output Parameter:
2958: . v3 - the result
2960: Level: beginner
2962: Note:
2963: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2964: call `MatMultTransposeAdd`(A,v1,v2,v1).
2966: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2967: @*/
2968: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2969: {
2970: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2972: PetscFunctionBegin;
2979: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2980: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2981: 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);
2982: 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);
2983: 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);
2984: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2985: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2986: MatCheckPreallocated(mat, 1);
2988: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2989: PetscCall(VecLockReadPush(v1));
2990: PetscCall((*op)(mat, v1, v2, v3));
2991: PetscCall(VecLockReadPop(v1));
2992: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2993: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2994: PetscFunctionReturn(PETSC_SUCCESS);
2995: }
2997: /*@
2998: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
3000: Neighbor-wise Collective
3002: Input Parameters:
3003: + mat - the matrix
3004: . v1 - the vector to be multiplied by the Hermitian transpose
3005: - v2 - the vector to be added to the result
3007: Output Parameter:
3008: . v3 - the result
3010: Level: beginner
3012: Note:
3013: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
3014: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
3016: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
3017: @*/
3018: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
3019: {
3020: PetscFunctionBegin;
3027: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3028: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3029: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
3030: 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);
3031: 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);
3032: 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);
3033: MatCheckPreallocated(mat, 1);
3035: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
3036: PetscCall(VecLockReadPush(v1));
3037: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
3038: else {
3039: Vec w, z;
3040: PetscCall(VecDuplicate(v1, &w));
3041: PetscCall(VecCopy(v1, w));
3042: PetscCall(VecConjugate(w));
3043: PetscCall(VecDuplicate(v3, &z));
3044: PetscCall(MatMultTranspose(mat, w, z));
3045: PetscCall(VecDestroy(&w));
3046: PetscCall(VecConjugate(z));
3047: if (v2 != v3) PetscCall(VecWAXPY(v3, 1.0, v2, z));
3048: else PetscCall(VecAXPY(v3, 1.0, z));
3049: PetscCall(VecDestroy(&z));
3050: }
3051: PetscCall(VecLockReadPop(v1));
3052: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
3053: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
3054: PetscFunctionReturn(PETSC_SUCCESS);
3055: }
3057: static PetscErrorCode MatADot_Default(Mat mat, Vec x, Vec y, PetscScalar *val)
3058: {
3059: PetscFunctionBegin;
3060: if (!mat->dot_vec) PetscCall(MatCreateVecs(mat, NULL, &mat->dot_vec));
3061: PetscCall(MatMult(mat, x, mat->dot_vec));
3062: PetscCall(VecDot(mat->dot_vec, y, val));
3063: PetscFunctionReturn(PETSC_SUCCESS);
3064: }
3066: static PetscErrorCode MatANorm_Default(Mat mat, Vec x, PetscReal *val)
3067: {
3068: PetscScalar sval;
3070: PetscFunctionBegin;
3071: PetscCall(MatADot(mat, x, x, &sval));
3072: PetscCheck(PetscRealPart(sval) >= 0.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not positive definite");
3073: PetscCheck(PetscAbsReal(PetscImaginaryPart(sval)) <= 100 * PETSC_MACHINE_EPSILON * PetscMax(1.0, PetscAbsScalar(sval)), PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not Hermitian");
3074: *val = PetscSqrtReal(PetscRealPart(sval));
3075: PetscFunctionReturn(PETSC_SUCCESS);
3076: }
3078: /*@
3079: 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)
3080: positive definite.
3082: Collective
3084: Input Parameters:
3085: + mat - matrix used to define the inner product
3086: . x - first vector
3087: - y - second vector
3089: Output Parameter:
3090: . val - the dot product with respect to `A`
3092: Level: intermediate
3094: Note:
3095: For complex vectors, `MatADot()` computes
3096: $$
3097: val = (x,y)_A = y^H A x,
3098: $$
3099: where $y^H$ denotes the conjugate transpose of `y`. Note that this corresponds to the "mathematicians" complex
3100: inner product where the SECOND argument gets the complex conjugate.
3102: .seealso: [](ch_matrices), `Mat`, `MatANorm()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3103: @*/
3104: PetscErrorCode MatADot(Mat mat, Vec x, Vec y, PetscScalar *val)
3105: {
3106: PetscFunctionBegin;
3110: VecCheckAssembled(x);
3112: VecCheckAssembled(y);
3115: PetscAssertPointer(val, 4);
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 == 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);
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 == 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);
3122: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3123: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_TRUE));
3124: MatCheckPreallocated(mat, 1);
3126: PetscCall(VecLockReadPush(x));
3127: PetscCall(VecLockReadPush(y));
3128: PetscCall(PetscLogEventBegin(MAT_ADot, mat, x, y, 0));
3129: if (mat->ops->adot) PetscUseTypeMethod(mat, adot, x, y, val);
3130: else PetscCall(MatADot_Default(mat, x, y, val));
3131: PetscCall(PetscLogEventEnd(MAT_ADot, mat, x, y, 0));
3132: PetscCall(VecLockReadPop(y));
3133: PetscCall(VecLockReadPop(x));
3134: PetscFunctionReturn(PETSC_SUCCESS);
3135: }
3137: /*@
3138: 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)
3139: positive definite.
3141: Collective
3143: Input Parameters:
3144: + mat - matrix used to define norm
3145: - x - the vector to compute the norm of
3147: Output Parameter:
3148: . val - the norm with respect to `A`
3150: Level: intermediate
3152: Note:
3153: For complex vectors, `MatANorm()` computes
3154: $$
3155: val = (x,x)_A^{1/2} = (x^H A x)^{1/2},
3156: $$
3157: where $x^H$ denotes the conjugate transpose of `x`.
3159: .seealso: [](ch_matrices), `Mat`, `MatADot()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3160: @*/
3161: PetscErrorCode MatANorm(Mat mat, Vec x, PetscReal *val)
3162: {
3163: PetscFunctionBegin;
3167: VecCheckAssembled(x);
3169: PetscAssertPointer(val, 3);
3170: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3171: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3172: 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);
3173: 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);
3174: 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);
3175: 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);
3176: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3177: MatCheckPreallocated(mat, 1);
3179: PetscCall(VecLockReadPush(x));
3180: PetscCall(PetscLogEventBegin(MAT_ANorm, mat, x, 0, 0));
3181: if (mat->ops->anorm) PetscUseTypeMethod(mat, anorm, x, val);
3182: else PetscCall(MatANorm_Default(mat, x, val));
3183: PetscCall(PetscLogEventEnd(MAT_ANorm, mat, x, 0, 0));
3184: PetscCall(VecLockReadPop(x));
3185: PetscFunctionReturn(PETSC_SUCCESS);
3186: }
3188: /*@
3189: MatGetFactorType - gets the type of factorization a matrix is
3191: Not Collective
3193: Input Parameter:
3194: . mat - the matrix
3196: Output Parameter:
3197: . 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`
3199: Level: intermediate
3201: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3202: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3203: @*/
3204: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3205: {
3206: PetscFunctionBegin;
3209: PetscAssertPointer(t, 2);
3210: *t = mat->factortype;
3211: PetscFunctionReturn(PETSC_SUCCESS);
3212: }
3214: /*@
3215: MatSetFactorType - sets the type of factorization a matrix is
3217: Logically Collective
3219: Input Parameters:
3220: + mat - the matrix
3221: - 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`
3223: Level: intermediate
3225: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3226: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3227: @*/
3228: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3229: {
3230: PetscFunctionBegin;
3233: mat->factortype = t;
3234: PetscFunctionReturn(PETSC_SUCCESS);
3235: }
3237: /*@
3238: MatGetInfo - Returns information about matrix storage (number of
3239: nonzeros, memory, etc.).
3241: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3243: Input Parameters:
3244: + mat - the matrix
3245: - flag - flag indicating the type of parameters to be returned (`MAT_LOCAL` - local matrix, `MAT_GLOBAL_MAX` - maximum over all processes, `MAT_GLOBAL_SUM` - sum over all processes)
3247: Output Parameter:
3248: . info - matrix information context
3250: Options Database Key:
3251: . -mat_view :[filename]:ascii_info - print the matrix information to `filename` or `stdout`, see `MatView()`
3253: Level: intermediate
3255: Notes:
3256: The `MatInfo` context contains a variety of matrix data, including
3257: number of nonzeros allocated and used, number of mallocs during
3258: matrix assembly, etc. Additional information for factored matrices
3259: is provided (such as the fill ratio, number of mallocs during
3260: factorization, etc.).
3262: Example:
3263: See the file `${PETSC_DIR}/include/petscmat.h` for a complete list of
3264: data within the `MatInfo` context. For example,
3265: .vb
3266: MatInfo info;
3267: Mat A;
3268: double mal, nz_a, nz_u;
3270: MatGetInfo(A, MAT_LOCAL, &info);
3271: mal = info.mallocs;
3272: nz_a = info.nz_allocated;
3273: .ve
3275: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3276: @*/
3277: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3278: {
3279: PetscFunctionBegin;
3282: PetscAssertPointer(info, 3);
3283: MatCheckPreallocated(mat, 1);
3284: PetscUseTypeMethod(mat, getinfo, flag, info);
3285: PetscFunctionReturn(PETSC_SUCCESS);
3286: }
3288: /*
3289: This is used by external packages where it is not easy to get the info from the actual
3290: matrix factorization.
3291: */
3292: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3293: {
3294: PetscFunctionBegin;
3295: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3296: PetscFunctionReturn(PETSC_SUCCESS);
3297: }
3299: /*@
3300: MatLUFactor - Performs in-place LU factorization of matrix.
3302: Collective
3304: Input Parameters:
3305: + mat - the matrix
3306: . row - row permutation
3307: . col - column permutation
3308: - info - options for factorization, includes
3309: .vb
3310: fill - expected fill as ratio of original fill.
3311: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3312: Run with the option -info to determine an optimal value to use
3313: .ve
3315: Level: developer
3317: Notes:
3318: Most users should employ the `KSP` interface for linear solvers
3319: instead of working directly with matrix algebra routines such as this.
3320: See, e.g., `KSPCreate()`.
3322: This changes the state of the matrix to a factored matrix; it cannot be used
3323: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3325: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3326: when not using `KSP`.
3328: Fortran Note:
3329: A valid (non-null) `info` argument must be provided
3331: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3332: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3333: @*/
3334: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3335: {
3336: MatFactorInfo tinfo;
3338: PetscFunctionBegin;
3342: if (info) PetscAssertPointer(info, 4);
3344: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3345: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3346: MatCheckPreallocated(mat, 1);
3347: if (!info) {
3348: PetscCall(MatFactorInfoInitialize(&tinfo));
3349: info = &tinfo;
3350: }
3352: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3353: PetscUseTypeMethod(mat, lufactor, row, col, info);
3354: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3355: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3356: PetscFunctionReturn(PETSC_SUCCESS);
3357: }
3359: /*@
3360: MatILUFactor - Performs in-place ILU factorization of matrix.
3362: Collective
3364: Input Parameters:
3365: + mat - the matrix
3366: . row - row permutation
3367: . col - column permutation
3368: - info - structure containing
3369: .vb
3370: levels - number of levels of fill.
3371: expected fill - as ratio of original fill.
3372: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3373: missing diagonal entries)
3374: .ve
3376: Level: developer
3378: Notes:
3379: Most users should employ the `KSP` interface for linear solvers
3380: instead of working directly with matrix algebra routines such as this.
3381: See, e.g., `KSPCreate()`.
3383: Probably really in-place only when level of fill is zero, otherwise allocates
3384: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatLUFactorNumeric()`
3385: when not using `KSP`.
3387: Fortran Note:
3388: A valid (non-null) `info` argument must be provided
3390: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3391: @*/
3392: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3393: {
3394: PetscFunctionBegin;
3398: PetscAssertPointer(info, 4);
3400: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3401: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3402: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3403: MatCheckPreallocated(mat, 1);
3405: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3406: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3407: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3408: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3409: PetscFunctionReturn(PETSC_SUCCESS);
3410: }
3412: /*@
3413: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3414: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3416: Collective
3418: Input Parameters:
3419: + fact - the factor matrix obtained with `MatGetFactor()`
3420: . mat - the matrix
3421: . row - the row permutation
3422: . col - the column permutation
3423: - info - options for factorization, includes
3424: .vb
3425: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3426: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3427: .ve
3429: Level: developer
3431: Notes:
3432: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3434: Most users should employ the simplified `KSP` interface for linear solvers
3435: instead of working directly with matrix algebra routines such as this.
3436: See, e.g., `KSPCreate()`.
3438: Fortran Note:
3439: A valid (non-null) `info` argument must be provided
3441: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3442: @*/
3443: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3444: {
3445: MatFactorInfo tinfo;
3447: PetscFunctionBegin;
3452: if (info) PetscAssertPointer(info, 5);
3455: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3456: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3457: MatCheckPreallocated(mat, 2);
3458: if (!info) {
3459: PetscCall(MatFactorInfoInitialize(&tinfo));
3460: info = &tinfo;
3461: }
3463: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3464: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3465: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3466: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3467: PetscFunctionReturn(PETSC_SUCCESS);
3468: }
3470: /*@
3471: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3472: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3474: Collective
3476: Input Parameters:
3477: + fact - the factor matrix obtained with `MatGetFactor()`
3478: . mat - the matrix
3479: - info - options for factorization
3481: Level: developer
3483: Notes:
3484: See `MatLUFactor()` for in-place factorization. See
3485: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
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`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3495: @*/
3496: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3497: {
3498: MatFactorInfo tinfo;
3500: PetscFunctionBegin;
3505: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3506: 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,
3507: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3509: MatCheckPreallocated(mat, 2);
3510: if (!info) {
3511: PetscCall(MatFactorInfoInitialize(&tinfo));
3512: info = &tinfo;
3513: }
3515: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3516: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3517: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3518: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3519: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3520: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3521: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3522: PetscFunctionReturn(PETSC_SUCCESS);
3523: }
3525: /*@
3526: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3527: symmetric matrix.
3529: Collective
3531: Input Parameters:
3532: + mat - the matrix
3533: . perm - row and column permutations
3534: - info - expected fill as ratio of original fill
3536: Level: developer
3538: Notes:
3539: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3540: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3542: Most users should employ the `KSP` interface for linear solvers
3543: instead of working directly with matrix algebra routines such as this.
3544: See, e.g., `KSPCreate()`.
3546: Fortran Note:
3547: A valid (non-null) `info` argument must be provided
3549: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`,
3550: `MatGetOrdering()`
3551: @*/
3552: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3553: {
3554: MatFactorInfo tinfo;
3556: PetscFunctionBegin;
3559: if (info) PetscAssertPointer(info, 3);
3561: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3562: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3563: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3564: MatCheckPreallocated(mat, 1);
3565: if (!info) {
3566: PetscCall(MatFactorInfoInitialize(&tinfo));
3567: info = &tinfo;
3568: }
3570: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3571: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3572: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3573: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3574: PetscFunctionReturn(PETSC_SUCCESS);
3575: }
3577: /*@
3578: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3579: of a symmetric matrix.
3581: Collective
3583: Input Parameters:
3584: + fact - the factor matrix obtained with `MatGetFactor()`
3585: . mat - the matrix
3586: . perm - row and column permutations
3587: - info - options for factorization, includes
3588: .vb
3589: fill - expected fill as ratio of original fill.
3590: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3591: Run with the option -info to determine an optimal value to use
3592: .ve
3594: Level: developer
3596: Notes:
3597: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3598: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3600: Most users should employ the `KSP` interface for linear solvers
3601: instead of working directly with matrix algebra routines such as this.
3602: See, e.g., `KSPCreate()`.
3604: Fortran Note:
3605: A valid (non-null) `info` argument must be provided
3607: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`,
3608: `MatGetOrdering()`
3609: @*/
3610: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3611: {
3612: MatFactorInfo tinfo;
3614: PetscFunctionBegin;
3618: if (info) PetscAssertPointer(info, 4);
3621: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3622: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3623: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3624: MatCheckPreallocated(mat, 2);
3625: if (!info) {
3626: PetscCall(MatFactorInfoInitialize(&tinfo));
3627: info = &tinfo;
3628: }
3630: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3631: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3632: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3633: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3634: PetscFunctionReturn(PETSC_SUCCESS);
3635: }
3637: /*@
3638: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3639: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3640: `MatCholeskyFactorSymbolic()`.
3642: Collective
3644: Input Parameters:
3645: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3646: . mat - the initial matrix that is to be factored
3647: - info - options for factorization
3649: Level: developer
3651: Note:
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: Fortran Note:
3657: A valid (non-null) `info` argument must be provided
3659: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3660: @*/
3661: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3662: {
3663: MatFactorInfo tinfo;
3665: PetscFunctionBegin;
3670: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3671: 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,
3672: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3673: MatCheckPreallocated(mat, 2);
3674: if (!info) {
3675: PetscCall(MatFactorInfoInitialize(&tinfo));
3676: info = &tinfo;
3677: }
3679: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3680: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3681: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3682: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3683: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3684: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3685: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3686: PetscFunctionReturn(PETSC_SUCCESS);
3687: }
3689: /*@
3690: MatQRFactor - Performs in-place QR factorization of matrix.
3692: Collective
3694: Input Parameters:
3695: + mat - the matrix
3696: . col - column permutation
3697: - info - options for factorization, includes
3698: .vb
3699: fill - expected fill as ratio of original fill.
3700: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3701: Run with the option -info to determine an optimal value to use
3702: .ve
3704: Level: developer
3706: Notes:
3707: Most users should employ the `KSP` interface for linear solvers
3708: instead of working directly with matrix algebra routines such as this.
3709: See, e.g., `KSPCreate()`.
3711: This changes the state of the matrix to a factored matrix; it cannot be used
3712: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3714: Fortran Note:
3715: A valid (non-null) `info` argument must be provided
3717: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3718: `MatSetUnfactored()`
3719: @*/
3720: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3721: {
3722: PetscFunctionBegin;
3725: if (info) PetscAssertPointer(info, 3);
3727: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3728: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3729: MatCheckPreallocated(mat, 1);
3730: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3731: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3732: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3733: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3734: PetscFunctionReturn(PETSC_SUCCESS);
3735: }
3737: /*@
3738: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3739: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3741: Collective
3743: Input Parameters:
3744: + fact - the factor matrix obtained with `MatGetFactor()`
3745: . mat - the matrix
3746: . col - column permutation
3747: - info - options for factorization, includes
3748: .vb
3749: fill - expected fill as ratio of original fill.
3750: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3751: Run with the option -info to determine an optimal value to use
3752: .ve
3754: Level: developer
3756: Note:
3757: Most users should employ the `KSP` interface for linear solvers
3758: instead of working directly with matrix algebra routines such as this.
3759: See, e.g., `KSPCreate()`.
3761: Fortran Note:
3762: A valid (non-null) `info` argument must be provided
3764: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3765: @*/
3766: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3767: {
3768: MatFactorInfo tinfo;
3770: PetscFunctionBegin;
3774: if (info) PetscAssertPointer(info, 4);
3777: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3778: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3779: MatCheckPreallocated(mat, 2);
3780: if (!info) {
3781: PetscCall(MatFactorInfoInitialize(&tinfo));
3782: info = &tinfo;
3783: }
3785: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3786: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3787: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3788: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3789: PetscFunctionReturn(PETSC_SUCCESS);
3790: }
3792: /*@
3793: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3794: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3796: Collective
3798: Input Parameters:
3799: + fact - the factor matrix obtained with `MatGetFactor()`
3800: . mat - the matrix
3801: - info - options for factorization
3803: Level: developer
3805: Notes:
3806: See `MatQRFactor()` for in-place factorization.
3808: Most users should employ the `KSP` interface for linear solvers
3809: instead of working directly with matrix algebra routines such as this.
3810: See, e.g., `KSPCreate()`.
3812: Fortran Note:
3813: A valid (non-null) `info` argument must be provided
3815: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3816: @*/
3817: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3818: {
3819: MatFactorInfo tinfo;
3821: PetscFunctionBegin;
3826: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3827: 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,
3828: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3830: MatCheckPreallocated(mat, 2);
3831: if (!info) {
3832: PetscCall(MatFactorInfoInitialize(&tinfo));
3833: info = &tinfo;
3834: }
3836: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3837: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3838: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3839: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3840: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3841: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3842: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3843: PetscFunctionReturn(PETSC_SUCCESS);
3844: }
3846: /*@
3847: MatSolve - Solves $A x = b$, given a factored matrix.
3849: Neighbor-wise Collective
3851: Input Parameters:
3852: + mat - the factored matrix
3853: - b - the right-hand-side vector
3855: Output Parameter:
3856: . x - the result vector
3858: Level: developer
3860: Notes:
3861: The vectors `b` and `x` cannot be the same. I.e., one cannot
3862: call `MatSolve`(A,x,x).
3864: Most users should employ the `KSP` interface for linear solvers
3865: instead of working directly with matrix algebra routines such as this.
3866: See, e.g., `KSPCreate()`.
3868: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3869: @*/
3870: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3871: {
3872: PetscFunctionBegin;
3877: PetscCheckSameComm(mat, 1, b, 2);
3878: PetscCheckSameComm(mat, 1, x, 3);
3879: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3880: 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);
3881: 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);
3882: 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);
3883: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3884: MatCheckPreallocated(mat, 1);
3886: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3887: PetscCall(VecFlag(x, mat->factorerrortype));
3888: if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3889: else PetscUseTypeMethod(mat, solve, b, x);
3890: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3891: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3892: PetscFunctionReturn(PETSC_SUCCESS);
3893: }
3895: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3896: {
3897: Vec b, x;
3898: PetscInt N;
3899: PetscErrorCode (*f)(Mat, Vec, Vec);
3900: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3902: PetscFunctionBegin;
3903: if (A->factorerrortype) {
3904: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3905: PetscCall(MatSetInf(X));
3906: PetscFunctionReturn(PETSC_SUCCESS);
3907: }
3908: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3909: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3910: PetscCall(MatBoundToCPU(A, &Abound));
3911: if (!Abound) {
3912: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3913: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3914: }
3915: #if PetscDefined(HAVE_CUDA)
3916: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3917: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3918: #elif PetscDefined(HAVE_HIP)
3919: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3920: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3921: #endif
3922: PetscCall(MatGetSize(B, NULL, &N));
3923: for (PetscInt i = 0; i < N; i++) {
3924: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3925: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3926: PetscCall((*f)(A, b, x));
3927: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3928: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3929: }
3930: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3931: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3932: PetscFunctionReturn(PETSC_SUCCESS);
3933: }
3935: /*@
3936: MatMatSolve - Solves $A X = B$, given a factored matrix.
3938: Neighbor-wise Collective
3940: Input Parameters:
3941: + A - the factored matrix
3942: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3944: Output Parameter:
3945: . X - the result matrix (dense matrix)
3947: Level: developer
3949: Note:
3950: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3951: otherwise, `B` and `X` cannot be the same.
3953: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3954: @*/
3955: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3956: {
3957: PetscFunctionBegin;
3962: PetscCheckSameComm(A, 1, B, 2);
3963: PetscCheckSameComm(A, 1, X, 3);
3964: 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);
3965: 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);
3966: 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");
3967: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3968: MatCheckPreallocated(A, 1);
3970: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3971: if (!A->ops->matsolve) {
3972: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3973: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3974: } else PetscUseTypeMethod(A, matsolve, B, X);
3975: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3976: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3977: PetscFunctionReturn(PETSC_SUCCESS);
3978: }
3980: /*@
3981: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3983: Neighbor-wise Collective
3985: Input Parameters:
3986: + A - the factored matrix
3987: - B - the right-hand-side matrix (`MATDENSE` matrix)
3989: Output Parameter:
3990: . X - the result matrix (dense matrix)
3992: Level: developer
3994: Note:
3995: The matrices `B` and `X` cannot be the same. I.e., one cannot
3996: call `MatMatSolveTranspose`(A,X,X).
3998: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3999: @*/
4000: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
4001: {
4002: PetscFunctionBegin;
4007: PetscCheckSameComm(A, 1, B, 2);
4008: PetscCheckSameComm(A, 1, X, 3);
4009: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
4010: PetscCheck(A->cmap->N == X->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat X: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->cmap->N, X->rmap->N);
4011: PetscCheck(A->rmap->N == B->rmap->N, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_SIZ, "Mat A,Mat B: global dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->N, B->rmap->N);
4012: PetscCheck(A->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Mat A,Mat B: local dim %" PetscInt_FMT " %" PetscInt_FMT, A->rmap->n, B->rmap->n);
4013: PetscCheck(X->cmap->N >= B->cmap->N, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Solution matrix must have same number of columns as rhs matrix");
4014: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4015: MatCheckPreallocated(A, 1);
4017: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
4018: if (!A->ops->matsolvetranspose) {
4019: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
4020: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
4021: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
4022: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
4023: PetscCall(PetscObjectStateIncrease((PetscObject)X));
4024: PetscFunctionReturn(PETSC_SUCCESS);
4025: }
4027: /*@
4028: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
4030: Neighbor-wise Collective
4032: Input Parameters:
4033: + A - the factored matrix
4034: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
4036: Output Parameter:
4037: . X - the result matrix (dense matrix)
4039: Level: developer
4041: Note:
4042: For MUMPS, it only supports centralized sparse compressed column format on the host process for right-hand side matrix. User must create `Bt` in sparse compressed row
4043: format on the host process and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
4045: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
4046: @*/
4047: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
4048: {
4049: PetscFunctionBegin;
4054: PetscCheckSameComm(A, 1, Bt, 2);
4055: PetscCheckSameComm(A, 1, X, 3);
4057: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
4058: 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);
4059: 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);
4060: 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");
4061: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4062: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
4063: MatCheckPreallocated(A, 1);
4065: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
4066: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
4067: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
4068: PetscCall(PetscObjectStateIncrease((PetscObject)X));
4069: PetscFunctionReturn(PETSC_SUCCESS);
4070: }
4072: /*@
4073: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
4074: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4076: Neighbor-wise Collective
4078: Input Parameters:
4079: + mat - the factored matrix
4080: - b - the right-hand-side vector
4082: Output Parameter:
4083: . x - the result vector
4085: Level: developer
4087: Notes:
4088: `MatSolve()` should be used for most applications, as it performs
4089: a forward solve followed by a backward solve.
4091: The vectors `b` and `x` cannot be the same, i.e., one cannot
4092: call `MatForwardSolve`(A,x,x).
4094: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4095: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4096: `MatForwardSolve()` solves $U^T*D y = b$, and
4097: `MatBackwardSolve()` solves $U x = y$.
4098: Thus they do not provide a symmetric preconditioner.
4100: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
4101: @*/
4102: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
4103: {
4104: PetscFunctionBegin;
4109: PetscCheckSameComm(mat, 1, b, 2);
4110: PetscCheckSameComm(mat, 1, x, 3);
4111: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4112: 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);
4113: 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);
4114: 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);
4115: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4116: MatCheckPreallocated(mat, 1);
4118: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
4119: PetscUseTypeMethod(mat, forwardsolve, b, x);
4120: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
4121: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4122: PetscFunctionReturn(PETSC_SUCCESS);
4123: }
4125: /*@
4126: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
4127: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4129: Neighbor-wise Collective
4131: Input Parameters:
4132: + mat - the factored matrix
4133: - b - the right-hand-side vector
4135: Output Parameter:
4136: . x - the result vector
4138: Level: developer
4140: Notes:
4141: `MatSolve()` should be used for most applications, as it performs
4142: a forward solve followed by a backward solve.
4144: The vectors `b` and `x` cannot be the same. I.e., one cannot
4145: call `MatBackwardSolve`(A,x,x).
4147: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4148: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4149: `MatForwardSolve()` solves $U^T*D y = b$, and
4150: `MatBackwardSolve()` solves $U x = y$.
4151: Thus they do not provide a symmetric preconditioner.
4153: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
4154: @*/
4155: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
4156: {
4157: PetscFunctionBegin;
4162: PetscCheckSameComm(mat, 1, b, 2);
4163: PetscCheckSameComm(mat, 1, x, 3);
4164: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4165: 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);
4166: 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);
4167: 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);
4168: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4169: MatCheckPreallocated(mat, 1);
4171: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
4172: PetscUseTypeMethod(mat, backwardsolve, b, x);
4173: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
4174: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4175: PetscFunctionReturn(PETSC_SUCCESS);
4176: }
4178: /*@
4179: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
4181: Neighbor-wise Collective
4183: Input Parameters:
4184: + mat - the factored matrix
4185: . b - the right-hand-side vector
4186: - y - the vector to be added to
4188: Output Parameter:
4189: . x - the result vector
4191: Level: developer
4193: Note:
4194: The vectors `b` and `x` cannot be the same. I.e., one cannot
4195: call `MatSolveAdd`(A,x,y,x).
4197: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4198: @*/
4199: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4200: {
4201: PetscScalar one = 1.0;
4202: Vec tmp;
4204: PetscFunctionBegin;
4210: PetscCheckSameComm(mat, 1, b, 2);
4211: PetscCheckSameComm(mat, 1, y, 3);
4212: PetscCheckSameComm(mat, 1, x, 4);
4213: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4214: 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);
4215: 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);
4216: 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);
4217: 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);
4218: 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);
4219: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4220: MatCheckPreallocated(mat, 1);
4222: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4223: PetscCall(VecFlag(x, mat->factorerrortype));
4224: if (mat->factorerrortype) {
4225: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4226: } else if (mat->ops->solveadd) {
4227: PetscUseTypeMethod(mat, solveadd, b, y, x);
4228: } else {
4229: /* do the solve then the add manually */
4230: if (x != y) {
4231: PetscCall(MatSolve(mat, b, x));
4232: PetscCall(VecAXPY(x, one, y));
4233: } else {
4234: PetscCall(VecDuplicate(x, &tmp));
4235: PetscCall(VecCopy(x, tmp));
4236: PetscCall(MatSolve(mat, b, x));
4237: PetscCall(VecAXPY(x, one, tmp));
4238: PetscCall(VecDestroy(&tmp));
4239: }
4240: }
4241: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4242: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4243: PetscFunctionReturn(PETSC_SUCCESS);
4244: }
4246: /*@
4247: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4249: Neighbor-wise Collective
4251: Input Parameters:
4252: + mat - the factored matrix
4253: - b - the right-hand-side vector
4255: Output Parameter:
4256: . x - the result vector
4258: Level: developer
4260: Notes:
4261: The vectors `b` and `x` cannot be the same. I.e., one cannot
4262: call `MatSolveTranspose`(A,x,x).
4264: Most users should employ the `KSP` interface for linear solvers
4265: instead of working directly with matrix algebra routines such as this.
4266: See, e.g., `KSPCreate()`.
4268: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4269: @*/
4270: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4271: {
4272: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4274: PetscFunctionBegin;
4279: PetscCheckSameComm(mat, 1, b, 2);
4280: PetscCheckSameComm(mat, 1, x, 3);
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: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4285: MatCheckPreallocated(mat, 1);
4286: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4287: PetscCall(VecFlag(x, mat->factorerrortype));
4288: if (mat->factorerrortype) {
4289: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4290: } else {
4291: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4292: PetscCall((*f)(mat, b, x));
4293: }
4294: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4295: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4296: PetscFunctionReturn(PETSC_SUCCESS);
4297: }
4299: /*@
4300: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4301: factored matrix.
4303: Neighbor-wise Collective
4305: Input Parameters:
4306: + mat - the factored matrix
4307: . b - the right-hand-side vector
4308: - y - the vector to be added to
4310: Output Parameter:
4311: . x - the result vector
4313: Level: developer
4315: Note:
4316: The vectors `b` and `x` cannot be the same. I.e., one cannot
4317: call `MatSolveTransposeAdd`(A,x,y,x).
4319: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4320: @*/
4321: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4322: {
4323: PetscScalar one = 1.0;
4324: Vec tmp;
4325: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4327: PetscFunctionBegin;
4333: PetscCheckSameComm(mat, 1, b, 2);
4334: PetscCheckSameComm(mat, 1, y, 3);
4335: PetscCheckSameComm(mat, 1, x, 4);
4336: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4337: 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);
4338: 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);
4339: 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);
4340: 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);
4341: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4342: MatCheckPreallocated(mat, 1);
4344: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4345: PetscCall(VecFlag(x, mat->factorerrortype));
4346: if (mat->factorerrortype) {
4347: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4348: } else if (f) {
4349: PetscCall((*f)(mat, b, y, x));
4350: } else {
4351: /* do the solve then the add manually */
4352: if (x != y) {
4353: PetscCall(MatSolveTranspose(mat, b, x));
4354: PetscCall(VecAXPY(x, one, y));
4355: } else {
4356: PetscCall(VecDuplicate(x, &tmp));
4357: PetscCall(VecCopy(x, tmp));
4358: PetscCall(MatSolveTranspose(mat, b, x));
4359: PetscCall(VecAXPY(x, one, tmp));
4360: PetscCall(VecDestroy(&tmp));
4361: }
4362: }
4363: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4364: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4365: PetscFunctionReturn(PETSC_SUCCESS);
4366: }
4368: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4369: /*@
4370: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4372: Neighbor-wise Collective
4374: Input Parameters:
4375: + mat - the matrix
4376: . b - the right-hand side
4377: . omega - the relaxation factor
4378: . flag - flag indicating the type of SOR (see below)
4379: . shift - diagonal shift
4380: . its - the number of iterations
4381: - lits - the number of local iterations
4383: Output Parameter:
4384: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4386: SOR Flags:
4387: + `SOR_FORWARD_SWEEP` - forward SOR
4388: . `SOR_BACKWARD_SWEEP` - backward SOR
4389: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4390: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4391: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4392: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4393: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4394: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies upper/lower triangular part of matrix to vector (with `omega`)
4395: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4397: Level: developer
4399: Notes:
4400: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4401: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4402: on each process.
4404: Application programmers will not generally use `MatSOR()` directly,
4405: but instead will employ `PCSOR` or `PCEISENSTAT`
4407: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with inodes, this does a block SOR smoothing, otherwise it does a pointwise smoothing.
4408: For `MATAIJ` matrices with inodes, the block sizes are determined by the inode sizes, not the block size set with `MatSetBlockSize()`
4410: Vectors `x` and `b` CANNOT be the same
4412: The flags are implemented as bitwise inclusive or operations.
4413: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4414: to specify a zero initial guess for SSOR.
4416: Developer Note:
4417: We should add block SOR support for `MATAIJ` matrices with block size set to greater than one and no inodes
4419: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4420: @*/
4421: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4422: {
4423: PetscFunctionBegin;
4428: PetscCheckSameComm(mat, 1, b, 2);
4429: PetscCheckSameComm(mat, 1, x, 8);
4430: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4431: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4432: 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);
4433: 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);
4434: 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);
4435: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4436: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4437: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4439: MatCheckPreallocated(mat, 1);
4440: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4441: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4442: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4443: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4444: PetscFunctionReturn(PETSC_SUCCESS);
4445: }
4447: /*
4448: Default matrix copy routine.
4449: */
4450: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4451: {
4452: PetscInt i, rstart = 0, rend = 0, nz;
4453: const PetscInt *cwork;
4454: const PetscScalar *vwork;
4456: PetscFunctionBegin;
4457: if (B->assembled) PetscCall(MatZeroEntries(B));
4458: if (str == SAME_NONZERO_PATTERN) {
4459: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4460: for (i = rstart; i < rend; i++) {
4461: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4462: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4463: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4464: }
4465: } else {
4466: PetscCall(MatAYPX(B, 0.0, A, str));
4467: }
4468: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4469: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4470: PetscFunctionReturn(PETSC_SUCCESS);
4471: }
4473: /*@
4474: MatCopy - Copies a matrix to another matrix.
4476: Collective
4478: Input Parameters:
4479: + A - the matrix
4480: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4482: Output Parameter:
4483: . B - where the copy is put
4485: Level: intermediate
4487: Notes:
4488: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4490: `MatCopy()` copies the matrix entries of a matrix to another existing
4491: matrix (after first zeroing the second matrix). A related routine is
4492: `MatConvert()`, which first creates a new matrix and then copies the data.
4494: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4495: @*/
4496: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4497: {
4498: PetscInt i;
4500: PetscFunctionBegin;
4505: PetscCheckSameComm(A, 1, B, 2);
4506: MatCheckPreallocated(B, 2);
4507: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4508: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4509: 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,
4510: A->cmap->N, B->cmap->N);
4511: MatCheckPreallocated(A, 1);
4512: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4514: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4515: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4516: else PetscCall(MatCopy_Basic(A, B, str));
4518: B->stencil.dim = A->stencil.dim;
4519: B->stencil.noc = A->stencil.noc;
4520: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4521: B->stencil.dims[i] = A->stencil.dims[i];
4522: B->stencil.starts[i] = A->stencil.starts[i];
4523: }
4525: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4526: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4527: PetscFunctionReturn(PETSC_SUCCESS);
4528: }
4530: /*@
4531: MatConvert - Converts a matrix to another matrix, either of the same
4532: or different type.
4534: Collective
4536: Input Parameters:
4537: + mat - the matrix
4538: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4539: same type as the original matrix.
4540: - reuse - denotes if the destination matrix is to be created or reused.
4541: 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
4542: `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).
4544: Output Parameter:
4545: . M - pointer to place new matrix
4547: Level: intermediate
4549: Notes:
4550: `MatConvert()` first creates a new matrix and then copies the data from
4551: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4552: entries of one matrix to another already existing matrix context.
4554: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4555: the MPI communicator of the generated matrix is always the same as the communicator
4556: of the input matrix.
4558: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4559: @*/
4560: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4561: {
4562: PetscBool sametype, issame, flg;
4563: PetscBool3 issymmetric, ishermitian, isspd;
4564: char convname[256], mtype[256];
4565: Mat B;
4567: PetscFunctionBegin;
4570: PetscAssertPointer(M, 4);
4571: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4572: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4573: MatCheckPreallocated(mat, 1);
4575: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4576: if (flg) newtype = mtype;
4578: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4579: PetscCall(PetscStrcmp(newtype, "same", &issame));
4580: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4581: if (reuse == MAT_REUSE_MATRIX) {
4583: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4584: }
4586: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4587: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4588: PetscFunctionReturn(PETSC_SUCCESS);
4589: }
4591: /* Cache Mat options because some converters use MatHeaderReplace() */
4592: issymmetric = mat->symmetric;
4593: ishermitian = mat->hermitian;
4594: isspd = mat->spd;
4596: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4597: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4598: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4599: } else {
4600: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4601: const char *prefix[3] = {"seq", "mpi", ""};
4602: PetscInt i;
4603: /*
4604: Order of precedence:
4605: 0) See if newtype is a superclass of the current matrix.
4606: 1) See if a specialized converter is known to the current matrix.
4607: 2) See if a specialized converter is known to the desired matrix class.
4608: 3) See if a good general converter is registered for the desired class
4609: (as of 6/27/03 only MATMPIADJ falls into this category).
4610: 4) See if a good general converter is known for the current matrix.
4611: 5) Use a really basic converter.
4612: */
4614: /* 0) See if newtype is a superclass of the current matrix.
4615: i.e mat is mpiaij and newtype is aij */
4616: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4617: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4618: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4619: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4620: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4621: if (flg) {
4622: if (reuse == MAT_INPLACE_MATRIX) {
4623: PetscCall(PetscInfo(mat, "Early return\n"));
4624: PetscFunctionReturn(PETSC_SUCCESS);
4625: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4626: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4627: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4628: PetscFunctionReturn(PETSC_SUCCESS);
4629: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4630: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4631: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4632: PetscFunctionReturn(PETSC_SUCCESS);
4633: }
4634: }
4635: }
4636: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4637: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4638: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4639: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4640: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4641: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4642: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4643: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4644: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4645: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4646: if (conv) goto foundconv;
4647: }
4649: /* 2) See if a specialized converter is known to the desired matrix class. */
4650: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4651: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4652: PetscCall(MatSetType(B, newtype));
4653: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4654: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4655: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4656: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4657: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4658: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4659: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4660: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4661: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4662: if (conv) {
4663: PetscCall(MatDestroy(&B));
4664: goto foundconv;
4665: }
4666: }
4668: /* 3) See if a good general converter is registered for the desired class */
4669: conv = B->ops->convertfrom;
4670: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4671: PetscCall(MatDestroy(&B));
4672: if (conv) goto foundconv;
4674: /* 4) See if a good general converter is known for the current matrix */
4675: if (mat->ops->convert) conv = mat->ops->convert;
4676: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4677: if (conv) goto foundconv;
4679: /* 5) Use a really basic converter. */
4680: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4681: conv = MatConvert_Basic;
4683: foundconv:
4684: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4685: PetscCall((*conv)(mat, newtype, reuse, M));
4686: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4687: /* the block sizes must be same if the mappings are copied over */
4688: (*M)->rmap->bs = mat->rmap->bs;
4689: (*M)->cmap->bs = mat->cmap->bs;
4690: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4691: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4692: (*M)->rmap->mapping = mat->rmap->mapping;
4693: (*M)->cmap->mapping = mat->cmap->mapping;
4694: }
4695: (*M)->stencil.dim = mat->stencil.dim;
4696: (*M)->stencil.noc = mat->stencil.noc;
4697: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4698: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4699: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4700: }
4701: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4702: }
4703: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4705: /* Reset Mat options */
4706: if (issymmetric != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PetscBool3ToBool(issymmetric)));
4707: if (ishermitian != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PetscBool3ToBool(ishermitian)));
4708: if (isspd != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SPD, PetscBool3ToBool(isspd)));
4709: PetscFunctionReturn(PETSC_SUCCESS);
4710: }
4712: /*@
4713: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4715: Not Collective
4717: Input Parameter:
4718: . mat - the matrix, must be a factored matrix
4720: Output Parameter:
4721: . type - the string name of the package (do not free this string)
4723: Level: intermediate
4725: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4726: @*/
4727: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4728: {
4729: PetscErrorCode (*conv)(Mat, MatSolverType *);
4731: PetscFunctionBegin;
4734: PetscAssertPointer(type, 2);
4735: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4736: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4737: if (conv) PetscCall((*conv)(mat, type));
4738: else *type = MATSOLVERPETSC;
4739: PetscFunctionReturn(PETSC_SUCCESS);
4740: }
4742: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4743: struct _MatSolverTypeForSpecifcType {
4744: MatType mtype;
4745: /* no entry for MAT_FACTOR_NONE */
4746: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4747: MatSolverTypeForSpecifcType next;
4748: };
4750: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4751: struct _MatSolverTypeHolder {
4752: char *name;
4753: MatSolverTypeForSpecifcType handlers;
4754: MatSolverTypeHolder next;
4755: };
4757: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4759: /*@
4760: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4762: Logically Collective, No Fortran Support
4764: Input Parameters:
4765: + package - name of the package, for example `petsc` or `superlu`
4766: . mtype - the matrix type that works with this package
4767: . ftype - the type of factorization supported by the package
4768: - createfactor - routine that will create the factored matrix ready to be used
4770: Level: developer
4772: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4773: `MatGetFactor()`
4774: @*/
4775: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4776: {
4777: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4778: PetscBool flg;
4779: MatSolverTypeForSpecifcType inext, iprev = NULL;
4781: PetscFunctionBegin;
4782: PetscCall(MatInitializePackage());
4783: if (!next) {
4784: PetscCall(PetscNew(&MatSolverTypeHolders));
4785: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4786: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4787: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4788: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4789: PetscFunctionReturn(PETSC_SUCCESS);
4790: }
4791: while (next) {
4792: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4793: if (flg) {
4794: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4795: inext = next->handlers;
4796: while (inext) {
4797: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4798: if (flg) {
4799: inext->createfactor[(int)ftype - 1] = createfactor;
4800: PetscFunctionReturn(PETSC_SUCCESS);
4801: }
4802: iprev = inext;
4803: inext = inext->next;
4804: }
4805: PetscCall(PetscNew(&iprev->next));
4806: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4807: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4808: PetscFunctionReturn(PETSC_SUCCESS);
4809: }
4810: prev = next;
4811: next = next->next;
4812: }
4813: PetscCall(PetscNew(&prev->next));
4814: PetscCall(PetscStrallocpy(package, &prev->next->name));
4815: PetscCall(PetscNew(&prev->next->handlers));
4816: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4817: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4818: PetscFunctionReturn(PETSC_SUCCESS);
4819: }
4821: /*@
4822: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4824: Input Parameters:
4825: + 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
4826: . ftype - the type of factorization supported by the type
4827: - mtype - the matrix type that works with this type
4829: Output Parameters:
4830: + foundtype - `PETSC_TRUE` if the type was registered
4831: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4832: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4834: Calling sequence of `createfactor`:
4835: + A - the matrix providing the factor matrix
4836: . ftype - the `MatFactorType` of the factor requested
4837: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4839: Level: developer
4841: Note:
4842: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4843: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4844: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4846: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4847: `MatInitializePackage()`
4848: @*/
4849: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4850: {
4851: MatSolverTypeHolder next = MatSolverTypeHolders;
4852: PetscBool flg;
4853: MatSolverTypeForSpecifcType inext;
4855: PetscFunctionBegin;
4856: if (foundtype) *foundtype = PETSC_FALSE;
4857: if (foundmtype) *foundmtype = PETSC_FALSE;
4858: if (createfactor) *createfactor = NULL;
4860: if (type) {
4861: while (next) {
4862: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4863: if (flg) {
4864: if (foundtype) *foundtype = PETSC_TRUE;
4865: inext = next->handlers;
4866: while (inext) {
4867: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4868: if (flg) {
4869: if (foundmtype) *foundmtype = PETSC_TRUE;
4870: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4871: PetscFunctionReturn(PETSC_SUCCESS);
4872: }
4873: inext = inext->next;
4874: }
4875: }
4876: next = next->next;
4877: }
4878: } else {
4879: while (next) {
4880: inext = next->handlers;
4881: while (inext) {
4882: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4883: if (flg && inext->createfactor[(int)ftype - 1]) {
4884: if (foundtype) *foundtype = PETSC_TRUE;
4885: if (foundmtype) *foundmtype = PETSC_TRUE;
4886: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4887: PetscFunctionReturn(PETSC_SUCCESS);
4888: }
4889: inext = inext->next;
4890: }
4891: next = next->next;
4892: }
4893: /* try with base classes inext->mtype */
4894: next = MatSolverTypeHolders;
4895: while (next) {
4896: inext = next->handlers;
4897: while (inext) {
4898: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4899: if (flg && inext->createfactor[(int)ftype - 1]) {
4900: if (foundtype) *foundtype = PETSC_TRUE;
4901: if (foundmtype) *foundmtype = PETSC_TRUE;
4902: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4903: PetscFunctionReturn(PETSC_SUCCESS);
4904: }
4905: inext = inext->next;
4906: }
4907: next = next->next;
4908: }
4909: }
4910: PetscFunctionReturn(PETSC_SUCCESS);
4911: }
4913: PetscErrorCode MatSolverTypeDestroy(void)
4914: {
4915: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4916: MatSolverTypeForSpecifcType inext, iprev;
4918: PetscFunctionBegin;
4919: while (next) {
4920: PetscCall(PetscFree(next->name));
4921: inext = next->handlers;
4922: while (inext) {
4923: PetscCall(PetscFree(inext->mtype));
4924: iprev = inext;
4925: inext = inext->next;
4926: PetscCall(PetscFree(iprev));
4927: }
4928: prev = next;
4929: next = next->next;
4930: PetscCall(PetscFree(prev));
4931: }
4932: MatSolverTypeHolders = NULL;
4933: PetscFunctionReturn(PETSC_SUCCESS);
4934: }
4936: static PetscErrorCode MatGetFactor_Private(Mat mat, MatFactorType ftype, PetscBool exact, PetscBool *found, Mat *f)
4937: {
4938: MatSolverTypeHolder next = MatSolverTypeHolders;
4939: MatSolverTypeForSpecifcType inext;
4940: PetscBool flg, same;
4942: PetscFunctionBegin;
4943: *found = PETSC_FALSE;
4944: *f = NULL;
4945: /* When no solver type is requested, MatGetFactor() must honor registration order, but a registered
4946: MatSolverType may only be able to reject a particular MatType at runtime by returning NULL in *f.
4947: Keep walking the registry until a matching backend actually creates a factor. */
4948: while (next) {
4949: inext = next->handlers;
4950: while (inext) {
4951: PetscCall(PetscStrcmp(((PetscObject)mat)->type_name, inext->mtype, &same));
4952: if (exact) flg = same;
4953: else {
4954: /* Do the base-type pass separately from the exact pass so exact registrations for the MatType
4955: are all tried before broader registrations such as implementation base classes. */
4956: PetscCall(PetscStrbeginswith(((PetscObject)mat)->type_name, inext->mtype, &flg));
4957: flg = (PetscBool)(flg && !same);
4958: }
4959: if (flg && inext->createfactor[(int)ftype - 1]) {
4960: *found = PETSC_TRUE;
4961: PetscCall((*inext->createfactor[(int)ftype - 1])(mat, ftype, f));
4962: if (*f) PetscFunctionReturn(PETSC_SUCCESS);
4963: }
4964: inext = inext->next;
4965: }
4966: next = next->next;
4967: }
4968: PetscFunctionReturn(PETSC_SUCCESS);
4969: }
4971: /*@
4972: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4974: Logically Collective
4976: Input Parameter:
4977: . mat - the matrix
4979: Output Parameter:
4980: . flg - `PETSC_TRUE` if uses the ordering
4982: Level: developer
4984: Note:
4985: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4986: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4988: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4989: @*/
4990: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4991: {
4992: PetscFunctionBegin;
4993: *flg = mat->canuseordering;
4994: PetscFunctionReturn(PETSC_SUCCESS);
4995: }
4997: /*@
4998: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
5000: Logically Collective
5002: Input Parameters:
5003: + mat - the matrix obtained with `MatGetFactor()`
5004: - ftype - the factorization type to be used
5006: Output Parameter:
5007: . otype - the preferred ordering type
5009: Level: developer
5011: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
5012: @*/
5013: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
5014: {
5015: PetscFunctionBegin;
5016: *otype = mat->preferredordering[ftype];
5017: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
5018: PetscFunctionReturn(PETSC_SUCCESS);
5019: }
5021: /*@
5022: MatGetFactor - Returns a matrix suitable to calls to routines such as `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
5023: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, and `MatCholeskyFactorNumeric()`
5025: Collective
5027: Input Parameters:
5028: + mat - the matrix
5029: . 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
5030: the other criteria is returned
5031: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
5033: Output Parameter:
5034: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
5036: Options Database Keys:
5037: + -pc_factor_mat_solver_type type - choose the type at run time. When using `KSP` solvers
5038: . -pc_factor_mat_factor_on_host (true|false) - do matrix factorization on host (with device matrices). Default is doing it on device
5039: - -pc_factor_mat_solve_on_host (true|false) - do matrix solve on host (with device matrices). Default is doing it on device
5041: Level: intermediate
5043: Notes:
5044: Some of the packages, such as MUMPS, have options for controlling the factorization, these are in the form `-prefix_mat_packagename_packageoption`
5045: (for example, `-mat_mumps_icntl_6 1`) where `prefix` is normally set automatically from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly,
5046: without using a `PC`, one can set the prefix by
5047: calling `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
5049: Some PETSc matrix formats have alternative solvers available that are provided by alternative packages
5050: such as PaStiX, SuperLU_DIST, MUMPS etc. PETSc must have been configured to use the external solver,
5051: using the corresponding `./configure` option such as `--download-package` or `--with-package-dir`.
5053: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
5054: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
5055: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
5057: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
5058: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
5060: Developer Note:
5061: This should actually be called `MatCreateFactor()` since it creates a new factor object
5063: The `MatGetFactor()` implementations should not be accessing the PETSc options database or making other decisions about solver options,
5064: that should be delayed until the later operations. This is to ensure the correct options prefix has been set in the factor matrix.
5066: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
5067: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`,
5068: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`,
5069: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
5070: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactorNumeric()`
5071: @*/
5072: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
5073: {
5074: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
5075: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
5077: PetscFunctionBegin;
5081: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5082: MatCheckPreallocated(mat, 1);
5084: PetscCall(MatIsShell(mat, &shell));
5085: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
5086: if (hasop) {
5087: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
5088: PetscFunctionReturn(PETSC_SUCCESS);
5089: }
5091: if (!type) {
5092: PetscBool foundbase;
5094: /* First try exact MatType registrations in solver registration order. If all matching backends
5095: decline this matrix instance by returning NULL, then try base-type registrations. */
5096: PetscCall(MatGetFactor_Private(mat, ftype, PETSC_TRUE, &foundtype, f));
5097: if (!*f) {
5098: PetscCall(MatGetFactor_Private(mat, ftype, PETSC_FALSE, &foundbase, f));
5099: foundtype = (PetscBool)(foundtype || foundbase);
5100: }
5101: 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);
5102: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
5103: PetscFunctionReturn(PETSC_SUCCESS);
5104: }
5106: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
5107: 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],
5108: ((PetscObject)mat)->type_name, type ? " Perhaps you must ./configure with --download-" : "", type ? type : "");
5109: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
5110: 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);
5112: PetscCall((*conv)(mat, ftype, f));
5113: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
5114: PetscFunctionReturn(PETSC_SUCCESS);
5115: }
5117: /*@
5118: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
5120: Not Collective
5122: Input Parameters:
5123: + mat - the matrix
5124: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
5125: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
5127: Output Parameter:
5128: . flg - `PETSC_TRUE` if the factorization is available
5130: Level: intermediate
5132: Notes:
5133: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
5134: such as `pastix`, `superlu`, `mumps`, etc.
5136: PETSc must have been configured with `./configure` to use the external solver using the option `--download-package` where package is the name of the package
5138: Developer Note:
5139: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
5141: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
5142: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
5143: @*/
5144: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
5145: {
5146: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
5148: PetscFunctionBegin;
5150: PetscAssertPointer(flg, 4);
5152: *flg = PETSC_FALSE;
5153: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
5155: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5156: MatCheckPreallocated(mat, 1);
5158: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
5159: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
5160: PetscFunctionReturn(PETSC_SUCCESS);
5161: }
5163: /*@
5164: MatDuplicate - Duplicates a matrix including the non-zero structure.
5166: Collective
5168: Input Parameters:
5169: + mat - the matrix
5170: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
5171: See the manual page for `MatDuplicateOption()` for an explanation of these options.
5173: Output Parameter:
5174: . M - pointer to place new matrix
5176: Level: intermediate
5178: Notes:
5179: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
5181: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
5183: 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.
5185: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
5186: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
5187: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
5189: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
5190: @*/
5191: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
5192: {
5193: Mat B;
5194: VecType vtype;
5195: PetscInt i;
5196: PetscObject dm, container_h, container_d;
5197: PetscErrorCodeFn *viewf;
5199: PetscFunctionBegin;
5202: PetscAssertPointer(M, 3);
5203: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
5204: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5205: MatCheckPreallocated(mat, 1);
5207: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
5208: PetscUseTypeMethod(mat, duplicate, op, M);
5209: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
5210: B = *M;
5212: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
5213: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
5214: PetscCall(MatGetVecType(mat, &vtype));
5215: PetscCall(MatSetVecType(B, vtype));
5217: B->stencil.dim = mat->stencil.dim;
5218: B->stencil.noc = mat->stencil.noc;
5219: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
5220: B->stencil.dims[i] = mat->stencil.dims[i];
5221: B->stencil.starts[i] = mat->stencil.starts[i];
5222: }
5224: B->nooffproczerorows = mat->nooffproczerorows;
5225: B->nooffprocentries = mat->nooffprocentries;
5227: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5228: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5229: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5230: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5231: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5232: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5233: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5234: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5235: PetscFunctionReturn(PETSC_SUCCESS);
5236: }
5238: /*@
5239: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5241: Logically Collective
5243: Input Parameter:
5244: . mat - the matrix
5246: Output Parameter:
5247: . v - the diagonal of the matrix
5249: Level: intermediate
5251: Note:
5252: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5253: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5254: is larger than `ndiag`, the values of the remaining entries are unspecified.
5256: Currently only correct in parallel for square matrices.
5258: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5259: @*/
5260: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5261: {
5262: PetscFunctionBegin;
5266: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5267: MatCheckPreallocated(mat, 1);
5268: if (PetscDefined(USE_DEBUG)) {
5269: PetscInt nv, row, col, ndiag;
5271: PetscCall(VecGetLocalSize(v, &nv));
5272: PetscCall(MatGetLocalSize(mat, &row, &col));
5273: ndiag = PetscMin(row, col);
5274: 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);
5275: }
5277: PetscUseTypeMethod(mat, getdiagonal, v);
5278: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5279: PetscFunctionReturn(PETSC_SUCCESS);
5280: }
5282: /*@
5283: MatGetRowMin - Gets the minimum value (of the real part) of each
5284: row of the matrix
5286: Logically Collective
5288: Input Parameter:
5289: . mat - the matrix
5291: Output Parameters:
5292: + v - the vector for storing the maximums
5293: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5295: Level: intermediate
5297: Note:
5298: The result of this call are the same as if one converted the matrix to dense format
5299: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5301: This code is only implemented for a couple of matrix formats.
5303: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5304: `MatGetRowMax()`
5305: @*/
5306: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5307: {
5308: PetscFunctionBegin;
5312: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5314: if (!mat->cmap->N) {
5315: PetscCall(VecSet(v, PETSC_MAX_REAL));
5316: if (idx) {
5317: PetscInt i, m = mat->rmap->n;
5318: for (i = 0; i < m; i++) idx[i] = -1;
5319: }
5320: } else {
5321: MatCheckPreallocated(mat, 1);
5322: }
5323: PetscUseTypeMethod(mat, getrowmin, v, idx);
5324: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5325: PetscFunctionReturn(PETSC_SUCCESS);
5326: }
5328: /*@
5329: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5330: row of the matrix
5332: Logically Collective
5334: Input Parameter:
5335: . mat - the matrix
5337: Output Parameters:
5338: + v - the vector for storing the minimums
5339: - idx - the indices of the column found for each row (or `NULL` if not needed)
5341: Level: intermediate
5343: Notes:
5344: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5345: row is 0 (the first column).
5347: This code is only implemented for a couple of matrix formats.
5349: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5350: @*/
5351: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5352: {
5353: PetscFunctionBegin;
5357: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5358: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5360: if (!mat->cmap->N) {
5361: PetscCall(VecSet(v, 0.0));
5362: if (idx) {
5363: PetscInt i, m = mat->rmap->n;
5364: for (i = 0; i < m; i++) idx[i] = -1;
5365: }
5366: } else {
5367: MatCheckPreallocated(mat, 1);
5368: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5369: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5370: }
5371: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5372: PetscFunctionReturn(PETSC_SUCCESS);
5373: }
5375: /*@
5376: MatGetRowMax - Gets the maximum value (of the real part) of each
5377: row of the matrix
5379: Logically Collective
5381: Input Parameter:
5382: . mat - the matrix
5384: Output Parameters:
5385: + v - the vector for storing the maximums
5386: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5388: Level: intermediate
5390: Notes:
5391: The result of this call are the same as if one converted the matrix to dense format
5392: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5394: This code is only implemented for a couple of matrix formats.
5396: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5397: @*/
5398: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5399: {
5400: PetscFunctionBegin;
5404: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5406: if (!mat->cmap->N) {
5407: PetscCall(VecSet(v, PETSC_MIN_REAL));
5408: if (idx) {
5409: PetscInt i, m = mat->rmap->n;
5410: for (i = 0; i < m; i++) idx[i] = -1;
5411: }
5412: } else {
5413: MatCheckPreallocated(mat, 1);
5414: PetscUseTypeMethod(mat, getrowmax, v, idx);
5415: }
5416: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5417: PetscFunctionReturn(PETSC_SUCCESS);
5418: }
5420: /*@
5421: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5422: row of the matrix
5424: Logically Collective
5426: Input Parameter:
5427: . mat - the matrix
5429: Output Parameters:
5430: + v - the vector for storing the maximums
5431: - idx - the indices of the column found for each row (or `NULL` if not needed)
5433: Level: intermediate
5435: Notes:
5436: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5437: row is 0 (the first column).
5439: This code is only implemented for a couple of matrix formats.
5441: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5442: @*/
5443: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5444: {
5445: PetscFunctionBegin;
5449: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5451: if (!mat->cmap->N) {
5452: PetscCall(VecSet(v, 0.0));
5453: if (idx) {
5454: PetscInt i, m = mat->rmap->n;
5455: for (i = 0; i < m; i++) idx[i] = -1;
5456: }
5457: } else {
5458: MatCheckPreallocated(mat, 1);
5459: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5460: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5461: }
5462: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5463: PetscFunctionReturn(PETSC_SUCCESS);
5464: }
5466: /*@
5467: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5469: Logically Collective
5471: Input Parameter:
5472: . mat - the matrix
5474: Output Parameter:
5475: . v - the vector for storing the sum
5477: Level: intermediate
5479: This code is only implemented for a couple of matrix formats.
5481: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5482: @*/
5483: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5484: {
5485: PetscFunctionBegin;
5489: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5491: if (!mat->cmap->N) PetscCall(VecSet(v, 0.0));
5492: else {
5493: MatCheckPreallocated(mat, 1);
5494: PetscUseTypeMethod(mat, getrowsumabs, v);
5495: }
5496: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5497: PetscFunctionReturn(PETSC_SUCCESS);
5498: }
5500: /*@
5501: MatGetRowSum - Gets the sum of each row of the matrix
5503: Logically or Neighborhood Collective
5505: Input Parameter:
5506: . mat - the matrix
5508: Output Parameter:
5509: . v - the vector for storing the sum of rows
5511: Level: intermediate
5513: Note:
5514: This code is slow since it is not currently specialized for different formats
5516: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5517: @*/
5518: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5519: {
5520: Vec ones;
5522: PetscFunctionBegin;
5526: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5527: MatCheckPreallocated(mat, 1);
5528: PetscCall(MatCreateVecs(mat, &ones, NULL));
5529: PetscCall(VecSet(ones, 1.));
5530: PetscCall(MatMult(mat, ones, v));
5531: PetscCall(VecDestroy(&ones));
5532: PetscFunctionReturn(PETSC_SUCCESS);
5533: }
5535: /*@
5536: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5537: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5539: Collective
5541: Input Parameter:
5542: . mat - the matrix to provide the transpose
5544: Output Parameter:
5545: . 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
5547: Level: advanced
5549: Note:
5550: 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
5551: routine allows bypassing that call.
5553: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5554: @*/
5555: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5556: {
5557: MatState *rb = NULL;
5559: PetscFunctionBegin;
5560: PetscCall(PetscNew(&rb));
5561: rb->id = ((PetscObject)mat)->id;
5562: rb->state = 0;
5563: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5564: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5565: PetscFunctionReturn(PETSC_SUCCESS);
5566: }
5568: static PetscErrorCode MatTranspose_Private(Mat mat, MatReuse reuse, Mat *B, PetscBool conjugate)
5569: {
5570: PetscContainer rB = NULL;
5571: MatState *rb = NULL;
5572: PetscErrorCode (*f)(Mat, MatReuse, Mat *) = NULL;
5574: PetscFunctionBegin;
5577: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5578: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5579: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5580: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5581: MatCheckPreallocated(mat, 1);
5582: if (reuse == MAT_REUSE_MATRIX) {
5583: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5584: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5585: PetscCall(PetscContainerGetPointer(rB, &rb));
5586: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5587: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5588: }
5590: if (conjugate) {
5591: f = mat->ops->hermitiantranspose;
5592: if (f) PetscCall((*f)(mat, reuse, B));
5593: }
5594: if (!f && !(reuse == MAT_INPLACE_MATRIX && mat->hermitian == PETSC_BOOL3_TRUE && conjugate)) {
5595: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5596: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5597: PetscUseTypeMethod(mat, transpose, reuse, B);
5598: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5599: }
5600: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5601: if (conjugate) PetscCall(MatConjugate(*B));
5602: }
5604: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5605: if (reuse != MAT_INPLACE_MATRIX) {
5606: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5607: PetscCall(PetscContainerGetPointer(rB, &rb));
5608: rb->state = ((PetscObject)mat)->state;
5609: rb->nonzerostate = mat->nonzerostate;
5610: }
5611: PetscFunctionReturn(PETSC_SUCCESS);
5612: }
5614: /*@
5615: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5617: Collective
5619: Input Parameters:
5620: + mat - the matrix to transpose
5621: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5623: Output Parameter:
5624: . B - the transpose of the matrix
5626: Level: intermediate
5628: Notes:
5629: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5631: `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
5632: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5634: 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.
5636: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5637: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5639: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5641: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5643: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5644: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5645: @*/
5646: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5647: {
5648: PetscFunctionBegin;
5649: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_FALSE));
5650: PetscFunctionReturn(PETSC_SUCCESS);
5651: }
5653: /*@
5654: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5656: Collective
5658: Input Parameter:
5659: . A - the matrix to transpose
5661: Output Parameter:
5662: . 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
5663: numerical portion.
5665: Level: intermediate
5667: Note:
5668: This is not supported for many matrix types, use `MatTranspose()` in those cases
5670: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5671: @*/
5672: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5673: {
5674: PetscFunctionBegin;
5677: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5678: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5679: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5680: PetscUseTypeMethod(A, transposesymbolic, B);
5681: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5683: PetscCall(MatTransposeSetPrecursor(A, *B));
5684: PetscFunctionReturn(PETSC_SUCCESS);
5685: }
5687: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5688: {
5689: PetscContainer rB;
5690: MatState *rb;
5692: PetscFunctionBegin;
5695: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5696: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5697: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5698: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5699: PetscCall(PetscContainerGetPointer(rB, &rb));
5700: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5701: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5702: PetscFunctionReturn(PETSC_SUCCESS);
5703: }
5705: /*@
5706: MatIsTranspose - Test whether a matrix is another one's transpose,
5707: or its own, in which case it tests symmetry.
5709: Collective
5711: Input Parameters:
5712: + A - the matrix to test
5713: . B - the matrix to test against, this can equal the first parameter
5714: - tol - tolerance, differences between entries smaller than this are counted as zero
5716: Output Parameter:
5717: . flg - the result
5719: Level: intermediate
5721: Notes:
5722: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5723: test involves parallel copies of the block off-diagonal parts of the matrix.
5725: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5726: @*/
5727: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5728: {
5729: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5731: PetscFunctionBegin;
5734: PetscAssertPointer(flg, 4);
5735: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5736: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5737: *flg = PETSC_FALSE;
5738: if (f && g) {
5739: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5740: PetscCall((*f)(A, B, tol, flg));
5741: } else {
5742: MatType mattype;
5744: PetscCall(MatGetType(f ? B : A, &mattype));
5745: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5746: }
5747: PetscFunctionReturn(PETSC_SUCCESS);
5748: }
5750: /*@
5751: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5753: Collective
5755: Input Parameters:
5756: + mat - the matrix to transpose and complex conjugate
5757: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5759: Output Parameter:
5760: . B - the Hermitian transpose
5762: Level: intermediate
5764: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5765: @*/
5766: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5767: {
5768: PetscFunctionBegin;
5769: PetscCall(MatTranspose_Private(mat, reuse, B, PetscDefined(USE_COMPLEX) ? PETSC_TRUE : PETSC_FALSE));
5770: PetscFunctionReturn(PETSC_SUCCESS);
5771: }
5773: /*@
5774: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5776: Collective
5778: Input Parameters:
5779: + A - the matrix to test
5780: . B - the matrix to test against, this can equal the first parameter
5781: - tol - tolerance, differences between entries smaller than this are counted as zero
5783: Output Parameter:
5784: . flg - the result
5786: Level: intermediate
5788: Notes:
5789: Only available for `MATAIJ` matrices.
5791: The sequential algorithm
5792: has a running time of the order of the number of nonzeros; the parallel
5793: test involves parallel copies of the block off-diagonal parts of the matrix.
5795: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5796: @*/
5797: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5798: {
5799: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5801: PetscFunctionBegin;
5804: PetscAssertPointer(flg, 4);
5805: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5806: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5807: if (f && g) {
5808: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5809: PetscCall((*f)(A, B, tol, flg));
5810: } else {
5811: MatType mattype;
5813: PetscCall(MatGetType(f ? B : A, &mattype));
5814: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for Hermitian transpose", mattype);
5815: }
5816: PetscFunctionReturn(PETSC_SUCCESS);
5817: }
5819: /*@
5820: MatPermute - Creates a new matrix with rows and columns permuted from the
5821: original.
5823: Collective
5825: Input Parameters:
5826: + mat - the matrix to permute
5827: . row - row permutation, each process supplies only the permutation for its rows
5828: - col - column permutation, each process supplies only the permutation for its columns
5830: Output Parameter:
5831: . B - the permuted matrix
5833: Level: advanced
5835: Note:
5836: The index sets map from `row`/`col` of permuted matrix to `row`/`col` of original matrix.
5837: The index sets should be on the same communicator as mat and have the same local sizes.
5838: `MATSEQSBAIJ` inputs may produce a `MATSEQBAIJ` matrix when the permutation does not preserve symmetry.
5840: Developer Note:
5841: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5842: exploit the fact that `row` and `col` are permutations, consider implementing the
5843: more general `MatCreateSubMatrix()` instead.
5845: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5846: @*/
5847: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5848: {
5849: PetscFunctionBegin;
5854: PetscAssertPointer(B, 4);
5855: PetscCheckSameComm(mat, 1, row, 2);
5856: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5857: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5858: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5859: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5860: MatCheckPreallocated(mat, 1);
5862: if (mat->ops->permute) {
5863: PetscUseTypeMethod(mat, permute, row, col, B);
5864: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5865: } else {
5866: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5867: }
5868: PetscFunctionReturn(PETSC_SUCCESS);
5869: }
5871: /*@
5872: MatEqual - Compares two matrices.
5874: Collective
5876: Input Parameters:
5877: + A - the first matrix
5878: - B - the second matrix
5880: Output Parameter:
5881: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5883: Level: intermediate
5885: Note:
5886: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5887: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5889: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5890: @*/
5891: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5892: {
5893: PetscFunctionBegin;
5898: PetscAssertPointer(flg, 3);
5899: PetscCheckSameComm(A, 1, B, 2);
5900: MatCheckPreallocated(A, 1);
5901: MatCheckPreallocated(B, 2);
5902: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5903: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5904: 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,
5905: B->cmap->N);
5906: if (A->ops->equal && A->ops->equal == B->ops->equal) PetscUseTypeMethod(A, equal, B, flg);
5907: else PetscCall(MatMultEqual(A, B, 10, flg));
5908: PetscFunctionReturn(PETSC_SUCCESS);
5909: }
5911: /*@
5912: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5913: matrices that are stored as vectors. Either of the two scaling
5914: matrices can be `NULL`.
5916: Collective
5918: Input Parameters:
5919: + mat - the matrix to be scaled
5920: . l - the left scaling vector (or `NULL`)
5921: - r - the right scaling vector (or `NULL`)
5923: Level: intermediate
5925: Note:
5926: `MatDiagonalScale()` computes $A = LAR$, where
5927: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5928: The L scales the rows of the matrix, the R scales the columns of the matrix.
5929: For `MATSEQSBAIJ`, if `l` and `r` are different `Vec` objects, `mat` changes to type `MATSEQBAIJ` because the result is not necessarily symmetric.
5931: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5932: @*/
5933: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5934: {
5935: PetscBool flg = PETSC_FALSE;
5937: PetscFunctionBegin;
5940: if (l) {
5942: PetscCheckSameComm(mat, 1, l, 2);
5943: }
5944: if (r) {
5946: PetscCheckSameComm(mat, 1, r, 3);
5947: }
5948: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5949: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5950: MatCheckPreallocated(mat, 1);
5951: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5953: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5954: PetscUseTypeMethod(mat, diagonalscale, l, r);
5955: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5956: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5957: if (l != r && (PetscBool3ToBool(mat->symmetric) || PetscBool3ToBool(mat->hermitian))) {
5958: if (!PetscDefined(USE_COMPLEX) || PetscBool3ToBool(mat->symmetric)) {
5959: if (l && r) PetscCall(VecEqual(l, r, &flg));
5960: if (!flg) {
5961: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &flg));
5962: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For MATMPISBAIJ, left and right scaling vectors must be the same");
5963: mat->symmetric = mat->spd = PETSC_BOOL3_FALSE;
5964: if (!PetscDefined(USE_COMPLEX)) mat->hermitian = PETSC_BOOL3_FALSE;
5965: else mat->hermitian = PETSC_BOOL3_UNKNOWN;
5966: }
5967: }
5968: if (PetscDefined(USE_COMPLEX) && PetscBool3ToBool(mat->hermitian)) {
5969: flg = PETSC_FALSE;
5970: if (l && r) {
5971: Vec conjugate;
5973: PetscCall(VecDuplicate(l, &conjugate));
5974: PetscCall(VecCopy(l, conjugate));
5975: PetscCall(VecConjugate(conjugate));
5976: PetscCall(VecEqual(conjugate, r, &flg));
5977: PetscCall(VecDestroy(&conjugate));
5978: }
5979: if (!flg) {
5980: PetscCall(PetscObjectTypeCompare((PetscObject)mat, MATMPISBAIJ, &flg));
5981: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For Hermitian MATMPISBAIJ, left and right scaling vectors must be conjugate one of the other");
5982: mat->hermitian = PETSC_BOOL3_FALSE;
5983: mat->symmetric = mat->spd = PETSC_BOOL3_UNKNOWN;
5984: }
5985: }
5986: }
5987: PetscFunctionReturn(PETSC_SUCCESS);
5988: }
5990: /*@
5991: MatScale - Scales all elements of a matrix by a given number.
5993: Logically Collective
5995: Input Parameters:
5996: + mat - the matrix to be scaled
5997: - a - the scaling value
5999: Level: intermediate
6001: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
6002: @*/
6003: PetscErrorCode MatScale(Mat mat, PetscScalar a)
6004: {
6005: PetscFunctionBegin;
6008: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6009: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6011: MatCheckPreallocated(mat, 1);
6013: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
6014: if (a != (PetscScalar)1.0) {
6015: PetscUseTypeMethod(mat, scale, a);
6016: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6017: }
6018: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
6019: PetscFunctionReturn(PETSC_SUCCESS);
6020: }
6022: /*@
6023: MatNorm - Calculates various norms of a matrix.
6025: Collective
6027: Input Parameters:
6028: + mat - the matrix
6029: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
6031: Output Parameter:
6032: . nrm - the resulting norm
6034: Level: intermediate
6036: .seealso: [](ch_matrices), `Mat`, `MatNormApproximate()`
6037: @*/
6038: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
6039: {
6040: PetscFunctionBegin;
6044: PetscAssertPointer(nrm, 3);
6046: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6047: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6048: MatCheckPreallocated(mat, 1);
6050: PetscUseTypeMethod(mat, norm, type, nrm);
6051: PetscFunctionReturn(PETSC_SUCCESS);
6052: }
6054: static PetscErrorCode VecSetFinalNormApp_Private(Vec x)
6055: {
6056: PetscScalar *ax, nm1;
6057: PetscInt st, en, n;
6059: PetscFunctionBegin;
6060: PetscCall(VecGetSize(x, &n));
6061: if (n < 2) PetscFunctionReturn(PETSC_SUCCESS);
6062: nm1 = n - 1;
6063: PetscCall(VecGetOwnershipRange(x, &st, &en));
6064: PetscCall(VecGetArrayWrite(x, &ax));
6065: for (PetscInt i = st; i < en; i++) {
6066: const PetscInt ii = i - st;
6067: const PetscScalar s = i % 2 ? -1.0 : 1.0;
6069: ax[ii] = s * (1.0 + i / nm1);
6070: }
6071: PetscCall(VecRestoreArrayWrite(x, &ax));
6072: PetscFunctionReturn(PETSC_SUCCESS);
6073: }
6075: static PetscErrorCode MatNormApproximateForwardOnly_Private(Mat A, NormType normtype, PetscInt maxit, PetscBool boundtocpu, PetscReal *n)
6076: {
6077: Vec x, y;
6078: PetscReal normx, normy;
6079: PetscInt i, N;
6080: PetscRandom rnd;
6082: PetscFunctionBegin;
6083: if (maxit < 0) maxit = 1;
6084: PetscCall(PetscRandomCreate(PetscObjectComm((PetscObject)A), &rnd));
6085: PetscCall(PetscRandomSetFromOptions(rnd));
6086: PetscCall(MatCreateVecs(A, &x, &y));
6087: PetscCall(VecBindToCPU(x, boundtocpu));
6088: PetscCall(VecBindToCPU(y, boundtocpu));
6089: PetscCall(VecGetSize(x, &N));
6090: *n = 0.0;
6091: for (i = 0; i < maxit; i++) {
6092: PetscCall(VecSetRandom(x, rnd));
6093: switch (normtype) {
6094: case NORM_1:
6095: PetscCall(VecNorm(x, NORM_1, &normx));
6096: if (normx > 0.0) PetscCall(VecScale(x, 1.0 / normx));
6097: break;
6098: case NORM_INFINITY:
6099: PetscCall(VecShift(x, -0.5));
6100: PetscCall(VecPointwiseSign(x, x, VEC_SIGN_ZERO_TO_SIGNED_UNIT));
6101: break;
6102: case NORM_2:
6103: PetscCall(VecNormalize(x, NULL));
6104: break;
6105: default:
6106: PetscUnreachable();
6107: }
6108: PetscCall(MatMult(A, x, y));
6109: PetscCall(VecNorm(y, normtype, &normy));
6110: *n = PetscMax(*n, normy);
6111: PetscCall(PetscInfo(A, "%s norm forward-only sample %" PetscInt_FMT " -> %g\n", NormTypes[normtype], i, (double)normy));
6112: }
6113: PetscCall(VecDestroy(&x));
6114: PetscCall(VecDestroy(&y));
6115: PetscCall(PetscRandomDestroy(&rnd));
6116: PetscFunctionReturn(PETSC_SUCCESS);
6117: }
6119: /*@
6120: MatNormApproximate - Approximate the norm of a matrix.
6122: Collective
6124: Input Parameters:
6125: + A - the matrix
6126: . normtype - the `NormType`
6127: - maxit - maximum number of iterations to use
6129: Output Parameter:
6130: . n - the norm estimate
6132: Level: intermediate
6134: Notes:
6135: 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`.
6137: If `maxit` is negative, a default number of iterations (10 for `NORM_1` and `NORM_INFINITY` and 20 for `NORM_2`) is performed.
6139: .seealso: [](ch_matrices), `Mat`, `MatNorm()`
6140: @*/
6141: PetscErrorCode MatNormApproximate(Mat A, NormType normtype, PetscInt maxit, PetscReal *n)
6142: {
6143: Vec x, y, w, z;
6144: PetscReal normz, adot;
6145: PetscScalar dot;
6146: PetscInt i, j, N, jold = -1;
6147: PetscBool boundtocpu = PETSC_TRUE, setherm, isherm, hasop;
6149: PetscFunctionBegin;
6154: PetscAssertPointer(n, 4);
6155: #if PetscDefined(HAVE_DEVICE)
6156: boundtocpu = A->boundtocpu;
6157: #endif
6158: PetscCall(MatHasOperation(A, MATOP_MULT_HERMITIAN_TRANSPOSE, &hasop));
6159: switch (normtype) {
6160: case NORM_INFINITY:
6161: case NORM_1:
6162: if (!hasop) {
6163: PetscCall(MatNormApproximateForwardOnly_Private(A, normtype, maxit, boundtocpu, n));
6164: i = maxit;
6165: break;
6166: } else {
6167: PetscCall(MatIsHermitianKnown(A, &setherm, &isherm));
6168: if ((setherm && isherm) || normtype == NORM_1) PetscCall(PetscObjectReference((PetscObject)A));
6169: else {
6170: Mat B;
6172: PetscCall(MatCreateHermitianTranspose(A, &B));
6173: A = B;
6174: }
6175: }
6176: if (maxit < 0) maxit = 10; /* pure guess */
6177: PetscCall(MatCreateVecs(A, &x, &y));
6178: PetscCall(MatCreateVecs(A, &z, &w));
6179: PetscCall(VecBindToCPU(x, boundtocpu));
6180: PetscCall(VecBindToCPU(y, boundtocpu));
6181: PetscCall(VecBindToCPU(z, boundtocpu));
6182: PetscCall(VecBindToCPU(w, boundtocpu));
6183: PetscCall(VecGetSize(x, &N));
6184: PetscCall(VecSet(x, 1. / N));
6185: *n = 0.0;
6186: for (i = 0; i < maxit; i++) {
6187: PetscCall(MatMult(A, x, y));
6188: PetscCall(VecNorm(y, NORM_1, n));
6189: if (PetscDefined(USE_COMPLEX)) {
6190: PetscCall(VecCopy(y, w));
6191: PetscCall(VecAbs(w));
6192: PetscCall(VecPointwiseDivide(w, y, w));
6193: } else PetscCall(VecPointwiseSign(w, y, VEC_SIGN_ZERO_TO_SIGNED_UNIT));
6194: PetscCall(MatMultHermitianTranspose(A, w, z));
6195: PetscCall(VecRealPart(z));
6196: PetscCall(VecNorm(z, NORM_INFINITY, &normz));
6197: PetscCall(VecDot(x, z, &dot));
6198: adot = PetscAbsScalar(dot);
6199: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> %g (%g %g)\n", NormTypes[normtype], i, (double)*n, (double)normz, (double)adot));
6200: if (normz <= adot && i > 0) {
6201: PetscCall(PetscInfo(A, "%s norm converged\n", NormTypes[normtype]));
6202: break;
6203: }
6204: PetscCall(VecAbs(z));
6205: PetscCall(VecMax(z, &j, &normz));
6206: if (j == jold) {
6207: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> breakdown (j==jold)\n", NormTypes[normtype], i));
6208: break;
6209: }
6210: jold = j;
6211: if (i < maxit - 1) PetscCall(VecSetStdBasis(x, j));
6212: }
6213: /* last check */
6214: if (N > 1) {
6215: PetscReal ny;
6217: PetscCall(VecSetFinalNormApp_Private(x));
6218: PetscCall(MatMult(A, x, y));
6219: PetscCall(VecNorm(y, NORM_1, &ny));
6220: ny = 2 * ny / (3 * N);
6221: PetscCall(PetscInfo(A, "%s norm final check: current %g test %g\n", NormTypes[normtype], (double)*n, (double)ny));
6222: *n = PetscMax(*n, ny);
6223: }
6224: PetscCall(MatDestroy(&A));
6225: PetscCall(VecDestroy(&x));
6226: PetscCall(VecDestroy(&w));
6227: PetscCall(VecDestroy(&y));
6228: PetscCall(VecDestroy(&z));
6229: break;
6230: case NORM_2:
6231: if (!hasop) {
6232: PetscCall(MatNormApproximateForwardOnly_Private(A, normtype, maxit, boundtocpu, n));
6233: i = maxit;
6234: break;
6235: }
6236: if (maxit < 0) maxit = 20; /* pure guess */
6237: PetscCall(MatCreateVecs(A, &x, &y));
6238: PetscCall(MatCreateVecs(A, &z, NULL));
6239: PetscCall(VecBindToCPU(x, boundtocpu));
6240: PetscCall(VecBindToCPU(y, boundtocpu));
6241: PetscCall(VecBindToCPU(z, boundtocpu));
6242: PetscCall(VecSetRandom(x, NULL));
6243: PetscCall(VecNormalize(x, NULL));
6244: *n = 0.0;
6245: for (i = 0; i < maxit; i++) {
6246: PetscCall(MatMult(A, x, y));
6247: PetscCall(VecNormalize(y, n));
6248: PetscCall(MatMultHermitianTranspose(A, y, z));
6249: PetscCall(VecNorm(z, NORM_2, &normz));
6250: PetscCall(VecDot(x, z, &dot));
6251: adot = PetscAbsScalar(dot);
6252: PetscCall(PetscInfo(A, "%s norm it %" PetscInt_FMT " -> %g (%g %g)\n", NormTypes[normtype], i, (double)*n, (double)normz, (double)adot));
6253: if (normz <= adot) {
6254: PetscCall(PetscInfo(A, "%s norm converged\n", NormTypes[normtype]));
6255: break;
6256: }
6257: if (i < maxit - 1) {
6258: Vec t;
6260: PetscCall(VecNormalize(z, NULL));
6261: t = x;
6262: x = z;
6263: z = t;
6264: }
6265: }
6266: PetscCall(VecDestroy(&x));
6267: PetscCall(VecDestroy(&y));
6268: PetscCall(VecDestroy(&z));
6269: break;
6270: default:
6271: SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "%s norm not supported", NormTypes[normtype]);
6272: }
6273: PetscCall(PetscInfo(A, "%s norm %g computed in %" PetscInt_FMT " iterations\n", NormTypes[normtype], (double)*n, i));
6274: PetscFunctionReturn(PETSC_SUCCESS);
6275: }
6277: /*
6278: This variable is used to prevent counting of MatAssemblyBegin() that
6279: are called from within a MatAssemblyEnd().
6280: */
6281: static PetscInt MatAssemblyEnd_InUse = 0;
6282: /*@
6283: MatAssemblyBegin - Begins assembling the matrix. This routine should
6284: be called after completing all calls to `MatSetValues()`.
6286: Collective
6288: Input Parameters:
6289: + mat - the matrix
6290: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
6292: Level: beginner
6294: Notes:
6295: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
6296: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
6298: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
6299: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
6300: using the matrix.
6302: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
6303: 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
6304: a global collective operation requiring all processes that share the matrix.
6306: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
6307: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
6308: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
6310: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
6311: @*/
6312: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
6313: {
6314: PetscFunctionBegin;
6317: MatCheckPreallocated(mat, 1);
6318: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
6319: if (mat->assembled) {
6320: mat->was_assembled = PETSC_TRUE;
6321: mat->assembled = PETSC_FALSE;
6322: }
6324: if (!MatAssemblyEnd_InUse) {
6325: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
6326: PetscTryTypeMethod(mat, assemblybegin, type);
6327: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
6328: } else PetscTryTypeMethod(mat, assemblybegin, type);
6329: PetscFunctionReturn(PETSC_SUCCESS);
6330: }
6332: /*@
6333: MatAssembled - Indicates if a matrix has been assembled and is ready for
6334: use; for example, in matrix-vector product.
6336: Not Collective
6338: Input Parameter:
6339: . mat - the matrix
6341: Output Parameter:
6342: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
6344: Level: advanced
6346: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
6347: @*/
6348: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
6349: {
6350: PetscFunctionBegin;
6352: PetscAssertPointer(assembled, 2);
6353: *assembled = mat->assembled;
6354: PetscFunctionReturn(PETSC_SUCCESS);
6355: }
6357: /*@
6358: MatAssemblyEnd - Completes assembling the matrix. This routine should
6359: be called after `MatAssemblyBegin()`.
6361: Collective
6363: Input Parameters:
6364: + mat - the matrix
6365: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
6367: Options Database Key:
6368: . -mat_view viewer_specification - Displays the matrix during this function call. See `PetscOptionsCreateViewer()` for the values of `viewer_specification`
6370: Level: beginner
6372: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`,
6373: `MatViewFromOptions()`, `PetscObjectViewFromOptions()`, `PetscOptionsCreateViewer()`
6374: @*/
6375: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
6376: {
6377: static PetscInt inassm = 0;
6378: PetscBool flg = PETSC_FALSE;
6380: PetscFunctionBegin;
6384: inassm++;
6385: MatAssemblyEnd_InUse++;
6386: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
6387: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
6388: PetscTryTypeMethod(mat, assemblyend, type);
6389: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
6390: } else PetscTryTypeMethod(mat, assemblyend, type);
6392: /* Flush assembly is not a true assembly */
6393: if (type != MAT_FLUSH_ASSEMBLY) {
6394: if (mat->num_ass) {
6395: if (!mat->symmetry_eternal) {
6396: mat->symmetric = PETSC_BOOL3_UNKNOWN;
6397: mat->hermitian = PETSC_BOOL3_UNKNOWN;
6398: }
6399: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
6400: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
6401: }
6402: mat->num_ass++;
6403: mat->assembled = PETSC_TRUE;
6404: mat->ass_nonzerostate = mat->nonzerostate;
6405: }
6407: mat->insertmode = NOT_SET_VALUES;
6408: MatAssemblyEnd_InUse--;
6409: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6410: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
6411: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6413: if (mat->checksymmetryonassembly) {
6414: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
6415: if (flg) {
6416: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6417: } else {
6418: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6419: }
6420: }
6421: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
6422: }
6423: inassm--;
6424: PetscFunctionReturn(PETSC_SUCCESS);
6425: }
6427: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
6428: /*@
6429: MatSetOption - Sets a parameter option for a matrix. Some options
6430: may be specific to certain storage formats. Some options
6431: determine how values will be inserted (or added). Sorted,
6432: row-oriented input will generally assemble the fastest. The default
6433: is row-oriented.
6435: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
6437: Input Parameters:
6438: + mat - the matrix
6439: . op - the option, one of those listed below (and possibly others),
6440: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6442: Options Describing Matrix Structure:
6443: + `MAT_SPD` - symmetric positive definite
6444: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
6445: . `MAT_HERMITIAN` - transpose is the complex conjugation
6446: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
6447: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
6448: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
6449: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
6451: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
6452: do not need to be computed (usually at a high cost)
6454: Options For Use with `MatSetValues()` and `MatGetValues()`:
6455: Insert a logically dense subblock, which can be
6456: . `MAT_ROW_ORIENTED` - row-oriented (default)
6458: These options reflect the data you pass in with `MatSetValues()` or receive with `MatGetValues()`; it has
6459: nothing to do with how the data is stored internally in the matrix
6460: data structure.
6462: When (re)assembling a matrix, we can restrict the input for
6463: efficiency/debugging purposes. These options include
6464: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
6465: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
6466: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-process entries
6467: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
6468: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
6469: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
6470: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
6471: performance for very large process counts.
6472: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
6473: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
6474: functions, instead sending only neighbor messages.
6476: Level: intermediate
6478: Notes:
6479: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
6481: Some options are relevant only for particular matrix types and
6482: are thus ignored by others. Other options are not supported by
6483: certain matrix types and will generate an error message if set.
6485: If using Fortran to compute a matrix, one may need to
6486: use the column-oriented option (or convert to the row-oriented
6487: format).
6489: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6490: that would generate a new entry in the nonzero structure is instead
6491: ignored. Thus, if memory has not already been allocated for this particular
6492: data, then the insertion is ignored. For dense matrices, in which
6493: the entire array is allocated, no entries are ever ignored.
6494: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6496: `MAT_NEW_NONZERO_LOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6497: that would generate a new entry in the nonzero structure instead produces
6498: 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
6500: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6501: that would generate a new entry that has not been preallocated will
6502: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6503: only.) This is a useful flag when debugging matrix memory preallocation.
6504: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6506: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6507: other processes should be dropped, rather than stashed.
6508: This is useful if you know that the "owning" process is also
6509: always generating the correct matrix entries, so that PETSc need
6510: not transfer duplicate entries generated on another process.
6512: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6513: searches during matrix assembly. When this flag is set, the hash table
6514: is created during the first matrix assembly. This hash table is
6515: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6516: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6517: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6518: supported by `MATMPIBAIJ` format only.
6520: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6521: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6523: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6524: a zero location in the matrix
6526: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6528: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6529: zero row routines and thus improves performance for very large process counts.
6531: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6532: part of the matrix (since they should match the upper triangular part).
6534: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6535: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6536: with finite difference schemes with non-periodic boundary conditions.
6538: Developer Note:
6539: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6540: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6541: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6542: not changed.
6544: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6545: @*/
6546: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6547: {
6548: PetscFunctionBegin;
6550: if (op > 0) {
6553: }
6555: 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);
6557: switch (op) {
6558: case MAT_FORCE_DIAGONAL_ENTRIES:
6559: mat->force_diagonals = flg;
6560: PetscFunctionReturn(PETSC_SUCCESS);
6561: case MAT_NO_OFF_PROC_ENTRIES:
6562: mat->nooffprocentries = flg;
6563: PetscFunctionReturn(PETSC_SUCCESS);
6564: case MAT_SUBSET_OFF_PROC_ENTRIES:
6565: mat->assembly_subset = flg;
6566: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6567: #if !PetscDefined(HAVE_MPIUNI)
6568: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6569: #endif
6570: mat->stash.first_assembly_done = PETSC_FALSE;
6571: }
6572: PetscFunctionReturn(PETSC_SUCCESS);
6573: case MAT_NO_OFF_PROC_ZERO_ROWS:
6574: mat->nooffproczerorows = flg;
6575: PetscFunctionReturn(PETSC_SUCCESS);
6576: case MAT_SPD:
6577: if (flg) {
6578: mat->spd = PETSC_BOOL3_TRUE;
6579: mat->symmetric = PETSC_BOOL3_TRUE;
6580: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6581: #if !PetscDefined(USE_COMPLEX)
6582: mat->hermitian = PETSC_BOOL3_TRUE;
6583: #endif
6584: } else {
6585: mat->spd = PETSC_BOOL3_FALSE;
6586: }
6587: break;
6588: case MAT_SYMMETRIC:
6589: mat->symmetric = PetscBoolToBool3(flg);
6590: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6591: #if !PetscDefined(USE_COMPLEX)
6592: mat->hermitian = PetscBoolToBool3(flg);
6593: #endif
6594: break;
6595: case MAT_HERMITIAN:
6596: mat->hermitian = PetscBoolToBool3(flg);
6597: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6598: #if !PetscDefined(USE_COMPLEX)
6599: mat->symmetric = PetscBoolToBool3(flg);
6600: #endif
6601: break;
6602: case MAT_STRUCTURALLY_SYMMETRIC:
6603: mat->structurally_symmetric = PetscBoolToBool3(flg);
6604: break;
6605: case MAT_SYMMETRY_ETERNAL:
6606: 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");
6607: mat->symmetry_eternal = flg;
6608: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6609: break;
6610: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6611: 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");
6612: mat->structural_symmetry_eternal = flg;
6613: break;
6614: case MAT_SPD_ETERNAL:
6615: 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");
6616: mat->spd_eternal = flg;
6617: if (flg) {
6618: mat->structural_symmetry_eternal = PETSC_TRUE;
6619: mat->symmetry_eternal = PETSC_TRUE;
6620: }
6621: break;
6622: case MAT_STRUCTURE_ONLY:
6623: mat->structure_only = flg;
6624: break;
6625: case MAT_SORTED_FULL:
6626: mat->sortedfull = flg;
6627: break;
6628: default:
6629: break;
6630: }
6631: 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");
6632: PetscTryTypeMethod(mat, setoption, op, flg);
6633: PetscFunctionReturn(PETSC_SUCCESS);
6634: }
6636: /*@
6637: MatGetOption - Gets a parameter option that has been set for a matrix.
6639: Logically Collective
6641: Input Parameters:
6642: + mat - the matrix
6643: - op - the option, this only responds to certain options, check the code for which ones
6645: Output Parameter:
6646: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6648: Level: intermediate
6650: Notes:
6651: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6653: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6654: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6656: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6657: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6658: @*/
6659: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6660: {
6661: PetscFunctionBegin;
6665: 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);
6666: 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()");
6668: switch (op) {
6669: case MAT_NO_OFF_PROC_ENTRIES:
6670: *flg = mat->nooffprocentries;
6671: break;
6672: case MAT_NO_OFF_PROC_ZERO_ROWS:
6673: *flg = mat->nooffproczerorows;
6674: break;
6675: case MAT_SYMMETRIC:
6676: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6677: break;
6678: case MAT_HERMITIAN:
6679: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6680: break;
6681: case MAT_STRUCTURALLY_SYMMETRIC:
6682: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6683: break;
6684: case MAT_SPD:
6685: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6686: break;
6687: case MAT_SYMMETRY_ETERNAL:
6688: *flg = mat->symmetry_eternal;
6689: break;
6690: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6691: *flg = mat->symmetry_eternal;
6692: break;
6693: default:
6694: break;
6695: }
6696: PetscFunctionReturn(PETSC_SUCCESS);
6697: }
6699: /*@
6700: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6701: this routine retains the old nonzero structure.
6703: Logically Collective
6705: Input Parameter:
6706: . mat - the matrix
6708: Level: intermediate
6710: Note:
6711: 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.
6712: See the Performance chapter of the users manual for information on preallocating matrices.
6714: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6715: @*/
6716: PetscErrorCode MatZeroEntries(Mat mat)
6717: {
6718: PetscFunctionBegin;
6721: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6722: 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");
6723: MatCheckPreallocated(mat, 1);
6725: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6726: PetscUseTypeMethod(mat, zeroentries);
6727: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6728: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6729: PetscFunctionReturn(PETSC_SUCCESS);
6730: }
6732: /*@
6733: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6734: of a set of rows and columns of a matrix.
6736: Collective
6738: Input Parameters:
6739: + mat - the matrix
6740: . numRows - the number of rows/columns to zero
6741: . rows - the global row indices
6742: . diag - value put in the diagonal of the eliminated rows
6743: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6744: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6746: Level: intermediate
6748: Notes:
6749: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6751: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6752: 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
6754: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6755: Krylov method to take advantage of the known solution on the zeroed rows.
6757: For the parallel case, all processes that share the matrix (i.e.,
6758: those in the communicator used for matrix creation) MUST call this
6759: routine, regardless of whether any rows being zeroed are owned by
6760: them.
6762: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6763: 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
6764: missing.
6766: Each process can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6767: list only rows local to itself).
6769: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6771: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6772: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6773: @*/
6774: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6775: {
6776: PetscFunctionBegin;
6779: if (numRows) PetscAssertPointer(rows, 3);
6780: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6781: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6782: MatCheckPreallocated(mat, 1);
6784: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6785: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6786: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6787: PetscFunctionReturn(PETSC_SUCCESS);
6788: }
6790: /*@
6791: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6792: of a set of rows and columns of a matrix.
6794: Collective
6796: Input Parameters:
6797: + mat - the matrix
6798: . is - the rows to zero
6799: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6800: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6801: - b - optional vector of right-hand side, that will be adjusted by provided solution
6803: Level: intermediate
6805: Note:
6806: See `MatZeroRowsColumns()` for details on how this routine operates.
6808: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6809: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6810: @*/
6811: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6812: {
6813: PetscInt numRows;
6814: const PetscInt *rows;
6816: PetscFunctionBegin;
6821: PetscCall(ISGetLocalSize(is, &numRows));
6822: PetscCall(ISGetIndices(is, &rows));
6823: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6824: PetscCall(ISRestoreIndices(is, &rows));
6825: PetscFunctionReturn(PETSC_SUCCESS);
6826: }
6828: /*@
6829: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6830: of a set of rows of a matrix.
6832: Collective
6834: Input Parameters:
6835: + mat - the matrix
6836: . numRows - the number of rows to zero
6837: . rows - the global row indices
6838: . diag - value put in the diagonal of the zeroed rows
6839: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6840: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6842: Level: intermediate
6844: Notes:
6845: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6847: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6849: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6850: Krylov method to take advantage of the known solution on the zeroed rows.
6852: 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)
6853: from the matrix.
6855: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6856: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6857: formats this does not alter the nonzero structure.
6859: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6860: of the matrix is not changed the values are
6861: merely zeroed.
6863: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6864: formats can optionally remove the main diagonal entry from the
6865: nonzero structure as well, by passing 0.0 as the final argument).
6867: For the parallel case, all processes that share the matrix (i.e.,
6868: those in the communicator used for matrix creation) MUST call this
6869: routine, regardless of whether any rows being zeroed are owned by
6870: them.
6872: Each process can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6873: list only rows local to itself).
6875: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6876: owns that are to be zeroed. This saves a global synchronization in the implementation.
6878: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6879: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6880: @*/
6881: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6882: {
6883: PetscFunctionBegin;
6886: if (numRows) PetscAssertPointer(rows, 3);
6887: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6888: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6889: MatCheckPreallocated(mat, 1);
6891: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6892: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6893: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6894: PetscFunctionReturn(PETSC_SUCCESS);
6895: }
6897: /*@
6898: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6899: of a set of rows of a matrix indicated by an `IS`
6901: Collective
6903: Input Parameters:
6904: + mat - the matrix
6905: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6906: . diag - value put in all diagonals of eliminated rows
6907: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6908: - b - optional vector of right-hand side, that will be adjusted by provided solution
6910: Level: intermediate
6912: Note:
6913: See `MatZeroRows()` for details on how this routine operates.
6915: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6916: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6917: @*/
6918: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6919: {
6920: PetscInt numRows = 0;
6921: const PetscInt *rows = NULL;
6923: PetscFunctionBegin;
6926: if (is) {
6928: PetscCall(ISGetLocalSize(is, &numRows));
6929: PetscCall(ISGetIndices(is, &rows));
6930: }
6931: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6932: if (is) PetscCall(ISRestoreIndices(is, &rows));
6933: PetscFunctionReturn(PETSC_SUCCESS);
6934: }
6936: /*@
6937: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6938: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6940: Collective
6942: Input Parameters:
6943: + mat - the matrix
6944: . numRows - the number of rows to remove
6945: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6946: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6947: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6948: - b - optional vector of right-hand side, that will be adjusted by provided solution
6950: Level: intermediate
6952: Notes:
6953: See `MatZeroRows()` for details on how this routine operates.
6955: The grid coordinates are across the entire grid, not just the local portion
6957: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6958: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6959: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6960: `DM_BOUNDARY_PERIODIC` boundary type.
6962: 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
6963: a single value per point) you can skip filling those indices.
6965: Fortran Note:
6966: `idxm` and `idxn` should be declared as
6967: .vb
6968: MatStencil idxm(4, m)
6969: .ve
6970: and the values inserted using
6971: .vb
6972: idxm(MatStencil_i, 1) = i
6973: idxm(MatStencil_j, 1) = j
6974: idxm(MatStencil_k, 1) = k
6975: idxm(MatStencil_c, 1) = c
6976: etc
6977: .ve
6979: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6980: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6981: @*/
6982: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6983: {
6984: PetscInt dim = mat->stencil.dim;
6985: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6986: PetscInt *dims = mat->stencil.dims + 1;
6987: PetscInt *starts = mat->stencil.starts;
6988: PetscInt *dxm = (PetscInt *)rows;
6989: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6991: PetscFunctionBegin;
6994: if (numRows) PetscAssertPointer(rows, 3);
6996: PetscCall(PetscMalloc1(numRows, &jdxm));
6997: for (i = 0; i < numRows; ++i) {
6998: /* Skip unused dimensions (they are ordered k, j, i, c) */
6999: for (j = 0; j < 3 - sdim; ++j) dxm++;
7000: /* Local index in X dir */
7001: tmp = *dxm++ - starts[0];
7002: /* Loop over remaining dimensions */
7003: for (j = 0; j < dim - 1; ++j) {
7004: /* If nonlocal, set index to be negative */
7005: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
7006: /* Update local index */
7007: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
7008: }
7009: /* Skip component slot if necessary */
7010: if (mat->stencil.noc) dxm++;
7011: /* Local row number */
7012: if (tmp >= 0) jdxm[numNewRows++] = tmp;
7013: }
7014: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
7015: PetscCall(PetscFree(jdxm));
7016: PetscFunctionReturn(PETSC_SUCCESS);
7017: }
7019: /*@
7020: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
7021: of a set of rows and columns of a matrix.
7023: Collective
7025: Input Parameters:
7026: + mat - the matrix
7027: . numRows - the number of rows/columns to remove
7028: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
7029: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
7030: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7031: - b - optional vector of right-hand side, that will be adjusted by provided solution
7033: Level: intermediate
7035: Notes:
7036: See `MatZeroRowsColumns()` for details on how this routine operates.
7038: The grid coordinates are across the entire grid, not just the local portion
7040: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
7041: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
7042: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
7043: `DM_BOUNDARY_PERIODIC` boundary type.
7045: 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
7046: a single value per point) you can skip filling those indices.
7048: Fortran Note:
7049: `idxm` and `idxn` should be declared as
7050: .vb
7051: MatStencil idxm(4, m)
7052: .ve
7053: and the values inserted using
7054: .vb
7055: idxm(MatStencil_i, 1) = i
7056: idxm(MatStencil_j, 1) = j
7057: idxm(MatStencil_k, 1) = k
7058: idxm(MatStencil_c, 1) = c
7059: etc
7060: .ve
7062: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7063: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
7064: @*/
7065: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
7066: {
7067: PetscInt dim = mat->stencil.dim;
7068: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
7069: PetscInt *dims = mat->stencil.dims + 1;
7070: PetscInt *starts = mat->stencil.starts;
7071: PetscInt *dxm = (PetscInt *)rows;
7072: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
7074: PetscFunctionBegin;
7077: if (numRows) PetscAssertPointer(rows, 3);
7079: PetscCall(PetscMalloc1(numRows, &jdxm));
7080: for (i = 0; i < numRows; ++i) {
7081: /* Skip unused dimensions (they are ordered k, j, i, c) */
7082: for (j = 0; j < 3 - sdim; ++j) dxm++;
7083: /* Local index in X dir */
7084: tmp = *dxm++ - starts[0];
7085: /* Loop over remaining dimensions */
7086: for (j = 0; j < dim - 1; ++j) {
7087: /* If nonlocal, set index to be negative */
7088: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
7089: /* Update local index */
7090: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
7091: }
7092: /* Skip component slot if necessary */
7093: if (mat->stencil.noc) dxm++;
7094: /* Local row number */
7095: if (tmp >= 0) jdxm[numNewRows++] = tmp;
7096: }
7097: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
7098: PetscCall(PetscFree(jdxm));
7099: PetscFunctionReturn(PETSC_SUCCESS);
7100: }
7102: /*@
7103: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
7104: of a set of rows of a matrix; using local numbering of rows.
7106: Collective
7108: Input Parameters:
7109: + mat - the matrix
7110: . numRows - the number of rows to remove
7111: . rows - the local row indices
7112: . diag - value put in all diagonals of eliminated rows
7113: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7114: - b - optional vector of right-hand side, that will be adjusted by provided solution
7116: Level: intermediate
7118: Notes:
7119: Before calling `MatZeroRowsLocal()`, the user must first set the
7120: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
7122: See `MatZeroRows()` for details on how this routine operates.
7124: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
7125: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7126: @*/
7127: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
7128: {
7129: PetscFunctionBegin;
7132: if (numRows) PetscAssertPointer(rows, 3);
7133: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7134: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7135: MatCheckPreallocated(mat, 1);
7137: if (mat->ops->zerorowslocal) {
7138: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
7139: } else {
7140: IS is, newis;
7141: PetscInt *newRows, nl = 0;
7143: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
7144: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
7145: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
7146: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
7147: for (PetscInt i = 0; i < numRows; i++)
7148: if (newRows[i] > -1) newRows[nl++] = newRows[i];
7149: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
7150: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
7151: PetscCall(ISDestroy(&newis));
7152: PetscCall(ISDestroy(&is));
7153: }
7154: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
7155: PetscFunctionReturn(PETSC_SUCCESS);
7156: }
7158: /*@
7159: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
7160: of a set of rows of a matrix; using local numbering of rows.
7162: Collective
7164: Input Parameters:
7165: + mat - the matrix
7166: . is - index set of rows to remove
7167: . diag - value put in all diagonals of eliminated rows
7168: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7169: - b - optional vector of right-hand side, that will be adjusted by provided solution
7171: Level: intermediate
7173: Notes:
7174: Before calling `MatZeroRowsLocalIS()`, the user must first set the
7175: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7177: See `MatZeroRows()` for details on how this routine operates.
7179: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7180: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7181: @*/
7182: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
7183: {
7184: PetscInt numRows;
7185: const PetscInt *rows;
7187: PetscFunctionBegin;
7191: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7192: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7193: MatCheckPreallocated(mat, 1);
7195: PetscCall(ISGetLocalSize(is, &numRows));
7196: PetscCall(ISGetIndices(is, &rows));
7197: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
7198: PetscCall(ISRestoreIndices(is, &rows));
7199: PetscFunctionReturn(PETSC_SUCCESS);
7200: }
7202: /*@
7203: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
7204: of a set of rows and columns of a matrix; using local numbering of rows.
7206: Collective
7208: Input Parameters:
7209: + mat - the matrix
7210: . numRows - the number of rows to remove
7211: . rows - the global row indices
7212: . diag - value put in all diagonals of eliminated rows
7213: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7214: - b - optional vector of right-hand side, that will be adjusted by provided solution
7216: Level: intermediate
7218: Notes:
7219: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
7220: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7222: See `MatZeroRowsColumns()` for details on how this routine operates.
7224: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7225: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7226: @*/
7227: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
7228: {
7229: PetscFunctionBegin;
7232: if (numRows) PetscAssertPointer(rows, 3);
7233: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7234: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7235: MatCheckPreallocated(mat, 1);
7237: if (mat->ops->zerorowscolumnslocal) {
7238: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
7239: } else {
7240: IS is, newis;
7241: PetscInt *newRows, nl = 0;
7243: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
7244: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
7245: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
7246: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
7247: for (PetscInt i = 0; i < numRows; i++)
7248: if (newRows[i] > -1) newRows[nl++] = newRows[i];
7249: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
7250: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
7251: PetscCall(ISDestroy(&newis));
7252: PetscCall(ISDestroy(&is));
7253: }
7254: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
7255: PetscFunctionReturn(PETSC_SUCCESS);
7256: }
7258: /*@
7259: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
7260: of a set of rows and columns of a matrix; using local numbering of rows.
7262: Collective
7264: Input Parameters:
7265: + mat - the matrix
7266: . is - index set of rows to remove
7267: . diag - value put in all diagonals of eliminated rows
7268: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
7269: - b - optional vector of right-hand side, that will be adjusted by provided solution
7271: Level: intermediate
7273: Notes:
7274: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
7275: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
7277: See `MatZeroRowsColumns()` for details on how this routine operates.
7279: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
7280: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
7281: @*/
7282: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
7283: {
7284: PetscInt numRows;
7285: const PetscInt *rows;
7287: PetscFunctionBegin;
7291: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7292: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7293: MatCheckPreallocated(mat, 1);
7295: PetscCall(ISGetLocalSize(is, &numRows));
7296: PetscCall(ISGetIndices(is, &rows));
7297: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
7298: PetscCall(ISRestoreIndices(is, &rows));
7299: PetscFunctionReturn(PETSC_SUCCESS);
7300: }
7302: /*@
7303: MatGetSize - Returns the numbers of rows and columns in a matrix.
7305: Not Collective
7307: Input Parameter:
7308: . mat - the matrix
7310: Output Parameters:
7311: + m - the number of global rows
7312: - n - the number of global columns
7314: Level: beginner
7316: Note:
7317: Both output parameters can be `NULL` on input.
7319: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
7320: @*/
7321: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
7322: {
7323: PetscFunctionBegin;
7325: if (m) *m = mat->rmap->N;
7326: if (n) *n = mat->cmap->N;
7327: PetscFunctionReturn(PETSC_SUCCESS);
7328: }
7330: /*@
7331: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
7332: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
7334: Not Collective
7336: Input Parameter:
7337: . mat - the matrix
7339: Output Parameters:
7340: + m - the number of local rows, use `NULL` to not obtain this value
7341: - n - the number of local columns, use `NULL` to not obtain this value
7343: Level: beginner
7345: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
7346: @*/
7347: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
7348: {
7349: PetscFunctionBegin;
7351: if (m) PetscAssertPointer(m, 2);
7352: if (n) PetscAssertPointer(n, 3);
7353: if (m) *m = mat->rmap->n;
7354: if (n) *n = mat->cmap->n;
7355: PetscFunctionReturn(PETSC_SUCCESS);
7356: }
7358: /*@
7359: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
7360: vector one multiplies this matrix by that are owned by this process.
7362: Not Collective, unless matrix has not been allocated, then collective
7364: Input Parameter:
7365: . mat - the matrix
7367: Output Parameters:
7368: + m - the global index of the first local column, use `NULL` to not obtain this value
7369: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
7371: Level: developer
7373: Notes:
7374: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7376: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7377: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7379: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7380: the local values in the matrix.
7382: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
7383: Layouts](sec_matlayout) for details on matrix layouts.
7385: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7386: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7387: @*/
7388: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
7389: {
7390: PetscFunctionBegin;
7393: if (m) PetscAssertPointer(m, 2);
7394: if (n) PetscAssertPointer(n, 3);
7395: MatCheckPreallocated(mat, 1);
7396: if (m) *m = mat->cmap->rstart;
7397: if (n) *n = mat->cmap->rend;
7398: PetscFunctionReturn(PETSC_SUCCESS);
7399: }
7401: /*@
7402: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
7403: this MPI process.
7405: Not Collective
7407: Input Parameter:
7408: . mat - the matrix
7410: Output Parameters:
7411: + m - the global index of the first local row, use `NULL` to not obtain this value
7412: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
7414: Level: beginner
7416: Notes:
7417: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7419: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7420: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7422: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7423: the local values in the matrix.
7425: The high argument is one more than the last element stored locally.
7427: For all matrices it returns the range of matrix rows associated with rows of a vector that
7428: would contain the result of a matrix vector product with this matrix. See [Matrix
7429: Layouts](sec_matlayout) for details on matrix layouts.
7431: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
7432: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7433: @*/
7434: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
7435: {
7436: PetscFunctionBegin;
7439: if (m) PetscAssertPointer(m, 2);
7440: if (n) PetscAssertPointer(n, 3);
7441: MatCheckPreallocated(mat, 1);
7442: if (m) *m = mat->rmap->rstart;
7443: if (n) *n = mat->rmap->rend;
7444: PetscFunctionReturn(PETSC_SUCCESS);
7445: }
7447: /*@
7448: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
7449: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
7451: Not Collective, unless matrix has not been allocated
7453: Input Parameter:
7454: . mat - the matrix
7456: Output Parameter:
7457: . ranges - start of each process's portion plus one more than the total length at the end, of length `size` + 1
7458: where `size` is the number of MPI processes used by `mat`
7460: Level: beginner
7462: Notes:
7463: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7465: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7466: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7468: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7469: the local values in the matrix.
7471: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
7472: would contain the result of a matrix vector product with this matrix. See [Matrix
7473: Layouts](sec_matlayout) for details on matrix layouts.
7475: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7476: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
7477: `DMDAGetGhostCorners()`, `DM`
7478: @*/
7479: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
7480: {
7481: PetscFunctionBegin;
7484: MatCheckPreallocated(mat, 1);
7485: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
7486: PetscFunctionReturn(PETSC_SUCCESS);
7487: }
7489: /*@
7490: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
7491: vector one multiplies this vector by that are owned by each process.
7493: Not Collective, unless matrix has not been allocated
7495: Input Parameter:
7496: . mat - the matrix
7498: Output Parameter:
7499: . ranges - start of each process's portion plus one more than the total length at the end
7501: Level: beginner
7503: Notes:
7504: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7506: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7507: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7509: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7510: the local values in the matrix.
7512: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7513: Layouts](sec_matlayout) for details on matrix layouts.
7515: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7516: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7517: `DMDAGetGhostCorners()`, `DM`
7518: @*/
7519: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7520: {
7521: PetscFunctionBegin;
7524: MatCheckPreallocated(mat, 1);
7525: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7526: PetscFunctionReturn(PETSC_SUCCESS);
7527: }
7529: /*@
7530: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7532: Not Collective
7534: Input Parameter:
7535: . A - matrix
7537: Output Parameters:
7538: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7539: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7541: Level: intermediate
7543: Note:
7544: You should call `ISDestroy()` on the returned `IS`
7546: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7547: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7548: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7549: details on matrix layouts.
7551: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7552: @*/
7553: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7554: {
7555: PetscErrorCode (*f)(Mat, IS *, IS *);
7557: PetscFunctionBegin;
7560: MatCheckPreallocated(A, 1);
7561: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7562: if (f) {
7563: PetscCall((*f)(A, rows, cols));
7564: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7565: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7566: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7567: }
7568: PetscFunctionReturn(PETSC_SUCCESS);
7569: }
7571: /*@
7572: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7573: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7574: to complete the factorization.
7576: Collective
7578: Input Parameters:
7579: + fact - the factorized matrix obtained with `MatGetFactor()`
7580: . mat - the matrix
7581: . row - row permutation
7582: . col - column permutation
7583: - info - structure containing
7584: .vb
7585: levels - number of levels of fill.
7586: expected fill - as ratio of original fill.
7587: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7588: missing diagonal entries)
7589: .ve
7591: Level: developer
7593: Notes:
7594: See [Matrix Factorization](sec_matfactor) for additional information.
7596: Most users should employ the `KSP` interface for linear solvers
7597: instead of working directly with matrix algebra routines such as this.
7598: See, e.g., `KSPCreate()`.
7600: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7602: Fortran Note:
7603: A valid (non-null) `info` argument must be provided
7605: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
7606: `MatGetOrdering()`, `MatFactorInfo`
7607: @*/
7608: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7609: {
7610: PetscFunctionBegin;
7615: PetscAssertPointer(info, 5);
7616: PetscAssertPointer(fact, 1);
7617: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7618: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7619: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7620: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7621: MatCheckPreallocated(mat, 2);
7623: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7624: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7625: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7626: PetscFunctionReturn(PETSC_SUCCESS);
7627: }
7629: /*@
7630: MatICCFactorSymbolic - Performs symbolic incomplete
7631: Cholesky factorization for a symmetric matrix. Use
7632: `MatCholeskyFactorNumeric()` to complete the factorization.
7634: Collective
7636: Input Parameters:
7637: + fact - the factorized matrix obtained with `MatGetFactor()`
7638: . mat - the matrix to be factored
7639: . perm - row and column permutation
7640: - info - structure containing
7641: .vb
7642: levels - number of levels of fill.
7643: expected fill - as ratio of original fill.
7644: .ve
7646: Level: developer
7648: Notes:
7649: Most users should employ the `KSP` interface for linear solvers
7650: instead of working directly with matrix algebra routines such as this.
7651: See, e.g., `KSPCreate()`.
7653: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7655: Fortran Note:
7656: A valid (non-null) `info` argument must be provided
7658: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7659: @*/
7660: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7661: {
7662: PetscFunctionBegin;
7666: PetscAssertPointer(info, 4);
7667: PetscAssertPointer(fact, 1);
7668: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7669: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7670: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7671: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7672: MatCheckPreallocated(mat, 2);
7674: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7675: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7676: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7677: PetscFunctionReturn(PETSC_SUCCESS);
7678: }
7680: /*@
7681: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7682: points to an array of valid matrices, they may be reused to store the new
7683: submatrices.
7685: Collective
7687: Input Parameters:
7688: + mat - the matrix
7689: . n - the number of submatrixes to be extracted (on this process, may be zero)
7690: . irow - index set of rows to extract
7691: . icol - index set of columns to extract
7692: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7694: Output Parameter:
7695: . submat - the array of submatrices
7697: Level: advanced
7699: Notes:
7700: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7701: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7702: to extract a parallel submatrix.
7704: Some matrix types place restrictions on the row and column
7705: indices, such as that they be sorted or that they be equal to each other.
7706: `MATSEQSBAIJ` inputs may produce `MATSEQBAIJ` submatrices when the row and column index sets do not preserve symmetry.
7708: The index sets may not have duplicate entries.
7710: When extracting submatrices from a parallel matrix, each process can
7711: form a different submatrix by setting the rows and columns of its
7712: individual index sets according to the local submatrix desired.
7714: When finished using the submatrices, the user should destroy
7715: them with `MatDestroySubMatrices()`.
7717: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7718: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7720: This routine creates the matrices in submat; you should NOT create them before
7721: calling it. It also allocates the array of matrix pointers submat.
7723: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7724: request one row/column in a block, they must request all rows/columns that are in
7725: that block. For example, if the block size is 2 you cannot request just row 0 and
7726: column 0.
7728: Fortran Note:
7729: .vb
7730: Mat, pointer :: submat(:)
7731: .ve
7733: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7734: @*/
7735: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7736: {
7737: PetscInt i;
7738: PetscBool eq;
7740: PetscFunctionBegin;
7743: if (n) {
7744: PetscAssertPointer(irow, 3);
7746: PetscAssertPointer(icol, 4);
7748: }
7749: PetscAssertPointer(submat, 6);
7750: if (n && scall == MAT_REUSE_MATRIX) {
7751: PetscAssertPointer(*submat, 6);
7753: }
7754: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7755: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7756: MatCheckPreallocated(mat, 1);
7757: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7758: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7759: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7760: for (i = 0; i < n; i++) {
7761: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7762: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7763: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7764: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
7765: if (mat->boundtocpu && mat->bindingpropagates) {
7766: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7767: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7768: }
7769: #endif
7770: }
7771: PetscFunctionReturn(PETSC_SUCCESS);
7772: }
7774: /*@
7775: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7777: Collective
7779: Input Parameters:
7780: + mat - the matrix
7781: . n - the number of submatrixes to be extracted
7782: . irow - index set of rows to extract
7783: . icol - index set of columns to extract
7784: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7786: Output Parameter:
7787: . submat - the array of submatrices
7789: Level: advanced
7791: Note:
7792: This is used by `PCGASM`
7794: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7795: @*/
7796: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7797: {
7798: PetscInt i;
7799: PetscBool eq;
7801: PetscFunctionBegin;
7804: if (n) {
7805: PetscAssertPointer(irow, 3);
7807: PetscAssertPointer(icol, 4);
7809: }
7810: PetscAssertPointer(submat, 6);
7811: if (n && scall == MAT_REUSE_MATRIX) {
7812: PetscAssertPointer(*submat, 6);
7814: }
7815: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7816: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7817: MatCheckPreallocated(mat, 1);
7819: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7820: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7821: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7822: for (i = 0; i < n; i++) {
7823: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7824: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7825: }
7826: PetscFunctionReturn(PETSC_SUCCESS);
7827: }
7829: /*@
7830: MatDestroyMatrices - Destroys an array of matrices
7832: Collective
7834: Input Parameters:
7835: + n - the number of local matrices
7836: - mat - the matrices (this is a pointer to the array of matrices)
7838: Level: advanced
7840: Notes:
7841: Frees not only the matrices, but also the array that contains the matrices
7843: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7845: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7846: @*/
7847: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7848: {
7849: PetscInt i;
7851: PetscFunctionBegin;
7852: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7853: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7854: PetscAssertPointer(mat, 2);
7856: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7858: /* memory is allocated even if n = 0 */
7859: PetscCall(PetscFree(*mat));
7860: PetscFunctionReturn(PETSC_SUCCESS);
7861: }
7863: /*@
7864: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7866: Collective
7868: Input Parameters:
7869: + n - the number of local matrices
7870: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7872: Level: advanced
7874: Note:
7875: Frees not only the matrices, but also the array that contains the matrices
7877: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7878: @*/
7879: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7880: {
7881: Mat mat0;
7883: PetscFunctionBegin;
7884: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7885: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7886: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7887: PetscAssertPointer(mat, 2);
7889: mat0 = (*mat)[0];
7890: if (mat0 && mat0->ops->destroysubmatrices) {
7891: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7892: } else {
7893: PetscCall(MatDestroyMatrices(n, mat));
7894: }
7895: PetscFunctionReturn(PETSC_SUCCESS);
7896: }
7898: /*@
7899: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7901: Collective
7903: Input Parameter:
7904: . mat - the matrix
7906: Output Parameter:
7907: . matstruct - the sequential matrix with the nonzero structure of `mat`
7909: Level: developer
7911: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7912: @*/
7913: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7914: {
7915: PetscFunctionBegin;
7917: PetscAssertPointer(matstruct, 2);
7920: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7921: MatCheckPreallocated(mat, 1);
7923: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7924: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7925: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7926: PetscFunctionReturn(PETSC_SUCCESS);
7927: }
7929: /*@
7930: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7932: Collective
7934: Input Parameter:
7935: . mat - the matrix
7937: Level: advanced
7939: Note:
7940: This is not needed, one can just call `MatDestroy()`
7942: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7943: @*/
7944: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7945: {
7946: PetscFunctionBegin;
7947: PetscAssertPointer(mat, 1);
7948: PetscCall(MatDestroy(mat));
7949: PetscFunctionReturn(PETSC_SUCCESS);
7950: }
7952: /*@
7953: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7954: replaces the index sets by larger ones that represent submatrices with
7955: additional overlap.
7957: Collective
7959: Input Parameters:
7960: + mat - the matrix
7961: . n - the number of index sets
7962: . is - the array of index sets (these index sets will changed during the call)
7963: - ov - the additional overlap requested
7965: Options Database Key:
7966: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7968: Level: developer
7970: Note:
7971: The computed overlap preserves the matrix block sizes when the blocks are square.
7972: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7973: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7975: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7976: @*/
7977: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7978: {
7979: PetscInt i, bs, cbs;
7981: PetscFunctionBegin;
7985: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7986: if (n) {
7987: PetscAssertPointer(is, 3);
7989: }
7990: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7991: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7992: MatCheckPreallocated(mat, 1);
7994: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7995: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7996: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7997: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7998: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7999: if (bs == cbs) {
8000: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
8001: }
8002: PetscFunctionReturn(PETSC_SUCCESS);
8003: }
8005: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
8007: /*@
8008: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
8009: a sub communicator, replaces the index sets by larger ones that represent submatrices with
8010: additional overlap.
8012: Collective
8014: Input Parameters:
8015: + mat - the matrix
8016: . n - the number of index sets
8017: . is - the array of index sets (these index sets will changed during the call)
8018: - ov - the additional overlap requested
8020: ` Options Database Key:
8021: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
8023: Level: developer
8025: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
8026: @*/
8027: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
8028: {
8029: PetscInt i;
8031: PetscFunctionBegin;
8034: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
8035: if (n) {
8036: PetscAssertPointer(is, 3);
8038: }
8039: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
8040: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8041: MatCheckPreallocated(mat, 1);
8042: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
8043: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
8044: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
8045: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
8046: PetscFunctionReturn(PETSC_SUCCESS);
8047: }
8049: /*@
8050: MatGetBlockSize - Returns the matrix block size.
8052: Not Collective
8054: Input Parameter:
8055: . mat - the matrix
8057: Output Parameter:
8058: . bs - block size
8060: Level: intermediate
8062: Notes:
8063: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
8065: If the block size has not been set yet this routine returns 1.
8067: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
8068: @*/
8069: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
8070: {
8071: PetscFunctionBegin;
8073: PetscAssertPointer(bs, 2);
8074: *bs = mat->rmap->bs;
8075: PetscFunctionReturn(PETSC_SUCCESS);
8076: }
8078: /*@
8079: MatGetBlockSizes - Returns the matrix block row and column sizes.
8081: Not Collective
8083: Input Parameter:
8084: . mat - the matrix
8086: Output Parameters:
8087: + rbs - row block size
8088: - cbs - column block size
8090: Level: intermediate
8092: Notes:
8093: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
8094: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8096: If a block size has not been set yet this routine returns 1.
8098: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
8099: @*/
8100: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
8101: {
8102: PetscFunctionBegin;
8104: if (rbs) PetscAssertPointer(rbs, 2);
8105: if (cbs) PetscAssertPointer(cbs, 3);
8106: if (rbs) *rbs = mat->rmap->bs;
8107: if (cbs) *cbs = mat->cmap->bs;
8108: PetscFunctionReturn(PETSC_SUCCESS);
8109: }
8111: /*@
8112: MatSetBlockSize - Sets the matrix block size.
8114: Logically Collective
8116: Input Parameters:
8117: + mat - the matrix
8118: - bs - block size
8120: Level: intermediate
8122: Notes:
8123: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
8124: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8126: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
8127: is compatible with the matrix local sizes.
8129: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
8130: @*/
8131: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
8132: {
8133: PetscFunctionBegin;
8136: PetscCall(MatSetBlockSizes(mat, bs, bs));
8137: PetscFunctionReturn(PETSC_SUCCESS);
8138: }
8140: typedef struct {
8141: PetscInt n;
8142: IS *is;
8143: Mat *mat;
8144: PetscObjectState nonzerostate;
8145: Mat C;
8146: } EnvelopeData;
8148: static PetscErrorCode EnvelopeDataDestroy(PetscCtxRt ptr)
8149: {
8150: EnvelopeData *edata = *(EnvelopeData **)ptr;
8152: PetscFunctionBegin;
8153: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
8154: PetscCall(PetscFree(edata->is));
8155: PetscCall(PetscFree(edata));
8156: PetscFunctionReturn(PETSC_SUCCESS);
8157: }
8159: /*@
8160: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
8161: the sizes of these blocks in the matrix. An individual block may lie over several processes.
8163: Collective
8165: Input Parameter:
8166: . mat - the matrix
8168: Level: intermediate
8170: Notes:
8171: There can be zeros within the blocks
8173: The blocks can overlap between processes, including laying on more than two processes
8175: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
8176: @*/
8177: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
8178: {
8179: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
8180: PetscInt *diag, *odiag, sc;
8181: VecScatter scatter;
8182: PetscScalar *seqv;
8183: const PetscScalar *parv;
8184: const PetscInt *ia, *ja;
8185: PetscBool set, flag, done;
8186: Mat AA = mat, A;
8187: MPI_Comm comm;
8188: PetscMPIInt rank, size, tag;
8189: MPI_Status status;
8190: PetscContainer container;
8191: EnvelopeData *edata;
8192: Vec seq, par;
8193: IS isglobal;
8195: PetscFunctionBegin;
8197: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
8198: if (!set || !flag) {
8199: /* TODO: only needs nonzero structure of transpose */
8200: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
8201: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
8202: }
8203: PetscCall(MatAIJGetLocalMat(AA, &A));
8204: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
8205: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
8207: PetscCall(MatGetLocalSize(mat, &n, NULL));
8208: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
8209: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
8210: PetscCallMPI(MPI_Comm_size(comm, &size));
8211: PetscCallMPI(MPI_Comm_rank(comm, &rank));
8213: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
8215: if (rank > 0) {
8216: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
8217: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
8218: }
8219: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
8220: for (i = 0; i < n; i++) {
8221: env = PetscMax(env, ja[ia[i + 1] - 1]);
8222: II = rstart + i;
8223: if (env == II) {
8224: starts[lblocks] = tbs;
8225: sizes[lblocks++] = 1 + II - tbs;
8226: tbs = 1 + II;
8227: }
8228: }
8229: if (rank < size - 1) {
8230: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
8231: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
8232: }
8234: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
8235: if (!set || !flag) PetscCall(MatDestroy(&AA));
8236: PetscCall(MatDestroy(&A));
8238: PetscCall(PetscNew(&edata));
8239: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
8240: edata->n = lblocks;
8241: /* create IS needed for extracting blocks from the original matrix */
8242: PetscCall(PetscMalloc1(lblocks, &edata->is));
8243: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
8245: /* Create the resulting inverse matrix nonzero structure with preallocation information */
8246: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
8247: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
8248: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
8249: PetscCall(MatSetType(edata->C, MATAIJ));
8251: /* Communicate the start and end of each row, from each block to the correct rank */
8252: /* TODO: Use PetscSF instead of VecScatter */
8253: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
8254: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
8255: PetscCall(VecGetArrayWrite(seq, &seqv));
8256: for (PetscInt i = 0; i < lblocks; i++) {
8257: for (PetscInt j = 0; j < sizes[i]; j++) {
8258: seqv[cnt] = starts[i];
8259: seqv[cnt + 1] = starts[i] + sizes[i];
8260: cnt += 2;
8261: }
8262: }
8263: PetscCall(VecRestoreArrayWrite(seq, &seqv));
8264: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
8265: sc -= cnt;
8266: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
8267: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
8268: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
8269: PetscCall(ISDestroy(&isglobal));
8270: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
8271: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
8272: PetscCall(VecScatterDestroy(&scatter));
8273: PetscCall(VecDestroy(&seq));
8274: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
8275: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
8276: PetscCall(VecGetArrayRead(par, &parv));
8277: cnt = 0;
8278: PetscCall(MatGetSize(mat, NULL, &n));
8279: for (PetscInt i = 0; i < mat->rmap->n; i++) {
8280: PetscInt start, end, d = 0, od = 0;
8282: start = (PetscInt)PetscRealPart(parv[cnt]);
8283: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
8284: cnt += 2;
8286: if (start < cstart) {
8287: od += cstart - start + n - cend;
8288: d += cend - cstart;
8289: } else if (start < cend) {
8290: od += n - cend;
8291: d += cend - start;
8292: } else od += n - start;
8293: if (end <= cstart) {
8294: od -= cstart - end + n - cend;
8295: d -= cend - cstart;
8296: } else if (end < cend) {
8297: od -= n - cend;
8298: d -= cend - end;
8299: } else od -= n - end;
8301: odiag[i] = od;
8302: diag[i] = d;
8303: }
8304: PetscCall(VecRestoreArrayRead(par, &parv));
8305: PetscCall(VecDestroy(&par));
8306: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
8307: PetscCall(PetscFree2(diag, odiag));
8308: PetscCall(PetscFree2(sizes, starts));
8310: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
8311: PetscCall(PetscContainerSetPointer(container, edata));
8312: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
8313: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
8314: PetscCall(PetscObjectDereference((PetscObject)container));
8315: PetscFunctionReturn(PETSC_SUCCESS);
8316: }
8318: /*@
8319: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
8321: Collective
8323: Input Parameters:
8324: + A - the matrix
8325: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
8327: Output Parameter:
8328: . C - matrix with inverted block diagonal of `A`
8330: Level: advanced
8332: Note:
8333: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
8335: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
8336: @*/
8337: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
8338: {
8339: PetscContainer container;
8340: EnvelopeData *edata;
8341: PetscObjectState nonzerostate;
8343: PetscFunctionBegin;
8344: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
8345: if (!container) {
8346: PetscCall(MatComputeVariableBlockEnvelope(A));
8347: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
8348: }
8349: PetscCall(PetscContainerGetPointer(container, &edata));
8350: PetscCall(MatGetNonzeroState(A, &nonzerostate));
8351: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
8352: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
8354: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
8355: *C = edata->C;
8357: for (PetscInt i = 0; i < edata->n; i++) {
8358: Mat D;
8359: PetscScalar *dvalues;
8361: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
8362: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
8363: PetscCall(MatSeqDenseInvert(D));
8364: PetscCall(MatDenseGetArray(D, &dvalues));
8365: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
8366: PetscCall(MatDestroy(&D));
8367: }
8368: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
8369: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
8370: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
8371: PetscFunctionReturn(PETSC_SUCCESS);
8372: }
8374: /*@
8375: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
8377: Not Collective
8379: Input Parameters:
8380: + mat - the matrix
8381: . nblocks - the number of blocks on this process, each block can only exist on a single MPI process
8382: - bsizes - the block sizes
8384: Level: intermediate
8386: Note:
8387: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
8389: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
8390: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
8391: @*/
8392: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
8393: {
8394: PetscInt ncnt = 0, nlocal;
8396: PetscFunctionBegin;
8398: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
8399: 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);
8400: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
8401: 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);
8402: PetscCall(PetscFree(mat->bsizes));
8403: mat->nblocks = nblocks;
8404: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
8405: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
8406: PetscFunctionReturn(PETSC_SUCCESS);
8407: }
8409: /*@
8410: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
8412: Not Collective; No Fortran Support
8414: Input Parameter:
8415: . mat - the matrix
8417: Output Parameters:
8418: + nblocks - the number of blocks on this process
8419: - bsizes - the block sizes
8421: Level: intermediate
8423: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8424: @*/
8425: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
8426: {
8427: PetscFunctionBegin;
8429: if (nblocks) *nblocks = mat->nblocks;
8430: if (bsizes) *bsizes = mat->bsizes;
8431: PetscFunctionReturn(PETSC_SUCCESS);
8432: }
8434: /*@
8435: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
8437: Not Collective
8439: Input Parameters:
8440: + subA - the submatrix
8441: . A - the original matrix
8442: - isrow - The `IS` of selected rows for the submatrix, must be sorted
8444: Level: developer
8446: Note:
8447: If the index set is not sorted or contains off-process entries, this function will do nothing.
8449: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8450: @*/
8451: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
8452: {
8453: const PetscInt *rows;
8454: PetscInt n, rStart, rEnd, Nb = 0;
8455: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
8457: PetscFunctionBegin;
8458: // The code for block size extraction does not support an unsorted IS
8459: if (flg) PetscCall(ISSorted(isrow, &flg));
8460: // We don't support originally off-diagonal blocks
8461: if (flg) {
8462: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
8463: PetscCall(ISGetLocalSize(isrow, &n));
8464: PetscCall(ISGetIndices(isrow, &rows));
8465: for (PetscInt i = 0; i < n && flg; ++i) {
8466: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
8467: }
8468: PetscCall(ISRestoreIndices(isrow, &rows));
8469: }
8470: // quiet return if we can't extract block size
8471: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
8472: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
8474: // extract block sizes
8475: PetscCall(ISGetIndices(isrow, &rows));
8476: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8477: PetscBool occupied = PETSC_FALSE;
8479: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8480: const PetscInt row = gr + br;
8482: if (i == n) break;
8483: if (rows[i] == row) {
8484: occupied = PETSC_TRUE;
8485: ++i;
8486: }
8487: while (i < n && rows[i] < row) ++i;
8488: }
8489: gr += A->bsizes[b];
8490: if (occupied) ++Nb;
8491: }
8492: subA->nblocks = Nb;
8493: PetscCall(PetscFree(subA->bsizes));
8494: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
8495: PetscInt sb = 0;
8496: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8497: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
8498: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8499: const PetscInt row = gr + br;
8501: if (i == n) break;
8502: if (rows[i] == row) {
8503: ++subA->bsizes[sb];
8504: ++i;
8505: }
8506: while (i < n && rows[i] < row) ++i;
8507: }
8508: gr += A->bsizes[b];
8509: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8510: }
8511: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8512: PetscInt nlocal, ncnt = 0;
8513: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8514: 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);
8515: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8516: 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);
8517: PetscCall(ISRestoreIndices(isrow, &rows));
8518: PetscFunctionReturn(PETSC_SUCCESS);
8519: }
8521: /*@
8522: MatSetBlockSizes - Sets the matrix block row and column sizes.
8524: Logically Collective
8526: Input Parameters:
8527: + mat - the matrix
8528: . rbs - row block size
8529: - cbs - column block size
8531: Level: intermediate
8533: Notes:
8534: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8535: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8536: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8538: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8539: are compatible with the matrix local sizes.
8541: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8543: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8544: @*/
8545: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8546: {
8547: PetscFunctionBegin;
8551: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8552: if (mat->rmap->refcnt) {
8553: ISLocalToGlobalMapping l2g = NULL;
8554: PetscLayout nmap = NULL;
8556: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8557: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8558: PetscCall(PetscLayoutDestroy(&mat->rmap));
8559: mat->rmap = nmap;
8560: mat->rmap->mapping = l2g;
8561: }
8562: if (mat->cmap->refcnt) {
8563: ISLocalToGlobalMapping l2g = NULL;
8564: PetscLayout nmap = NULL;
8566: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8567: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8568: PetscCall(PetscLayoutDestroy(&mat->cmap));
8569: mat->cmap = nmap;
8570: mat->cmap->mapping = l2g;
8571: }
8572: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8573: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8574: PetscFunctionReturn(PETSC_SUCCESS);
8575: }
8577: /*@
8578: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8580: Logically Collective
8582: Input Parameters:
8583: + mat - the matrix
8584: . fromRow - matrix from which to copy row block size
8585: - fromCol - matrix from which to copy column block size (can be same as `fromRow`)
8587: Level: developer
8589: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8590: @*/
8591: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8592: {
8593: PetscFunctionBegin;
8597: PetscTryTypeMethod(mat, setblocksizes, fromRow->rmap->bs, fromCol->cmap->bs);
8598: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8599: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8600: PetscFunctionReturn(PETSC_SUCCESS);
8601: }
8603: /*@
8604: MatResidual - Default routine to calculate the residual $r = b - Ax$
8606: Collective
8608: Input Parameters:
8609: + mat - the matrix
8610: . b - the right-hand-side
8611: - x - the approximate solution
8613: Output Parameter:
8614: . r - location to store the residual
8616: Level: developer
8618: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8619: @*/
8620: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8621: {
8622: PetscFunctionBegin;
8628: MatCheckPreallocated(mat, 1);
8629: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8630: if (!mat->ops->residual) {
8631: PetscCall(MatMult(mat, x, r));
8632: PetscCall(VecAYPX(r, -1.0, b));
8633: } else {
8634: PetscUseTypeMethod(mat, residual, b, x, r);
8635: }
8636: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8637: PetscFunctionReturn(PETSC_SUCCESS);
8638: }
8640: /*@
8641: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8643: Collective
8645: Input Parameters:
8646: + mat - the matrix
8647: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8648: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8649: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8650: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8651: always used.
8653: Output Parameters:
8654: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8655: . 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
8656: . ja - the column indices, use `NULL` if not needed
8657: - done - indicates if the routine actually worked and returned appropriate `ia` and `ja` arrays; callers
8658: are responsible for handling the case when done is `PETSC_FALSE` and `ia` and `ja` are not provided
8660: Level: developer
8662: Notes:
8663: You CANNOT change any of the `ia` or `ja` values.
8665: Use `MatRestoreRowIJ()` when you are finished accessing the `ia` and `ja` values.
8667: Fortran Notes:
8668: Use
8669: .vb
8670: PetscInt, pointer :: ia(:),ja(:)
8671: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8672: ! Access the ith and jth entries via ia(i) and ja(j)
8673: .ve
8675: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8676: @*/
8677: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8678: {
8679: PetscFunctionBegin;
8682: if (n) PetscAssertPointer(n, 5);
8683: if (ia) PetscAssertPointer(ia, 6);
8684: if (ja) PetscAssertPointer(ja, 7);
8685: if (done) PetscAssertPointer(done, 8);
8686: MatCheckPreallocated(mat, 1);
8687: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8688: else {
8689: if (done) *done = PETSC_TRUE;
8690: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8691: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8692: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8693: }
8694: PetscFunctionReturn(PETSC_SUCCESS);
8695: }
8697: /*@
8698: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8700: Collective
8702: Input Parameters:
8703: + mat - the matrix
8704: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8705: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8706: symmetrized
8707: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8708: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8709: always used.
8711: Output Parameters:
8712: + n - number of columns in the (possibly compressed) matrix
8713: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8714: . ja - the row indices
8715: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8717: Level: developer
8719: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8720: @*/
8721: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8722: {
8723: PetscFunctionBegin;
8726: PetscAssertPointer(n, 5);
8727: if (ia) PetscAssertPointer(ia, 6);
8728: if (ja) PetscAssertPointer(ja, 7);
8729: PetscAssertPointer(done, 8);
8730: MatCheckPreallocated(mat, 1);
8731: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8732: else {
8733: *done = PETSC_TRUE;
8734: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8735: }
8736: PetscFunctionReturn(PETSC_SUCCESS);
8737: }
8739: /*@
8740: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8742: Collective
8744: Input Parameters:
8745: + mat - the matrix
8746: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8747: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8748: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8749: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8750: always used.
8751: . n - size of (possibly compressed) matrix, or `NULL`
8752: . ia - the row pointers, or `NULL`
8753: - ja - the column indices, or `NULL`
8755: Output Parameter:
8756: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8758: Level: developer
8760: Note:
8761: This routine zeros out `n`, `ia`, and `ja` if they are provided. Use of `ia` or `ja` after `MatRestoreRowIJ()` is always invalid.
8763: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8764: @*/
8765: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8766: {
8767: PetscFunctionBegin;
8770: if (ia) PetscAssertPointer(ia, 6);
8771: if (ja) PetscAssertPointer(ja, 7);
8772: if (done) PetscAssertPointer(done, 8);
8773: MatCheckPreallocated(mat, 1);
8775: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8776: else {
8777: if (done) *done = PETSC_TRUE;
8778: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8779: if (n) *n = 0;
8780: if (ia) *ia = NULL;
8781: if (ja) *ja = NULL;
8782: }
8783: PetscFunctionReturn(PETSC_SUCCESS);
8784: }
8786: /*@
8787: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8789: Collective
8791: Input Parameters:
8792: + mat - the matrix
8793: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8794: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8795: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8796: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8797: always used.
8799: Output Parameters:
8800: + n - size of (possibly compressed) matrix
8801: . ia - the column pointers
8802: . ja - the row indices
8803: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8805: Level: developer
8807: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8808: @*/
8809: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8810: {
8811: PetscFunctionBegin;
8814: if (ia) PetscAssertPointer(ia, 6);
8815: if (ja) PetscAssertPointer(ja, 7);
8816: PetscAssertPointer(done, 8);
8817: MatCheckPreallocated(mat, 1);
8819: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8820: else {
8821: *done = PETSC_TRUE;
8822: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8823: if (n) *n = 0;
8824: if (ia) *ia = NULL;
8825: if (ja) *ja = NULL;
8826: }
8827: PetscFunctionReturn(PETSC_SUCCESS);
8828: }
8830: /*@
8831: MatColoringPatch - Utility routine used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8832: `MatGetColumnIJ()`.
8834: Collective
8836: Input Parameters:
8837: + mat - the matrix
8838: . ncolors - maximum color value
8839: . n - number of entries in `colorarray`
8840: - colorarray - array indicating color for each column
8842: Output Parameter:
8843: . iscoloring - coloring generated using `colorarray` information
8845: Level: developer
8847: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8848: @*/
8849: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8850: {
8851: PetscFunctionBegin;
8854: PetscAssertPointer(colorarray, 4);
8855: PetscAssertPointer(iscoloring, 5);
8856: MatCheckPreallocated(mat, 1);
8858: if (!mat->ops->coloringpatch) {
8859: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8860: } else {
8861: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8862: }
8863: PetscFunctionReturn(PETSC_SUCCESS);
8864: }
8866: /*@
8867: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8869: Logically Collective
8871: Input Parameter:
8872: . mat - the factored matrix to be reset
8874: Level: developer
8876: Notes:
8877: This routine should be used only with factored matrices formed by in-place
8878: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8879: format). This option can save memory, for example, when solving nonlinear
8880: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8881: ILU(0) preconditioner.
8883: One can specify in-place ILU(0) factorization by calling
8884: .vb
8885: PCType(pc,PCILU);
8886: PCFactorSeUseInPlace(pc);
8887: .ve
8888: or by using the options -pc_type ilu -pc_factor_in_place
8890: In-place factorization ILU(0) can also be used as a local
8891: solver for the blocks within the block Jacobi or additive Schwarz
8892: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8893: for details on setting local solver options.
8895: Most users should employ the `KSP` interface for linear solvers
8896: instead of working directly with matrix algebra routines such as this.
8897: See, e.g., `KSPCreate()`.
8899: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8900: @*/
8901: PetscErrorCode MatSetUnfactored(Mat mat)
8902: {
8903: PetscFunctionBegin;
8906: MatCheckPreallocated(mat, 1);
8907: mat->factortype = MAT_FACTOR_NONE;
8908: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8909: PetscUseTypeMethod(mat, setunfactored);
8910: PetscFunctionReturn(PETSC_SUCCESS);
8911: }
8913: /*@
8914: MatCreateSubMatrix - Gets a single submatrix on the same number of processes
8915: as the original matrix.
8917: Collective
8919: Input Parameters:
8920: + mat - the original matrix
8921: . isrow - parallel `IS` containing the rows this process should obtain
8922: . 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.
8923: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8925: Output Parameter:
8926: . newmat - the new submatrix, of the same type as the original matrix (except potentially for `MATSEQSBAIJ`)
8928: Level: advanced
8930: Notes:
8931: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8933: Some matrix types place restrictions on the row and column indices, such
8934: 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;
8935: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8936: `MATSEQSBAIJ` inputs may produce a `MATSEQBAIJ` matrix when the row and column index sets do not preserve symmetry.
8938: The index sets may not have duplicate entries.
8940: The first time this is called you should use a `cll` of `MAT_INITIAL_MATRIX`,
8941: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8942: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8943: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8944: you are finished using it.
8946: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8947: the input matrix.
8949: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8951: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8952: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8954: Example usage:
8955: Consider the following 8x8 matrix with 34 non-zero values, that is
8956: assembled across 3 processes. Let's assume that proc0 owns 3 rows,
8957: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8958: as follows
8959: .vb
8960: 1 2 0 | 0 3 0 | 0 4
8961: Proc0 0 5 6 | 7 0 0 | 8 0
8962: 9 0 10 | 11 0 0 | 12 0
8963: -------------------------------------
8964: 13 0 14 | 15 16 17 | 0 0
8965: Proc1 0 18 0 | 19 20 21 | 0 0
8966: 0 0 0 | 22 23 0 | 24 0
8967: -------------------------------------
8968: Proc2 25 26 27 | 0 0 28 | 29 0
8969: 30 0 0 | 31 32 33 | 0 34
8970: .ve
8972: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8974: .vb
8975: 2 0 | 0 3 0 | 0
8976: Proc0 5 6 | 7 0 0 | 8
8977: -------------------------------
8978: Proc1 18 0 | 19 20 21 | 0
8979: -------------------------------
8980: Proc2 26 27 | 0 0 28 | 29
8981: 0 0 | 31 32 33 | 0
8982: .ve
8984: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8985: @*/
8986: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8987: {
8988: PetscMPIInt size;
8989: Mat *local;
8990: IS iscoltmp;
8991: PetscBool flg;
8993: PetscFunctionBegin;
8997: PetscAssertPointer(newmat, 5);
9000: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9001: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
9002: PetscCheck(cll != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_INPLACE_MATRIX");
9004: MatCheckPreallocated(mat, 1);
9005: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9007: if (!iscol || isrow == iscol) {
9008: PetscBool stride;
9009: PetscMPIInt grab = 0;
9010: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
9011: if (stride) {
9012: PetscInt first, step, n, rstart, rend;
9013: PetscCall(ISStrideGetInfo(isrow, &first, &step));
9014: if (step == 1) {
9015: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
9016: if (rstart == first) {
9017: PetscCall(ISGetLocalSize(isrow, &n));
9018: if (n == rend - rstart) grab = 1;
9019: }
9020: }
9021: }
9022: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
9023: if (grab) {
9024: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
9025: if (cll == MAT_INITIAL_MATRIX) {
9026: *newmat = mat;
9027: PetscCall(PetscObjectReference((PetscObject)mat));
9028: }
9029: PetscFunctionReturn(PETSC_SUCCESS);
9030: }
9031: }
9033: if (!iscol) {
9034: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
9035: } else {
9036: iscoltmp = iscol;
9037: }
9039: /* if original matrix is on just one process then use submatrix generated */
9040: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
9041: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
9042: goto setproperties;
9043: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
9044: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
9045: *newmat = *local;
9046: PetscCall(PetscFree(local));
9047: goto setproperties;
9048: } else if (!mat->ops->createsubmatrix) {
9049: /* Create a new matrix type that implements the operation using the full matrix */
9050: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
9051: switch (cll) {
9052: case MAT_INITIAL_MATRIX:
9053: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
9054: break;
9055: case MAT_REUSE_MATRIX:
9056: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
9057: break;
9058: default:
9059: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
9060: }
9061: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
9062: goto setproperties;
9063: }
9065: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
9066: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
9067: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
9069: setproperties:
9070: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
9071: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
9072: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
9073: }
9074: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
9075: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
9076: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
9077: PetscFunctionReturn(PETSC_SUCCESS);
9078: }
9080: /*@
9081: MatPropagateSymmetryOptions - Propagates symmetry properties set on a matrix to another matrix
9083: Not Collective
9085: Input Parameters:
9086: + A - the matrix we wish to propagate properties from
9087: - B - the matrix we wish to propagate properties to
9089: Level: beginner
9091: Note:
9092: Propagates the properties associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
9094: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
9095: @*/
9096: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
9097: {
9098: PetscFunctionBegin;
9101: B->symmetry_eternal = A->symmetry_eternal;
9102: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
9103: B->symmetric = A->symmetric;
9104: B->structurally_symmetric = A->structurally_symmetric;
9105: B->spd = A->spd;
9106: B->hermitian = A->hermitian;
9107: PetscFunctionReturn(PETSC_SUCCESS);
9108: }
9110: /*@
9111: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
9112: used during the assembly process to store values that belong to
9113: other processes.
9115: Not Collective
9117: Input Parameters:
9118: + mat - the matrix
9119: . size - the initial size of the stash.
9120: - bsize - the initial size of the block-stash(if used).
9122: Options Database Key:
9123: . -matstash_initial_size (size|size0,size1,...,sizep-1) - set initial size of stash for all or each of the MPI processes, sets both block and non-block stash sizes
9125: Level: intermediate
9127: Notes:
9128: The block-stash is used for values set with `MatSetValuesBlocked()` while
9129: the stash is used for values set with `MatSetValues()`
9131: Run with the option `-info` and look for output of the form
9132: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
9133: to determine the appropriate value, MM, to use for size and
9134: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
9135: to determine the value, BMM to use for bsize
9137: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
9138: @*/
9139: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
9140: {
9141: PetscFunctionBegin;
9144: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
9145: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
9146: PetscFunctionReturn(PETSC_SUCCESS);
9147: }
9149: /*@
9150: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
9151: the matrix
9153: Neighbor-wise Collective
9155: Input Parameters:
9156: + A - the matrix
9157: . x - the vector to be multiplied by the interpolation operator
9158: - y - the vector to be added to the result
9160: Output Parameter:
9161: . w - the resulting vector
9163: Level: intermediate
9165: Notes:
9166: `w` may be the same vector as `y`.
9168: This allows one to use either the restriction or interpolation (its transpose)
9169: matrix to do the interpolation
9171: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
9172: @*/
9173: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
9174: {
9175: PetscInt M, N, Ny;
9177: PetscFunctionBegin;
9182: PetscCall(MatGetSize(A, &M, &N));
9183: PetscCall(VecGetSize(y, &Ny));
9184: if (M == Ny) PetscCall(MatMultAdd(A, x, y, w));
9185: else PetscCall(MatMultTransposeAdd(A, x, y, w));
9186: PetscFunctionReturn(PETSC_SUCCESS);
9187: }
9189: /*@
9190: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
9191: the matrix
9193: Neighbor-wise Collective
9195: Input Parameters:
9196: + A - the matrix
9197: - x - the vector to be interpolated
9199: Output Parameter:
9200: . y - the resulting vector
9202: Level: intermediate
9204: Note:
9205: This allows one to use either the restriction or interpolation (its transpose)
9206: matrix to do the interpolation
9208: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
9209: @*/
9210: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
9211: {
9212: PetscInt M, N, Ny;
9214: PetscFunctionBegin;
9218: PetscCall(MatGetSize(A, &M, &N));
9219: PetscCall(VecGetSize(y, &Ny));
9220: if (M == Ny) PetscCall(MatMult(A, x, y));
9221: else PetscCall(MatMultTranspose(A, x, y));
9222: PetscFunctionReturn(PETSC_SUCCESS);
9223: }
9225: /*@
9226: MatRestrict - $y = A*x$ or $A^T*x$
9228: Neighbor-wise Collective
9230: Input Parameters:
9231: + A - the matrix
9232: - x - the vector to be restricted
9234: Output Parameter:
9235: . y - the resulting vector
9237: Level: intermediate
9239: Note:
9240: This allows one to use either the restriction or interpolation (its transpose)
9241: matrix to do the restriction
9243: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
9244: @*/
9245: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
9246: {
9247: PetscInt M, N, Nx;
9249: PetscFunctionBegin;
9253: PetscCall(MatGetSize(A, &M, &N));
9254: PetscCall(VecGetSize(x, &Nx));
9255: if (M == Nx) PetscCall(MatMultTranspose(A, x, y));
9256: else PetscCall(MatMult(A, x, y));
9257: PetscFunctionReturn(PETSC_SUCCESS);
9258: }
9260: /*@
9261: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
9263: Neighbor-wise Collective
9265: Input Parameters:
9266: + A - the matrix
9267: . x - the input dense matrix to be multiplied
9268: - w - the input dense matrix to be added to the result
9270: Output Parameter:
9271: . y - the output dense matrix
9273: Level: intermediate
9275: Note:
9276: This allows one to use either the restriction or interpolation (its transpose)
9277: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
9278: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9280: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
9281: @*/
9282: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
9283: {
9284: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
9285: PetscBool trans = PETSC_TRUE;
9286: MatReuse reuse = MAT_INITIAL_MATRIX;
9288: PetscFunctionBegin;
9294: PetscCall(MatGetSize(A, &M, &N));
9295: PetscCall(MatGetSize(x, &Mx, &Nx));
9296: if (N == Mx) trans = PETSC_FALSE;
9297: 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);
9298: Mo = trans ? N : M;
9299: if (*y) {
9300: PetscCall(MatGetSize(*y, &My, &Ny));
9301: if (Mo == My && Nx == Ny) reuse = MAT_REUSE_MATRIX;
9302: else {
9303: 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);
9304: PetscCall(MatDestroy(y));
9305: }
9306: }
9308: if (w && *y == w) { /* this is to minimize changes in PCMG */
9309: PetscBool flg;
9311: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
9312: if (w) {
9313: PetscInt My, Ny, Mw, Nw;
9315: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
9316: PetscCall(MatGetSize(*y, &My, &Ny));
9317: PetscCall(MatGetSize(w, &Mw, &Nw));
9318: if (!flg || My != Mw || Ny != Nw) w = NULL;
9319: }
9320: if (!w) {
9321: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
9322: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
9323: PetscCall(PetscObjectDereference((PetscObject)w));
9324: } else PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
9325: }
9326: if (!trans) PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
9327: else PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
9328: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
9329: PetscFunctionReturn(PETSC_SUCCESS);
9330: }
9332: /*@
9333: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
9335: Neighbor-wise Collective
9337: Input Parameters:
9338: + A - the matrix
9339: - x - the input dense matrix
9341: Output Parameter:
9342: . y - the output dense matrix
9344: Level: intermediate
9346: Note:
9347: This allows one to use either the restriction or interpolation (its transpose)
9348: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
9349: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9351: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
9352: @*/
9353: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
9354: {
9355: PetscFunctionBegin;
9356: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9357: PetscFunctionReturn(PETSC_SUCCESS);
9358: }
9360: /*@
9361: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
9363: Neighbor-wise Collective
9365: Input Parameters:
9366: + A - the matrix
9367: - x - the input dense matrix
9369: Output Parameter:
9370: . y - the output dense matrix
9372: Level: intermediate
9374: Note:
9375: This allows one to use either the restriction or interpolation (its transpose)
9376: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
9377: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9379: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
9380: @*/
9381: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
9382: {
9383: PetscFunctionBegin;
9384: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9385: PetscFunctionReturn(PETSC_SUCCESS);
9386: }
9388: /*@
9389: MatGetNullSpace - retrieves the null space of a matrix that was provided with `MatSetNullSpace()`
9391: Logically Collective
9393: Input Parameters:
9394: + mat - the matrix
9395: - nullsp - the null space object
9397: Level: developer
9399: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
9400: @*/
9401: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
9402: {
9403: PetscFunctionBegin;
9405: PetscAssertPointer(nullsp, 2);
9406: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
9407: PetscFunctionReturn(PETSC_SUCCESS);
9408: }
9410: /*@
9411: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices that were supplied with `MatSetNullSpace()`,
9412: `MatSetTransposeNullSpace()`, and `MatSetNearNullSpace()`
9414: Logically Collective
9416: Input Parameters:
9417: + n - the number of matrices
9418: - mat - the array of matrices
9420: Output Parameters:
9421: . nullsp - an array of null spaces, `NULL` will be inserted for each matrix that does not have a null space, length 3 * `n`
9423: Level: developer
9425: Note:
9426: Call `MatRestoreNullspaces()` to provide these to another array of matrices
9428: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9429: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`, `MatNullSpace`
9430: @*/
9431: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9432: {
9433: PetscFunctionBegin;
9434: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9435: PetscAssertPointer(mat, 2);
9436: PetscAssertPointer(nullsp, 3);
9438: PetscCall(PetscCalloc1(3 * n, nullsp));
9439: for (PetscInt i = 0; i < n; i++) {
9441: (*nullsp)[i] = mat[i]->nullsp;
9442: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
9443: (*nullsp)[n + i] = mat[i]->nearnullsp;
9444: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
9445: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
9446: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
9447: }
9448: PetscFunctionReturn(PETSC_SUCCESS);
9449: }
9451: /*@
9452: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
9454: Logically Collective
9456: Input Parameters:
9457: + n - the number of matrices
9458: . mat - the array of matrices
9459: - nullsp - an array of null spaces, of length 3 * `n`
9461: Level: developer
9463: Notes:
9464: Call `MatGetNullSpaces()` to create `nullsp`.
9466: Frees `nullsp`.
9468: Developer Note:
9469: The name of this function is confusing. Traditionally in PETSc, a restore operation undoes something that was previously done on an object (or objects) with a get operation.
9470: This restore routine does something to a new set of objects using the results of a get operation on a previous set of objects. Perhaps this routine
9471: should have simply been called `MatSetNullSpaces()`
9473: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9474: `MatNullSpaceRemove()`, `MatGetNullSpaces()`, `MatNullSpace`
9475: @*/
9476: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9477: {
9478: PetscFunctionBegin;
9479: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9480: PetscAssertPointer(mat, 2);
9481: PetscAssertPointer(nullsp, 3);
9482: PetscAssertPointer(*nullsp, 3);
9484: for (PetscInt i = 0; i < n; i++) {
9486: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
9487: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9488: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9489: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9490: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9491: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9492: }
9493: PetscCall(PetscFree(*nullsp));
9494: PetscFunctionReturn(PETSC_SUCCESS);
9495: }
9497: /*@
9498: MatSetNullSpace - attaches a null space to a matrix.
9500: Logically Collective
9502: Input Parameters:
9503: + mat - the matrix
9504: - nullsp - the null space object
9506: Level: advanced
9508: Notes:
9509: This null space is used by the `KSP` linear solvers to solve singular systems.
9511: 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`
9513: 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
9514: to zero but the linear system will still be solved in a least squares sense.
9516: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9517: 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 $A^T$, $R(A^T)$.
9518: 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
9519: $n(A^T))$ 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
9520: 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 $n(A^T)$.
9521: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9523: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9524: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9525: routine also automatically calls `MatSetTransposeNullSpace()`.
9527: The user should call `MatNullSpaceDestroy()`.
9529: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9530: `KSPSetPCSide()`, `MatNullSpace`
9531: @*/
9532: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9533: {
9534: PetscFunctionBegin;
9537: PetscCall(PetscObjectReference((PetscObject)nullsp));
9538: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9539: mat->nullsp = nullsp;
9540: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9541: PetscFunctionReturn(PETSC_SUCCESS);
9542: }
9544: /*@
9545: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix that was set with `MatSetTransposeNullSpace()`
9547: Logically Collective
9549: Input Parameters:
9550: + mat - the matrix
9551: - nullsp - the null space object
9553: Level: developer
9555: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9556: @*/
9557: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9558: {
9559: PetscFunctionBegin;
9562: PetscAssertPointer(nullsp, 2);
9563: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9564: PetscFunctionReturn(PETSC_SUCCESS);
9565: }
9567: /*@
9568: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9570: Logically Collective
9572: Input Parameters:
9573: + mat - the matrix
9574: - nullsp - the null space object
9576: Level: advanced
9578: Notes:
9579: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9581: See `MatSetNullSpace()`
9583: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9584: @*/
9585: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9586: {
9587: PetscFunctionBegin;
9590: PetscCall(PetscObjectReference((PetscObject)nullsp));
9591: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9592: mat->transnullsp = nullsp;
9593: PetscFunctionReturn(PETSC_SUCCESS);
9594: }
9596: /*@
9597: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9598: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9600: Logically Collective
9602: Input Parameters:
9603: + mat - the matrix
9604: - nullsp - the null space object
9606: Level: advanced
9608: Notes:
9609: Overwrites any previous near null space that may have been attached
9611: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9613: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9614: @*/
9615: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9616: {
9617: PetscFunctionBegin;
9621: MatCheckPreallocated(mat, 1);
9622: PetscCall(PetscObjectReference((PetscObject)nullsp));
9623: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9624: mat->nearnullsp = nullsp;
9625: PetscFunctionReturn(PETSC_SUCCESS);
9626: }
9628: /*@
9629: MatGetNearNullSpace - Get null space from a matrix that was attached with `MatSetNearNullSpace()`
9631: Not Collective
9633: Input Parameter:
9634: . mat - the matrix
9636: Output Parameter:
9637: . nullsp - the null space object, `NULL` if not set
9639: Level: advanced
9641: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9642: @*/
9643: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9644: {
9645: PetscFunctionBegin;
9648: PetscAssertPointer(nullsp, 2);
9649: MatCheckPreallocated(mat, 1);
9650: *nullsp = mat->nearnullsp;
9651: PetscFunctionReturn(PETSC_SUCCESS);
9652: }
9654: /*@
9655: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9657: Collective
9659: Input Parameters:
9660: + mat - the matrix
9661: . row - row/column permutation
9662: - info - information on desired factorization process
9664: Level: developer
9666: Notes:
9667: Probably really in-place only when level of fill is zero, otherwise allocates
9668: new space to store factored matrix and deletes previous memory.
9670: Most users should employ the `KSP` interface for linear solvers
9671: instead of working directly with matrix algebra routines such as this.
9672: See, e.g., `KSPCreate()`.
9674: Fortran Note:
9675: A valid (non-null) `info` argument must be provided
9677: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9678: @*/
9679: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9680: {
9681: PetscFunctionBegin;
9685: PetscAssertPointer(info, 3);
9686: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9687: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9688: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9689: MatCheckPreallocated(mat, 1);
9690: PetscUseTypeMethod(mat, iccfactor, row, info);
9691: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9692: PetscFunctionReturn(PETSC_SUCCESS);
9693: }
9695: /*@
9696: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9697: ghosted ones.
9699: Not Collective
9701: Input Parameters:
9702: + mat - the matrix
9703: - diag - the diagonal values, including ghost ones
9705: Level: developer
9707: Notes:
9708: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9710: `diag` is a sequential vector that has a length which is the same as the local (ghosted) length of the vector associated with
9711: the matrix's `ISLocalToGlobalMapping` set with `MatSetLocalToGlobalMapping()`.
9713: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`.
9715: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`, `MatSetLocalToGlobalMapping()`, `ISLocalToGlobalMapping`
9716: @*/
9717: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9718: {
9719: PetscMPIInt size;
9721: PetscFunctionBegin;
9726: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9727: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9728: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9729: if (size == 1) {
9730: PetscInt n, m;
9731: PetscCall(VecGetSize(diag, &n));
9732: PetscCall(MatGetSize(mat, NULL, &m));
9733: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9734: PetscCall(MatDiagonalScale(mat, NULL, diag));
9735: } else PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9736: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9737: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9738: PetscFunctionReturn(PETSC_SUCCESS);
9739: }
9741: /*@
9742: MatGetInertia - Gets the inertia from a factored matrix
9744: Collective
9746: Input Parameter:
9747: . mat - the matrix
9749: Output Parameters:
9750: + nneg - number of negative eigenvalues
9751: . nzero - number of zero eigenvalues
9752: - npos - number of positive eigenvalues
9754: Level: advanced
9756: Note:
9757: Matrix must have been factored by `MatCholeskyFactor()`
9759: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9760: @*/
9761: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9762: {
9763: PetscFunctionBegin;
9766: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9767: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9768: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9769: PetscFunctionReturn(PETSC_SUCCESS);
9770: }
9772: /*@
9773: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9775: Neighbor-wise Collective
9777: Input Parameters:
9778: + mat - the factored matrix obtained with `MatGetFactor()`
9779: - b - the right-hand-side vectors
9781: Output Parameter:
9782: . x - the result vectors
9784: Level: developer
9786: Note:
9787: The vectors `b` and `x` cannot be the same. I.e., one cannot
9788: call `MatSolves`(A,x,x).
9790: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatGetFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9791: @*/
9792: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9793: {
9794: PetscFunctionBegin;
9797: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9798: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9799: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9801: MatCheckPreallocated(mat, 1);
9802: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9803: PetscUseTypeMethod(mat, solves, b, x);
9804: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9805: PetscFunctionReturn(PETSC_SUCCESS);
9806: }
9808: /*@
9809: MatIsSymmetric - Test whether a matrix is symmetric
9811: Collective
9813: Input Parameters:
9814: + A - the matrix to test
9815: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9817: Output Parameter:
9818: . flg - the result
9820: Level: intermediate
9822: Notes:
9823: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9825: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9827: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9828: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`). If these properties
9829: have been set then `MatIsSymmetric()` does not need to perform any computations and returns immediately with the result.
9831: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9832: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9833: @*/
9834: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9835: {
9836: PetscFunctionBegin;
9838: PetscAssertPointer(flg, 3);
9839: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9840: else {
9841: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9842: else PetscCall(MatIsTranspose(A, A, tol, flg));
9843: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9844: }
9845: PetscFunctionReturn(PETSC_SUCCESS);
9846: }
9848: /*@
9849: MatIsHermitian - Test whether a matrix is Hermitian
9851: Collective
9853: Input Parameters:
9854: + A - the matrix to test
9855: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9857: Output Parameter:
9858: . flg - the result
9860: Level: intermediate
9862: Notes:
9863: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9865: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9867: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9868: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`). If these properties
9869: have been set then `MatIsHermitian()` does not need to perform any computations and returns immediately with the result.
9871: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9872: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9873: @*/
9874: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9875: {
9876: PetscFunctionBegin;
9878: PetscAssertPointer(flg, 3);
9879: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9880: else {
9881: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9882: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9883: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9884: }
9885: PetscFunctionReturn(PETSC_SUCCESS);
9886: }
9888: /*@
9889: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9891: Not Collective
9893: Input Parameter:
9894: . A - the matrix to check
9896: Output Parameters:
9897: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9898: - flg - the result (only valid if set is `PETSC_TRUE`)
9900: Level: advanced
9902: Notes:
9903: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9904: if you want it explicitly checked
9906: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9907: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9909: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9910: @*/
9911: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9912: {
9913: PetscFunctionBegin;
9915: PetscAssertPointer(set, 2);
9916: PetscAssertPointer(flg, 3);
9917: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9918: *set = PETSC_TRUE;
9919: *flg = PetscBool3ToBool(A->symmetric);
9920: } else *set = PETSC_FALSE;
9921: PetscFunctionReturn(PETSC_SUCCESS);
9922: }
9924: /*@
9925: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9927: Not Collective
9929: Input Parameter:
9930: . A - the matrix to check
9932: Output Parameters:
9933: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9934: - flg - the result (only valid if set is `PETSC_TRUE`)
9936: Level: advanced
9938: Notes:
9939: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9941: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9942: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9944: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9945: @*/
9946: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9947: {
9948: PetscFunctionBegin;
9950: PetscAssertPointer(set, 2);
9951: PetscAssertPointer(flg, 3);
9952: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9953: *set = PETSC_TRUE;
9954: *flg = PetscBool3ToBool(A->spd);
9955: } else *set = PETSC_FALSE;
9956: PetscFunctionReturn(PETSC_SUCCESS);
9957: }
9959: /*@
9960: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9962: Not Collective
9964: Input Parameter:
9965: . A - the matrix to check
9967: Output Parameters:
9968: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9969: - flg - the result (only valid if set is `PETSC_TRUE`)
9971: Level: advanced
9973: Notes:
9974: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9975: if you want it explicitly checked
9977: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9978: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9980: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9981: @*/
9982: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9983: {
9984: PetscFunctionBegin;
9986: PetscAssertPointer(set, 2);
9987: PetscAssertPointer(flg, 3);
9988: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9989: *set = PETSC_TRUE;
9990: *flg = PetscBool3ToBool(A->hermitian);
9991: } else *set = PETSC_FALSE;
9992: PetscFunctionReturn(PETSC_SUCCESS);
9993: }
9995: /*@
9996: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9998: Collective
10000: Input Parameter:
10001: . A - the matrix to test
10003: Output Parameter:
10004: . flg - the result
10006: Level: intermediate
10008: Notes:
10009: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
10011: 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
10012: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`). If these properties
10013: have been set then `MatIsStructurallySymmetric()` does not need to perform any computations and returns immediately with the result.
10015: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
10016: @*/
10017: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
10018: {
10019: PetscFunctionBegin;
10021: PetscAssertPointer(flg, 2);
10022: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) *flg = PetscBool3ToBool(A->structurally_symmetric);
10023: else {
10024: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
10025: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
10026: }
10027: PetscFunctionReturn(PETSC_SUCCESS);
10028: }
10030: /*@
10031: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
10033: Not Collective
10035: Input Parameter:
10036: . A - the matrix to check
10038: Output Parameters:
10039: + set - `PETSC_TRUE` if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
10040: - flg - the result (only valid if set is `PETSC_TRUE`)
10042: Level: advanced
10044: Notes:
10045: 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
10046: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
10048: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
10050: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
10051: @*/
10052: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
10053: {
10054: PetscFunctionBegin;
10056: PetscAssertPointer(set, 2);
10057: PetscAssertPointer(flg, 3);
10058: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
10059: *set = PETSC_TRUE;
10060: *flg = PetscBool3ToBool(A->structurally_symmetric);
10061: } else *set = PETSC_FALSE;
10062: PetscFunctionReturn(PETSC_SUCCESS);
10063: }
10065: /*@
10066: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
10067: to be communicated to other processes during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
10069: Not Collective
10071: Input Parameter:
10072: . mat - the matrix
10074: Output Parameters:
10075: + nstash - the size of the stash
10076: . reallocs - the number of additional mallocs incurred.
10077: . bnstash - the size of the block stash
10078: - breallocs - the number of additional mallocs incurred.in the block stash
10080: Level: advanced
10082: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
10083: @*/
10084: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
10085: {
10086: PetscFunctionBegin;
10087: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
10088: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
10089: PetscFunctionReturn(PETSC_SUCCESS);
10090: }
10092: /*@
10093: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
10094: parallel layout, `PetscLayout` for rows and columns
10096: Collective
10098: Input Parameter:
10099: . mat - the matrix
10101: Output Parameters:
10102: + right - (optional) vector that the matrix can be multiplied against
10103: - left - (optional) vector that the matrix vector product can be stored in
10105: Options Database Key:
10106: . -mat_vec_type type - set the `VecType` of the created vectors during `MatSetFromOptions()`
10108: Level: advanced
10110: Notes:
10111: 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()`.
10113: 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`.
10115: These are new vectors which are not owned by the `mat`, they should be destroyed with `VecDestroy()` when no longer needed.
10117: PETSc `Vec` always have all zero entries when created with `MatCreateVecs()` until routines such as `VecSet()` or `VecSetValues()`
10118: are used to change the values. There is no reason to call `VecZeroEntries()` after creation.
10120: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`, `MatSetVecType()`
10121: @*/
10122: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
10123: {
10124: PetscFunctionBegin;
10127: if (mat->ops->getvecs) {
10128: PetscUseTypeMethod(mat, getvecs, right, left);
10129: } else {
10130: if (right) {
10131: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
10132: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
10133: PetscCall(VecSetType(*right, mat->defaultvectype));
10134: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
10135: if (mat->boundtocpu && mat->bindingpropagates) {
10136: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
10137: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
10138: }
10139: #endif
10140: }
10141: if (left) {
10142: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
10143: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
10144: PetscCall(VecSetType(*left, mat->defaultvectype));
10145: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
10146: if (mat->boundtocpu && mat->bindingpropagates) {
10147: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
10148: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
10149: }
10150: #endif
10151: }
10152: }
10153: PetscFunctionReturn(PETSC_SUCCESS);
10154: }
10156: /*@
10157: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
10158: with default values.
10160: Not Collective
10162: Input Parameter:
10163: . info - the `MatFactorInfo` data structure
10165: Level: developer
10167: Notes:
10168: The solvers are generally used through the `KSP` and `PC` objects, for example
10169: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
10171: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
10173: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
10174: @*/
10175: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
10176: {
10177: PetscFunctionBegin;
10178: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
10179: PetscFunctionReturn(PETSC_SUCCESS);
10180: }
10182: /*@
10183: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
10185: Collective
10187: Input Parameters:
10188: + mat - the factored matrix
10189: - is - the index set defining the Schur indices (0-based)
10191: Level: advanced
10193: Notes:
10194: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
10196: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
10198: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
10200: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
10201: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
10202: @*/
10203: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
10204: {
10205: PetscErrorCode (*f)(Mat, IS);
10207: PetscFunctionBegin;
10212: PetscCheckSameComm(mat, 1, is, 2);
10213: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
10214: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
10215: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
10216: PetscCall((*f)(mat, is));
10217: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
10218: PetscFunctionReturn(PETSC_SUCCESS);
10219: }
10221: /*@
10222: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
10224: Logically Collective
10226: Input Parameters:
10227: + F - the factored matrix obtained by calling `MatGetFactor()`
10228: . S - location where to return the Schur complement, can be `NULL`
10229: - status - the status of the Schur complement matrix, can be `NULL`
10231: Level: advanced
10233: Notes:
10234: You must call `MatFactorSetSchurIS()` before calling this routine.
10236: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
10238: The routine provides a copy of the Schur matrix stored within the solver data structures.
10239: The caller must destroy the object when it is no longer needed.
10240: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
10242: 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)
10244: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
10246: Developer Note:
10247: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
10248: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
10250: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
10251: @*/
10252: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
10253: {
10254: PetscFunctionBegin;
10256: if (S) PetscAssertPointer(S, 2);
10257: if (status) PetscAssertPointer(status, 3);
10258: if (S) {
10259: PetscErrorCode (*f)(Mat, Mat *);
10261: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
10262: if (f) PetscCall((*f)(F, S));
10263: else PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
10264: }
10265: if (status) *status = F->schur_status;
10266: PetscFunctionReturn(PETSC_SUCCESS);
10267: }
10269: /*@
10270: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
10272: Logically Collective
10274: Input Parameters:
10275: + F - the factored matrix obtained by calling `MatGetFactor()`
10276: . S - location where to return the Schur complement, can be `NULL`
10277: - status - the status of the Schur complement matrix, can be `NULL`
10279: Level: advanced
10281: Notes:
10282: You must call `MatFactorSetSchurIS()` before calling this routine.
10284: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
10286: The routine returns a the Schur Complement stored within the data structures of the solver.
10288: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
10290: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
10292: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
10294: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
10296: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
10297: @*/
10298: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
10299: {
10300: PetscFunctionBegin;
10302: if (S) {
10303: PetscAssertPointer(S, 2);
10304: *S = F->schur;
10305: }
10306: if (status) {
10307: PetscAssertPointer(status, 3);
10308: *status = F->schur_status;
10309: }
10310: PetscFunctionReturn(PETSC_SUCCESS);
10311: }
10313: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
10314: {
10315: Mat S = F->schur;
10317: PetscFunctionBegin;
10318: switch (F->schur_status) {
10319: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
10320: case MAT_FACTOR_SCHUR_INVERTED:
10321: if (S) {
10322: S->ops->solve = NULL;
10323: S->ops->matsolve = NULL;
10324: S->ops->solvetranspose = NULL;
10325: S->ops->matsolvetranspose = NULL;
10326: S->ops->solveadd = NULL;
10327: S->ops->solvetransposeadd = NULL;
10328: S->factortype = MAT_FACTOR_NONE;
10329: PetscCall(PetscFree(S->solvertype));
10330: }
10331: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
10332: break;
10333: default:
10334: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10335: }
10336: PetscFunctionReturn(PETSC_SUCCESS);
10337: }
10339: /*@
10340: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
10342: Logically Collective
10344: Input Parameters:
10345: + F - the factored matrix obtained by calling `MatGetFactor()`
10346: . S - location where the Schur complement is stored
10347: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
10349: Level: advanced
10351: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
10352: @*/
10353: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
10354: {
10355: PetscFunctionBegin;
10357: if (S) {
10359: *S = NULL;
10360: }
10361: F->schur_status = status;
10362: PetscCall(MatFactorUpdateSchurStatus_Private(F));
10363: PetscFunctionReturn(PETSC_SUCCESS);
10364: }
10366: /*@
10367: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
10369: Logically Collective
10371: Input Parameters:
10372: + F - the factored matrix obtained by calling `MatGetFactor()`
10373: . rhs - location where the right-hand side of the Schur complement system is stored
10374: - sol - location where the solution of the Schur complement system has to be returned
10376: Level: advanced
10378: Notes:
10379: The sizes of the vectors should match the size of the Schur complement
10381: Must be called after `MatFactorSetSchurIS()`
10383: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
10384: @*/
10385: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
10386: {
10387: PetscFunctionBegin;
10394: PetscCheckSameComm(F, 1, rhs, 2);
10395: PetscCheckSameComm(F, 1, sol, 3);
10396: PetscCall(MatFactorFactorizeSchurComplement(F));
10397: switch (F->schur_status) {
10398: case MAT_FACTOR_SCHUR_FACTORED:
10399: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
10400: break;
10401: case MAT_FACTOR_SCHUR_INVERTED:
10402: PetscCall(MatMultTranspose(F->schur, rhs, sol));
10403: break;
10404: default:
10405: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10406: }
10407: PetscFunctionReturn(PETSC_SUCCESS);
10408: }
10410: /*@
10411: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
10413: Logically Collective
10415: Input Parameters:
10416: + F - the factored matrix obtained by calling `MatGetFactor()`
10417: . rhs - location where the right-hand side of the Schur complement system is stored
10418: - sol - location where the solution of the Schur complement system has to be returned
10420: Level: advanced
10422: Notes:
10423: The sizes of the vectors should match the size of the Schur complement
10425: Must be called after `MatFactorSetSchurIS()`
10427: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
10428: @*/
10429: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
10430: {
10431: PetscFunctionBegin;
10438: PetscCheckSameComm(F, 1, rhs, 2);
10439: PetscCheckSameComm(F, 1, sol, 3);
10440: PetscCall(MatFactorFactorizeSchurComplement(F));
10441: switch (F->schur_status) {
10442: case MAT_FACTOR_SCHUR_FACTORED:
10443: PetscCall(MatSolve(F->schur, rhs, sol));
10444: break;
10445: case MAT_FACTOR_SCHUR_INVERTED:
10446: PetscCall(MatMult(F->schur, rhs, sol));
10447: break;
10448: default:
10449: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10450: }
10451: PetscFunctionReturn(PETSC_SUCCESS);
10452: }
10454: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
10455: #if PetscDefined(HAVE_CUDA)
10456: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
10457: #endif
10459: /* Schur status updated in the interface */
10460: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
10461: {
10462: Mat S = F->schur;
10464: PetscFunctionBegin;
10465: if (S) {
10466: PetscMPIInt size;
10467: PetscBool isdense, isdensecuda;
10469: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
10470: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
10471: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
10472: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
10473: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
10474: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
10475: if (isdense) {
10476: PetscCall(MatSeqDenseInvertFactors_Private(S));
10477: } else if (isdensecuda) {
10478: #if PetscDefined(HAVE_CUDA)
10479: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
10480: #endif
10481: }
10482: // HIP??????????????
10483: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
10484: }
10485: PetscFunctionReturn(PETSC_SUCCESS);
10486: }
10488: /*@
10489: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
10491: Logically Collective
10493: Input Parameter:
10494: . F - the factored matrix obtained by calling `MatGetFactor()`
10496: Level: advanced
10498: Notes:
10499: Must be called after `MatFactorSetSchurIS()`.
10501: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
10503: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10504: @*/
10505: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10506: {
10507: PetscFunctionBegin;
10510: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10511: PetscCall(MatFactorFactorizeSchurComplement(F));
10512: PetscCall(MatFactorInvertSchurComplement_Private(F));
10513: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10514: PetscFunctionReturn(PETSC_SUCCESS);
10515: }
10517: /*@
10518: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10520: Logically Collective
10522: Input Parameter:
10523: . F - the factored matrix obtained by calling `MatGetFactor()`
10525: Level: advanced
10527: Note:
10528: Must be called after `MatFactorSetSchurIS()`
10530: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10531: @*/
10532: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10533: {
10534: MatFactorInfo info;
10536: PetscFunctionBegin;
10539: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10540: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10541: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10542: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10543: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10544: } else {
10545: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10546: }
10547: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10548: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10549: PetscFunctionReturn(PETSC_SUCCESS);
10550: }
10552: /*@
10553: MatPtAP - Creates the matrix product $C = P^T * A * P$
10555: Neighbor-wise Collective
10557: Input Parameters:
10558: + A - the matrix
10559: . P - the projection matrix
10560: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10561: - 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
10562: if the result is a dense matrix this is irrelevant
10564: Output Parameter:
10565: . C - the product matrix
10567: Level: intermediate
10569: Notes:
10570: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10572: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_PtAP`
10573: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10575: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10577: Developer Note:
10578: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10580: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10581: @*/
10582: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10583: {
10584: PetscFunctionBegin;
10585: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10586: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10588: if (scall == MAT_INITIAL_MATRIX) {
10589: PetscCall(MatProductCreate(A, P, NULL, C));
10590: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10591: PetscCall(MatProductSetAlgorithm(*C, "default"));
10592: PetscCall(MatProductSetFill(*C, fill));
10594: (*C)->product->api_user = PETSC_TRUE;
10595: PetscCall(MatProductSetFromOptions(*C));
10596: 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);
10597: PetscCall(MatProductSymbolic(*C));
10598: } else { /* scall == MAT_REUSE_MATRIX */
10599: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10600: }
10602: PetscCall(MatProductNumeric(*C));
10603: if (A->symmetric == PETSC_BOOL3_TRUE) {
10604: PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10605: (*C)->spd = A->spd;
10606: }
10607: PetscFunctionReturn(PETSC_SUCCESS);
10608: }
10610: /*@
10611: MatRARt - Creates the matrix product $C = R * A * R^T$
10613: Neighbor-wise Collective
10615: Input Parameters:
10616: + A - the matrix
10617: . R - the projection matrix
10618: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10619: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10620: if the result is a dense matrix this is irrelevant
10622: Output Parameter:
10623: . C - the product matrix
10625: Level: intermediate
10627: Notes:
10628: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10630: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_RARt`
10631: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10633: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10634: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10635: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10636: We recommend using `MatPtAP()` when possible.
10638: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10640: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10641: @*/
10642: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10643: {
10644: PetscFunctionBegin;
10645: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10646: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10648: if (scall == MAT_INITIAL_MATRIX) {
10649: PetscCall(MatProductCreate(A, R, NULL, C));
10650: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10651: PetscCall(MatProductSetAlgorithm(*C, "default"));
10652: PetscCall(MatProductSetFill(*C, fill));
10654: (*C)->product->api_user = PETSC_TRUE;
10655: PetscCall(MatProductSetFromOptions(*C));
10656: 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);
10657: PetscCall(MatProductSymbolic(*C));
10658: } else { /* scall == MAT_REUSE_MATRIX */
10659: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10660: }
10662: PetscCall(MatProductNumeric(*C));
10663: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10664: PetscFunctionReturn(PETSC_SUCCESS);
10665: }
10667: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10668: {
10669: PetscBool flg = PETSC_TRUE;
10671: PetscFunctionBegin;
10672: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10673: if (scall == MAT_INITIAL_MATRIX) {
10674: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10675: PetscCall(MatProductCreate(A, B, NULL, C));
10676: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10677: PetscCall(MatProductSetFill(*C, fill));
10678: } else { /* scall == MAT_REUSE_MATRIX */
10679: Mat_Product *product = (*C)->product;
10681: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10682: if (flg && product && product->type != ptype) {
10683: PetscCall(MatProductClear(*C));
10684: product = NULL;
10685: }
10686: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10687: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10688: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10689: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10690: product = (*C)->product;
10691: product->fill = fill;
10692: product->clear = PETSC_TRUE;
10693: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10694: flg = PETSC_FALSE;
10695: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10696: }
10697: }
10698: if (flg) {
10699: (*C)->product->api_user = PETSC_TRUE;
10700: PetscCall(MatProductSetType(*C, ptype));
10701: PetscCall(MatProductSetFromOptions(*C));
10702: PetscCall(MatProductSymbolic(*C));
10703: }
10704: PetscCall(MatProductNumeric(*C));
10705: PetscFunctionReturn(PETSC_SUCCESS);
10706: }
10708: /*@
10709: MatMatMult - Performs matrix-matrix multiplication $ C=A*B $.
10711: Neighbor-wise Collective
10713: Input Parameters:
10714: + A - the left matrix
10715: . B - the right matrix
10716: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10717: - 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
10718: if the result is a dense matrix this is irrelevant
10720: Output Parameter:
10721: . C - the product matrix
10723: Notes:
10724: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10726: `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
10727: call to this function with `MAT_INITIAL_MATRIX`.
10729: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10731: 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`,
10732: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10734: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10736: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AB`
10737: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10739: Example of Usage:
10740: .vb
10741: MatProductCreate(A,B,NULL,&C);
10742: MatProductSetType(C,MATPRODUCT_AB);
10743: MatProductSymbolic(C);
10744: MatProductNumeric(C); // compute C=A * B
10745: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10746: MatProductNumeric(C);
10747: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10748: MatProductNumeric(C);
10749: .ve
10751: Level: intermediate
10753: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10754: @*/
10755: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10756: {
10757: PetscFunctionBegin;
10758: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10759: PetscFunctionReturn(PETSC_SUCCESS);
10760: }
10762: /*@
10763: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10765: Neighbor-wise Collective
10767: Input Parameters:
10768: + A - the left matrix
10769: . B - the right matrix
10770: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10771: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10773: Output Parameter:
10774: . C - the product matrix
10776: Options Database Key:
10777: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10778: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10779: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10781: Level: intermediate
10783: Notes:
10784: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10786: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10788: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10789: actually needed.
10791: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10792: and for pairs of `MATMPIDENSE` matrices.
10794: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABt`
10795: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10797: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10799: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()`, `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10800: @*/
10801: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10802: {
10803: PetscFunctionBegin;
10804: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10805: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10806: PetscFunctionReturn(PETSC_SUCCESS);
10807: }
10809: /*@
10810: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10812: Neighbor-wise Collective
10814: Input Parameters:
10815: + A - the left matrix
10816: . B - the right matrix
10817: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10818: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10820: Output Parameter:
10821: . C - the product matrix
10823: Level: intermediate
10825: Notes:
10826: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10828: `MAT_REUSE_MATRIX` can only be used if `A` and `B` have the same nonzero pattern as in the previous call.
10830: This is a convenience routine that wraps the use of `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AtB`
10831: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10833: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10834: actually needed.
10836: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10837: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10839: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10841: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10842: @*/
10843: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10844: {
10845: PetscFunctionBegin;
10846: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10847: PetscFunctionReturn(PETSC_SUCCESS);
10848: }
10850: /*@
10851: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10853: Neighbor-wise Collective
10855: Input Parameters:
10856: + A - the left matrix
10857: . B - the middle matrix
10858: . C - the right matrix
10859: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10860: - 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
10861: if the result is a dense matrix this is irrelevant
10863: Output Parameter:
10864: . D - the product matrix
10866: Level: intermediate
10868: Notes:
10869: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10871: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10873: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABC`
10874: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10876: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10877: actually needed.
10879: If you have many matrices with the same non-zero structure to multiply, you
10880: should use `MAT_REUSE_MATRIX` in all calls but the first
10882: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10884: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10885: @*/
10886: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10887: {
10888: PetscFunctionBegin;
10889: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10890: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10892: if (scall == MAT_INITIAL_MATRIX) {
10893: PetscCall(MatProductCreate(A, B, C, D));
10894: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10895: PetscCall(MatProductSetAlgorithm(*D, "default"));
10896: PetscCall(MatProductSetFill(*D, fill));
10898: (*D)->product->api_user = PETSC_TRUE;
10899: PetscCall(MatProductSetFromOptions(*D));
10900: 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,
10901: ((PetscObject)C)->type_name);
10902: PetscCall(MatProductSymbolic(*D));
10903: } else { /* user may change input matrices when REUSE */
10904: PetscCall(MatProductReplaceMats(A, B, C, *D));
10905: }
10906: PetscCall(MatProductNumeric(*D));
10907: PetscFunctionReturn(PETSC_SUCCESS);
10908: }
10910: /*@
10911: MatCreateRedundantMatrix - Create redundant matrices and put them into processes of subcommunicators.
10913: Collective
10915: Input Parameters:
10916: + mat - the matrix
10917: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10918: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10919: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10921: Output Parameter:
10922: . matredundant - redundant matrix
10924: Level: advanced
10926: Notes:
10927: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10928: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10930: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10931: calling it.
10933: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10935: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10936: @*/
10937: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10938: {
10939: MPI_Comm comm;
10940: PetscMPIInt size;
10941: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10942: Mat_Redundant *redund = NULL;
10943: PetscSubcomm psubcomm = NULL;
10944: MPI_Comm subcomm_in = subcomm;
10945: Mat *matseq;
10946: IS isrow, iscol;
10947: PetscBool newsubcomm = PETSC_FALSE;
10949: PetscFunctionBegin;
10951: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10952: PetscAssertPointer(*matredundant, 5);
10954: }
10956: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10957: if (size == 1 || nsubcomm == 1) {
10958: if (reuse == MAT_INITIAL_MATRIX) {
10959: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10960: } else {
10961: 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");
10962: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10963: }
10964: PetscFunctionReturn(PETSC_SUCCESS);
10965: }
10967: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10968: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10969: MatCheckPreallocated(mat, 1);
10971: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10972: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10973: /* create psubcomm, then get subcomm */
10974: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10975: PetscCallMPI(MPI_Comm_size(comm, &size));
10976: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10978: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10979: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10980: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10981: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10982: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10983: newsubcomm = PETSC_TRUE;
10984: PetscCall(PetscSubcommDestroy(&psubcomm));
10985: }
10987: /* get isrow, iscol and a local sequential matrix matseq[0] */
10988: if (reuse == MAT_INITIAL_MATRIX) {
10989: mloc_sub = PETSC_DECIDE;
10990: nloc_sub = PETSC_DECIDE;
10991: if (bs < 1) {
10992: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10993: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10994: } else {
10995: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10996: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10997: }
10998: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10999: rstart = rend - mloc_sub;
11000: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
11001: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
11002: PetscCall(ISSetIdentity(iscol));
11003: } else { /* reuse == MAT_REUSE_MATRIX */
11004: 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");
11005: /* retrieve subcomm */
11006: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
11007: redund = (*matredundant)->redundant;
11008: isrow = redund->isrow;
11009: iscol = redund->iscol;
11010: matseq = redund->matseq;
11011: }
11012: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
11014: /* get matredundant over subcomm */
11015: if (reuse == MAT_INITIAL_MATRIX) {
11016: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
11018: /* create a supporting struct and attach it to C for reuse */
11019: PetscCall(PetscNew(&redund));
11020: (*matredundant)->redundant = redund;
11021: redund->isrow = isrow;
11022: redund->iscol = iscol;
11023: redund->matseq = matseq;
11024: if (newsubcomm) {
11025: redund->subcomm = subcomm;
11026: } else {
11027: redund->subcomm = MPI_COMM_NULL;
11028: }
11029: } else {
11030: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
11031: }
11032: #if PetscDefined(HAVE_VIENNACL) || PetscDefined(HAVE_CUDA) || PetscDefined(HAVE_HIP)
11033: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
11034: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
11035: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
11036: }
11037: #endif
11038: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
11039: PetscFunctionReturn(PETSC_SUCCESS);
11040: }
11042: /*@
11043: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
11044: a given `Mat`. Each submatrix can span multiple procs.
11046: Collective
11048: Input Parameters:
11049: + mat - the matrix
11050: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
11051: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11053: Output Parameter:
11054: . subMat - parallel sub-matrices each spanning a given `subcomm`
11056: Level: advanced
11058: Notes:
11059: The submatrix partition across processes is dictated by `subComm` a
11060: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
11061: is not restricted to be grouped with consecutive original MPI processes.
11063: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
11064: map directly to the layout of the original matrix [wrt the local
11065: row,col partitioning]. So the original 'DiagonalMat' naturally maps
11066: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
11067: the `subMat`. However the offDiagMat looses some columns - and this is
11068: reconstructed with `MatSetValues()`
11070: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
11072: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
11073: @*/
11074: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
11075: {
11076: PetscMPIInt commsize, subCommSize;
11078: PetscFunctionBegin;
11079: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
11080: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
11081: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
11083: 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");
11084: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
11085: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
11086: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
11087: PetscFunctionReturn(PETSC_SUCCESS);
11088: }
11090: /*@
11091: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
11093: Not Collective
11095: Input Parameters:
11096: + mat - matrix to extract local submatrix from
11097: . isrow - local row indices for submatrix
11098: - iscol - local column indices for submatrix
11100: Output Parameter:
11101: . submat - the submatrix
11103: Level: intermediate
11105: Notes:
11106: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
11108: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
11109: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
11111: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
11112: `MatSetValuesBlockedLocal()` will also be implemented.
11114: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
11115: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
11117: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
11118: @*/
11119: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
11120: {
11121: PetscFunctionBegin;
11125: PetscCheckSameComm(isrow, 2, iscol, 3);
11126: PetscAssertPointer(submat, 4);
11127: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
11129: if (mat->ops->getlocalsubmatrix) {
11130: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
11131: } else {
11132: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
11133: }
11134: (*submat)->assembled = mat->assembled;
11135: PetscFunctionReturn(PETSC_SUCCESS);
11136: }
11138: /*@
11139: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
11141: Not Collective
11143: Input Parameters:
11144: + mat - matrix to extract local submatrix from
11145: . isrow - local row indices for submatrix
11146: . iscol - local column indices for submatrix
11147: - submat - the submatrix
11149: Level: intermediate
11151: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
11152: @*/
11153: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
11154: {
11155: PetscFunctionBegin;
11159: PetscCheckSameComm(isrow, 2, iscol, 3);
11160: PetscAssertPointer(submat, 4);
11163: if (mat->ops->restorelocalsubmatrix) {
11164: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
11165: } else {
11166: PetscCall(MatDestroy(submat));
11167: }
11168: *submat = NULL;
11169: PetscFunctionReturn(PETSC_SUCCESS);
11170: }
11172: /*@
11173: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
11175: Collective
11177: Input Parameter:
11178: . mat - the matrix
11180: Output Parameter:
11181: . is - if any rows have zero diagonals this contains the list of them
11183: Level: developer
11185: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
11186: @*/
11187: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
11188: {
11189: PetscFunctionBegin;
11192: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11193: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11195: if (!mat->ops->findzerodiagonals) {
11196: Vec diag;
11197: const PetscScalar *a;
11198: PetscInt *rows;
11199: PetscInt rStart, rEnd, r, nrow = 0;
11201: PetscCall(MatCreateVecs(mat, &diag, NULL));
11202: PetscCall(MatGetDiagonal(mat, diag));
11203: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
11204: PetscCall(VecGetArrayRead(diag, &a));
11205: for (r = 0; r < rEnd - rStart; ++r)
11206: if (a[r] == 0.0) ++nrow;
11207: PetscCall(PetscMalloc1(nrow, &rows));
11208: nrow = 0;
11209: for (r = 0; r < rEnd - rStart; ++r)
11210: if (a[r] == 0.0) rows[nrow++] = r + rStart;
11211: PetscCall(VecRestoreArrayRead(diag, &a));
11212: PetscCall(VecDestroy(&diag));
11213: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
11214: } else {
11215: PetscUseTypeMethod(mat, findzerodiagonals, is);
11216: }
11217: PetscFunctionReturn(PETSC_SUCCESS);
11218: }
11220: /*@
11221: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
11223: Collective
11225: Input Parameter:
11226: . mat - the matrix
11228: Output Parameter:
11229: . is - contains the list of rows with off block diagonal entries
11231: Level: developer
11233: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
11234: @*/
11235: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
11236: {
11237: PetscFunctionBegin;
11240: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11241: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11243: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
11244: PetscFunctionReturn(PETSC_SUCCESS);
11245: }
11247: /*@
11248: MatInvertBlockDiagonal - Inverts the block diagonal entries.
11250: Collective; No Fortran Support
11252: Input Parameter:
11253: . mat - the matrix
11255: Output Parameter:
11256: . values - the block inverses in column major order (FORTRAN-like)
11258: Level: advanced
11260: Notes:
11261: The size of the blocks is determined by the block size of the matrix.
11263: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
11265: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
11267: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
11268: @*/
11269: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
11270: {
11271: PetscFunctionBegin;
11273: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11274: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11275: PetscUseTypeMethod(mat, invertblockdiagonal, values);
11276: PetscFunctionReturn(PETSC_SUCCESS);
11277: }
11279: /*@
11280: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
11282: Collective; No Fortran Support
11284: Input Parameters:
11285: + mat - the matrix
11286: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
11287: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
11289: Output Parameter:
11290: . values - the block inverses in column major order (FORTRAN-like)
11292: Level: advanced
11294: Notes:
11295: Use `MatInvertBlockDiagonal()` if all blocks have the same size
11297: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
11299: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
11300: @*/
11301: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
11302: {
11303: PetscFunctionBegin;
11305: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
11306: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
11307: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
11308: PetscFunctionReturn(PETSC_SUCCESS);
11309: }
11311: /*@
11312: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
11314: Collective
11316: Input Parameters:
11317: + A - the matrix
11318: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
11320: Level: advanced
11322: Note:
11323: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
11325: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
11326: @*/
11327: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
11328: {
11329: const PetscScalar *vals;
11330: PetscInt *dnnz;
11331: PetscInt m, rstart, rend, bs, i, j;
11333: PetscFunctionBegin;
11334: PetscCall(MatInvertBlockDiagonal(A, &vals));
11335: PetscCall(MatGetBlockSize(A, &bs));
11336: PetscCall(MatGetLocalSize(A, &m, NULL));
11337: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
11338: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
11339: PetscCall(PetscMalloc1(m / bs, &dnnz));
11340: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
11341: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
11342: PetscCall(PetscFree(dnnz));
11343: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
11344: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
11345: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
11346: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
11347: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
11348: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
11349: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
11350: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
11351: PetscFunctionReturn(PETSC_SUCCESS);
11352: }
11354: /*@
11355: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
11356: via `MatTransposeColoringCreate()`.
11358: Collective
11360: Input Parameter:
11361: . c - coloring context
11363: Level: intermediate
11365: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
11366: @*/
11367: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
11368: {
11369: MatTransposeColoring matcolor = *c;
11371: PetscFunctionBegin;
11372: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
11373: if (--((PetscObject)matcolor)->refct > 0) {
11374: matcolor = NULL;
11375: PetscFunctionReturn(PETSC_SUCCESS);
11376: }
11378: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
11379: PetscCall(PetscFree(matcolor->rows));
11380: PetscCall(PetscFree(matcolor->den2sp));
11381: PetscCall(PetscFree(matcolor->colorforcol));
11382: PetscCall(PetscFree(matcolor->columns));
11383: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
11384: PetscCall(PetscHeaderDestroy(c));
11385: PetscFunctionReturn(PETSC_SUCCESS);
11386: }
11388: /*@
11389: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
11390: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
11391: `MatTransposeColoring` to sparse `B`.
11393: Collective
11395: Input Parameters:
11396: + coloring - coloring context created with `MatTransposeColoringCreate()`
11397: - B - sparse matrix
11399: Output Parameter:
11400: . Btdense - dense matrix $B^T$
11402: Level: developer
11404: Note:
11405: These are used internally for some implementations of `MatRARt()`
11407: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
11408: @*/
11409: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
11410: {
11411: PetscFunctionBegin;
11416: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
11417: PetscFunctionReturn(PETSC_SUCCESS);
11418: }
11420: /*@
11421: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
11422: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
11423: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
11424: $C_{sp}$ from $C_{den}$.
11426: Collective
11428: Input Parameters:
11429: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
11430: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
11432: Output Parameter:
11433: . Csp - sparse matrix
11435: Level: developer
11437: Note:
11438: These are used internally for some implementations of `MatRARt()`
11440: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
11441: @*/
11442: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
11443: {
11444: PetscFunctionBegin;
11449: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
11450: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
11451: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
11452: PetscFunctionReturn(PETSC_SUCCESS);
11453: }
11455: /*@
11456: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
11458: Collective
11460: Input Parameters:
11461: + mat - the matrix product C
11462: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
11464: Output Parameter:
11465: . color - the new coloring context
11467: Level: intermediate
11469: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
11470: `MatTransColoringApplyDenToSp()`
11471: @*/
11472: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
11473: {
11474: MatTransposeColoring c;
11475: MPI_Comm comm;
11477: PetscFunctionBegin;
11478: PetscAssertPointer(color, 3);
11480: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11481: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
11482: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
11483: c->ctype = iscoloring->ctype;
11484: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
11485: *color = c;
11486: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11487: PetscFunctionReturn(PETSC_SUCCESS);
11488: }
11490: /*@
11491: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
11492: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
11494: Not Collective
11496: Input Parameter:
11497: . mat - the matrix
11499: Output Parameter:
11500: . state - the current state
11502: Level: intermediate
11504: Notes:
11505: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11506: different matrices
11508: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
11510: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
11512: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11513: @*/
11514: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11515: {
11516: PetscFunctionBegin;
11518: *state = mat->nonzerostate;
11519: PetscFunctionReturn(PETSC_SUCCESS);
11520: }
11522: /*@
11523: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11524: matrices from each process
11526: Collective
11528: Input Parameters:
11529: + comm - the communicators the parallel matrix will live on
11530: . seqmat - the input sequential matrices
11531: . n - number of local columns (or `PETSC_DECIDE`)
11532: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11534: Output Parameter:
11535: . mpimat - the parallel matrix generated
11537: Level: developer
11539: Note:
11540: The number of columns of the matrix in EACH process MUST be the same.
11542: .seealso: [](ch_matrices), `Mat`
11543: @*/
11544: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11545: {
11546: PetscMPIInt size;
11548: PetscFunctionBegin;
11549: PetscCallMPI(MPI_Comm_size(comm, &size));
11550: if (size == 1) {
11551: if (reuse == MAT_INITIAL_MATRIX) {
11552: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11553: } else {
11554: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11555: }
11556: PetscFunctionReturn(PETSC_SUCCESS);
11557: }
11559: 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");
11561: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11562: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11563: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11564: PetscFunctionReturn(PETSC_SUCCESS);
11565: }
11567: /*@
11568: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11570: Collective
11572: Input Parameters:
11573: + A - the matrix to create subdomains from
11574: - N - requested number of subdomains
11576: Output Parameters:
11577: + n - number of subdomains resulting on this MPI process
11578: - iss - `IS` list with indices of subdomains on this MPI process
11580: Level: advanced
11582: Note:
11583: The number of subdomains must be smaller than the communicator size
11585: .seealso: [](ch_matrices), `Mat`, `IS`
11586: @*/
11587: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11588: {
11589: MPI_Comm comm, subcomm;
11590: PetscMPIInt size, rank, color;
11591: PetscInt rstart, rend, k;
11593: PetscFunctionBegin;
11594: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11595: PetscCallMPI(MPI_Comm_size(comm, &size));
11596: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11597: 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);
11598: *n = 1;
11599: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11600: color = rank / k;
11601: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11602: PetscCall(PetscMalloc1(1, iss));
11603: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11604: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11605: PetscCallMPI(MPI_Comm_free(&subcomm));
11606: PetscFunctionReturn(PETSC_SUCCESS);
11607: }
11609: /*@
11610: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11612: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11613: If they are not the same, uses `MatMatMatMult()`.
11615: Once the coarse grid problem is constructed, correct for interpolation operators
11616: that are not of full rank, which can legitimately happen in the case of non-nested
11617: geometric multigrid.
11619: Input Parameters:
11620: + restrct - restriction operator
11621: . dA - fine grid matrix
11622: . interpolate - interpolation operator
11623: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11624: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11626: Output Parameter:
11627: . A - the Galerkin coarse matrix
11629: Options Database Key:
11630: . -pc_mg_galerkin (both|pmat|mat|none) - for what matrices the Galerkin process should be used
11632: Level: developer
11634: Note:
11635: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11637: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11638: @*/
11639: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11640: {
11641: IS zerorows;
11642: Vec diag;
11644: PetscFunctionBegin;
11645: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11646: /* Construct the coarse grid matrix */
11647: if (interpolate == restrct) {
11648: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11649: } else {
11650: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11651: }
11653: /* If the interpolation matrix is not of full rank, A will have zero rows.
11654: This can legitimately happen in the case of non-nested geometric multigrid.
11655: In that event, we set the rows of the matrix to the rows of the identity,
11656: ignoring the equations (as the RHS will also be zero). */
11658: PetscCall(MatFindZeroRows(*A, &zerorows));
11660: if (zerorows != NULL) { /* if there are any zero rows */
11661: PetscCall(MatCreateVecs(*A, &diag, NULL));
11662: PetscCall(MatGetDiagonal(*A, diag));
11663: PetscCall(VecISSet(diag, zerorows, 1.0));
11664: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11665: PetscCall(VecDestroy(&diag));
11666: PetscCall(ISDestroy(&zerorows));
11667: }
11668: PetscFunctionReturn(PETSC_SUCCESS);
11669: }
11671: /*@
11672: MatSetOperation - Allows user to set a matrix operation for any matrix type
11674: Logically Collective
11676: Input Parameters:
11677: + mat - the matrix
11678: . op - the name of the operation
11679: - f - the function that provides the operation
11681: Level: developer
11683: Example Usage:
11684: .vb
11685: extern PetscErrorCode usermult(Mat, Vec, Vec);
11687: PetscCall(MatCreateXXX(comm, ..., &A));
11688: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11689: .ve
11691: Notes:
11692: See the file `include/petscmat.h` for a complete list of matrix
11693: operations, which all have the form MATOP_<OPERATION>, where
11694: <OPERATION> is the name (in all capital letters) of the
11695: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11697: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11698: sequence as the usual matrix interface routines, since they
11699: are intended to be accessed via the usual matrix interface
11700: routines, e.g.,
11701: .vb
11702: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11703: .ve
11705: In particular each function MUST return `PETSC_SUCCESS` on success and
11706: nonzero on failure.
11708: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11710: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11711: @*/
11712: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11713: {
11714: PetscFunctionBegin;
11717: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11718: #if !PetscDefined(USE_COMPLEX)
11719: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11720: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11721: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11722: #endif
11723: (((PetscErrorCodeFn **)mat->ops)[op]) = f;
11724: PetscFunctionReturn(PETSC_SUCCESS);
11725: }
11727: /*@
11728: MatGetOperation - Gets a matrix operation for any matrix type.
11730: Not Collective
11732: Input Parameters:
11733: + mat - the matrix
11734: - op - the name of the operation
11736: Output Parameter:
11737: . f - the function that provides the operation
11739: Level: developer
11741: Example Usage:
11742: .vb
11743: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11745: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11746: .ve
11748: Notes:
11749: See the file `include/petscmat.h` for a complete list of matrix
11750: operations, which all have the form MATOP_<OPERATION>, where
11751: <OPERATION> is the name (in all capital letters) of the
11752: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11754: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11756: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11757: @*/
11758: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11759: {
11760: PetscFunctionBegin;
11762: PetscAssertPointer(f, 3);
11763: #if !PetscDefined(USE_COMPLEX)
11764: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11765: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11766: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11767: #endif
11768: *f = (((PetscErrorCodeFn **)mat->ops)[op]);
11769: PetscFunctionReturn(PETSC_SUCCESS);
11770: }
11772: /*@
11773: MatHasOperation - Determines whether the given matrix supports the particular operation.
11775: Not Collective
11777: Input Parameters:
11778: + mat - the matrix
11779: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11781: Output Parameter:
11782: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11784: Level: advanced
11786: Note:
11787: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11789: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11790: @*/
11791: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11792: {
11793: PetscFunctionBegin;
11795: PetscAssertPointer(has, 3);
11796: #if !PetscDefined(USE_COMPLEX)
11797: if (op == MATOP_MULT_HERMITIAN_TRANSPOSE) op = MATOP_MULT_TRANSPOSE;
11798: else if (op == MATOP_MULT_HERMITIAN_TRANS_ADD) op = MATOP_MULT_TRANSPOSE_ADD;
11799: else if (op == MATOP_HERMITIAN_TRANSPOSE) op = MATOP_TRANSPOSE;
11800: #endif
11801: if (op == MATOP_ADOT || op == MATOP_ANORM) {
11802: /* MatADot() and MatANorm() fall back to MatMult() when the type has no method */
11803: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11804: else PetscCall(MatHasOperation(mat, MATOP_MULT, has));
11805: PetscFunctionReturn(PETSC_SUCCESS);
11806: }
11807: if (mat->ops->hasoperation) {
11808: PetscUseTypeMethod(mat, hasoperation, op, has);
11809: } else {
11810: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11811: else {
11812: *has = PETSC_FALSE;
11813: if (op == MATOP_CREATE_SUBMATRIX) {
11814: PetscMPIInt size;
11816: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11817: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11818: }
11819: }
11820: }
11821: PetscFunctionReturn(PETSC_SUCCESS);
11822: }
11824: /*@
11825: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11827: Collective
11829: Input Parameter:
11830: . mat - the matrix
11832: Output Parameter:
11833: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11835: Level: beginner
11837: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11838: @*/
11839: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11840: {
11841: PetscFunctionBegin;
11844: PetscAssertPointer(cong, 2);
11845: if (!mat->rmap || !mat->cmap) {
11846: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11847: PetscFunctionReturn(PETSC_SUCCESS);
11848: }
11849: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11850: PetscCall(PetscLayoutSetUp(mat->rmap));
11851: PetscCall(PetscLayoutSetUp(mat->cmap));
11852: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11853: if (*cong) mat->congruentlayouts = 1;
11854: else mat->congruentlayouts = 0;
11855: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11856: PetscFunctionReturn(PETSC_SUCCESS);
11857: }
11859: /*@
11860: MatSetInf - Set every entry (of a given nonzero pattern) of a matrix to positive infinity.
11862: Logically Collective
11864: Input Parameter:
11865: . A - the matrix
11867: Level: developer
11869: Notes:
11870: Only the dense types (`MATSEQDENSE`, `MATMPIDENSE`, and their device variants) currently implement this operation, which is used to flag a block of solutions that a linear solver failed to compute, as `VecFlag()` does for a single solution.
11872: The state of `A` is increased, so an outer solver that tracks it detects the failure even when the entries were already infinite.
11874: .seealso: `Mat`, `MatZeroEntries()`, `MatSetValues()`, `VecFlag()`
11875: @*/
11876: PetscErrorCode MatSetInf(Mat A)
11877: {
11878: PetscFunctionBegin;
11881: MatCheckPreallocated(A, 1);
11882: PetscUseTypeMethod(A, setinf);
11883: PetscCall(PetscObjectStateIncrease((PetscObject)A));
11884: PetscFunctionReturn(PETSC_SUCCESS);
11885: }
11887: /*@
11888: 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
11889: and possibly removes small values from the graph structure.
11891: Collective
11893: Input Parameters:
11894: + A - the matrix
11895: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11896: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11897: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with $|entries| \le filter$
11898: . num_idx - size of `index` array
11899: - index - array of block indices to use for graph strength of connection weight
11901: Output Parameter:
11902: . graph - the resulting graph
11904: Level: advanced
11906: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11907: @*/
11908: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11909: {
11910: PetscFunctionBegin;
11914: PetscAssertPointer(graph, 7);
11915: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11916: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11917: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11918: PetscFunctionReturn(PETSC_SUCCESS);
11919: }
11921: /*@
11922: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11923: meaning the same memory is used for the matrix, and no new memory is allocated.
11925: Collective
11927: Input Parameters:
11928: + A - the matrix
11929: - 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
11931: Level: intermediate
11933: Developer Note:
11934: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11935: of the arrays in the data structure may be no longer needed to represent the matrix.
11937: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11938: @*/
11939: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11940: {
11941: PetscFunctionBegin;
11943: PetscUseTypeMethod(A, eliminatezeros, keep);
11944: PetscFunctionReturn(PETSC_SUCCESS);
11945: }
11947: /*@
11948: MatGetCurrentMemType - Get the memory location of the matrix
11950: Not Collective, but the result will be the same on all MPI processes
11952: Input Parameter:
11953: . A - the matrix whose memory type we are checking
11955: Output Parameter:
11956: . m - the memory type, see `PetscMemType`
11958: Level: intermediate
11960: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11961: @*/
11962: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11963: {
11964: PetscFunctionBegin;
11966: PetscAssertPointer(m, 2);
11967: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11968: else *m = PETSC_MEMTYPE_HOST;
11969: PetscFunctionReturn(PETSC_SUCCESS);
11970: }