Actual source code: matrix.c
1: /*
2: This is where the abstract matrix operations are defined
3: Portions of this code are under:
4: Copyright (c) 2022 Advanced Micro Devices, Inc. All rights reserved.
5: */
7: #include <petsc/private/matimpl.h>
8: #include <petsc/private/isimpl.h>
9: #include <petsc/private/vecimpl.h>
11: /* Logging support */
12: PetscClassId MAT_CLASSID;
13: PetscClassId MAT_COLORING_CLASSID;
14: PetscClassId MAT_FDCOLORING_CLASSID;
15: PetscClassId MAT_TRANSPOSECOLORING_CLASSID;
17: PetscLogEvent MAT_Mult, MAT_MultAdd, MAT_MultTranspose;
18: PetscLogEvent MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve, MAT_MatTrSolve;
19: PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
20: PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
21: PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
22: PetscLogEvent MAT_QRFactorNumeric, MAT_QRFactorSymbolic, MAT_QRFactor;
23: PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
24: PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_PartitioningND, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
25: PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply, MAT_Transpose, MAT_FDColoringFunction, MAT_CreateSubMat;
26: PetscLogEvent MAT_TransposeColoringCreate;
27: PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
28: PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric, MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
29: PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
30: PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
31: PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
32: PetscLogEvent MAT_MultHermitianTranspose, MAT_MultHermitianTransposeAdd;
33: PetscLogEvent MAT_Getsymtransreduced, MAT_GetBrowsOfAcols;
34: PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
35: PetscLogEvent MAT_GetMultiProcBlock;
36: PetscLogEvent MAT_CUSPARSECopyToGPU, MAT_CUSPARSECopyFromGPU, MAT_CUSPARSEGenerateTranspose, MAT_CUSPARSESolveAnalysis;
37: PetscLogEvent MAT_HIPSPARSECopyToGPU, MAT_HIPSPARSECopyFromGPU, MAT_HIPSPARSEGenerateTranspose, MAT_HIPSPARSESolveAnalysis;
38: PetscLogEvent MAT_PreallCOO, MAT_SetVCOO;
39: PetscLogEvent MAT_CreateGraph;
40: PetscLogEvent MAT_SetValuesBatch;
41: PetscLogEvent MAT_ViennaCLCopyToGPU;
42: PetscLogEvent MAT_CUDACopyToGPU, MAT_HIPCopyToGPU;
43: PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
44: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
45: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
46: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
47: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;
49: const char *const MatFactorTypes[] = {"NONE", "LU", "CHOLESKY", "ILU", "ICC", "ILUDT", "QR", "MatFactorType", "MAT_FACTOR_", NULL};
51: /*@
52: MatSetRandom - Sets all components of a matrix to random numbers.
54: Logically Collective
56: Input Parameters:
57: + x - the matrix
58: - rctx - the `PetscRandom` object, formed by `PetscRandomCreate()`, or `NULL` and
59: it will create one internally.
61: Example:
62: .vb
63: PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
64: MatSetRandom(x,rctx);
65: PetscRandomDestroy(rctx);
66: .ve
68: Level: intermediate
70: Notes:
71: For sparse matrices that have been preallocated but not been assembled, it randomly selects appropriate locations,
73: for sparse matrices that already have nonzero locations, it fills the locations with random numbers.
75: It generates an error if used on unassembled sparse matrices that have not been preallocated.
77: .seealso: [](ch_matrices), `Mat`, `PetscRandom`, `PetscRandomCreate()`, `MatZeroEntries()`, `MatSetValues()`, `PetscRandomDestroy()`
78: @*/
79: PetscErrorCode MatSetRandom(Mat x, PetscRandom rctx)
80: {
81: PetscRandom randObj = NULL;
83: PetscFunctionBegin;
87: MatCheckPreallocated(x, 1);
89: if (!rctx) {
90: MPI_Comm comm;
91: PetscCall(PetscObjectGetComm((PetscObject)x, &comm));
92: PetscCall(PetscRandomCreate(comm, &randObj));
93: PetscCall(PetscRandomSetType(randObj, x->defaultrandtype));
94: PetscCall(PetscRandomSetFromOptions(randObj));
95: rctx = randObj;
96: }
97: PetscCall(PetscLogEventBegin(MAT_SetRandom, x, rctx, 0, 0));
98: PetscUseTypeMethod(x, setrandom, rctx);
99: PetscCall(PetscLogEventEnd(MAT_SetRandom, x, rctx, 0, 0));
101: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
102: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
103: PetscCall(PetscRandomDestroy(&randObj));
104: PetscFunctionReturn(PETSC_SUCCESS);
105: }
107: /*@
108: MatCopyHashToXAIJ - copy hash table entries into an XAIJ matrix type
110: Logically Collective
112: Input Parameter:
113: . A - A matrix in unassembled, hash table form
115: Output Parameter:
116: . B - The XAIJ matrix. This can either be `A` or some matrix of equivalent size, e.g. obtained from `A` via `MatDuplicate()`
118: Example:
119: .vb
120: PetscCall(MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, &B));
121: PetscCall(MatCopyHashToXAIJ(A, B));
122: .ve
124: Level: advanced
126: Notes:
127: If `B` is `A`, then the hash table data structure will be destroyed. `B` is assembled
129: .seealso: [](ch_matrices), `Mat`, `MAT_USE_HASH_TABLE`
130: @*/
131: PetscErrorCode MatCopyHashToXAIJ(Mat A, Mat B)
132: {
133: PetscFunctionBegin;
135: PetscUseTypeMethod(A, copyhashtoxaij, B);
136: PetscFunctionReturn(PETSC_SUCCESS);
137: }
139: /*@
140: MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in
142: Logically Collective
144: Input Parameter:
145: . mat - the factored matrix
147: Output Parameters:
148: + pivot - the pivot value computed
149: - row - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
150: the share the matrix
152: Level: advanced
154: Notes:
155: This routine does not work for factorizations done with external packages.
157: This routine should only be called if `MatGetFactorError()` returns a value of `MAT_FACTOR_NUMERIC_ZEROPIVOT`
159: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
161: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
162: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
163: `MAT_FACTOR_NUMERIC_ZEROPIVOT`
164: @*/
165: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
166: {
167: PetscFunctionBegin;
169: PetscAssertPointer(pivot, 2);
170: PetscAssertPointer(row, 3);
171: *pivot = mat->factorerror_zeropivot_value;
172: *row = mat->factorerror_zeropivot_row;
173: PetscFunctionReturn(PETSC_SUCCESS);
174: }
176: /*@
177: MatFactorGetError - gets the error code from a factorization
179: Logically Collective
181: Input Parameter:
182: . mat - the factored matrix
184: Output Parameter:
185: . err - the error code
187: Level: advanced
189: Note:
190: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
192: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
193: `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
194: @*/
195: PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
196: {
197: PetscFunctionBegin;
199: PetscAssertPointer(err, 2);
200: *err = mat->factorerrortype;
201: PetscFunctionReturn(PETSC_SUCCESS);
202: }
204: /*@
205: MatFactorClearError - clears the error code in a factorization
207: Logically Collective
209: Input Parameter:
210: . mat - the factored matrix
212: Level: developer
214: Note:
215: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
217: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
218: `MatGetErrorCode()`, `MatFactorError`
219: @*/
220: PetscErrorCode MatFactorClearError(Mat mat)
221: {
222: PetscFunctionBegin;
224: mat->factorerrortype = MAT_FACTOR_NOERROR;
225: mat->factorerror_zeropivot_value = 0.0;
226: mat->factorerror_zeropivot_row = 0;
227: PetscFunctionReturn(PETSC_SUCCESS);
228: }
230: PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
231: {
232: Vec r, l;
233: const PetscScalar *al;
234: PetscInt i, nz, gnz, N, n, st;
236: PetscFunctionBegin;
237: PetscCall(MatCreateVecs(mat, &r, &l));
238: if (!cols) { /* nonzero rows */
239: PetscCall(MatGetOwnershipRange(mat, &st, NULL));
240: PetscCall(MatGetSize(mat, &N, NULL));
241: PetscCall(MatGetLocalSize(mat, &n, NULL));
242: PetscCall(VecSet(l, 0.0));
243: PetscCall(VecSetRandom(r, NULL));
244: PetscCall(MatMult(mat, r, l));
245: PetscCall(VecGetArrayRead(l, &al));
246: } else { /* nonzero columns */
247: PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
248: PetscCall(MatGetSize(mat, NULL, &N));
249: PetscCall(MatGetLocalSize(mat, NULL, &n));
250: PetscCall(VecSet(r, 0.0));
251: PetscCall(VecSetRandom(l, NULL));
252: PetscCall(MatMultTranspose(mat, l, r));
253: PetscCall(VecGetArrayRead(r, &al));
254: }
255: if (tol <= 0.0) {
256: for (i = 0, nz = 0; i < n; i++)
257: if (al[i] != 0.0) nz++;
258: } else {
259: for (i = 0, nz = 0; i < n; i++)
260: if (PetscAbsScalar(al[i]) > tol) nz++;
261: }
262: PetscCallMPI(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
263: if (gnz != N) {
264: PetscInt *nzr;
265: PetscCall(PetscMalloc1(nz, &nzr));
266: if (nz) {
267: if (tol < 0) {
268: for (i = 0, nz = 0; i < n; i++)
269: if (al[i] != 0.0) nzr[nz++] = i + st;
270: } else {
271: for (i = 0, nz = 0; i < n; i++)
272: if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
273: }
274: }
275: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
276: } else *nonzero = NULL;
277: if (!cols) { /* nonzero rows */
278: PetscCall(VecRestoreArrayRead(l, &al));
279: } else {
280: PetscCall(VecRestoreArrayRead(r, &al));
281: }
282: PetscCall(VecDestroy(&l));
283: PetscCall(VecDestroy(&r));
284: PetscFunctionReturn(PETSC_SUCCESS);
285: }
287: /*@
288: MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix
290: Input Parameter:
291: . mat - the matrix
293: Output Parameter:
294: . keptrows - the rows that are not completely zero
296: Level: intermediate
298: Note:
299: `keptrows` is set to `NULL` if all rows are nonzero.
301: Developer Note:
302: If `keptrows` is not `NULL`, it must be sorted.
304: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
305: @*/
306: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
307: {
308: PetscFunctionBegin;
311: PetscAssertPointer(keptrows, 2);
312: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
313: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
314: if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
315: else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
316: if (keptrows && *keptrows) PetscCall(ISSetInfo(*keptrows, IS_SORTED, IS_GLOBAL, PETSC_FALSE, PETSC_TRUE));
317: PetscFunctionReturn(PETSC_SUCCESS);
318: }
320: /*@
321: MatFindZeroRows - Locate all rows that are completely zero in the matrix
323: Input Parameter:
324: . mat - the matrix
326: Output Parameter:
327: . zerorows - the rows that are completely zero
329: Level: intermediate
331: Note:
332: `zerorows` is set to `NULL` if no rows are zero.
334: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
335: @*/
336: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
337: {
338: IS keptrows;
339: PetscInt m, n;
341: PetscFunctionBegin;
344: PetscAssertPointer(zerorows, 2);
345: PetscCall(MatFindNonzeroRows(mat, &keptrows));
346: /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
347: In keeping with this convention, we set zerorows to NULL if there are no zero
348: rows. */
349: if (keptrows == NULL) {
350: *zerorows = NULL;
351: } else {
352: PetscCall(MatGetOwnershipRange(mat, &m, &n));
353: PetscCall(ISComplement(keptrows, m, n, zerorows));
354: PetscCall(ISDestroy(&keptrows));
355: }
356: PetscFunctionReturn(PETSC_SUCCESS);
357: }
359: /*@
360: MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling
362: Not Collective
364: Input Parameter:
365: . A - the matrix
367: Output Parameter:
368: . a - the diagonal part (which is a SEQUENTIAL matrix)
370: Level: advanced
372: Notes:
373: See `MatCreateAIJ()` for more information on the "diagonal part" of the matrix.
375: Use caution, as the reference count on the returned matrix is not incremented and it is used as part of `A`'s normal operation.
377: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
378: @*/
379: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
380: {
381: PetscFunctionBegin;
384: PetscAssertPointer(a, 2);
385: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
386: if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
387: else {
388: PetscMPIInt size;
390: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
391: PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
392: *a = A;
393: }
394: PetscFunctionReturn(PETSC_SUCCESS);
395: }
397: /*@
398: MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.
400: Collective
402: Input Parameter:
403: . mat - the matrix
405: Output Parameter:
406: . trace - the sum of the diagonal entries
408: Level: advanced
410: .seealso: [](ch_matrices), `Mat`
411: @*/
412: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
413: {
414: Vec diag;
416: PetscFunctionBegin;
418: PetscAssertPointer(trace, 2);
419: PetscCall(MatCreateVecs(mat, &diag, NULL));
420: PetscCall(MatGetDiagonal(mat, diag));
421: PetscCall(VecSum(diag, trace));
422: PetscCall(VecDestroy(&diag));
423: PetscFunctionReturn(PETSC_SUCCESS);
424: }
426: /*@
427: MatRealPart - Zeros out the imaginary part of the matrix
429: Logically Collective
431: Input Parameter:
432: . mat - the matrix
434: Level: advanced
436: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
437: @*/
438: PetscErrorCode MatRealPart(Mat mat)
439: {
440: PetscFunctionBegin;
443: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
444: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
445: MatCheckPreallocated(mat, 1);
446: PetscUseTypeMethod(mat, realpart);
447: PetscFunctionReturn(PETSC_SUCCESS);
448: }
450: /*@C
451: MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix
453: Collective
455: Input Parameter:
456: . mat - the matrix
458: Output Parameters:
459: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
460: - ghosts - the global indices of the ghost points
462: Level: advanced
464: Note:
465: `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`
467: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
468: @*/
469: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
470: {
471: PetscFunctionBegin;
474: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
475: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
476: if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
477: else {
478: if (nghosts) *nghosts = 0;
479: if (ghosts) *ghosts = NULL;
480: }
481: PetscFunctionReturn(PETSC_SUCCESS);
482: }
484: /*@
485: MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part
487: Logically Collective
489: Input Parameter:
490: . mat - the matrix
492: Level: advanced
494: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
495: @*/
496: PetscErrorCode MatImaginaryPart(Mat mat)
497: {
498: PetscFunctionBegin;
501: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
502: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
503: MatCheckPreallocated(mat, 1);
504: PetscUseTypeMethod(mat, imaginarypart);
505: PetscFunctionReturn(PETSC_SUCCESS);
506: }
508: /*@
509: MatMissingDiagonal - Determine if sparse matrix is missing a diagonal entry (or block entry for `MATBAIJ` and `MATSBAIJ` matrices) in the nonzero structure
511: Not Collective
513: Input Parameter:
514: . mat - the matrix
516: Output Parameters:
517: + missing - is any diagonal entry missing
518: - dd - first diagonal entry that is missing (optional) on this process
520: Level: advanced
522: Note:
523: This does not return diagonal entries that are in the nonzero structure but happen to have a zero numerical value
525: .seealso: [](ch_matrices), `Mat`
526: @*/
527: PetscErrorCode MatMissingDiagonal(Mat mat, PetscBool *missing, PetscInt *dd)
528: {
529: PetscFunctionBegin;
532: PetscAssertPointer(missing, 2);
533: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix %s", ((PetscObject)mat)->type_name);
534: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
535: PetscUseTypeMethod(mat, missingdiagonal, missing, dd);
536: PetscFunctionReturn(PETSC_SUCCESS);
537: }
539: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
540: /*@C
541: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
542: for each row that you get to ensure that your application does
543: not bleed memory.
545: Not Collective
547: Input Parameters:
548: + mat - the matrix
549: - row - the row to get
551: Output Parameters:
552: + ncols - if not `NULL`, the number of nonzeros in `row`
553: . cols - if not `NULL`, the column numbers
554: - vals - if not `NULL`, the numerical values
556: Level: advanced
558: Notes:
559: This routine is provided for people who need to have direct access
560: to the structure of a matrix. We hope that we provide enough
561: high-level matrix routines that few users will need it.
563: `MatGetRow()` always returns 0-based column indices, regardless of
564: whether the internal representation is 0-based (default) or 1-based.
566: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
567: not wish to extract these quantities.
569: The user can only examine the values extracted with `MatGetRow()`;
570: the values CANNOT be altered. To change the matrix entries, one
571: must use `MatSetValues()`.
573: You can only have one call to `MatGetRow()` outstanding for a particular
574: matrix at a time, per processor. `MatGetRow()` can only obtain rows
575: associated with the given processor, it cannot get rows from the
576: other processors; for that we suggest using `MatCreateSubMatrices()`, then
577: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
578: is in the global number of rows.
580: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
582: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
584: Fortran Note:
585: .vb
586: PetscInt, pointer :: cols(:)
587: PetscScalar, pointer :: vals(:)
588: .ve
590: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
591: @*/
592: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
593: {
594: PetscInt incols;
596: PetscFunctionBegin;
599: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
600: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
601: MatCheckPreallocated(mat, 1);
602: PetscCheck(row >= mat->rmap->rstart && row < mat->rmap->rend, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Only for local rows, %" PetscInt_FMT " not in [%" PetscInt_FMT ",%" PetscInt_FMT ")", row, mat->rmap->rstart, mat->rmap->rend);
603: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
604: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
605: if (ncols) *ncols = incols;
606: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
607: PetscFunctionReturn(PETSC_SUCCESS);
608: }
610: /*@
611: MatConjugate - replaces the matrix values with their complex conjugates
613: Logically Collective
615: Input Parameter:
616: . mat - the matrix
618: Level: advanced
620: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
621: @*/
622: PetscErrorCode MatConjugate(Mat mat)
623: {
624: PetscFunctionBegin;
626: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
627: if (PetscDefined(USE_COMPLEX) && mat->hermitian != PETSC_BOOL3_TRUE) {
628: PetscUseTypeMethod(mat, conjugate);
629: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
630: }
631: PetscFunctionReturn(PETSC_SUCCESS);
632: }
634: /*@C
635: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
637: Not Collective
639: Input Parameters:
640: + mat - the matrix
641: . row - the row to get
642: . ncols - the number of nonzeros
643: . cols - the columns of the nonzeros
644: - vals - if nonzero the column values
646: Level: advanced
648: Notes:
649: This routine should be called after you have finished examining the entries.
651: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
652: us of the array after it has been restored. If you pass `NULL`, it will
653: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
655: Fortran Note:
656: .vb
657: PetscInt, pointer :: cols(:)
658: PetscScalar, pointer :: vals(:)
659: .ve
661: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
662: @*/
663: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
664: {
665: PetscFunctionBegin;
667: if (ncols) PetscAssertPointer(ncols, 3);
668: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
669: PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
670: if (ncols) *ncols = 0;
671: if (cols) *cols = NULL;
672: if (vals) *vals = NULL;
673: PetscFunctionReturn(PETSC_SUCCESS);
674: }
676: /*@
677: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
678: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
680: Not Collective
682: Input Parameter:
683: . mat - the matrix
685: Level: advanced
687: Note:
688: The flag is to ensure that users are aware that `MatGetRow()` only provides the upper triangular part of the row for the matrices in `MATSBAIJ` format.
690: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
691: @*/
692: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
693: {
694: PetscFunctionBegin;
697: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
698: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
699: MatCheckPreallocated(mat, 1);
700: PetscTryTypeMethod(mat, getrowuppertriangular);
701: PetscFunctionReturn(PETSC_SUCCESS);
702: }
704: /*@
705: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
707: Not Collective
709: Input Parameter:
710: . mat - the matrix
712: Level: advanced
714: Note:
715: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
717: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
718: @*/
719: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
720: {
721: PetscFunctionBegin;
724: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
725: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
726: MatCheckPreallocated(mat, 1);
727: PetscTryTypeMethod(mat, restorerowuppertriangular);
728: PetscFunctionReturn(PETSC_SUCCESS);
729: }
731: /*@
732: MatSetOptionsPrefix - Sets the prefix used for searching for all
733: `Mat` options in the database.
735: Logically Collective
737: Input Parameters:
738: + A - the matrix
739: - prefix - the prefix to prepend to all option names
741: Level: advanced
743: Notes:
744: A hyphen (-) must NOT be given at the beginning of the prefix name.
745: The first character of all runtime options is AUTOMATICALLY the hyphen.
747: This is NOT used for options for the factorization of the matrix. Normally the
748: prefix is automatically passed in from the PC calling the factorization. To set
749: it directly use `MatSetOptionsPrefixFactor()`
751: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
752: @*/
753: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
754: {
755: PetscFunctionBegin;
757: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
758: PetscFunctionReturn(PETSC_SUCCESS);
759: }
761: /*@
762: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
763: for matrices created with `MatGetFactor()`
765: Logically Collective
767: Input Parameters:
768: + A - the matrix
769: - prefix - the prefix to prepend to all option names for the factored matrix
771: Level: developer
773: Notes:
774: A hyphen (-) must NOT be given at the beginning of the prefix name.
775: The first character of all runtime options is AUTOMATICALLY the hyphen.
777: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
778: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
780: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
781: @*/
782: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
783: {
784: PetscFunctionBegin;
786: if (prefix) {
787: PetscAssertPointer(prefix, 2);
788: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
789: if (prefix != A->factorprefix) {
790: PetscCall(PetscFree(A->factorprefix));
791: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
792: }
793: } else PetscCall(PetscFree(A->factorprefix));
794: PetscFunctionReturn(PETSC_SUCCESS);
795: }
797: /*@
798: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
799: for matrices created with `MatGetFactor()`
801: Logically Collective
803: Input Parameters:
804: + A - the matrix
805: - prefix - the prefix to prepend to all option names for the factored matrix
807: Level: developer
809: Notes:
810: A hyphen (-) must NOT be given at the beginning of the prefix name.
811: The first character of all runtime options is AUTOMATICALLY the hyphen.
813: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
814: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
816: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
817: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
818: `MatSetOptionsPrefix()`
819: @*/
820: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
821: {
822: size_t len1, len2, new_len;
824: PetscFunctionBegin;
826: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
827: if (!A->factorprefix) {
828: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
829: PetscFunctionReturn(PETSC_SUCCESS);
830: }
831: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
833: PetscCall(PetscStrlen(A->factorprefix, &len1));
834: PetscCall(PetscStrlen(prefix, &len2));
835: new_len = len1 + len2 + 1;
836: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
837: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
838: PetscFunctionReturn(PETSC_SUCCESS);
839: }
841: /*@
842: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
843: matrix options in the database.
845: Logically Collective
847: Input Parameters:
848: + A - the matrix
849: - prefix - the prefix to prepend to all option names
851: Level: advanced
853: Note:
854: A hyphen (-) must NOT be given at the beginning of the prefix name.
855: The first character of all runtime options is AUTOMATICALLY the hyphen.
857: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
858: @*/
859: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
860: {
861: PetscFunctionBegin;
863: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
864: PetscFunctionReturn(PETSC_SUCCESS);
865: }
867: /*@
868: MatGetOptionsPrefix - Gets the prefix used for searching for all
869: matrix options in the database.
871: Not Collective
873: Input Parameter:
874: . A - the matrix
876: Output Parameter:
877: . prefix - pointer to the prefix string used
879: Level: advanced
881: Fortran Note:
882: The user should pass in a string `prefix` of
883: sufficient length to hold the prefix.
885: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
886: @*/
887: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
888: {
889: PetscFunctionBegin;
891: PetscAssertPointer(prefix, 2);
892: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
893: PetscFunctionReturn(PETSC_SUCCESS);
894: }
896: /*@
897: MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`
899: Not Collective
901: Input Parameter:
902: . A - the matrix
904: Output Parameter:
905: . state - the object state
907: Level: advanced
909: Note:
910: Object state is an integer which gets increased every time
911: the object is changed. By saving and later querying the object state
912: one can determine whether information about the object is still current.
914: See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.
916: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
917: @*/
918: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
919: {
920: PetscFunctionBegin;
922: PetscAssertPointer(state, 2);
923: PetscCall(PetscObjectStateGet((PetscObject)A, state));
924: PetscFunctionReturn(PETSC_SUCCESS);
925: }
927: /*@
928: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
930: Collective
932: Input Parameter:
933: . A - the matrix
935: Level: beginner
937: Notes:
938: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
939: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
940: makes all of the preallocation space available
942: Current values in the matrix are lost in this call.
944: Currently only supported for `MATAIJ` matrices.
946: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
947: @*/
948: PetscErrorCode MatResetPreallocation(Mat A)
949: {
950: PetscFunctionBegin;
953: PetscCheck(A->insertmode == NOT_SET_VALUES, PETSC_COMM_SELF, PETSC_ERR_SUP, "Cannot reset preallocation after setting some values but not yet calling MatAssemblyBegin()/MatAssemblyEnd()");
954: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
955: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
956: PetscFunctionReturn(PETSC_SUCCESS);
957: }
959: /*@
960: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
962: Collective
964: Input Parameter:
965: . A - the matrix
967: Level: intermediate
969: Notes:
970: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
972: Currently only supported for `MATAIJ` matrices.
974: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
975: @*/
976: PetscErrorCode MatResetHash(Mat A)
977: {
978: PetscFunctionBegin;
981: 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()");
982: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
983: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
984: /* These flags are used to determine whether certain setups occur */
985: A->was_assembled = PETSC_FALSE;
986: A->assembled = PETSC_FALSE;
987: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
988: PetscCall(PetscObjectStateIncrease((PetscObject)A));
989: PetscFunctionReturn(PETSC_SUCCESS);
990: }
992: /*@
993: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
995: Collective
997: Input Parameter:
998: . A - the matrix
1000: Level: advanced
1002: Notes:
1003: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
1004: setting values in the matrix.
1006: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
1008: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
1009: @*/
1010: PetscErrorCode MatSetUp(Mat A)
1011: {
1012: PetscFunctionBegin;
1014: if (!((PetscObject)A)->type_name) {
1015: PetscMPIInt size;
1017: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
1018: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
1019: }
1020: if (!A->preallocated) PetscTryTypeMethod(A, setup);
1021: PetscCall(PetscLayoutSetUp(A->rmap));
1022: PetscCall(PetscLayoutSetUp(A->cmap));
1023: A->preallocated = PETSC_TRUE;
1024: PetscFunctionReturn(PETSC_SUCCESS);
1025: }
1027: #if defined(PETSC_HAVE_SAWS)
1028: #include <petscviewersaws.h>
1029: #endif
1031: /*
1032: If threadsafety is on extraneous matrices may be printed
1034: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1035: */
1036: #if !defined(PETSC_HAVE_THREADSAFETY)
1037: static PetscInt insidematview = 0;
1038: #endif
1040: /*@
1041: MatViewFromOptions - View properties of the matrix based on options set in the options database
1043: Collective
1045: Input Parameters:
1046: + A - the matrix
1047: . obj - optional additional object that provides the options prefix to use
1048: - name - command line option
1050: Options Database Key:
1051: . -mat_view [viewertype]:... - the viewer and its options
1053: Level: intermediate
1055: Note:
1056: .vb
1057: If no value is provided ascii:stdout is used
1058: ascii[:[filename][:[format][:append]]] defaults to stdout - format can be one of ascii_info, ascii_info_detail, or ascii_matlab,
1059: for example ascii::ascii_info prints just the information about the object not all details
1060: unless :append is given filename opens in write mode, overwriting what was already there
1061: binary[:[filename][:[format][:append]]] defaults to the file binaryoutput
1062: draw[:drawtype[:filename]] for example, draw:tikz, draw:tikz:figure.tex or draw:x
1063: socket[:port] defaults to the standard output port
1064: saws[:communicatorname] publishes object to the Scientific Application Webserver (SAWs)
1065: .ve
1067: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1068: @*/
1069: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1070: {
1071: PetscFunctionBegin;
1073: #if !defined(PETSC_HAVE_THREADSAFETY)
1074: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1075: #endif
1076: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1077: PetscFunctionReturn(PETSC_SUCCESS);
1078: }
1080: /*@
1081: MatView - display information about a matrix in a variety ways
1083: Collective on viewer
1085: Input Parameters:
1086: + mat - the matrix
1087: - viewer - visualization context
1089: Options Database Keys:
1090: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1091: . -mat_view ::ascii_info_detail - Prints more detailed info
1092: . -mat_view - Prints matrix in ASCII format
1093: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1094: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1095: . -display <name> - Sets display name (default is host)
1096: . -draw_pause <sec> - Sets number of seconds to pause after display
1097: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1098: . -viewer_socket_machine <machine> - -
1099: . -viewer_socket_port <port> - -
1100: . -mat_view binary - save matrix to file in binary format
1101: - -viewer_binary_filename <name> - -
1103: Level: beginner
1105: Notes:
1106: The available visualization contexts include
1107: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1108: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1109: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1110: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1112: The user can open alternative visualization contexts with
1113: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1114: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1115: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1116: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1118: The user can call `PetscViewerPushFormat()` to specify the output
1119: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1120: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1121: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1122: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1123: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1124: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1125: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1126: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1127: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1129: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1130: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1132: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1134: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1135: viewer is used.
1137: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1138: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1140: One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1141: and then use the following mouse functions.
1142: .vb
1143: left mouse: zoom in
1144: middle mouse: zoom out
1145: right mouse: continue with the simulation
1146: .ve
1148: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1149: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1150: @*/
1151: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1152: {
1153: PetscInt rows, cols, rbs, cbs;
1154: PetscBool isascii, isstring, issaws;
1155: PetscViewerFormat format;
1156: PetscMPIInt size;
1158: PetscFunctionBegin;
1161: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1164: PetscCall(PetscViewerGetFormat(viewer, &format));
1165: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1166: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1168: #if !defined(PETSC_HAVE_THREADSAFETY)
1169: insidematview++;
1170: #endif
1171: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1172: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1173: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1174: PetscCheck((isascii && (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL)) || !mat->factortype, PetscObjectComm((PetscObject)viewer), PETSC_ERR_ARG_WRONGSTATE, "No viewers for factored matrix except ASCII, info, or info_detail");
1176: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1177: if (isascii) {
1178: if (!mat->preallocated) {
1179: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1180: #if !defined(PETSC_HAVE_THREADSAFETY)
1181: insidematview--;
1182: #endif
1183: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1184: PetscFunctionReturn(PETSC_SUCCESS);
1185: }
1186: if (!mat->assembled) {
1187: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1188: #if !defined(PETSC_HAVE_THREADSAFETY)
1189: insidematview--;
1190: #endif
1191: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1192: PetscFunctionReturn(PETSC_SUCCESS);
1193: }
1194: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1195: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1196: MatNullSpace nullsp, transnullsp;
1198: PetscCall(PetscViewerASCIIPushTab(viewer));
1199: PetscCall(MatGetSize(mat, &rows, &cols));
1200: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1201: if (rbs != 1 || cbs != 1) {
1202: 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" : ""));
1203: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1204: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1205: if (mat->factortype) {
1206: MatSolverType solver;
1207: PetscCall(MatFactorGetSolverType(mat, &solver));
1208: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1209: }
1210: if (mat->ops->getinfo) {
1211: MatInfo info;
1212: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1213: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1214: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1215: }
1216: PetscCall(MatGetNullSpace(mat, &nullsp));
1217: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1218: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1219: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1220: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1221: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1222: PetscCall(PetscViewerASCIIPushTab(viewer));
1223: PetscCall(MatProductView(mat, viewer));
1224: PetscCall(PetscViewerASCIIPopTab(viewer));
1225: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1226: IS tmp;
1228: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1229: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1230: PetscCall(PetscViewerASCIIPushTab(viewer));
1231: PetscCall(ISView(tmp, viewer));
1232: PetscCall(PetscViewerASCIIPopTab(viewer));
1233: PetscCall(ISDestroy(&tmp));
1234: }
1235: }
1236: } else if (issaws) {
1237: #if defined(PETSC_HAVE_SAWS)
1238: PetscMPIInt rank;
1240: PetscCall(PetscObjectName((PetscObject)mat));
1241: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1242: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1243: #endif
1244: } else if (isstring) {
1245: const char *type;
1246: PetscCall(MatGetType(mat, &type));
1247: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1248: PetscTryTypeMethod(mat, view, viewer);
1249: }
1250: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1251: PetscCall(PetscViewerASCIIPushTab(viewer));
1252: PetscUseTypeMethod(mat, viewnative, viewer);
1253: PetscCall(PetscViewerASCIIPopTab(viewer));
1254: } else if (mat->ops->view) {
1255: PetscCall(PetscViewerASCIIPushTab(viewer));
1256: PetscUseTypeMethod(mat, view, viewer);
1257: PetscCall(PetscViewerASCIIPopTab(viewer));
1258: }
1259: if (isascii) {
1260: PetscCall(PetscViewerGetFormat(viewer, &format));
1261: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1262: }
1263: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1264: #if !defined(PETSC_HAVE_THREADSAFETY)
1265: insidematview--;
1266: #endif
1267: PetscFunctionReturn(PETSC_SUCCESS);
1268: }
1270: #if defined(PETSC_USE_DEBUG)
1271: #include <../src/sys/totalview/tv_data_display.h>
1272: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1273: {
1274: TV_add_row("Local rows", "int", &mat->rmap->n);
1275: TV_add_row("Local columns", "int", &mat->cmap->n);
1276: TV_add_row("Global rows", "int", &mat->rmap->N);
1277: TV_add_row("Global columns", "int", &mat->cmap->N);
1278: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1279: return TV_format_OK;
1280: }
1281: #endif
1283: /*@
1284: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1285: with `MatView()`. The matrix format is determined from the options database.
1286: Generates a parallel MPI matrix if the communicator has more than one
1287: processor. The default matrix type is `MATAIJ`.
1289: Collective
1291: Input Parameters:
1292: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1293: or some related function before a call to `MatLoad()`
1294: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1296: Options Database Key:
1297: . -matload_block_size <bs> - set block size
1299: Level: beginner
1301: Notes:
1302: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1303: `Mat` before calling this routine if you wish to set it from the options database.
1305: `MatLoad()` automatically loads into the options database any options
1306: given in the file filename.info where filename is the name of the file
1307: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1308: file will be ignored if you use the -viewer_binary_skip_info option.
1310: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1311: sets the default matrix type AIJ and sets the local and global sizes.
1312: If type and/or size is already set, then the same are used.
1314: In parallel, each processor can load a subset of rows (or the
1315: entire matrix). This routine is especially useful when a large
1316: matrix is stored on disk and only part of it is desired on each
1317: processor. For example, a parallel solver may access only some of
1318: the rows from each processor. The algorithm used here reads
1319: relatively small blocks of data rather than reading the entire
1320: matrix and then subsetting it.
1322: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1323: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1324: or the sequence like
1325: .vb
1326: `PetscViewer` v;
1327: `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1328: `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1329: `PetscViewerSetFromOptions`(v);
1330: `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1331: `PetscViewerFileSetName`(v,"datafile");
1332: .ve
1333: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1334: $ -viewer_type {binary, hdf5}
1336: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1337: and src/mat/tutorials/ex10.c with the second approach.
1339: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1340: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1341: Multiple objects, both matrices and vectors, can be stored within the same file.
1342: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1344: Most users should not need to know the details of the binary storage
1345: format, since `MatLoad()` and `MatView()` completely hide these details.
1346: But for anyone who is interested, the standard binary matrix storage
1347: format is
1349: .vb
1350: PetscInt MAT_FILE_CLASSID
1351: PetscInt number of rows
1352: PetscInt number of columns
1353: PetscInt total number of nonzeros
1354: PetscInt *number nonzeros in each row
1355: PetscInt *column indices of all nonzeros (starting index is zero)
1356: PetscScalar *values of all nonzeros
1357: .ve
1358: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1359: 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
1360: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1362: PETSc automatically does the byte swapping for
1363: machines that store the bytes reversed. Thus if you write your own binary
1364: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1365: and `PetscBinaryWrite()` to see how this may be done.
1367: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1368: Each processor's chunk is loaded independently by its owning MPI process.
1369: Multiple objects, both matrices and vectors, can be stored within the same file.
1370: They are looked up by their PetscObject name.
1372: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1373: by default the same structure and naming of the AIJ arrays and column count
1374: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1375: $ save example.mat A b -v7.3
1376: can be directly read by this routine (see Reference 1 for details).
1378: Depending on your MATLAB version, this format might be a default,
1379: otherwise you can set it as default in Preferences.
1381: Unless -nocompression flag is used to save the file in MATLAB,
1382: PETSc must be configured with ZLIB package.
1384: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1386: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1388: Corresponding `MatView()` is not yet implemented.
1390: The loaded matrix is actually a transpose of the original one in MATLAB,
1391: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1392: With this format, matrix is automatically transposed by PETSc,
1393: unless the matrix is marked as SPD or symmetric
1394: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1396: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1398: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1399: @*/
1400: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1401: {
1402: PetscBool flg;
1404: PetscFunctionBegin;
1408: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1410: flg = PETSC_FALSE;
1411: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1412: if (flg) {
1413: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1414: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1415: }
1416: flg = PETSC_FALSE;
1417: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1418: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1420: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1421: PetscUseTypeMethod(mat, load, viewer);
1422: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1423: PetscFunctionReturn(PETSC_SUCCESS);
1424: }
1426: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1427: {
1428: Mat_Redundant *redund = *redundant;
1430: PetscFunctionBegin;
1431: if (redund) {
1432: if (redund->matseq) { /* via MatCreateSubMatrices() */
1433: PetscCall(ISDestroy(&redund->isrow));
1434: PetscCall(ISDestroy(&redund->iscol));
1435: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1436: } else {
1437: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1438: PetscCall(PetscFree(redund->sbuf_j));
1439: PetscCall(PetscFree(redund->sbuf_a));
1440: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1441: PetscCall(PetscFree(redund->rbuf_j[i]));
1442: PetscCall(PetscFree(redund->rbuf_a[i]));
1443: }
1444: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1445: }
1447: if (redund->subcomm) PetscCall(PetscCommDestroy(&redund->subcomm));
1448: PetscCall(PetscFree(redund));
1449: }
1450: PetscFunctionReturn(PETSC_SUCCESS);
1451: }
1453: /*@
1454: MatDestroy - Frees space taken by a matrix.
1456: Collective
1458: Input Parameter:
1459: . A - the matrix
1461: Level: beginner
1463: Developer Note:
1464: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1465: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1466: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1467: if changes are needed here.
1469: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1470: @*/
1471: PetscErrorCode MatDestroy(Mat *A)
1472: {
1473: PetscFunctionBegin;
1474: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1476: if (--((PetscObject)*A)->refct > 0) {
1477: *A = NULL;
1478: PetscFunctionReturn(PETSC_SUCCESS);
1479: }
1481: /* if memory was published with SAWs then destroy it */
1482: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1483: PetscTryTypeMethod(*A, destroy);
1485: PetscCall(PetscFree((*A)->factorprefix));
1486: PetscCall(PetscFree((*A)->defaultvectype));
1487: PetscCall(PetscFree((*A)->defaultrandtype));
1488: PetscCall(PetscFree((*A)->bsizes));
1489: PetscCall(PetscFree((*A)->solvertype));
1490: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1491: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1492: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1493: PetscCall(MatProductClear(*A));
1494: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1495: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1496: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1497: PetscCall(MatDestroy(&(*A)->schur));
1498: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1499: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1500: PetscCall(PetscHeaderDestroy(A));
1501: PetscFunctionReturn(PETSC_SUCCESS);
1502: }
1504: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1505: /*@
1506: MatSetValues - Inserts or adds a block of values into a matrix.
1507: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1508: MUST be called after all calls to `MatSetValues()` have been completed.
1510: Not Collective
1512: Input Parameters:
1513: + mat - the matrix
1514: . v - a logically two-dimensional array of values
1515: . m - the number of rows
1516: . idxm - the global indices of the rows
1517: . n - the number of columns
1518: . idxn - the global indices of the columns
1519: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1521: Level: beginner
1523: Notes:
1524: By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.
1526: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1527: options cannot be mixed without intervening calls to the assembly
1528: routines.
1530: `MatSetValues()` uses 0-based row and column numbers in Fortran
1531: as well as in C.
1533: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1534: simply ignored. This allows easily inserting element stiffness matrices
1535: with homogeneous Dirichlet boundary conditions that you don't want represented
1536: in the matrix.
1538: Efficiency Alert:
1539: The routine `MatSetValuesBlocked()` may offer much better efficiency
1540: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1542: Fortran Notes:
1543: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1544: .vb
1545: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1546: .ve
1548: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1550: Developer Note:
1551: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1552: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1554: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1555: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1556: @*/
1557: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1558: {
1559: PetscFunctionBeginHot;
1562: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1563: PetscAssertPointer(idxm, 3);
1564: PetscAssertPointer(idxn, 5);
1565: MatCheckPreallocated(mat, 1);
1567: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1568: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1570: if (PetscDefined(USE_DEBUG)) {
1571: PetscInt i, j;
1573: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1574: if (v) {
1575: for (i = 0; i < m; i++) {
1576: for (j = 0; j < n; j++) {
1577: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1578: #if defined(PETSC_USE_COMPLEX)
1579: 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]);
1580: #else
1581: 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]);
1582: #endif
1583: }
1584: }
1585: }
1586: 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);
1587: 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);
1588: }
1590: if (mat->assembled) {
1591: mat->was_assembled = PETSC_TRUE;
1592: mat->assembled = PETSC_FALSE;
1593: }
1594: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1595: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1596: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1597: PetscFunctionReturn(PETSC_SUCCESS);
1598: }
1600: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1601: /*@
1602: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1603: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1604: MUST be called after all calls to `MatSetValues()` have been completed.
1606: Not Collective
1608: Input Parameters:
1609: + mat - the matrix
1610: . v - a logically two-dimensional array of values
1611: . ism - the rows to provide
1612: . isn - the columns to provide
1613: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1615: Level: beginner
1617: Notes:
1618: By default the values, `v`, are stored row-oriented. See `MatSetOption()` for other options.
1620: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1621: options cannot be mixed without intervening calls to the assembly
1622: routines.
1624: `MatSetValues()` uses 0-based row and column numbers in Fortran
1625: as well as in C.
1627: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1628: simply ignored. This allows easily inserting element stiffness matrices
1629: with homogeneous Dirichlet boundary conditions that you don't want represented
1630: in the matrix.
1632: Efficiency Alert:
1633: The routine `MatSetValuesBlocked()` may offer much better efficiency
1634: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1636: This is currently not optimized for any particular `ISType`
1638: Developer Note:
1639: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1640: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1642: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1643: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1644: @*/
1645: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1646: {
1647: PetscInt m, n;
1648: const PetscInt *rows, *cols;
1650: PetscFunctionBeginHot;
1652: PetscCall(ISGetIndices(ism, &rows));
1653: PetscCall(ISGetIndices(isn, &cols));
1654: PetscCall(ISGetLocalSize(ism, &m));
1655: PetscCall(ISGetLocalSize(isn, &n));
1656: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1657: PetscCall(ISRestoreIndices(ism, &rows));
1658: PetscCall(ISRestoreIndices(isn, &cols));
1659: PetscFunctionReturn(PETSC_SUCCESS);
1660: }
1662: /*@
1663: MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1664: values into a matrix
1666: Not Collective
1668: Input Parameters:
1669: + mat - the matrix
1670: . row - the (block) row to set
1671: - v - a logically two-dimensional array of values
1673: Level: intermediate
1675: Notes:
1676: The values, `v`, are column-oriented (for the block version) and sorted
1678: All the nonzero values in `row` must be provided
1680: The matrix must have previously had its column indices set, likely by having been assembled.
1682: `row` must belong to this MPI process
1684: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1685: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1686: @*/
1687: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1688: {
1689: PetscInt globalrow;
1691: PetscFunctionBegin;
1694: PetscAssertPointer(v, 3);
1695: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1696: PetscCall(MatSetValuesRow(mat, globalrow, v));
1697: PetscFunctionReturn(PETSC_SUCCESS);
1698: }
1700: /*@
1701: MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1702: values into a matrix
1704: Not Collective
1706: Input Parameters:
1707: + mat - the matrix
1708: . row - the (block) row to set
1709: - v - a logically two-dimensional (column major) array of values for block matrices with blocksize larger than one, otherwise a one dimensional array of values
1711: Level: advanced
1713: Notes:
1714: The values, `v`, are column-oriented for the block version.
1716: All the nonzeros in `row` must be provided
1718: THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.
1720: `row` must belong to this process
1722: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1723: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1724: @*/
1725: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1726: {
1727: PetscFunctionBeginHot;
1730: MatCheckPreallocated(mat, 1);
1731: PetscAssertPointer(v, 3);
1732: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1733: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1734: mat->insertmode = INSERT_VALUES;
1736: if (mat->assembled) {
1737: mat->was_assembled = PETSC_TRUE;
1738: mat->assembled = PETSC_FALSE;
1739: }
1740: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1741: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1742: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1743: PetscFunctionReturn(PETSC_SUCCESS);
1744: }
1746: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1747: /*@
1748: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1749: Using structured grid indexing
1751: Not Collective
1753: Input Parameters:
1754: + mat - the matrix
1755: . m - number of rows being entered
1756: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1757: . n - number of columns being entered
1758: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1759: . v - a logically two-dimensional array of values
1760: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1762: Level: beginner
1764: Notes:
1765: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1767: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1768: options cannot be mixed without intervening calls to the assembly
1769: routines.
1771: The grid coordinates are across the entire grid, not just the local portion
1773: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1774: as well as in C.
1776: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1778: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1779: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1781: The columns and rows in the stencil passed in MUST be contained within the
1782: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1783: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1784: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1785: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1787: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1788: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1789: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1790: `DM_BOUNDARY_PERIODIC` boundary type.
1792: 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
1793: a single value per point) you can skip filling those indices.
1795: Inspired by the structured grid interface to the HYPRE package
1796: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1798: Efficiency Alert:
1799: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1800: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1802: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1803: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1804: @*/
1805: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1806: {
1807: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1808: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1809: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1811: PetscFunctionBegin;
1812: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1815: PetscAssertPointer(idxm, 3);
1816: PetscAssertPointer(idxn, 5);
1818: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1819: jdxm = buf;
1820: jdxn = buf + m;
1821: } else {
1822: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1823: jdxm = bufm;
1824: jdxn = bufn;
1825: }
1826: for (i = 0; i < m; i++) {
1827: for (j = 0; j < 3 - sdim; j++) dxm++;
1828: tmp = *dxm++ - starts[0];
1829: for (j = 0; j < dim - 1; j++) {
1830: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1831: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1832: }
1833: if (mat->stencil.noc) dxm++;
1834: jdxm[i] = tmp;
1835: }
1836: for (i = 0; i < n; i++) {
1837: for (j = 0; j < 3 - sdim; j++) dxn++;
1838: tmp = *dxn++ - starts[0];
1839: for (j = 0; j < dim - 1; j++) {
1840: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1841: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1842: }
1843: if (mat->stencil.noc) dxn++;
1844: jdxn[i] = tmp;
1845: }
1846: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1847: PetscCall(PetscFree2(bufm, bufn));
1848: PetscFunctionReturn(PETSC_SUCCESS);
1849: }
1851: /*@
1852: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1853: Using structured grid indexing
1855: Not Collective
1857: Input Parameters:
1858: + mat - the matrix
1859: . m - number of rows being entered
1860: . idxm - grid coordinates for matrix rows being entered
1861: . n - number of columns being entered
1862: . idxn - grid coordinates for matrix columns being entered
1863: . v - a logically two-dimensional array of values
1864: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1866: Level: beginner
1868: Notes:
1869: By default the values, `v`, are row-oriented and unsorted.
1870: See `MatSetOption()` for other options.
1872: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1873: options cannot be mixed without intervening calls to the assembly
1874: routines.
1876: The grid coordinates are across the entire grid, not just the local portion
1878: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1879: as well as in C.
1881: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1883: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1884: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1886: The columns and rows in the stencil passed in MUST be contained within the
1887: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1888: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1889: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1890: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1892: Negative indices may be passed in idxm and idxn, these rows and columns are
1893: simply ignored. This allows easily inserting element stiffness matrices
1894: with homogeneous Dirichlet boundary conditions that you don't want represented
1895: in the matrix.
1897: Inspired by the structured grid interface to the HYPRE package
1898: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1900: Fortran Note:
1901: `idxm` and `idxn` should be declared as
1902: $ MatStencil idxm(4,m),idxn(4,n)
1903: and the values inserted using
1904: .vb
1905: idxm(MatStencil_i,1) = i
1906: idxm(MatStencil_j,1) = j
1907: idxm(MatStencil_k,1) = k
1908: etc
1909: .ve
1911: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1912: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1913: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1914: @*/
1915: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1916: {
1917: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1918: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1919: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1921: PetscFunctionBegin;
1922: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1925: PetscAssertPointer(idxm, 3);
1926: PetscAssertPointer(idxn, 5);
1927: PetscAssertPointer(v, 6);
1929: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1930: jdxm = buf;
1931: jdxn = buf + m;
1932: } else {
1933: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1934: jdxm = bufm;
1935: jdxn = bufn;
1936: }
1937: for (i = 0; i < m; i++) {
1938: for (j = 0; j < 3 - sdim; j++) dxm++;
1939: tmp = *dxm++ - starts[0];
1940: for (j = 0; j < sdim - 1; j++) {
1941: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1942: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1943: }
1944: dxm++;
1945: jdxm[i] = tmp;
1946: }
1947: for (i = 0; i < n; i++) {
1948: for (j = 0; j < 3 - sdim; j++) dxn++;
1949: tmp = *dxn++ - starts[0];
1950: for (j = 0; j < sdim - 1; j++) {
1951: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1952: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1953: }
1954: dxn++;
1955: jdxn[i] = tmp;
1956: }
1957: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1958: PetscCall(PetscFree2(bufm, bufn));
1959: PetscFunctionReturn(PETSC_SUCCESS);
1960: }
1962: /*@
1963: MatSetStencil - Sets the grid information for setting values into a matrix via
1964: `MatSetValuesStencil()`
1966: Not Collective
1968: Input Parameters:
1969: + mat - the matrix
1970: . dim - dimension of the grid 1, 2, or 3
1971: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1972: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1973: - dof - number of degrees of freedom per node
1975: Level: beginner
1977: Notes:
1978: Inspired by the structured grid interface to the HYPRE package
1979: (www.llnl.gov/CASC/hyper)
1981: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1982: user.
1984: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`
1985: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1986: @*/
1987: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1988: {
1989: PetscFunctionBegin;
1991: PetscAssertPointer(dims, 3);
1992: PetscAssertPointer(starts, 4);
1994: mat->stencil.dim = dim + (dof > 1);
1995: for (PetscInt i = 0; i < dim; i++) {
1996: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
1997: mat->stencil.starts[i] = starts[dim - i - 1];
1998: }
1999: mat->stencil.dims[dim] = dof;
2000: mat->stencil.starts[dim] = 0;
2001: mat->stencil.noc = (PetscBool)(dof == 1);
2002: PetscFunctionReturn(PETSC_SUCCESS);
2003: }
2005: /*@
2006: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
2008: Not Collective
2010: Input Parameters:
2011: + mat - the matrix
2012: . v - a logically two-dimensional array of values
2013: . m - the number of block rows
2014: . idxm - the global block indices
2015: . n - the number of block columns
2016: . idxn - the global block indices
2017: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
2019: Level: intermediate
2021: Notes:
2022: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2023: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2025: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2026: NOT the total number of rows/columns; for example, if the block size is 2 and
2027: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2028: The values in `idxm` would be 1 2; that is the first index for each block divided by
2029: the block size.
2031: You must call `MatSetBlockSize()` when constructing this matrix (before
2032: preallocating it).
2034: By default the values, `v`, are row-oriented, so the layout of
2035: `v` is the same as for `MatSetValues()`. See `MatSetOption()` for other options.
2037: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2038: options cannot be mixed without intervening calls to the assembly
2039: routines.
2041: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2042: as well as in C.
2044: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2045: simply ignored. This allows easily inserting element stiffness matrices
2046: with homogeneous Dirichlet boundary conditions that you don't want represented
2047: in the matrix.
2049: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2050: internal searching must be done to determine where to place the
2051: data in the matrix storage space. By instead inserting blocks of
2052: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2053: reduced.
2055: Example:
2056: .vb
2057: Suppose m=n=2 and block size(bs) = 2 The array is
2059: 1 2 | 3 4
2060: 5 6 | 7 8
2061: - - - | - - -
2062: 9 10 | 11 12
2063: 13 14 | 15 16
2065: v[] should be passed in like
2066: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2068: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2069: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2070: .ve
2072: Fortran Notes:
2073: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2074: .vb
2075: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2076: .ve
2078: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2080: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2081: @*/
2082: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2083: {
2084: PetscFunctionBeginHot;
2087: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2088: PetscAssertPointer(idxm, 3);
2089: PetscAssertPointer(idxn, 5);
2090: MatCheckPreallocated(mat, 1);
2091: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2092: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2093: if (PetscDefined(USE_DEBUG)) {
2094: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2095: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2096: }
2097: if (PetscDefined(USE_DEBUG)) {
2098: PetscInt rbs, cbs, M, N, i;
2099: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2100: PetscCall(MatGetSize(mat, &M, &N));
2101: 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);
2102: for (i = 0; i < n; i++)
2103: 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);
2104: }
2105: if (mat->assembled) {
2106: mat->was_assembled = PETSC_TRUE;
2107: mat->assembled = PETSC_FALSE;
2108: }
2109: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2110: if (mat->ops->setvaluesblocked) {
2111: PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2112: } else {
2113: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2114: PetscInt i, j, bs, cbs;
2116: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2117: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2118: iidxm = buf;
2119: iidxn = buf + m * bs;
2120: } else {
2121: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2122: iidxm = bufr;
2123: iidxn = bufc;
2124: }
2125: for (i = 0; i < m; i++) {
2126: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2127: }
2128: if (m != n || bs != cbs || idxm != idxn) {
2129: for (i = 0; i < n; i++) {
2130: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2131: }
2132: } else iidxn = iidxm;
2133: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2134: PetscCall(PetscFree2(bufr, bufc));
2135: }
2136: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2137: PetscFunctionReturn(PETSC_SUCCESS);
2138: }
2140: /*@
2141: MatGetValues - Gets a block of local values from a matrix.
2143: Not Collective; can only return values that are owned by the give process
2145: Input Parameters:
2146: + mat - the matrix
2147: . v - a logically two-dimensional array for storing the values
2148: . m - the number of rows
2149: . idxm - the global indices of the rows
2150: . n - the number of columns
2151: - idxn - the global indices of the columns
2153: Level: advanced
2155: Notes:
2156: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2157: The values, `v`, are then returned in a row-oriented format,
2158: analogous to that used by default in `MatSetValues()`.
2160: `MatGetValues()` uses 0-based row and column numbers in
2161: Fortran as well as in C.
2163: `MatGetValues()` requires that the matrix has been assembled
2164: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2165: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2166: without intermediate matrix assembly.
2168: Negative row or column indices will be ignored and those locations in `v` will be
2169: left unchanged.
2171: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2172: That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2173: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2175: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2176: @*/
2177: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2178: {
2179: PetscFunctionBegin;
2182: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2183: PetscAssertPointer(idxm, 3);
2184: PetscAssertPointer(idxn, 5);
2185: PetscAssertPointer(v, 6);
2186: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2187: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2188: MatCheckPreallocated(mat, 1);
2190: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2191: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2192: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2193: PetscFunctionReturn(PETSC_SUCCESS);
2194: }
2196: /*@
2197: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2198: defined previously by `MatSetLocalToGlobalMapping()`
2200: Not Collective
2202: Input Parameters:
2203: + mat - the matrix
2204: . nrow - number of rows
2205: . irow - the row local indices
2206: . ncol - number of columns
2207: - icol - the column local indices
2209: Output Parameter:
2210: . y - a logically two-dimensional array of values
2212: Level: advanced
2214: Notes:
2215: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2217: 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,
2218: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2219: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2220: with `MatSetLocalToGlobalMapping()`.
2222: Developer Note:
2223: This is labelled with C so does not automatically generate Fortran stubs and interfaces
2224: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
2226: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2227: `MatSetValuesLocal()`, `MatGetValues()`
2228: @*/
2229: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2230: {
2231: PetscFunctionBeginHot;
2234: MatCheckPreallocated(mat, 1);
2235: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2236: PetscAssertPointer(irow, 3);
2237: PetscAssertPointer(icol, 5);
2238: if (PetscDefined(USE_DEBUG)) {
2239: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2240: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2241: }
2242: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2243: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2244: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2245: else {
2246: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2247: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2248: irowm = buf;
2249: icolm = buf + nrow;
2250: } else {
2251: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2252: irowm = bufr;
2253: icolm = bufc;
2254: }
2255: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2256: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2257: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2258: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2259: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2260: PetscCall(PetscFree2(bufr, bufc));
2261: }
2262: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2263: PetscFunctionReturn(PETSC_SUCCESS);
2264: }
2266: /*@
2267: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2268: the same size. Currently, this can only be called once and creates the given matrix.
2270: Not Collective
2272: Input Parameters:
2273: + mat - the matrix
2274: . nb - the number of blocks
2275: . bs - the number of rows (and columns) in each block
2276: . rows - a concatenation of the rows for each block
2277: - v - a concatenation of logically two-dimensional arrays of values
2279: Level: advanced
2281: Notes:
2282: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2284: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2286: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2287: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2288: @*/
2289: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2290: {
2291: PetscFunctionBegin;
2294: PetscAssertPointer(rows, 4);
2295: PetscAssertPointer(v, 5);
2296: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2298: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2299: if (mat->ops->setvaluesbatch) PetscUseTypeMethod(mat, setvaluesbatch, nb, bs, rows, v);
2300: else {
2301: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2302: }
2303: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2304: PetscFunctionReturn(PETSC_SUCCESS);
2305: }
2307: /*@
2308: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2309: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2310: using a local (per-processor) numbering.
2312: Not Collective
2314: Input Parameters:
2315: + x - the matrix
2316: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2317: - cmapping - column mapping
2319: Level: intermediate
2321: Note:
2322: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2324: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2325: @*/
2326: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2327: {
2328: PetscFunctionBegin;
2333: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2334: else {
2335: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2336: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2337: }
2338: PetscFunctionReturn(PETSC_SUCCESS);
2339: }
2341: /*@
2342: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2344: Not Collective
2346: Input Parameter:
2347: . A - the matrix
2349: Output Parameters:
2350: + rmapping - row mapping
2351: - cmapping - column mapping
2353: Level: advanced
2355: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2356: @*/
2357: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2358: {
2359: PetscFunctionBegin;
2362: if (rmapping) {
2363: PetscAssertPointer(rmapping, 2);
2364: *rmapping = A->rmap->mapping;
2365: }
2366: if (cmapping) {
2367: PetscAssertPointer(cmapping, 3);
2368: *cmapping = A->cmap->mapping;
2369: }
2370: PetscFunctionReturn(PETSC_SUCCESS);
2371: }
2373: /*@
2374: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2376: Logically Collective
2378: Input Parameters:
2379: + A - the matrix
2380: . rmap - row layout
2381: - cmap - column layout
2383: Level: advanced
2385: Note:
2386: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2388: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2389: @*/
2390: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2391: {
2392: PetscFunctionBegin;
2394: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2395: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2396: PetscFunctionReturn(PETSC_SUCCESS);
2397: }
2399: /*@
2400: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2402: Not Collective
2404: Input Parameter:
2405: . A - the matrix
2407: Output Parameters:
2408: + rmap - row layout
2409: - cmap - column layout
2411: Level: advanced
2413: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2414: @*/
2415: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2416: {
2417: PetscFunctionBegin;
2420: if (rmap) {
2421: PetscAssertPointer(rmap, 2);
2422: *rmap = A->rmap;
2423: }
2424: if (cmap) {
2425: PetscAssertPointer(cmap, 3);
2426: *cmap = A->cmap;
2427: }
2428: PetscFunctionReturn(PETSC_SUCCESS);
2429: }
2431: /*@
2432: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2433: using a local numbering of the rows and columns.
2435: Not Collective
2437: Input Parameters:
2438: + mat - the matrix
2439: . nrow - number of rows
2440: . irow - the row local indices
2441: . ncol - number of columns
2442: . icol - the column local indices
2443: . y - a logically two-dimensional array of values
2444: - addv - either `INSERT_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2446: Level: intermediate
2448: Notes:
2449: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2451: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2452: options cannot be mixed without intervening calls to the assembly
2453: routines.
2455: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2456: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2458: Fortran Notes:
2459: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2460: .vb
2461: call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2462: .ve
2464: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2466: Developer Note:
2467: This is labeled with C so does not automatically generate Fortran stubs and interfaces
2468: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
2470: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2471: `MatGetValuesLocal()`
2472: @*/
2473: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2474: {
2475: PetscFunctionBeginHot;
2478: MatCheckPreallocated(mat, 1);
2479: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2480: PetscAssertPointer(irow, 3);
2481: PetscAssertPointer(icol, 5);
2482: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2483: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2484: if (PetscDefined(USE_DEBUG)) {
2485: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2486: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2487: }
2489: if (mat->assembled) {
2490: mat->was_assembled = PETSC_TRUE;
2491: mat->assembled = PETSC_FALSE;
2492: }
2493: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2494: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2495: else {
2496: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2497: const PetscInt *irowm, *icolm;
2499: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2500: bufr = buf;
2501: bufc = buf + nrow;
2502: irowm = bufr;
2503: icolm = bufc;
2504: } else {
2505: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2506: irowm = bufr;
2507: icolm = bufc;
2508: }
2509: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2510: else irowm = irow;
2511: if (mat->cmap->mapping) {
2512: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2513: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2514: } else icolm = irowm;
2515: } else icolm = icol;
2516: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2517: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2518: }
2519: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2520: PetscFunctionReturn(PETSC_SUCCESS);
2521: }
2523: /*@
2524: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2525: using a local ordering of the nodes a block at a time.
2527: Not Collective
2529: Input Parameters:
2530: + mat - the matrix
2531: . nrow - number of rows
2532: . irow - the row local indices
2533: . ncol - number of columns
2534: . icol - the column local indices
2535: . y - a logically two-dimensional array of values
2536: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2538: Level: intermediate
2540: Notes:
2541: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2542: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2544: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2545: options cannot be mixed without intervening calls to the assembly
2546: routines.
2548: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2549: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2551: Fortran Notes:
2552: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2553: .vb
2554: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2555: .ve
2557: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2559: Developer Note:
2560: This is labeled with C so does not automatically generate Fortran stubs and interfaces
2561: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
2563: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2564: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2565: @*/
2566: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2567: {
2568: PetscFunctionBeginHot;
2571: MatCheckPreallocated(mat, 1);
2572: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2573: PetscAssertPointer(irow, 3);
2574: PetscAssertPointer(icol, 5);
2575: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2576: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2577: if (PetscDefined(USE_DEBUG)) {
2578: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2579: 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);
2580: }
2582: if (mat->assembled) {
2583: mat->was_assembled = PETSC_TRUE;
2584: mat->assembled = PETSC_FALSE;
2585: }
2586: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2587: PetscInt irbs, rbs;
2588: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2589: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2590: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2591: }
2592: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2593: PetscInt icbs, cbs;
2594: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2595: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2596: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2597: }
2598: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2599: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2600: else {
2601: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2602: const PetscInt *irowm, *icolm;
2604: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2605: bufr = buf;
2606: bufc = buf + nrow;
2607: irowm = bufr;
2608: icolm = bufc;
2609: } else {
2610: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2611: irowm = bufr;
2612: icolm = bufc;
2613: }
2614: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2615: else irowm = irow;
2616: if (mat->cmap->mapping) {
2617: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) {
2618: PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2619: } else icolm = irowm;
2620: } else icolm = icol;
2621: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2622: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2623: }
2624: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2625: PetscFunctionReturn(PETSC_SUCCESS);
2626: }
2628: /*@
2629: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2631: Collective
2633: Input Parameters:
2634: + mat - the matrix
2635: - x - the vector to be multiplied
2637: Output Parameter:
2638: . y - the result
2640: Level: developer
2642: Note:
2643: The vectors `x` and `y` cannot be the same. I.e., one cannot
2644: call `MatMultDiagonalBlock`(A,y,y).
2646: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2647: @*/
2648: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2649: {
2650: PetscFunctionBegin;
2656: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2657: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2658: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2659: MatCheckPreallocated(mat, 1);
2661: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2662: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2663: PetscFunctionReturn(PETSC_SUCCESS);
2664: }
2666: /*@
2667: MatMult - Computes the matrix-vector product, $y = Ax$.
2669: Neighbor-wise Collective
2671: Input Parameters:
2672: + mat - the matrix
2673: - x - the vector to be multiplied
2675: Output Parameter:
2676: . y - the result
2678: Level: beginner
2680: Note:
2681: The vectors `x` and `y` cannot be the same. I.e., one cannot
2682: call `MatMult`(A,y,y).
2684: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2685: @*/
2686: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2687: {
2688: PetscFunctionBegin;
2692: VecCheckAssembled(x);
2694: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2695: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2696: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2697: 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);
2698: 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);
2699: 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);
2700: 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);
2701: PetscCall(VecSetErrorIfLocked(y, 3));
2702: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2703: MatCheckPreallocated(mat, 1);
2705: PetscCall(VecLockReadPush(x));
2706: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2707: PetscUseTypeMethod(mat, mult, x, y);
2708: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2709: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2710: PetscCall(VecLockReadPop(x));
2711: PetscFunctionReturn(PETSC_SUCCESS);
2712: }
2714: /*@
2715: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2717: Neighbor-wise Collective
2719: Input Parameters:
2720: + mat - the matrix
2721: - x - the vector to be multiplied
2723: Output Parameter:
2724: . y - the result
2726: Level: beginner
2728: Notes:
2729: The vectors `x` and `y` cannot be the same. I.e., one cannot
2730: call `MatMultTranspose`(A,y,y).
2732: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2733: use `MatMultHermitianTranspose()`
2735: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2736: @*/
2737: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2738: {
2739: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2741: PetscFunctionBegin;
2745: VecCheckAssembled(x);
2748: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2749: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2750: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2751: 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);
2752: 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);
2753: 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);
2754: 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);
2755: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2756: MatCheckPreallocated(mat, 1);
2758: if (!mat->ops->multtranspose) {
2759: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2760: 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);
2761: } else op = mat->ops->multtranspose;
2762: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2763: PetscCall(VecLockReadPush(x));
2764: PetscCall((*op)(mat, x, y));
2765: PetscCall(VecLockReadPop(x));
2766: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2767: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2768: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2769: PetscFunctionReturn(PETSC_SUCCESS);
2770: }
2772: /*@
2773: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2775: Neighbor-wise Collective
2777: Input Parameters:
2778: + mat - the matrix
2779: - x - the vector to be multiplied
2781: Output Parameter:
2782: . y - the result
2784: Level: beginner
2786: Notes:
2787: The vectors `x` and `y` cannot be the same. I.e., one cannot
2788: call `MatMultHermitianTranspose`(A,y,y).
2790: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2792: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2794: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2795: @*/
2796: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2797: {
2798: PetscFunctionBegin;
2804: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2805: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2806: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2807: 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);
2808: 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);
2809: 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);
2810: 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);
2811: MatCheckPreallocated(mat, 1);
2813: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2814: #if defined(PETSC_USE_COMPLEX)
2815: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2816: PetscCall(VecLockReadPush(x));
2817: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2818: else PetscUseTypeMethod(mat, mult, x, y);
2819: PetscCall(VecLockReadPop(x));
2820: } else {
2821: Vec w;
2822: PetscCall(VecDuplicate(x, &w));
2823: PetscCall(VecCopy(x, w));
2824: PetscCall(VecConjugate(w));
2825: PetscCall(MatMultTranspose(mat, w, y));
2826: PetscCall(VecDestroy(&w));
2827: PetscCall(VecConjugate(y));
2828: }
2829: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2830: #else
2831: PetscCall(MatMultTranspose(mat, x, y));
2832: #endif
2833: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2834: PetscFunctionReturn(PETSC_SUCCESS);
2835: }
2837: /*@
2838: MatMultAdd - Computes $v3 = v2 + A * v1$.
2840: Neighbor-wise Collective
2842: Input Parameters:
2843: + mat - the matrix
2844: . v1 - the vector to be multiplied by `mat`
2845: - v2 - the vector to be added to the result
2847: Output Parameter:
2848: . v3 - the result
2850: Level: beginner
2852: Note:
2853: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2854: call `MatMultAdd`(A,v1,v2,v1).
2856: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2857: @*/
2858: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2859: {
2860: 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(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);
2870: /* 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);
2871: 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); */
2872: 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);
2873: 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);
2874: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2875: MatCheckPreallocated(mat, 1);
2877: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2878: PetscCall(VecLockReadPush(v1));
2879: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2880: PetscCall(VecLockReadPop(v1));
2881: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2882: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2883: PetscFunctionReturn(PETSC_SUCCESS);
2884: }
2886: /*@
2887: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2889: Neighbor-wise Collective
2891: Input Parameters:
2892: + mat - the matrix
2893: . v1 - the vector to be multiplied by the transpose of the matrix
2894: - v2 - the vector to be added to the result
2896: Output Parameter:
2897: . v3 - the result
2899: Level: beginner
2901: Note:
2902: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2903: call `MatMultTransposeAdd`(A,v1,v2,v1).
2905: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2906: @*/
2907: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2908: {
2909: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2911: PetscFunctionBegin;
2918: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2919: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2920: 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);
2921: 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);
2922: 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);
2923: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2924: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2925: MatCheckPreallocated(mat, 1);
2927: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2928: PetscCall(VecLockReadPush(v1));
2929: PetscCall((*op)(mat, v1, v2, v3));
2930: PetscCall(VecLockReadPop(v1));
2931: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2932: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2933: PetscFunctionReturn(PETSC_SUCCESS);
2934: }
2936: /*@
2937: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2939: Neighbor-wise Collective
2941: Input Parameters:
2942: + mat - the matrix
2943: . v1 - the vector to be multiplied by the Hermitian transpose
2944: - v2 - the vector to be added to the result
2946: Output Parameter:
2947: . v3 - the result
2949: Level: beginner
2951: Note:
2952: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2953: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2955: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2956: @*/
2957: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2958: {
2959: PetscFunctionBegin;
2966: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2967: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2968: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2969: 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);
2970: 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);
2971: 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);
2972: MatCheckPreallocated(mat, 1);
2974: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2975: PetscCall(VecLockReadPush(v1));
2976: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2977: else {
2978: Vec w, z;
2979: PetscCall(VecDuplicate(v1, &w));
2980: PetscCall(VecCopy(v1, w));
2981: PetscCall(VecConjugate(w));
2982: PetscCall(VecDuplicate(v3, &z));
2983: PetscCall(MatMultTranspose(mat, w, z));
2984: PetscCall(VecDestroy(&w));
2985: PetscCall(VecConjugate(z));
2986: if (v2 != v3) {
2987: PetscCall(VecWAXPY(v3, 1.0, v2, z));
2988: } else {
2989: PetscCall(VecAXPY(v3, 1.0, z));
2990: }
2991: PetscCall(VecDestroy(&z));
2992: }
2993: PetscCall(VecLockReadPop(v1));
2994: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2995: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2996: PetscFunctionReturn(PETSC_SUCCESS);
2997: }
2999: /*@
3000: MatGetFactorType - gets the type of factorization a matrix is
3002: Not Collective
3004: Input Parameter:
3005: . mat - the matrix
3007: Output Parameter:
3008: . 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`
3010: Level: intermediate
3012: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3013: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3014: @*/
3015: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3016: {
3017: PetscFunctionBegin;
3020: PetscAssertPointer(t, 2);
3021: *t = mat->factortype;
3022: PetscFunctionReturn(PETSC_SUCCESS);
3023: }
3025: /*@
3026: MatSetFactorType - sets the type of factorization a matrix is
3028: Logically Collective
3030: Input Parameters:
3031: + mat - the matrix
3032: - 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`
3034: Level: intermediate
3036: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3037: `MAT_FACTOR_ICC`,`MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3038: @*/
3039: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3040: {
3041: PetscFunctionBegin;
3044: mat->factortype = t;
3045: PetscFunctionReturn(PETSC_SUCCESS);
3046: }
3048: /*@
3049: MatGetInfo - Returns information about matrix storage (number of
3050: nonzeros, memory, etc.).
3052: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3054: Input Parameters:
3055: + mat - the matrix
3056: - flag - flag indicating the type of parameters to be returned (`MAT_LOCAL` - local matrix, `MAT_GLOBAL_MAX` - maximum over all processors, `MAT_GLOBAL_SUM` - sum over all processors)
3058: Output Parameter:
3059: . info - matrix information context
3061: Options Database Key:
3062: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3064: Level: intermediate
3066: Notes:
3067: The `MatInfo` context contains a variety of matrix data, including
3068: number of nonzeros allocated and used, number of mallocs during
3069: matrix assembly, etc. Additional information for factored matrices
3070: is provided (such as the fill ratio, number of mallocs during
3071: factorization, etc.).
3073: Example:
3074: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3075: data within the `MatInfo` context. For example,
3076: .vb
3077: MatInfo info;
3078: Mat A;
3079: double mal, nz_a, nz_u;
3081: MatGetInfo(A, MAT_LOCAL, &info);
3082: mal = info.mallocs;
3083: nz_a = info.nz_allocated;
3084: .ve
3086: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3087: @*/
3088: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3089: {
3090: PetscFunctionBegin;
3093: PetscAssertPointer(info, 3);
3094: MatCheckPreallocated(mat, 1);
3095: PetscUseTypeMethod(mat, getinfo, flag, info);
3096: PetscFunctionReturn(PETSC_SUCCESS);
3097: }
3099: /*
3100: This is used by external packages where it is not easy to get the info from the actual
3101: matrix factorization.
3102: */
3103: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3104: {
3105: PetscFunctionBegin;
3106: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3107: PetscFunctionReturn(PETSC_SUCCESS);
3108: }
3110: /*@
3111: MatLUFactor - Performs in-place LU factorization of matrix.
3113: Collective
3115: Input Parameters:
3116: + mat - the matrix
3117: . row - row permutation
3118: . col - column permutation
3119: - info - options for factorization, includes
3120: .vb
3121: fill - expected fill as ratio of original fill.
3122: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3123: Run with the option -info to determine an optimal value to use
3124: .ve
3126: Level: developer
3128: Notes:
3129: Most users should employ the `KSP` interface for linear solvers
3130: instead of working directly with matrix algebra routines such as this.
3131: See, e.g., `KSPCreate()`.
3133: This changes the state of the matrix to a factored matrix; it cannot be used
3134: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3136: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3137: when not using `KSP`.
3139: Developer Note:
3140: The Fortran interface is not autogenerated as the
3141: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3143: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3144: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3145: @*/
3146: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3147: {
3148: MatFactorInfo tinfo;
3150: PetscFunctionBegin;
3154: if (info) PetscAssertPointer(info, 4);
3156: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3157: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3158: MatCheckPreallocated(mat, 1);
3159: if (!info) {
3160: PetscCall(MatFactorInfoInitialize(&tinfo));
3161: info = &tinfo;
3162: }
3164: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3165: PetscUseTypeMethod(mat, lufactor, row, col, info);
3166: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3167: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3168: PetscFunctionReturn(PETSC_SUCCESS);
3169: }
3171: /*@
3172: MatILUFactor - Performs in-place ILU factorization of matrix.
3174: Collective
3176: Input Parameters:
3177: + mat - the matrix
3178: . row - row permutation
3179: . col - column permutation
3180: - info - structure containing
3181: .vb
3182: levels - number of levels of fill.
3183: expected fill - as ratio of original fill.
3184: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3185: missing diagonal entries)
3186: .ve
3188: Level: developer
3190: Notes:
3191: Most users should employ the `KSP` interface for linear solvers
3192: instead of working directly with matrix algebra routines such as this.
3193: See, e.g., `KSPCreate()`.
3195: Probably really in-place only when level of fill is zero, otherwise allocates
3196: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3197: when not using `KSP`.
3199: Developer Note:
3200: The Fortran interface is not autogenerated as the
3201: interface definition cannot be generated correctly [due to MatFactorInfo]
3203: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3204: @*/
3205: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3206: {
3207: PetscFunctionBegin;
3211: PetscAssertPointer(info, 4);
3213: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3214: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3215: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3216: MatCheckPreallocated(mat, 1);
3218: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3219: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3220: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3221: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3222: PetscFunctionReturn(PETSC_SUCCESS);
3223: }
3225: /*@
3226: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3227: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3229: Collective
3231: Input Parameters:
3232: + fact - the factor matrix obtained with `MatGetFactor()`
3233: . mat - the matrix
3234: . row - the row permutation
3235: . col - the column permutation
3236: - info - options for factorization, includes
3237: .vb
3238: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3239: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3240: .ve
3242: Level: developer
3244: Notes:
3245: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3247: Most users should employ the simplified `KSP` interface for linear solvers
3248: instead of working directly with matrix algebra routines such as this.
3249: See, e.g., `KSPCreate()`.
3251: Developer Note:
3252: The Fortran interface is not autogenerated as the
3253: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3255: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3256: @*/
3257: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3258: {
3259: MatFactorInfo tinfo;
3261: PetscFunctionBegin;
3266: if (info) PetscAssertPointer(info, 5);
3269: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3270: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3271: MatCheckPreallocated(mat, 2);
3272: if (!info) {
3273: PetscCall(MatFactorInfoInitialize(&tinfo));
3274: info = &tinfo;
3275: }
3277: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3278: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3279: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3280: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3281: PetscFunctionReturn(PETSC_SUCCESS);
3282: }
3284: /*@
3285: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3286: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3288: Collective
3290: Input Parameters:
3291: + fact - the factor matrix obtained with `MatGetFactor()`
3292: . mat - the matrix
3293: - info - options for factorization
3295: Level: developer
3297: Notes:
3298: See `MatLUFactor()` for in-place factorization. See
3299: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3301: Most users should employ the `KSP` interface for linear solvers
3302: instead of working directly with matrix algebra routines such as this.
3303: See, e.g., `KSPCreate()`.
3305: Developer Note:
3306: The Fortran interface is not autogenerated as the
3307: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3309: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3310: @*/
3311: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3312: {
3313: MatFactorInfo tinfo;
3315: PetscFunctionBegin;
3320: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3321: 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,
3322: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3324: MatCheckPreallocated(mat, 2);
3325: if (!info) {
3326: PetscCall(MatFactorInfoInitialize(&tinfo));
3327: info = &tinfo;
3328: }
3330: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3331: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3332: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3333: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3334: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3335: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3336: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3337: PetscFunctionReturn(PETSC_SUCCESS);
3338: }
3340: /*@
3341: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3342: symmetric matrix.
3344: Collective
3346: Input Parameters:
3347: + mat - the matrix
3348: . perm - row and column permutations
3349: - info - expected fill as ratio of original fill
3351: Level: developer
3353: Notes:
3354: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3355: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3357: Most users should employ the `KSP` interface for linear solvers
3358: instead of working directly with matrix algebra routines such as this.
3359: See, e.g., `KSPCreate()`.
3361: Developer Note:
3362: The Fortran interface is not autogenerated as the
3363: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3365: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`
3366: `MatGetOrdering()`
3367: @*/
3368: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3369: {
3370: MatFactorInfo tinfo;
3372: PetscFunctionBegin;
3375: if (info) PetscAssertPointer(info, 3);
3377: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3378: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3379: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3380: MatCheckPreallocated(mat, 1);
3381: if (!info) {
3382: PetscCall(MatFactorInfoInitialize(&tinfo));
3383: info = &tinfo;
3384: }
3386: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3387: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3388: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3389: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3390: PetscFunctionReturn(PETSC_SUCCESS);
3391: }
3393: /*@
3394: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3395: of a symmetric matrix.
3397: Collective
3399: Input Parameters:
3400: + fact - the factor matrix obtained with `MatGetFactor()`
3401: . mat - the matrix
3402: . perm - row and column permutations
3403: - info - options for factorization, includes
3404: .vb
3405: fill - expected fill as ratio of original fill.
3406: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3407: Run with the option -info to determine an optimal value to use
3408: .ve
3410: Level: developer
3412: Notes:
3413: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3414: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3416: Most users should employ the `KSP` interface for linear solvers
3417: instead of working directly with matrix algebra routines such as this.
3418: See, e.g., `KSPCreate()`.
3420: Developer Note:
3421: The Fortran interface is not autogenerated as the
3422: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3424: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`
3425: `MatGetOrdering()`
3426: @*/
3427: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3428: {
3429: MatFactorInfo tinfo;
3431: PetscFunctionBegin;
3435: if (info) PetscAssertPointer(info, 4);
3438: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3439: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3440: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3441: MatCheckPreallocated(mat, 2);
3442: if (!info) {
3443: PetscCall(MatFactorInfoInitialize(&tinfo));
3444: info = &tinfo;
3445: }
3447: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3448: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3449: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3450: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3451: PetscFunctionReturn(PETSC_SUCCESS);
3452: }
3454: /*@
3455: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3456: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3457: `MatCholeskyFactorSymbolic()`.
3459: Collective
3461: Input Parameters:
3462: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3463: . mat - the initial matrix that is to be factored
3464: - info - options for factorization
3466: Level: developer
3468: Note:
3469: Most users should employ the `KSP` interface for linear solvers
3470: instead of working directly with matrix algebra routines such as this.
3471: See, e.g., `KSPCreate()`.
3473: Developer Note:
3474: The Fortran interface is not autogenerated as the
3475: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3477: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3478: @*/
3479: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3480: {
3481: MatFactorInfo tinfo;
3483: PetscFunctionBegin;
3488: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3489: 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,
3490: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3491: MatCheckPreallocated(mat, 2);
3492: if (!info) {
3493: PetscCall(MatFactorInfoInitialize(&tinfo));
3494: info = &tinfo;
3495: }
3497: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3498: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3499: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3500: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3501: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3502: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3503: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3504: PetscFunctionReturn(PETSC_SUCCESS);
3505: }
3507: /*@
3508: MatQRFactor - Performs in-place QR factorization of matrix.
3510: Collective
3512: Input Parameters:
3513: + mat - the matrix
3514: . col - column permutation
3515: - info - options for factorization, includes
3516: .vb
3517: fill - expected fill as ratio of original fill.
3518: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3519: Run with the option -info to determine an optimal value to use
3520: .ve
3522: Level: developer
3524: Notes:
3525: Most users should employ the `KSP` interface for linear solvers
3526: instead of working directly with matrix algebra routines such as this.
3527: See, e.g., `KSPCreate()`.
3529: This changes the state of the matrix to a factored matrix; it cannot be used
3530: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3532: Developer Note:
3533: The Fortran interface is not autogenerated as the
3534: interface definition cannot be generated correctly [due to MatFactorInfo]
3536: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3537: `MatSetUnfactored()`
3538: @*/
3539: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3540: {
3541: PetscFunctionBegin;
3544: if (info) PetscAssertPointer(info, 3);
3546: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3547: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3548: MatCheckPreallocated(mat, 1);
3549: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3550: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3551: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3552: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3553: PetscFunctionReturn(PETSC_SUCCESS);
3554: }
3556: /*@
3557: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3558: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3560: Collective
3562: Input Parameters:
3563: + fact - the factor matrix obtained with `MatGetFactor()`
3564: . mat - the matrix
3565: . col - column permutation
3566: - info - options for factorization, includes
3567: .vb
3568: fill - expected fill as ratio of original fill.
3569: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3570: Run with the option -info to determine an optimal value to use
3571: .ve
3573: Level: developer
3575: Note:
3576: Most users should employ the `KSP` interface for linear solvers
3577: instead of working directly with matrix algebra routines such as this.
3578: See, e.g., `KSPCreate()`.
3580: Developer Note:
3581: The Fortran interface is not autogenerated as the
3582: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3584: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3585: @*/
3586: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3587: {
3588: MatFactorInfo tinfo;
3590: PetscFunctionBegin;
3594: if (info) PetscAssertPointer(info, 4);
3597: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3598: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3599: MatCheckPreallocated(mat, 2);
3600: if (!info) {
3601: PetscCall(MatFactorInfoInitialize(&tinfo));
3602: info = &tinfo;
3603: }
3605: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3606: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3607: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3608: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3609: PetscFunctionReturn(PETSC_SUCCESS);
3610: }
3612: /*@
3613: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3614: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3616: Collective
3618: Input Parameters:
3619: + fact - the factor matrix obtained with `MatGetFactor()`
3620: . mat - the matrix
3621: - info - options for factorization
3623: Level: developer
3625: Notes:
3626: See `MatQRFactor()` for in-place factorization.
3628: Most users should employ the `KSP` interface for linear solvers
3629: instead of working directly with matrix algebra routines such as this.
3630: See, e.g., `KSPCreate()`.
3632: Developer Note:
3633: The Fortran interface is not autogenerated as the
3634: interface definition cannot be generated correctly [due to `MatFactorInfo`]
3636: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3637: @*/
3638: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3639: {
3640: MatFactorInfo tinfo;
3642: PetscFunctionBegin;
3647: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3648: 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,
3649: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3651: MatCheckPreallocated(mat, 2);
3652: if (!info) {
3653: PetscCall(MatFactorInfoInitialize(&tinfo));
3654: info = &tinfo;
3655: }
3657: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3658: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3659: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3660: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3661: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3662: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3663: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3664: PetscFunctionReturn(PETSC_SUCCESS);
3665: }
3667: /*@
3668: MatSolve - Solves $A x = b$, given a factored matrix.
3670: Neighbor-wise Collective
3672: Input Parameters:
3673: + mat - the factored matrix
3674: - b - the right-hand-side vector
3676: Output Parameter:
3677: . x - the result vector
3679: Level: developer
3681: Notes:
3682: The vectors `b` and `x` cannot be the same. I.e., one cannot
3683: call `MatSolve`(A,x,x).
3685: Most users should employ the `KSP` interface for linear solvers
3686: instead of working directly with matrix algebra routines such as this.
3687: See, e.g., `KSPCreate()`.
3689: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3690: @*/
3691: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3692: {
3693: PetscFunctionBegin;
3698: PetscCheckSameComm(mat, 1, b, 2);
3699: PetscCheckSameComm(mat, 1, x, 3);
3700: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3701: 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);
3702: 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);
3703: 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);
3704: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3705: MatCheckPreallocated(mat, 1);
3707: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3708: PetscCall(VecFlag(x, mat->factorerrortype));
3709: if (mat->factorerrortype) {
3710: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3711: } else PetscUseTypeMethod(mat, solve, b, x);
3712: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3713: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3714: PetscFunctionReturn(PETSC_SUCCESS);
3715: }
3717: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3718: {
3719: Vec b, x;
3720: PetscInt N, i;
3721: PetscErrorCode (*f)(Mat, Vec, Vec);
3722: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3724: PetscFunctionBegin;
3725: if (A->factorerrortype) {
3726: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3727: PetscCall(MatSetInf(X));
3728: PetscFunctionReturn(PETSC_SUCCESS);
3729: }
3730: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3731: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3732: PetscCall(MatBoundToCPU(A, &Abound));
3733: if (!Abound) {
3734: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3735: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3736: }
3737: #if PetscDefined(HAVE_CUDA)
3738: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3739: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3740: #elif PetscDefined(HAVE_HIP)
3741: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3742: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3743: #endif
3744: PetscCall(MatGetSize(B, NULL, &N));
3745: for (i = 0; i < N; i++) {
3746: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3747: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3748: PetscCall((*f)(A, b, x));
3749: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3750: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3751: }
3752: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3753: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3754: PetscFunctionReturn(PETSC_SUCCESS);
3755: }
3757: /*@
3758: MatMatSolve - Solves $A X = B$, given a factored matrix.
3760: Neighbor-wise Collective
3762: Input Parameters:
3763: + A - the factored matrix
3764: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3766: Output Parameter:
3767: . X - the result matrix (dense matrix)
3769: Level: developer
3771: Note:
3772: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3773: otherwise, `B` and `X` cannot be the same.
3775: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3776: @*/
3777: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3778: {
3779: PetscFunctionBegin;
3784: PetscCheckSameComm(A, 1, B, 2);
3785: PetscCheckSameComm(A, 1, X, 3);
3786: 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);
3787: 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);
3788: 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");
3789: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3790: MatCheckPreallocated(A, 1);
3792: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3793: if (!A->ops->matsolve) {
3794: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3795: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3796: } else PetscUseTypeMethod(A, matsolve, B, X);
3797: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3798: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3799: PetscFunctionReturn(PETSC_SUCCESS);
3800: }
3802: /*@
3803: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3805: Neighbor-wise Collective
3807: Input Parameters:
3808: + A - the factored matrix
3809: - B - the right-hand-side matrix (`MATDENSE` matrix)
3811: Output Parameter:
3812: . X - the result matrix (dense matrix)
3814: Level: developer
3816: Note:
3817: The matrices `B` and `X` cannot be the same. I.e., one cannot
3818: call `MatMatSolveTranspose`(A,X,X).
3820: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3821: @*/
3822: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3823: {
3824: PetscFunctionBegin;
3829: PetscCheckSameComm(A, 1, B, 2);
3830: PetscCheckSameComm(A, 1, X, 3);
3831: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3832: 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);
3833: 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);
3834: 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);
3835: 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");
3836: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3837: MatCheckPreallocated(A, 1);
3839: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3840: if (!A->ops->matsolvetranspose) {
3841: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3842: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3843: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3844: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3845: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3846: PetscFunctionReturn(PETSC_SUCCESS);
3847: }
3849: /*@
3850: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3852: Neighbor-wise Collective
3854: Input Parameters:
3855: + A - the factored matrix
3856: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3858: Output Parameter:
3859: . X - the result matrix (dense matrix)
3861: Level: developer
3863: Note:
3864: For MUMPS, it only supports centralized sparse compressed column format on the host processor for right-hand side matrix. User must create `Bt` in sparse compressed row
3865: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3867: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3868: @*/
3869: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3870: {
3871: PetscFunctionBegin;
3876: PetscCheckSameComm(A, 1, Bt, 2);
3877: PetscCheckSameComm(A, 1, X, 3);
3879: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3880: 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);
3881: 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);
3882: 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");
3883: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3884: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3885: MatCheckPreallocated(A, 1);
3887: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3888: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3889: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3890: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3891: PetscFunctionReturn(PETSC_SUCCESS);
3892: }
3894: /*@
3895: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3896: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3898: Neighbor-wise Collective
3900: Input Parameters:
3901: + mat - the factored matrix
3902: - b - the right-hand-side vector
3904: Output Parameter:
3905: . x - the result vector
3907: Level: developer
3909: Notes:
3910: `MatSolve()` should be used for most applications, as it performs
3911: a forward solve followed by a backward solve.
3913: The vectors `b` and `x` cannot be the same, i.e., one cannot
3914: call `MatForwardSolve`(A,x,x).
3916: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3917: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3918: `MatForwardSolve()` solves $U^T*D y = b$, and
3919: `MatBackwardSolve()` solves $U x = y$.
3920: Thus they do not provide a symmetric preconditioner.
3922: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
3923: @*/
3924: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
3925: {
3926: PetscFunctionBegin;
3931: PetscCheckSameComm(mat, 1, b, 2);
3932: PetscCheckSameComm(mat, 1, x, 3);
3933: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3934: 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);
3935: 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);
3936: 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);
3937: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3938: MatCheckPreallocated(mat, 1);
3940: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
3941: PetscUseTypeMethod(mat, forwardsolve, b, x);
3942: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
3943: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3944: PetscFunctionReturn(PETSC_SUCCESS);
3945: }
3947: /*@
3948: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
3949: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3951: Neighbor-wise Collective
3953: Input Parameters:
3954: + mat - the factored matrix
3955: - b - the right-hand-side vector
3957: Output Parameter:
3958: . x - the result vector
3960: Level: developer
3962: Notes:
3963: `MatSolve()` should be used for most applications, as it performs
3964: a forward solve followed by a backward solve.
3966: The vectors `b` and `x` cannot be the same. I.e., one cannot
3967: call `MatBackwardSolve`(A,x,x).
3969: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3970: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3971: `MatForwardSolve()` solves $U^T*D y = b$, and
3972: `MatBackwardSolve()` solves $U x = y$.
3973: Thus they do not provide a symmetric preconditioner.
3975: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
3976: @*/
3977: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
3978: {
3979: PetscFunctionBegin;
3984: PetscCheckSameComm(mat, 1, b, 2);
3985: PetscCheckSameComm(mat, 1, x, 3);
3986: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3987: 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);
3988: 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);
3989: 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);
3990: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3991: MatCheckPreallocated(mat, 1);
3993: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
3994: PetscUseTypeMethod(mat, backwardsolve, b, x);
3995: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
3996: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3997: PetscFunctionReturn(PETSC_SUCCESS);
3998: }
4000: /*@
4001: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
4003: Neighbor-wise Collective
4005: Input Parameters:
4006: + mat - the factored matrix
4007: . b - the right-hand-side vector
4008: - y - the vector to be added to
4010: Output Parameter:
4011: . x - the result vector
4013: Level: developer
4015: Note:
4016: The vectors `b` and `x` cannot be the same. I.e., one cannot
4017: call `MatSolveAdd`(A,x,y,x).
4019: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4020: @*/
4021: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4022: {
4023: PetscScalar one = 1.0;
4024: Vec tmp;
4026: PetscFunctionBegin;
4032: PetscCheckSameComm(mat, 1, b, 2);
4033: PetscCheckSameComm(mat, 1, y, 3);
4034: PetscCheckSameComm(mat, 1, x, 4);
4035: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4036: 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);
4037: 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);
4038: 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);
4039: 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);
4040: 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);
4041: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4042: MatCheckPreallocated(mat, 1);
4044: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4045: PetscCall(VecFlag(x, mat->factorerrortype));
4046: if (mat->factorerrortype) {
4047: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4048: } else if (mat->ops->solveadd) {
4049: PetscUseTypeMethod(mat, solveadd, b, y, x);
4050: } else {
4051: /* do the solve then the add manually */
4052: if (x != y) {
4053: PetscCall(MatSolve(mat, b, x));
4054: PetscCall(VecAXPY(x, one, y));
4055: } else {
4056: PetscCall(VecDuplicate(x, &tmp));
4057: PetscCall(VecCopy(x, tmp));
4058: PetscCall(MatSolve(mat, b, x));
4059: PetscCall(VecAXPY(x, one, tmp));
4060: PetscCall(VecDestroy(&tmp));
4061: }
4062: }
4063: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4064: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4065: PetscFunctionReturn(PETSC_SUCCESS);
4066: }
4068: /*@
4069: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4071: Neighbor-wise Collective
4073: Input Parameters:
4074: + mat - the factored matrix
4075: - b - the right-hand-side vector
4077: Output Parameter:
4078: . x - the result vector
4080: Level: developer
4082: Notes:
4083: The vectors `b` and `x` cannot be the same. I.e., one cannot
4084: call `MatSolveTranspose`(A,x,x).
4086: Most users should employ the `KSP` interface for linear solvers
4087: instead of working directly with matrix algebra routines such as this.
4088: See, e.g., `KSPCreate()`.
4090: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4091: @*/
4092: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4093: {
4094: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4096: PetscFunctionBegin;
4101: PetscCheckSameComm(mat, 1, b, 2);
4102: PetscCheckSameComm(mat, 1, x, 3);
4103: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4104: 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);
4105: 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);
4106: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4107: MatCheckPreallocated(mat, 1);
4108: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4109: PetscCall(VecFlag(x, mat->factorerrortype));
4110: if (mat->factorerrortype) {
4111: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4112: } else {
4113: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4114: PetscCall((*f)(mat, b, x));
4115: }
4116: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4117: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4118: PetscFunctionReturn(PETSC_SUCCESS);
4119: }
4121: /*@
4122: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4123: factored matrix.
4125: Neighbor-wise Collective
4127: Input Parameters:
4128: + mat - the factored matrix
4129: . b - the right-hand-side vector
4130: - y - the vector to be added to
4132: Output Parameter:
4133: . x - the result vector
4135: Level: developer
4137: Note:
4138: The vectors `b` and `x` cannot be the same. I.e., one cannot
4139: call `MatSolveTransposeAdd`(A,x,y,x).
4141: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4142: @*/
4143: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4144: {
4145: PetscScalar one = 1.0;
4146: Vec tmp;
4147: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4149: PetscFunctionBegin;
4155: PetscCheckSameComm(mat, 1, b, 2);
4156: PetscCheckSameComm(mat, 1, y, 3);
4157: PetscCheckSameComm(mat, 1, x, 4);
4158: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4159: PetscCheck(mat->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);
4160: 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);
4161: 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);
4162: 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);
4163: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4164: MatCheckPreallocated(mat, 1);
4166: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4167: PetscCall(VecFlag(x, mat->factorerrortype));
4168: if (mat->factorerrortype) {
4169: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4170: } else if (f) {
4171: PetscCall((*f)(mat, b, y, x));
4172: } else {
4173: /* do the solve then the add manually */
4174: if (x != y) {
4175: PetscCall(MatSolveTranspose(mat, b, x));
4176: PetscCall(VecAXPY(x, one, y));
4177: } else {
4178: PetscCall(VecDuplicate(x, &tmp));
4179: PetscCall(VecCopy(x, tmp));
4180: PetscCall(MatSolveTranspose(mat, b, x));
4181: PetscCall(VecAXPY(x, one, tmp));
4182: PetscCall(VecDestroy(&tmp));
4183: }
4184: }
4185: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4186: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4187: PetscFunctionReturn(PETSC_SUCCESS);
4188: }
4190: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4191: /*@
4192: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4194: Neighbor-wise Collective
4196: Input Parameters:
4197: + mat - the matrix
4198: . b - the right-hand side
4199: . omega - the relaxation factor
4200: . flag - flag indicating the type of SOR (see below)
4201: . shift - diagonal shift
4202: . its - the number of iterations
4203: - lits - the number of local iterations
4205: Output Parameter:
4206: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4208: SOR Flags:
4209: + `SOR_FORWARD_SWEEP` - forward SOR
4210: . `SOR_BACKWARD_SWEEP` - backward SOR
4211: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4212: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4213: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4214: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4215: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4216: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies
4217: upper/lower triangular part of matrix to
4218: vector (with omega)
4219: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4221: Level: developer
4223: Notes:
4224: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4225: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4226: on each processor.
4228: Application programmers will not generally use `MatSOR()` directly,
4229: but instead will employ the `KSP`/`PC` interface.
4231: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with Inodes this does a block SOR smoothing, otherwise it does a pointwise smoothing
4233: Most users should employ the `KSP` interface for linear solvers
4234: instead of working directly with matrix algebra routines such as this.
4235: See, e.g., `KSPCreate()`.
4237: Vectors `x` and `b` CANNOT be the same
4239: The flags are implemented as bitwise inclusive or operations.
4240: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4241: to specify a zero initial guess for SSOR.
4243: Developer Note:
4244: We should add block SOR support for `MATAIJ` matrices with block size set to great than one and no inodes
4246: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4247: @*/
4248: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4249: {
4250: PetscFunctionBegin;
4255: PetscCheckSameComm(mat, 1, b, 2);
4256: PetscCheckSameComm(mat, 1, x, 8);
4257: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4258: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4259: 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);
4260: 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);
4261: 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);
4262: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4263: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4264: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4266: MatCheckPreallocated(mat, 1);
4267: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4268: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4269: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4270: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4271: PetscFunctionReturn(PETSC_SUCCESS);
4272: }
4274: /*
4275: Default matrix copy routine.
4276: */
4277: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4278: {
4279: PetscInt i, rstart = 0, rend = 0, nz;
4280: const PetscInt *cwork;
4281: const PetscScalar *vwork;
4283: PetscFunctionBegin;
4284: if (B->assembled) PetscCall(MatZeroEntries(B));
4285: if (str == SAME_NONZERO_PATTERN) {
4286: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4287: for (i = rstart; i < rend; i++) {
4288: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4289: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4290: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4291: }
4292: } else {
4293: PetscCall(MatAYPX(B, 0.0, A, str));
4294: }
4295: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4296: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4297: PetscFunctionReturn(PETSC_SUCCESS);
4298: }
4300: /*@
4301: MatCopy - Copies a matrix to another matrix.
4303: Collective
4305: Input Parameters:
4306: + A - the matrix
4307: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4309: Output Parameter:
4310: . B - where the copy is put
4312: Level: intermediate
4314: Notes:
4315: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4317: `MatCopy()` copies the matrix entries of a matrix to another existing
4318: matrix (after first zeroing the second matrix). A related routine is
4319: `MatConvert()`, which first creates a new matrix and then copies the data.
4321: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4322: @*/
4323: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4324: {
4325: PetscInt i;
4327: PetscFunctionBegin;
4332: PetscCheckSameComm(A, 1, B, 2);
4333: MatCheckPreallocated(B, 2);
4334: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4335: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4336: 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,
4337: A->cmap->N, B->cmap->N);
4338: MatCheckPreallocated(A, 1);
4339: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4341: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4342: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4343: else PetscCall(MatCopy_Basic(A, B, str));
4345: B->stencil.dim = A->stencil.dim;
4346: B->stencil.noc = A->stencil.noc;
4347: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4348: B->stencil.dims[i] = A->stencil.dims[i];
4349: B->stencil.starts[i] = A->stencil.starts[i];
4350: }
4352: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4353: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4354: PetscFunctionReturn(PETSC_SUCCESS);
4355: }
4357: /*@
4358: MatConvert - Converts a matrix to another matrix, either of the same
4359: or different type.
4361: Collective
4363: Input Parameters:
4364: + mat - the matrix
4365: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4366: same type as the original matrix.
4367: - reuse - denotes if the destination matrix is to be created or reused.
4368: 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
4369: `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).
4371: Output Parameter:
4372: . M - pointer to place new matrix
4374: Level: intermediate
4376: Notes:
4377: `MatConvert()` first creates a new matrix and then copies the data from
4378: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4379: entries of one matrix to another already existing matrix context.
4381: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4382: the MPI communicator of the generated matrix is always the same as the communicator
4383: of the input matrix.
4385: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4386: @*/
4387: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4388: {
4389: PetscBool sametype, issame, flg;
4390: PetscBool3 issymmetric, ishermitian;
4391: char convname[256], mtype[256];
4392: Mat B;
4394: PetscFunctionBegin;
4397: PetscAssertPointer(M, 4);
4398: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4399: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4400: MatCheckPreallocated(mat, 1);
4402: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4403: if (flg) newtype = mtype;
4405: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4406: PetscCall(PetscStrcmp(newtype, "same", &issame));
4407: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4408: if (reuse == MAT_REUSE_MATRIX) {
4410: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4411: }
4413: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4414: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4415: PetscFunctionReturn(PETSC_SUCCESS);
4416: }
4418: /* Cache Mat options because some converters use MatHeaderReplace */
4419: issymmetric = mat->symmetric;
4420: ishermitian = mat->hermitian;
4422: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4423: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4424: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4425: } else {
4426: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4427: const char *prefix[3] = {"seq", "mpi", ""};
4428: PetscInt i;
4429: /*
4430: Order of precedence:
4431: 0) See if newtype is a superclass of the current matrix.
4432: 1) See if a specialized converter is known to the current matrix.
4433: 2) See if a specialized converter is known to the desired matrix class.
4434: 3) See if a good general converter is registered for the desired class
4435: (as of 6/27/03 only MATMPIADJ falls into this category).
4436: 4) See if a good general converter is known for the current matrix.
4437: 5) Use a really basic converter.
4438: */
4440: /* 0) See if newtype is a superclass of the current matrix.
4441: i.e mat is mpiaij and newtype is aij */
4442: for (i = 0; i < 2; i++) {
4443: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4444: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4445: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4446: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4447: if (flg) {
4448: if (reuse == MAT_INPLACE_MATRIX) {
4449: PetscCall(PetscInfo(mat, "Early return\n"));
4450: PetscFunctionReturn(PETSC_SUCCESS);
4451: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4452: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4453: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4454: PetscFunctionReturn(PETSC_SUCCESS);
4455: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4456: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4457: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4458: PetscFunctionReturn(PETSC_SUCCESS);
4459: }
4460: }
4461: }
4462: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4463: for (i = 0; i < 3; i++) {
4464: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4465: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4466: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4467: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4468: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4469: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4470: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4471: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4472: if (conv) goto foundconv;
4473: }
4475: /* 2) See if a specialized converter is known to the desired matrix class. */
4476: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4477: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4478: PetscCall(MatSetType(B, newtype));
4479: for (i = 0; i < 3; i++) {
4480: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4481: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4482: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4483: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4484: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4485: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4486: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4487: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4488: if (conv) {
4489: PetscCall(MatDestroy(&B));
4490: goto foundconv;
4491: }
4492: }
4494: /* 3) See if a good general converter is registered for the desired class */
4495: conv = B->ops->convertfrom;
4496: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4497: PetscCall(MatDestroy(&B));
4498: if (conv) goto foundconv;
4500: /* 4) See if a good general converter is known for the current matrix */
4501: if (mat->ops->convert) conv = mat->ops->convert;
4502: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4503: if (conv) goto foundconv;
4505: /* 5) Use a really basic converter. */
4506: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4507: conv = MatConvert_Basic;
4509: foundconv:
4510: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4511: PetscCall((*conv)(mat, newtype, reuse, M));
4512: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4513: /* the block sizes must be same if the mappings are copied over */
4514: (*M)->rmap->bs = mat->rmap->bs;
4515: (*M)->cmap->bs = mat->cmap->bs;
4516: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4517: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4518: (*M)->rmap->mapping = mat->rmap->mapping;
4519: (*M)->cmap->mapping = mat->cmap->mapping;
4520: }
4521: (*M)->stencil.dim = mat->stencil.dim;
4522: (*M)->stencil.noc = mat->stencil.noc;
4523: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4524: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4525: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4526: }
4527: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4528: }
4529: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4531: /* Copy Mat options */
4532: if (issymmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_TRUE));
4533: else if (issymmetric == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PETSC_FALSE));
4534: if (ishermitian == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_TRUE));
4535: else if (ishermitian == PETSC_BOOL3_FALSE) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PETSC_FALSE));
4536: PetscFunctionReturn(PETSC_SUCCESS);
4537: }
4539: /*@
4540: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4542: Not Collective
4544: Input Parameter:
4545: . mat - the matrix, must be a factored matrix
4547: Output Parameter:
4548: . type - the string name of the package (do not free this string)
4550: Level: intermediate
4552: Fortran Note:
4553: Pass in an empty string that is long enough and the package name will be copied into it.
4555: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4556: @*/
4557: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4558: {
4559: PetscErrorCode (*conv)(Mat, MatSolverType *);
4561: PetscFunctionBegin;
4564: PetscAssertPointer(type, 2);
4565: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4566: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4567: if (conv) PetscCall((*conv)(mat, type));
4568: else *type = MATSOLVERPETSC;
4569: PetscFunctionReturn(PETSC_SUCCESS);
4570: }
4572: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4573: struct _MatSolverTypeForSpecifcType {
4574: MatType mtype;
4575: /* no entry for MAT_FACTOR_NONE */
4576: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4577: MatSolverTypeForSpecifcType next;
4578: };
4580: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4581: struct _MatSolverTypeHolder {
4582: char *name;
4583: MatSolverTypeForSpecifcType handlers;
4584: MatSolverTypeHolder next;
4585: };
4587: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4589: /*@C
4590: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4592: Logically Collective, No Fortran Support
4594: Input Parameters:
4595: + package - name of the package, for example petsc or superlu
4596: . mtype - the matrix type that works with this package
4597: . ftype - the type of factorization supported by the package
4598: - createfactor - routine that will create the factored matrix ready to be used
4600: Level: developer
4602: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4603: `MatGetFactor()`
4604: @*/
4605: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4606: {
4607: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4608: PetscBool flg;
4609: MatSolverTypeForSpecifcType inext, iprev = NULL;
4611: PetscFunctionBegin;
4612: PetscCall(MatInitializePackage());
4613: if (!next) {
4614: PetscCall(PetscNew(&MatSolverTypeHolders));
4615: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4616: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4617: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4618: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4619: PetscFunctionReturn(PETSC_SUCCESS);
4620: }
4621: while (next) {
4622: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4623: if (flg) {
4624: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4625: inext = next->handlers;
4626: while (inext) {
4627: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4628: if (flg) {
4629: inext->createfactor[(int)ftype - 1] = createfactor;
4630: PetscFunctionReturn(PETSC_SUCCESS);
4631: }
4632: iprev = inext;
4633: inext = inext->next;
4634: }
4635: PetscCall(PetscNew(&iprev->next));
4636: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4637: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4638: PetscFunctionReturn(PETSC_SUCCESS);
4639: }
4640: prev = next;
4641: next = next->next;
4642: }
4643: PetscCall(PetscNew(&prev->next));
4644: PetscCall(PetscStrallocpy(package, &prev->next->name));
4645: PetscCall(PetscNew(&prev->next->handlers));
4646: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4647: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4648: PetscFunctionReturn(PETSC_SUCCESS);
4649: }
4651: /*@C
4652: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4654: Input Parameters:
4655: + 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
4656: . ftype - the type of factorization supported by the type
4657: - mtype - the matrix type that works with this type
4659: Output Parameters:
4660: + foundtype - `PETSC_TRUE` if the type was registered
4661: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4662: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4664: Calling sequence of `createfactor`:
4665: + A - the matrix providing the factor matrix
4666: . ftype - the `MatFactorType` of the factor requested
4667: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4669: Level: developer
4671: Note:
4672: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4673: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4674: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4676: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4677: `MatInitializePackage()`
4678: @*/
4679: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4680: {
4681: MatSolverTypeHolder next = MatSolverTypeHolders;
4682: PetscBool flg;
4683: MatSolverTypeForSpecifcType inext;
4685: PetscFunctionBegin;
4686: if (foundtype) *foundtype = PETSC_FALSE;
4687: if (foundmtype) *foundmtype = PETSC_FALSE;
4688: if (createfactor) *createfactor = NULL;
4690: if (type) {
4691: while (next) {
4692: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4693: if (flg) {
4694: if (foundtype) *foundtype = PETSC_TRUE;
4695: inext = next->handlers;
4696: while (inext) {
4697: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4698: if (flg) {
4699: if (foundmtype) *foundmtype = PETSC_TRUE;
4700: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4701: PetscFunctionReturn(PETSC_SUCCESS);
4702: }
4703: inext = inext->next;
4704: }
4705: }
4706: next = next->next;
4707: }
4708: } else {
4709: while (next) {
4710: inext = next->handlers;
4711: while (inext) {
4712: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4713: if (flg && inext->createfactor[(int)ftype - 1]) {
4714: if (foundtype) *foundtype = PETSC_TRUE;
4715: if (foundmtype) *foundmtype = PETSC_TRUE;
4716: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4717: PetscFunctionReturn(PETSC_SUCCESS);
4718: }
4719: inext = inext->next;
4720: }
4721: next = next->next;
4722: }
4723: /* try with base classes inext->mtype */
4724: next = MatSolverTypeHolders;
4725: while (next) {
4726: inext = next->handlers;
4727: while (inext) {
4728: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4729: if (flg && inext->createfactor[(int)ftype - 1]) {
4730: if (foundtype) *foundtype = PETSC_TRUE;
4731: if (foundmtype) *foundmtype = PETSC_TRUE;
4732: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4733: PetscFunctionReturn(PETSC_SUCCESS);
4734: }
4735: inext = inext->next;
4736: }
4737: next = next->next;
4738: }
4739: }
4740: PetscFunctionReturn(PETSC_SUCCESS);
4741: }
4743: PetscErrorCode MatSolverTypeDestroy(void)
4744: {
4745: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4746: MatSolverTypeForSpecifcType inext, iprev;
4748: PetscFunctionBegin;
4749: while (next) {
4750: PetscCall(PetscFree(next->name));
4751: inext = next->handlers;
4752: while (inext) {
4753: PetscCall(PetscFree(inext->mtype));
4754: iprev = inext;
4755: inext = inext->next;
4756: PetscCall(PetscFree(iprev));
4757: }
4758: prev = next;
4759: next = next->next;
4760: PetscCall(PetscFree(prev));
4761: }
4762: MatSolverTypeHolders = NULL;
4763: PetscFunctionReturn(PETSC_SUCCESS);
4764: }
4766: /*@
4767: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4769: Logically Collective
4771: Input Parameter:
4772: . mat - the matrix
4774: Output Parameter:
4775: . flg - `PETSC_TRUE` if uses the ordering
4777: Level: developer
4779: Note:
4780: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4781: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4783: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4784: @*/
4785: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4786: {
4787: PetscFunctionBegin;
4788: *flg = mat->canuseordering;
4789: PetscFunctionReturn(PETSC_SUCCESS);
4790: }
4792: /*@
4793: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4795: Logically Collective
4797: Input Parameters:
4798: + mat - the matrix obtained with `MatGetFactor()`
4799: - ftype - the factorization type to be used
4801: Output Parameter:
4802: . otype - the preferred ordering type
4804: Level: developer
4806: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4807: @*/
4808: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4809: {
4810: PetscFunctionBegin;
4811: *otype = mat->preferredordering[ftype];
4812: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4813: PetscFunctionReturn(PETSC_SUCCESS);
4814: }
4816: /*@
4817: MatGetFactor - Returns a matrix suitable to calls to MatXXFactorSymbolic,Numeric()
4819: Collective
4821: Input Parameters:
4822: + mat - the matrix
4823: . type - name of solver type, for example, superlu, petsc (to use PETSc's solver if it is available), if this is 'NULL', then the first result that satisfies
4824: the other criteria is returned
4825: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4827: Output Parameter:
4828: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4830: Options Database Keys:
4831: + -pc_factor_mat_solver_type <type> - choose the type at run time. When using `KSP` solvers
4832: - -mat_factor_bind_factorization <host, device> - Where to do matrix factorization? Default is device (might consume more device memory.
4833: One can choose host to save device memory). Currently only supported with `MATSEQAIJCUSPARSE` matrices.
4835: Level: intermediate
4837: Notes:
4838: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4839: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4841: Users usually access the factorization solvers via `KSP`
4843: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4844: such as pastix, superlu, mumps etc. PETSc must have been ./configure to use the external solver, using the option --download-package or --with-package-dir
4846: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4847: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4848: For example if one configuration had --download-mumps while a different one had --download-superlu_dist.
4850: Some of the packages have options for controlling the factorization, these are in the form -prefix_mat_packagename_packageoption
4851: where prefix is normally obtained from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly one can set
4852: call `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4854: Developer Note:
4855: This should actually be called `MatCreateFactor()` since it creates a new factor object
4857: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4858: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`
4859: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`
4860: @*/
4861: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4862: {
4863: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4864: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4866: PetscFunctionBegin;
4870: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4871: MatCheckPreallocated(mat, 1);
4873: PetscCall(MatIsShell(mat, &shell));
4874: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4875: if (hasop) {
4876: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4877: PetscFunctionReturn(PETSC_SUCCESS);
4878: }
4880: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4881: if (!foundtype) {
4882: if (type) {
4883: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "Could not locate solver type %s for factorization type %s and matrix type %s. Perhaps you must ./configure with --download-%s", type, MatFactorTypes[ftype],
4884: ((PetscObject)mat)->type_name, type);
4885: } else {
4886: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "Could not locate a solver type for factorization type %s and matrix type %s.", MatFactorTypes[ftype], ((PetscObject)mat)->type_name);
4887: }
4888: }
4889: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4890: 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);
4892: PetscCall((*conv)(mat, ftype, f));
4893: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4894: PetscFunctionReturn(PETSC_SUCCESS);
4895: }
4897: /*@
4898: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4900: Not Collective
4902: Input Parameters:
4903: + mat - the matrix
4904: . type - name of solver type, for example, superlu, petsc (to use PETSc's default)
4905: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4907: Output Parameter:
4908: . flg - PETSC_TRUE if the factorization is available
4910: Level: intermediate
4912: Notes:
4913: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4914: such as pastix, superlu, mumps etc.
4916: PETSc must have been ./configure to use the external solver, using the option --download-package
4918: Developer Note:
4919: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
4921: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
4922: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
4923: @*/
4924: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
4925: {
4926: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
4928: PetscFunctionBegin;
4930: PetscAssertPointer(flg, 4);
4932: *flg = PETSC_FALSE;
4933: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
4935: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4936: MatCheckPreallocated(mat, 1);
4938: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
4939: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
4940: PetscFunctionReturn(PETSC_SUCCESS);
4941: }
4943: /*@
4944: MatDuplicate - Duplicates a matrix including the non-zero structure.
4946: Collective
4948: Input Parameters:
4949: + mat - the matrix
4950: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
4951: See the manual page for `MatDuplicateOption()` for an explanation of these options.
4953: Output Parameter:
4954: . M - pointer to place new matrix
4956: Level: intermediate
4958: Notes:
4959: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
4961: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
4963: 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.
4965: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
4966: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
4967: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
4969: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
4970: @*/
4971: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
4972: {
4973: Mat B;
4974: VecType vtype;
4975: PetscInt i;
4976: PetscObject dm, container_h, container_d;
4977: void (*viewf)(void);
4979: PetscFunctionBegin;
4982: PetscAssertPointer(M, 3);
4983: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
4984: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4985: MatCheckPreallocated(mat, 1);
4987: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4988: PetscUseTypeMethod(mat, duplicate, op, M);
4989: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4990: B = *M;
4992: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
4993: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
4994: PetscCall(MatGetVecType(mat, &vtype));
4995: PetscCall(MatSetVecType(B, vtype));
4997: B->stencil.dim = mat->stencil.dim;
4998: B->stencil.noc = mat->stencil.noc;
4999: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
5000: B->stencil.dims[i] = mat->stencil.dims[i];
5001: B->stencil.starts[i] = mat->stencil.starts[i];
5002: }
5004: B->nooffproczerorows = mat->nooffproczerorows;
5005: B->nooffprocentries = mat->nooffprocentries;
5007: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5008: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5009: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5010: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5011: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5012: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5013: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5014: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5015: PetscFunctionReturn(PETSC_SUCCESS);
5016: }
5018: /*@
5019: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5021: Logically Collective
5023: Input Parameter:
5024: . mat - the matrix
5026: Output Parameter:
5027: . v - the diagonal of the matrix
5029: Level: intermediate
5031: Note:
5032: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5033: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5034: is larger than `ndiag`, the values of the remaining entries are unspecified.
5036: Currently only correct in parallel for square matrices.
5038: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5039: @*/
5040: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5041: {
5042: PetscFunctionBegin;
5046: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5047: MatCheckPreallocated(mat, 1);
5048: if (PetscDefined(USE_DEBUG)) {
5049: PetscInt nv, row, col, ndiag;
5051: PetscCall(VecGetLocalSize(v, &nv));
5052: PetscCall(MatGetLocalSize(mat, &row, &col));
5053: ndiag = PetscMin(row, col);
5054: 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);
5055: }
5057: PetscUseTypeMethod(mat, getdiagonal, v);
5058: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5059: PetscFunctionReturn(PETSC_SUCCESS);
5060: }
5062: /*@
5063: MatGetRowMin - Gets the minimum value (of the real part) of each
5064: row of the matrix
5066: Logically Collective
5068: Input Parameter:
5069: . mat - the matrix
5071: Output Parameters:
5072: + v - the vector for storing the maximums
5073: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5075: Level: intermediate
5077: Note:
5078: The result of this call are the same as if one converted the matrix to dense format
5079: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5081: This code is only implemented for a couple of matrix formats.
5083: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5084: `MatGetRowMax()`
5085: @*/
5086: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5087: {
5088: PetscFunctionBegin;
5092: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5094: if (!mat->cmap->N) {
5095: PetscCall(VecSet(v, PETSC_MAX_REAL));
5096: if (idx) {
5097: PetscInt i, m = mat->rmap->n;
5098: for (i = 0; i < m; i++) idx[i] = -1;
5099: }
5100: } else {
5101: MatCheckPreallocated(mat, 1);
5102: }
5103: PetscUseTypeMethod(mat, getrowmin, v, idx);
5104: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5105: PetscFunctionReturn(PETSC_SUCCESS);
5106: }
5108: /*@
5109: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5110: row of the matrix
5112: Logically Collective
5114: Input Parameter:
5115: . mat - the matrix
5117: Output Parameters:
5118: + v - the vector for storing the minimums
5119: - idx - the indices of the column found for each row (or `NULL` if not needed)
5121: Level: intermediate
5123: Notes:
5124: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5125: row is 0 (the first column).
5127: This code is only implemented for a couple of matrix formats.
5129: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5130: @*/
5131: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5132: {
5133: PetscFunctionBegin;
5137: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5138: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5140: if (!mat->cmap->N) {
5141: PetscCall(VecSet(v, 0.0));
5142: if (idx) {
5143: PetscInt i, m = mat->rmap->n;
5144: for (i = 0; i < m; i++) idx[i] = -1;
5145: }
5146: } else {
5147: MatCheckPreallocated(mat, 1);
5148: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5149: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5150: }
5151: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5152: PetscFunctionReturn(PETSC_SUCCESS);
5153: }
5155: /*@
5156: MatGetRowMax - Gets the maximum value (of the real part) of each
5157: row of the matrix
5159: Logically Collective
5161: Input Parameter:
5162: . mat - the matrix
5164: Output Parameters:
5165: + v - the vector for storing the maximums
5166: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5168: Level: intermediate
5170: Notes:
5171: The result of this call are the same as if one converted the matrix to dense format
5172: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5174: This code is only implemented for a couple of matrix formats.
5176: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5177: @*/
5178: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5179: {
5180: PetscFunctionBegin;
5184: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5186: if (!mat->cmap->N) {
5187: PetscCall(VecSet(v, PETSC_MIN_REAL));
5188: if (idx) {
5189: PetscInt i, m = mat->rmap->n;
5190: for (i = 0; i < m; i++) idx[i] = -1;
5191: }
5192: } else {
5193: MatCheckPreallocated(mat, 1);
5194: PetscUseTypeMethod(mat, getrowmax, v, idx);
5195: }
5196: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5197: PetscFunctionReturn(PETSC_SUCCESS);
5198: }
5200: /*@
5201: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5202: row of the matrix
5204: Logically Collective
5206: Input Parameter:
5207: . mat - the matrix
5209: Output Parameters:
5210: + v - the vector for storing the maximums
5211: - idx - the indices of the column found for each row (or `NULL` if not needed)
5213: Level: intermediate
5215: Notes:
5216: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5217: row is 0 (the first column).
5219: This code is only implemented for a couple of matrix formats.
5221: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5222: @*/
5223: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5224: {
5225: PetscFunctionBegin;
5229: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5231: if (!mat->cmap->N) {
5232: PetscCall(VecSet(v, 0.0));
5233: if (idx) {
5234: PetscInt i, m = mat->rmap->n;
5235: for (i = 0; i < m; i++) idx[i] = -1;
5236: }
5237: } else {
5238: MatCheckPreallocated(mat, 1);
5239: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5240: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5241: }
5242: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5243: PetscFunctionReturn(PETSC_SUCCESS);
5244: }
5246: /*@
5247: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5249: Logically Collective
5251: Input Parameter:
5252: . mat - the matrix
5254: Output Parameter:
5255: . v - the vector for storing the sum
5257: Level: intermediate
5259: This code is only implemented for a couple of matrix formats.
5261: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5262: @*/
5263: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5264: {
5265: PetscFunctionBegin;
5269: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5271: if (!mat->cmap->N) {
5272: PetscCall(VecSet(v, 0.0));
5273: } else {
5274: MatCheckPreallocated(mat, 1);
5275: PetscUseTypeMethod(mat, getrowsumabs, v);
5276: }
5277: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5278: PetscFunctionReturn(PETSC_SUCCESS);
5279: }
5281: /*@
5282: MatGetRowSum - Gets the sum of each row of the matrix
5284: Logically or Neighborhood Collective
5286: Input Parameter:
5287: . mat - the matrix
5289: Output Parameter:
5290: . v - the vector for storing the sum of rows
5292: Level: intermediate
5294: Note:
5295: This code is slow since it is not currently specialized for different formats
5297: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5298: @*/
5299: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5300: {
5301: Vec ones;
5303: PetscFunctionBegin;
5307: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5308: MatCheckPreallocated(mat, 1);
5309: PetscCall(MatCreateVecs(mat, &ones, NULL));
5310: PetscCall(VecSet(ones, 1.));
5311: PetscCall(MatMult(mat, ones, v));
5312: PetscCall(VecDestroy(&ones));
5313: PetscFunctionReturn(PETSC_SUCCESS);
5314: }
5316: /*@
5317: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5318: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5320: Collective
5322: Input Parameter:
5323: . mat - the matrix to provide the transpose
5325: Output Parameter:
5326: . 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
5328: Level: advanced
5330: Note:
5331: 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
5332: routine allows bypassing that call.
5334: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5335: @*/
5336: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5337: {
5338: MatParentState *rb = NULL;
5340: PetscFunctionBegin;
5341: PetscCall(PetscNew(&rb));
5342: rb->id = ((PetscObject)mat)->id;
5343: rb->state = 0;
5344: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5345: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5346: PetscFunctionReturn(PETSC_SUCCESS);
5347: }
5349: /*@
5350: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5352: Collective
5354: Input Parameters:
5355: + mat - the matrix to transpose
5356: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5358: Output Parameter:
5359: . B - the transpose of the matrix
5361: Level: intermediate
5363: Notes:
5364: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5366: `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
5367: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5369: 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.
5371: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5372: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5374: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5376: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5378: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5379: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5380: @*/
5381: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5382: {
5383: PetscContainer rB = NULL;
5384: MatParentState *rb = NULL;
5386: PetscFunctionBegin;
5389: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5390: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5391: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5392: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5393: MatCheckPreallocated(mat, 1);
5394: if (reuse == MAT_REUSE_MATRIX) {
5395: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5396: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5397: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5398: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5399: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5400: }
5402: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5403: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5404: PetscUseTypeMethod(mat, transpose, reuse, B);
5405: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5406: }
5407: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5409: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5410: if (reuse != MAT_INPLACE_MATRIX) {
5411: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5412: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5413: rb->state = ((PetscObject)mat)->state;
5414: rb->nonzerostate = mat->nonzerostate;
5415: }
5416: PetscFunctionReturn(PETSC_SUCCESS);
5417: }
5419: /*@
5420: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5422: Collective
5424: Input Parameter:
5425: . A - the matrix to transpose
5427: Output Parameter:
5428: . 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
5429: numerical portion.
5431: Level: intermediate
5433: Note:
5434: This is not supported for many matrix types, use `MatTranspose()` in those cases
5436: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5437: @*/
5438: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5439: {
5440: PetscFunctionBegin;
5443: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5444: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5445: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5446: PetscUseTypeMethod(A, transposesymbolic, B);
5447: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5449: PetscCall(MatTransposeSetPrecursor(A, *B));
5450: PetscFunctionReturn(PETSC_SUCCESS);
5451: }
5453: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5454: {
5455: PetscContainer rB;
5456: MatParentState *rb;
5458: PetscFunctionBegin;
5461: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5462: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5463: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5464: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5465: PetscCall(PetscContainerGetPointer(rB, (void **)&rb));
5466: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5467: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5468: PetscFunctionReturn(PETSC_SUCCESS);
5469: }
5471: /*@
5472: MatIsTranspose - Test whether a matrix is another one's transpose,
5473: or its own, in which case it tests symmetry.
5475: Collective
5477: Input Parameters:
5478: + A - the matrix to test
5479: . B - the matrix to test against, this can equal the first parameter
5480: - tol - tolerance, differences between entries smaller than this are counted as zero
5482: Output Parameter:
5483: . flg - the result
5485: Level: intermediate
5487: Notes:
5488: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5489: test involves parallel copies of the block off-diagonal parts of the matrix.
5491: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5492: @*/
5493: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5494: {
5495: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5497: PetscFunctionBegin;
5500: PetscAssertPointer(flg, 4);
5501: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5502: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5503: *flg = PETSC_FALSE;
5504: if (f && g) {
5505: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5506: PetscCall((*f)(A, B, tol, flg));
5507: } else {
5508: MatType mattype;
5510: PetscCall(MatGetType(f ? B : A, &mattype));
5511: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5512: }
5513: PetscFunctionReturn(PETSC_SUCCESS);
5514: }
5516: /*@
5517: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5519: Collective
5521: Input Parameters:
5522: + mat - the matrix to transpose and complex conjugate
5523: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5525: Output Parameter:
5526: . B - the Hermitian transpose
5528: Level: intermediate
5530: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5531: @*/
5532: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5533: {
5534: PetscFunctionBegin;
5535: PetscCall(MatTranspose(mat, reuse, B));
5536: #if defined(PETSC_USE_COMPLEX)
5537: PetscCall(MatConjugate(*B));
5538: #endif
5539: PetscFunctionReturn(PETSC_SUCCESS);
5540: }
5542: /*@
5543: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5545: Collective
5547: Input Parameters:
5548: + A - the matrix to test
5549: . B - the matrix to test against, this can equal the first parameter
5550: - tol - tolerance, differences between entries smaller than this are counted as zero
5552: Output Parameter:
5553: . flg - the result
5555: Level: intermediate
5557: Notes:
5558: Only available for `MATAIJ` matrices.
5560: The sequential algorithm
5561: has a running time of the order of the number of nonzeros; the parallel
5562: test involves parallel copies of the block off-diagonal parts of the matrix.
5564: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5565: @*/
5566: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5567: {
5568: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5570: PetscFunctionBegin;
5573: PetscAssertPointer(flg, 4);
5574: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5575: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5576: if (f && g) {
5577: PetscCheck(f != g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5578: PetscCall((*f)(A, B, tol, flg));
5579: }
5580: PetscFunctionReturn(PETSC_SUCCESS);
5581: }
5583: /*@
5584: MatPermute - Creates a new matrix with rows and columns permuted from the
5585: original.
5587: Collective
5589: Input Parameters:
5590: + mat - the matrix to permute
5591: . row - row permutation, each processor supplies only the permutation for its rows
5592: - col - column permutation, each processor supplies only the permutation for its columns
5594: Output Parameter:
5595: . B - the permuted matrix
5597: Level: advanced
5599: Note:
5600: The index sets map from row/col of permuted matrix to row/col of original matrix.
5601: The index sets should be on the same communicator as mat and have the same local sizes.
5603: Developer Note:
5604: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5605: exploit the fact that row and col are permutations, consider implementing the
5606: more general `MatCreateSubMatrix()` instead.
5608: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5609: @*/
5610: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5611: {
5612: PetscFunctionBegin;
5617: PetscAssertPointer(B, 4);
5618: PetscCheckSameComm(mat, 1, row, 2);
5619: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5620: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5621: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5622: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5623: MatCheckPreallocated(mat, 1);
5625: if (mat->ops->permute) {
5626: PetscUseTypeMethod(mat, permute, row, col, B);
5627: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5628: } else {
5629: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5630: }
5631: PetscFunctionReturn(PETSC_SUCCESS);
5632: }
5634: /*@
5635: MatEqual - Compares two matrices.
5637: Collective
5639: Input Parameters:
5640: + A - the first matrix
5641: - B - the second matrix
5643: Output Parameter:
5644: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5646: Level: intermediate
5648: Note:
5649: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing the results of several matrix-vector product
5650: using several randomly created vectors, see `MatMultEqual()`.
5652: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5653: @*/
5654: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5655: {
5656: PetscFunctionBegin;
5661: PetscAssertPointer(flg, 3);
5662: PetscCheckSameComm(A, 1, B, 2);
5663: MatCheckPreallocated(A, 1);
5664: MatCheckPreallocated(B, 2);
5665: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5666: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5667: 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,
5668: B->cmap->N);
5669: if (A->ops->equal && A->ops->equal == B->ops->equal) {
5670: PetscUseTypeMethod(A, equal, B, flg);
5671: } else {
5672: PetscCall(MatMultEqual(A, B, 10, flg));
5673: }
5674: PetscFunctionReturn(PETSC_SUCCESS);
5675: }
5677: /*@
5678: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5679: matrices that are stored as vectors. Either of the two scaling
5680: matrices can be `NULL`.
5682: Collective
5684: Input Parameters:
5685: + mat - the matrix to be scaled
5686: . l - the left scaling vector (or `NULL`)
5687: - r - the right scaling vector (or `NULL`)
5689: Level: intermediate
5691: Note:
5692: `MatDiagonalScale()` computes $A = LAR$, where
5693: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5694: The L scales the rows of the matrix, the R scales the columns of the matrix.
5696: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5697: @*/
5698: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5699: {
5700: PetscFunctionBegin;
5703: if (l) {
5705: PetscCheckSameComm(mat, 1, l, 2);
5706: }
5707: if (r) {
5709: PetscCheckSameComm(mat, 1, r, 3);
5710: }
5711: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5712: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5713: MatCheckPreallocated(mat, 1);
5714: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5716: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5717: PetscUseTypeMethod(mat, diagonalscale, l, r);
5718: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5719: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5720: if (l != r) mat->symmetric = PETSC_BOOL3_FALSE;
5721: PetscFunctionReturn(PETSC_SUCCESS);
5722: }
5724: /*@
5725: MatScale - Scales all elements of a matrix by a given number.
5727: Logically Collective
5729: Input Parameters:
5730: + mat - the matrix to be scaled
5731: - a - the scaling value
5733: Level: intermediate
5735: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5736: @*/
5737: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5738: {
5739: PetscFunctionBegin;
5742: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5743: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5745: MatCheckPreallocated(mat, 1);
5747: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5748: if (a != (PetscScalar)1.0) {
5749: PetscUseTypeMethod(mat, scale, a);
5750: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5751: }
5752: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5753: PetscFunctionReturn(PETSC_SUCCESS);
5754: }
5756: /*@
5757: MatNorm - Calculates various norms of a matrix.
5759: Collective
5761: Input Parameters:
5762: + mat - the matrix
5763: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5765: Output Parameter:
5766: . nrm - the resulting norm
5768: Level: intermediate
5770: .seealso: [](ch_matrices), `Mat`
5771: @*/
5772: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5773: {
5774: PetscFunctionBegin;
5777: PetscAssertPointer(nrm, 3);
5779: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5780: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5781: MatCheckPreallocated(mat, 1);
5783: PetscUseTypeMethod(mat, norm, type, nrm);
5784: PetscFunctionReturn(PETSC_SUCCESS);
5785: }
5787: /*
5788: This variable is used to prevent counting of MatAssemblyBegin() that
5789: are called from within a MatAssemblyEnd().
5790: */
5791: static PetscInt MatAssemblyEnd_InUse = 0;
5792: /*@
5793: MatAssemblyBegin - Begins assembling the matrix. This routine should
5794: be called after completing all calls to `MatSetValues()`.
5796: Collective
5798: Input Parameters:
5799: + mat - the matrix
5800: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5802: Level: beginner
5804: Notes:
5805: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5806: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5808: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5809: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5810: using the matrix.
5812: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5813: 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
5814: a global collective operation requiring all processes that share the matrix.
5816: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5817: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5818: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5820: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5821: @*/
5822: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5823: {
5824: PetscFunctionBegin;
5827: MatCheckPreallocated(mat, 1);
5828: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5829: if (mat->assembled) {
5830: mat->was_assembled = PETSC_TRUE;
5831: mat->assembled = PETSC_FALSE;
5832: }
5834: if (!MatAssemblyEnd_InUse) {
5835: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5836: PetscTryTypeMethod(mat, assemblybegin, type);
5837: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5838: } else PetscTryTypeMethod(mat, assemblybegin, type);
5839: PetscFunctionReturn(PETSC_SUCCESS);
5840: }
5842: /*@
5843: MatAssembled - Indicates if a matrix has been assembled and is ready for
5844: use; for example, in matrix-vector product.
5846: Not Collective
5848: Input Parameter:
5849: . mat - the matrix
5851: Output Parameter:
5852: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5854: Level: advanced
5856: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5857: @*/
5858: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5859: {
5860: PetscFunctionBegin;
5862: PetscAssertPointer(assembled, 2);
5863: *assembled = mat->assembled;
5864: PetscFunctionReturn(PETSC_SUCCESS);
5865: }
5867: /*@
5868: MatAssemblyEnd - Completes assembling the matrix. This routine should
5869: be called after `MatAssemblyBegin()`.
5871: Collective
5873: Input Parameters:
5874: + mat - the matrix
5875: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5877: Options Database Keys:
5878: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
5879: . -mat_view ::ascii_info_detail - Prints more detailed info
5880: . -mat_view - Prints matrix in ASCII format
5881: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
5882: . -mat_view draw - draws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
5883: . -display <name> - Sets display name (default is host)
5884: . -draw_pause <sec> - Sets number of seconds to pause after display
5885: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (See [Using MATLAB with PETSc](ch_matlab))
5886: . -viewer_socket_machine <machine> - Machine to use for socket
5887: . -viewer_socket_port <port> - Port number to use for socket
5888: - -mat_view binary:filename[:append] - Save matrix to file in binary format
5890: Level: beginner
5892: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`
5893: @*/
5894: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
5895: {
5896: static PetscInt inassm = 0;
5897: PetscBool flg = PETSC_FALSE;
5899: PetscFunctionBegin;
5903: inassm++;
5904: MatAssemblyEnd_InUse++;
5905: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
5906: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
5907: PetscTryTypeMethod(mat, assemblyend, type);
5908: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
5909: } else PetscTryTypeMethod(mat, assemblyend, type);
5911: /* Flush assembly is not a true assembly */
5912: if (type != MAT_FLUSH_ASSEMBLY) {
5913: if (mat->num_ass) {
5914: if (!mat->symmetry_eternal) {
5915: mat->symmetric = PETSC_BOOL3_UNKNOWN;
5916: mat->hermitian = PETSC_BOOL3_UNKNOWN;
5917: }
5918: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
5919: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
5920: }
5921: mat->num_ass++;
5922: mat->assembled = PETSC_TRUE;
5923: mat->ass_nonzerostate = mat->nonzerostate;
5924: }
5926: mat->insertmode = NOT_SET_VALUES;
5927: MatAssemblyEnd_InUse--;
5928: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5929: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
5930: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
5932: if (mat->checksymmetryonassembly) {
5933: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
5934: if (flg) {
5935: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5936: } else {
5937: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
5938: }
5939: }
5940: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
5941: }
5942: inassm--;
5943: PetscFunctionReturn(PETSC_SUCCESS);
5944: }
5946: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
5947: /*@
5948: MatSetOption - Sets a parameter option for a matrix. Some options
5949: may be specific to certain storage formats. Some options
5950: determine how values will be inserted (or added). Sorted,
5951: row-oriented input will generally assemble the fastest. The default
5952: is row-oriented.
5954: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
5956: Input Parameters:
5957: + mat - the matrix
5958: . op - the option, one of those listed below (and possibly others),
5959: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
5961: Options Describing Matrix Structure:
5962: + `MAT_SPD` - symmetric positive definite
5963: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
5964: . `MAT_HERMITIAN` - transpose is the complex conjugation
5965: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
5966: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
5967: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
5968: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
5970: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
5971: do not need to be computed (usually at a high cost)
5973: Options For Use with `MatSetValues()`:
5974: Insert a logically dense subblock, which can be
5975: . `MAT_ROW_ORIENTED` - row-oriented (default)
5977: These options reflect the data you pass in with `MatSetValues()`; it has
5978: nothing to do with how the data is stored internally in the matrix
5979: data structure.
5981: When (re)assembling a matrix, we can restrict the input for
5982: efficiency/debugging purposes. These options include
5983: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
5984: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
5985: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
5986: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
5987: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
5988: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
5989: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
5990: performance for very large process counts.
5991: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
5992: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
5993: functions, instead sending only neighbor messages.
5995: Level: intermediate
5997: Notes:
5998: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
6000: Some options are relevant only for particular matrix types and
6001: are thus ignored by others. Other options are not supported by
6002: certain matrix types and will generate an error message if set.
6004: If using Fortran to compute a matrix, one may need to
6005: use the column-oriented option (or convert to the row-oriented
6006: format).
6008: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6009: that would generate a new entry in the nonzero structure is instead
6010: ignored. Thus, if memory has not already been allocated for this particular
6011: data, then the insertion is ignored. For dense matrices, in which
6012: the entire array is allocated, no entries are ever ignored.
6013: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6015: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6016: that would generate a new entry in the nonzero structure instead produces
6017: 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
6019: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6020: that would generate a new entry that has not been preallocated will
6021: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6022: only.) This is a useful flag when debugging matrix memory preallocation.
6023: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6025: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6026: other processors should be dropped, rather than stashed.
6027: This is useful if you know that the "owning" processor is also
6028: always generating the correct matrix entries, so that PETSc need
6029: not transfer duplicate entries generated on another processor.
6031: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6032: searches during matrix assembly. When this flag is set, the hash table
6033: is created during the first matrix assembly. This hash table is
6034: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6035: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6036: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6037: supported by `MATMPIBAIJ` format only.
6039: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6040: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6042: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6043: a zero location in the matrix
6045: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6047: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6048: zero row routines and thus improves performance for very large process counts.
6050: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6051: part of the matrix (since they should match the upper triangular part).
6053: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6054: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6055: with finite difference schemes with non-periodic boundary conditions.
6057: Developer Note:
6058: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6059: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6060: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6061: not changed.
6063: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6064: @*/
6065: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6066: {
6067: PetscFunctionBegin;
6069: if (op > 0) {
6072: }
6074: 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);
6076: switch (op) {
6077: case MAT_FORCE_DIAGONAL_ENTRIES:
6078: mat->force_diagonals = flg;
6079: PetscFunctionReturn(PETSC_SUCCESS);
6080: case MAT_NO_OFF_PROC_ENTRIES:
6081: mat->nooffprocentries = flg;
6082: PetscFunctionReturn(PETSC_SUCCESS);
6083: case MAT_SUBSET_OFF_PROC_ENTRIES:
6084: mat->assembly_subset = flg;
6085: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6086: #if !defined(PETSC_HAVE_MPIUNI)
6087: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6088: #endif
6089: mat->stash.first_assembly_done = PETSC_FALSE;
6090: }
6091: PetscFunctionReturn(PETSC_SUCCESS);
6092: case MAT_NO_OFF_PROC_ZERO_ROWS:
6093: mat->nooffproczerorows = flg;
6094: PetscFunctionReturn(PETSC_SUCCESS);
6095: case MAT_SPD:
6096: if (flg) {
6097: mat->spd = PETSC_BOOL3_TRUE;
6098: mat->symmetric = PETSC_BOOL3_TRUE;
6099: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6100: } else {
6101: mat->spd = PETSC_BOOL3_FALSE;
6102: }
6103: break;
6104: case MAT_SYMMETRIC:
6105: mat->symmetric = PetscBoolToBool3(flg);
6106: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6107: #if !defined(PETSC_USE_COMPLEX)
6108: mat->hermitian = PetscBoolToBool3(flg);
6109: #endif
6110: break;
6111: case MAT_HERMITIAN:
6112: mat->hermitian = PetscBoolToBool3(flg);
6113: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6114: #if !defined(PETSC_USE_COMPLEX)
6115: mat->symmetric = PetscBoolToBool3(flg);
6116: #endif
6117: break;
6118: case MAT_STRUCTURALLY_SYMMETRIC:
6119: mat->structurally_symmetric = PetscBoolToBool3(flg);
6120: break;
6121: case MAT_SYMMETRY_ETERNAL:
6122: 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");
6123: mat->symmetry_eternal = flg;
6124: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6125: break;
6126: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6127: 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");
6128: mat->structural_symmetry_eternal = flg;
6129: break;
6130: case MAT_SPD_ETERNAL:
6131: 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");
6132: mat->spd_eternal = flg;
6133: if (flg) {
6134: mat->structural_symmetry_eternal = PETSC_TRUE;
6135: mat->symmetry_eternal = PETSC_TRUE;
6136: }
6137: break;
6138: case MAT_STRUCTURE_ONLY:
6139: mat->structure_only = flg;
6140: break;
6141: case MAT_SORTED_FULL:
6142: mat->sortedfull = flg;
6143: break;
6144: default:
6145: break;
6146: }
6147: PetscTryTypeMethod(mat, setoption, op, flg);
6148: PetscFunctionReturn(PETSC_SUCCESS);
6149: }
6151: /*@
6152: MatGetOption - Gets a parameter option that has been set for a matrix.
6154: Logically Collective
6156: Input Parameters:
6157: + mat - the matrix
6158: - op - the option, this only responds to certain options, check the code for which ones
6160: Output Parameter:
6161: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6163: Level: intermediate
6165: Notes:
6166: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6168: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6169: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6171: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6172: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6173: @*/
6174: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6175: {
6176: PetscFunctionBegin;
6180: 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);
6181: 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()");
6183: switch (op) {
6184: case MAT_NO_OFF_PROC_ENTRIES:
6185: *flg = mat->nooffprocentries;
6186: break;
6187: case MAT_NO_OFF_PROC_ZERO_ROWS:
6188: *flg = mat->nooffproczerorows;
6189: break;
6190: case MAT_SYMMETRIC:
6191: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6192: break;
6193: case MAT_HERMITIAN:
6194: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6195: break;
6196: case MAT_STRUCTURALLY_SYMMETRIC:
6197: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6198: break;
6199: case MAT_SPD:
6200: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6201: break;
6202: case MAT_SYMMETRY_ETERNAL:
6203: *flg = mat->symmetry_eternal;
6204: break;
6205: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6206: *flg = mat->symmetry_eternal;
6207: break;
6208: default:
6209: break;
6210: }
6211: PetscFunctionReturn(PETSC_SUCCESS);
6212: }
6214: /*@
6215: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6216: this routine retains the old nonzero structure.
6218: Logically Collective
6220: Input Parameter:
6221: . mat - the matrix
6223: Level: intermediate
6225: Note:
6226: 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.
6227: See the Performance chapter of the users manual for information on preallocating matrices.
6229: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6230: @*/
6231: PetscErrorCode MatZeroEntries(Mat mat)
6232: {
6233: PetscFunctionBegin;
6236: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6237: 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");
6238: MatCheckPreallocated(mat, 1);
6240: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6241: PetscUseTypeMethod(mat, zeroentries);
6242: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6243: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6244: PetscFunctionReturn(PETSC_SUCCESS);
6245: }
6247: /*@
6248: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6249: of a set of rows and columns of a matrix.
6251: Collective
6253: Input Parameters:
6254: + mat - the matrix
6255: . numRows - the number of rows/columns to zero
6256: . rows - the global row indices
6257: . diag - value put in the diagonal of the eliminated rows
6258: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6259: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6261: Level: intermediate
6263: Notes:
6264: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6266: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6267: 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
6269: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6270: Krylov method to take advantage of the known solution on the zeroed rows.
6272: For the parallel case, all processes that share the matrix (i.e.,
6273: those in the communicator used for matrix creation) MUST call this
6274: routine, regardless of whether any rows being zeroed are owned by
6275: them.
6277: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6278: 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
6279: missing.
6281: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6282: list only rows local to itself).
6284: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6286: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6287: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6288: @*/
6289: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6290: {
6291: PetscFunctionBegin;
6294: if (numRows) PetscAssertPointer(rows, 3);
6295: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6296: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6297: MatCheckPreallocated(mat, 1);
6299: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6300: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6301: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6302: PetscFunctionReturn(PETSC_SUCCESS);
6303: }
6305: /*@
6306: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6307: of a set of rows and columns of a matrix.
6309: Collective
6311: Input Parameters:
6312: + mat - the matrix
6313: . is - the rows to zero
6314: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6315: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6316: - b - optional vector of right-hand side, that will be adjusted by provided solution
6318: Level: intermediate
6320: Note:
6321: See `MatZeroRowsColumns()` for details on how this routine operates.
6323: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6324: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6325: @*/
6326: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6327: {
6328: PetscInt numRows;
6329: const PetscInt *rows;
6331: PetscFunctionBegin;
6336: PetscCall(ISGetLocalSize(is, &numRows));
6337: PetscCall(ISGetIndices(is, &rows));
6338: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6339: PetscCall(ISRestoreIndices(is, &rows));
6340: PetscFunctionReturn(PETSC_SUCCESS);
6341: }
6343: /*@
6344: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6345: of a set of rows of a matrix.
6347: Collective
6349: Input Parameters:
6350: + mat - the matrix
6351: . numRows - the number of rows to zero
6352: . rows - the global row indices
6353: . diag - value put in the diagonal of the zeroed rows
6354: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6355: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6357: Level: intermediate
6359: Notes:
6360: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6362: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6364: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6365: Krylov method to take advantage of the known solution on the zeroed rows.
6367: 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)
6368: from the matrix.
6370: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6371: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6372: formats this does not alter the nonzero structure.
6374: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6375: of the matrix is not changed the values are
6376: merely zeroed.
6378: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6379: formats can optionally remove the main diagonal entry from the
6380: nonzero structure as well, by passing 0.0 as the final argument).
6382: For the parallel case, all processes that share the matrix (i.e.,
6383: those in the communicator used for matrix creation) MUST call this
6384: routine, regardless of whether any rows being zeroed are owned by
6385: them.
6387: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6388: list only rows local to itself).
6390: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6391: owns that are to be zeroed. This saves a global synchronization in the implementation.
6393: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6394: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6395: @*/
6396: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6397: {
6398: PetscFunctionBegin;
6401: if (numRows) PetscAssertPointer(rows, 3);
6402: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6403: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6404: MatCheckPreallocated(mat, 1);
6406: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6407: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6408: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6409: PetscFunctionReturn(PETSC_SUCCESS);
6410: }
6412: /*@
6413: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6414: of a set of rows of a matrix indicated by an `IS`
6416: Collective
6418: Input Parameters:
6419: + mat - the matrix
6420: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6421: . diag - value put in all diagonals of eliminated rows
6422: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6423: - b - optional vector of right-hand side, that will be adjusted by provided solution
6425: Level: intermediate
6427: Note:
6428: See `MatZeroRows()` for details on how this routine operates.
6430: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6431: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6432: @*/
6433: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6434: {
6435: PetscInt numRows = 0;
6436: const PetscInt *rows = NULL;
6438: PetscFunctionBegin;
6441: if (is) {
6443: PetscCall(ISGetLocalSize(is, &numRows));
6444: PetscCall(ISGetIndices(is, &rows));
6445: }
6446: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6447: if (is) PetscCall(ISRestoreIndices(is, &rows));
6448: PetscFunctionReturn(PETSC_SUCCESS);
6449: }
6451: /*@
6452: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6453: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6455: Collective
6457: Input Parameters:
6458: + mat - the matrix
6459: . numRows - the number of rows to remove
6460: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6461: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6462: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6463: - b - optional vector of right-hand side, that will be adjusted by provided solution
6465: Level: intermediate
6467: Notes:
6468: See `MatZeroRows()` for details on how this routine operates.
6470: The grid coordinates are across the entire grid, not just the local portion
6472: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6473: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6474: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6475: `DM_BOUNDARY_PERIODIC` boundary type.
6477: 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
6478: a single value per point) you can skip filling those indices.
6480: Fortran Note:
6481: `idxm` and `idxn` should be declared as
6482: $ MatStencil idxm(4, m)
6483: and the values inserted using
6484: .vb
6485: idxm(MatStencil_i, 1) = i
6486: idxm(MatStencil_j, 1) = j
6487: idxm(MatStencil_k, 1) = k
6488: idxm(MatStencil_c, 1) = c
6489: etc
6490: .ve
6492: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6493: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6494: @*/
6495: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6496: {
6497: PetscInt dim = mat->stencil.dim;
6498: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6499: PetscInt *dims = mat->stencil.dims + 1;
6500: PetscInt *starts = mat->stencil.starts;
6501: PetscInt *dxm = (PetscInt *)rows;
6502: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6504: PetscFunctionBegin;
6507: if (numRows) PetscAssertPointer(rows, 3);
6509: PetscCall(PetscMalloc1(numRows, &jdxm));
6510: for (i = 0; i < numRows; ++i) {
6511: /* Skip unused dimensions (they are ordered k, j, i, c) */
6512: for (j = 0; j < 3 - sdim; ++j) dxm++;
6513: /* Local index in X dir */
6514: tmp = *dxm++ - starts[0];
6515: /* Loop over remaining dimensions */
6516: for (j = 0; j < dim - 1; ++j) {
6517: /* If nonlocal, set index to be negative */
6518: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6519: /* Update local index */
6520: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6521: }
6522: /* Skip component slot if necessary */
6523: if (mat->stencil.noc) dxm++;
6524: /* Local row number */
6525: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6526: }
6527: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6528: PetscCall(PetscFree(jdxm));
6529: PetscFunctionReturn(PETSC_SUCCESS);
6530: }
6532: /*@
6533: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6534: of a set of rows and columns of a matrix.
6536: Collective
6538: Input Parameters:
6539: + mat - the matrix
6540: . numRows - the number of rows/columns to remove
6541: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6542: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6543: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6544: - b - optional vector of right-hand side, that will be adjusted by provided solution
6546: Level: intermediate
6548: Notes:
6549: See `MatZeroRowsColumns()` for details on how this routine operates.
6551: The grid coordinates are across the entire grid, not just the local portion
6553: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6554: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6555: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6556: `DM_BOUNDARY_PERIODIC` boundary type.
6558: 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
6559: a single value per point) you can skip filling those indices.
6561: Fortran Note:
6562: `idxm` and `idxn` should be declared as
6563: $ MatStencil idxm(4, m)
6564: and the values inserted using
6565: .vb
6566: idxm(MatStencil_i, 1) = i
6567: idxm(MatStencil_j, 1) = j
6568: idxm(MatStencil_k, 1) = k
6569: idxm(MatStencil_c, 1) = c
6570: etc
6571: .ve
6573: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6574: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6575: @*/
6576: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6577: {
6578: PetscInt dim = mat->stencil.dim;
6579: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6580: PetscInt *dims = mat->stencil.dims + 1;
6581: PetscInt *starts = mat->stencil.starts;
6582: PetscInt *dxm = (PetscInt *)rows;
6583: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6585: PetscFunctionBegin;
6588: if (numRows) PetscAssertPointer(rows, 3);
6590: PetscCall(PetscMalloc1(numRows, &jdxm));
6591: for (i = 0; i < numRows; ++i) {
6592: /* Skip unused dimensions (they are ordered k, j, i, c) */
6593: for (j = 0; j < 3 - sdim; ++j) dxm++;
6594: /* Local index in X dir */
6595: tmp = *dxm++ - starts[0];
6596: /* Loop over remaining dimensions */
6597: for (j = 0; j < dim - 1; ++j) {
6598: /* If nonlocal, set index to be negative */
6599: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6600: /* Update local index */
6601: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6602: }
6603: /* Skip component slot if necessary */
6604: if (mat->stencil.noc) dxm++;
6605: /* Local row number */
6606: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6607: }
6608: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6609: PetscCall(PetscFree(jdxm));
6610: PetscFunctionReturn(PETSC_SUCCESS);
6611: }
6613: /*@
6614: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6615: of a set of rows of a matrix; using local numbering of rows.
6617: Collective
6619: Input Parameters:
6620: + mat - the matrix
6621: . numRows - the number of rows to remove
6622: . rows - the local row indices
6623: . diag - value put in all diagonals of eliminated rows
6624: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6625: - b - optional vector of right-hand side, that will be adjusted by provided solution
6627: Level: intermediate
6629: Notes:
6630: Before calling `MatZeroRowsLocal()`, the user must first set the
6631: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6633: See `MatZeroRows()` for details on how this routine operates.
6635: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6636: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6637: @*/
6638: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6639: {
6640: PetscFunctionBegin;
6643: if (numRows) PetscAssertPointer(rows, 3);
6644: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6645: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6646: MatCheckPreallocated(mat, 1);
6648: if (mat->ops->zerorowslocal) {
6649: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6650: } else {
6651: IS is, newis;
6652: const PetscInt *newRows;
6654: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6655: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6656: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6657: PetscCall(ISGetIndices(newis, &newRows));
6658: PetscUseTypeMethod(mat, zerorows, numRows, newRows, diag, x, b);
6659: PetscCall(ISRestoreIndices(newis, &newRows));
6660: PetscCall(ISDestroy(&newis));
6661: PetscCall(ISDestroy(&is));
6662: }
6663: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6664: PetscFunctionReturn(PETSC_SUCCESS);
6665: }
6667: /*@
6668: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6669: of a set of rows of a matrix; using local numbering of rows.
6671: Collective
6673: Input Parameters:
6674: + mat - the matrix
6675: . is - index set of rows to remove
6676: . diag - value put in all diagonals of eliminated rows
6677: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6678: - b - optional vector of right-hand side, that will be adjusted by provided solution
6680: Level: intermediate
6682: Notes:
6683: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6684: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6686: See `MatZeroRows()` for details on how this routine operates.
6688: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6689: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6690: @*/
6691: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6692: {
6693: PetscInt numRows;
6694: const PetscInt *rows;
6696: PetscFunctionBegin;
6700: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6701: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6702: MatCheckPreallocated(mat, 1);
6704: PetscCall(ISGetLocalSize(is, &numRows));
6705: PetscCall(ISGetIndices(is, &rows));
6706: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6707: PetscCall(ISRestoreIndices(is, &rows));
6708: PetscFunctionReturn(PETSC_SUCCESS);
6709: }
6711: /*@
6712: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6713: of a set of rows and columns of a matrix; using local numbering of rows.
6715: Collective
6717: Input Parameters:
6718: + mat - the matrix
6719: . numRows - the number of rows to remove
6720: . rows - the global row indices
6721: . diag - value put in all diagonals of eliminated rows
6722: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6723: - b - optional vector of right-hand side, that will be adjusted by provided solution
6725: Level: intermediate
6727: Notes:
6728: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6729: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6731: See `MatZeroRowsColumns()` for details on how this routine operates.
6733: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6734: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6735: @*/
6736: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6737: {
6738: IS is, newis;
6739: const PetscInt *newRows;
6741: PetscFunctionBegin;
6744: if (numRows) PetscAssertPointer(rows, 3);
6745: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6746: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6747: MatCheckPreallocated(mat, 1);
6749: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6750: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_COPY_VALUES, &is));
6751: PetscCall(ISLocalToGlobalMappingApplyIS(mat->cmap->mapping, is, &newis));
6752: PetscCall(ISGetIndices(newis, &newRows));
6753: PetscUseTypeMethod(mat, zerorowscolumns, numRows, newRows, diag, x, b);
6754: PetscCall(ISRestoreIndices(newis, &newRows));
6755: PetscCall(ISDestroy(&newis));
6756: PetscCall(ISDestroy(&is));
6757: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6758: PetscFunctionReturn(PETSC_SUCCESS);
6759: }
6761: /*@
6762: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6763: of a set of rows and columns of a matrix; using local numbering of rows.
6765: Collective
6767: Input Parameters:
6768: + mat - the matrix
6769: . is - index set of rows to remove
6770: . diag - value put in all diagonals of eliminated rows
6771: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6772: - b - optional vector of right-hand side, that will be adjusted by provided solution
6774: Level: intermediate
6776: Notes:
6777: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6778: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6780: See `MatZeroRowsColumns()` for details on how this routine operates.
6782: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6783: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6784: @*/
6785: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6786: {
6787: PetscInt numRows;
6788: const PetscInt *rows;
6790: PetscFunctionBegin;
6794: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6795: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6796: MatCheckPreallocated(mat, 1);
6798: PetscCall(ISGetLocalSize(is, &numRows));
6799: PetscCall(ISGetIndices(is, &rows));
6800: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6801: PetscCall(ISRestoreIndices(is, &rows));
6802: PetscFunctionReturn(PETSC_SUCCESS);
6803: }
6805: /*@
6806: MatGetSize - Returns the numbers of rows and columns in a matrix.
6808: Not Collective
6810: Input Parameter:
6811: . mat - the matrix
6813: Output Parameters:
6814: + m - the number of global rows
6815: - n - the number of global columns
6817: Level: beginner
6819: Note:
6820: Both output parameters can be `NULL` on input.
6822: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6823: @*/
6824: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6825: {
6826: PetscFunctionBegin;
6828: if (m) *m = mat->rmap->N;
6829: if (n) *n = mat->cmap->N;
6830: PetscFunctionReturn(PETSC_SUCCESS);
6831: }
6833: /*@
6834: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6835: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6837: Not Collective
6839: Input Parameter:
6840: . mat - the matrix
6842: Output Parameters:
6843: + m - the number of local rows, use `NULL` to not obtain this value
6844: - n - the number of local columns, use `NULL` to not obtain this value
6846: Level: beginner
6848: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6849: @*/
6850: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6851: {
6852: PetscFunctionBegin;
6854: if (m) PetscAssertPointer(m, 2);
6855: if (n) PetscAssertPointer(n, 3);
6856: if (m) *m = mat->rmap->n;
6857: if (n) *n = mat->cmap->n;
6858: PetscFunctionReturn(PETSC_SUCCESS);
6859: }
6861: /*@
6862: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6863: vector one multiplies this matrix by that are owned by this processor.
6865: Not Collective, unless matrix has not been allocated, then collective
6867: Input Parameter:
6868: . mat - the matrix
6870: Output Parameters:
6871: + m - the global index of the first local column, use `NULL` to not obtain this value
6872: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
6874: Level: developer
6876: Notes:
6877: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6879: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6880: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6882: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6883: the local values in the matrix.
6885: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
6886: Layouts](sec_matlayout) for details on matrix layouts.
6888: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6889: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6890: @*/
6891: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
6892: {
6893: PetscFunctionBegin;
6896: if (m) PetscAssertPointer(m, 2);
6897: if (n) PetscAssertPointer(n, 3);
6898: MatCheckPreallocated(mat, 1);
6899: if (m) *m = mat->cmap->rstart;
6900: if (n) *n = mat->cmap->rend;
6901: PetscFunctionReturn(PETSC_SUCCESS);
6902: }
6904: /*@
6905: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
6906: this MPI process.
6908: Not Collective
6910: Input Parameter:
6911: . mat - the matrix
6913: Output Parameters:
6914: + m - the global index of the first local row, use `NULL` to not obtain this value
6915: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
6917: Level: beginner
6919: Notes:
6920: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6922: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6923: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6925: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6926: the local values in the matrix.
6928: The high argument is one more than the last element stored locally.
6930: For all matrices it returns the range of matrix rows associated with rows of a vector that
6931: would contain the result of a matrix vector product with this matrix. See [Matrix
6932: Layouts](sec_matlayout) for details on matrix layouts.
6934: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
6935: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
6936: @*/
6937: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
6938: {
6939: PetscFunctionBegin;
6942: if (m) PetscAssertPointer(m, 2);
6943: if (n) PetscAssertPointer(n, 3);
6944: MatCheckPreallocated(mat, 1);
6945: if (m) *m = mat->rmap->rstart;
6946: if (n) *n = mat->rmap->rend;
6947: PetscFunctionReturn(PETSC_SUCCESS);
6948: }
6950: /*@C
6951: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
6952: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
6954: Not Collective, unless matrix has not been allocated
6956: Input Parameter:
6957: . mat - the matrix
6959: Output Parameter:
6960: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
6961: where `size` is the number of MPI processes used by `mat`
6963: Level: beginner
6965: Notes:
6966: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
6968: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
6969: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
6971: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
6972: the local values in the matrix.
6974: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
6975: would contain the result of a matrix vector product with this matrix. See [Matrix
6976: Layouts](sec_matlayout) for details on matrix layouts.
6978: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
6979: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
6980: `DMDAGetGhostCorners()`, `DM`
6981: @*/
6982: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
6983: {
6984: PetscFunctionBegin;
6987: MatCheckPreallocated(mat, 1);
6988: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
6989: PetscFunctionReturn(PETSC_SUCCESS);
6990: }
6992: /*@C
6993: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
6994: vector one multiplies this vector by that are owned by each processor.
6996: Not Collective, unless matrix has not been allocated
6998: Input Parameter:
6999: . mat - the matrix
7001: Output Parameter:
7002: . ranges - start of each processors portion plus one more than the total length at the end
7004: Level: beginner
7006: Notes:
7007: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7009: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7010: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7012: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7013: the local values in the matrix.
7015: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7016: Layouts](sec_matlayout) for details on matrix layouts.
7018: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7019: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7020: `DMDAGetGhostCorners()`, `DM`
7021: @*/
7022: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7023: {
7024: PetscFunctionBegin;
7027: MatCheckPreallocated(mat, 1);
7028: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7029: PetscFunctionReturn(PETSC_SUCCESS);
7030: }
7032: /*@
7033: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7035: Not Collective
7037: Input Parameter:
7038: . A - matrix
7040: Output Parameters:
7041: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7042: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7044: Level: intermediate
7046: Note:
7047: You should call `ISDestroy()` on the returned `IS`
7049: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7050: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7051: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7052: details on matrix layouts.
7054: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7055: @*/
7056: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7057: {
7058: PetscErrorCode (*f)(Mat, IS *, IS *);
7060: PetscFunctionBegin;
7063: MatCheckPreallocated(A, 1);
7064: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7065: if (f) {
7066: PetscCall((*f)(A, rows, cols));
7067: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7068: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7069: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7070: }
7071: PetscFunctionReturn(PETSC_SUCCESS);
7072: }
7074: /*@
7075: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7076: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7077: to complete the factorization.
7079: Collective
7081: Input Parameters:
7082: + fact - the factorized matrix obtained with `MatGetFactor()`
7083: . mat - the matrix
7084: . row - row permutation
7085: . col - column permutation
7086: - info - structure containing
7087: .vb
7088: levels - number of levels of fill.
7089: expected fill - as ratio of original fill.
7090: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7091: missing diagonal entries)
7092: .ve
7094: Level: developer
7096: Notes:
7097: See [Matrix Factorization](sec_matfactor) for additional information.
7099: Most users should employ the `KSP` interface for linear solvers
7100: instead of working directly with matrix algebra routines such as this.
7101: See, e.g., `KSPCreate()`.
7103: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7105: Developer Note:
7106: The Fortran interface is not autogenerated as the
7107: interface definition cannot be generated correctly [due to `MatFactorInfo`]
7109: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
7110: `MatGetOrdering()`, `MatFactorInfo`
7111: @*/
7112: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7113: {
7114: PetscFunctionBegin;
7119: PetscAssertPointer(info, 5);
7120: PetscAssertPointer(fact, 1);
7121: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7122: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7123: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7124: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7125: MatCheckPreallocated(mat, 2);
7127: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7128: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7129: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7130: PetscFunctionReturn(PETSC_SUCCESS);
7131: }
7133: /*@
7134: MatICCFactorSymbolic - Performs symbolic incomplete
7135: Cholesky factorization for a symmetric matrix. Use
7136: `MatCholeskyFactorNumeric()` to complete the factorization.
7138: Collective
7140: Input Parameters:
7141: + fact - the factorized matrix obtained with `MatGetFactor()`
7142: . mat - the matrix to be factored
7143: . perm - row and column permutation
7144: - info - structure containing
7145: .vb
7146: levels - number of levels of fill.
7147: expected fill - as ratio of original fill.
7148: .ve
7150: Level: developer
7152: Notes:
7153: Most users should employ the `KSP` interface for linear solvers
7154: instead of working directly with matrix algebra routines such as this.
7155: See, e.g., `KSPCreate()`.
7157: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7159: Developer Note:
7160: The Fortran interface is not autogenerated as the
7161: interface definition cannot be generated correctly [due to `MatFactorInfo`]
7163: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7164: @*/
7165: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7166: {
7167: PetscFunctionBegin;
7171: PetscAssertPointer(info, 4);
7172: PetscAssertPointer(fact, 1);
7173: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7174: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7175: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7176: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7177: MatCheckPreallocated(mat, 2);
7179: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7180: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7181: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7182: PetscFunctionReturn(PETSC_SUCCESS);
7183: }
7185: /*@C
7186: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7187: points to an array of valid matrices, they may be reused to store the new
7188: submatrices.
7190: Collective
7192: Input Parameters:
7193: + mat - the matrix
7194: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7195: . irow - index set of rows to extract
7196: . icol - index set of columns to extract
7197: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7199: Output Parameter:
7200: . submat - the array of submatrices
7202: Level: advanced
7204: Notes:
7205: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7206: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7207: to extract a parallel submatrix.
7209: Some matrix types place restrictions on the row and column
7210: indices, such as that they be sorted or that they be equal to each other.
7212: The index sets may not have duplicate entries.
7214: When extracting submatrices from a parallel matrix, each processor can
7215: form a different submatrix by setting the rows and columns of its
7216: individual index sets according to the local submatrix desired.
7218: When finished using the submatrices, the user should destroy
7219: them with `MatDestroySubMatrices()`.
7221: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7222: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7224: This routine creates the matrices in submat; you should NOT create them before
7225: calling it. It also allocates the array of matrix pointers submat.
7227: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7228: request one row/column in a block, they must request all rows/columns that are in
7229: that block. For example, if the block size is 2 you cannot request just row 0 and
7230: column 0.
7232: Fortran Note:
7233: .vb
7234: Mat, pointer :: submat(:)
7235: .ve
7237: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7238: @*/
7239: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7240: {
7241: PetscInt i;
7242: PetscBool eq;
7244: PetscFunctionBegin;
7247: if (n) {
7248: PetscAssertPointer(irow, 3);
7250: PetscAssertPointer(icol, 4);
7252: }
7253: PetscAssertPointer(submat, 6);
7254: if (n && scall == MAT_REUSE_MATRIX) {
7255: PetscAssertPointer(*submat, 6);
7257: }
7258: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7259: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7260: MatCheckPreallocated(mat, 1);
7261: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7262: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7263: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7264: for (i = 0; i < n; i++) {
7265: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7266: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7267: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7268: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7269: if (mat->boundtocpu && mat->bindingpropagates) {
7270: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7271: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7272: }
7273: #endif
7274: }
7275: PetscFunctionReturn(PETSC_SUCCESS);
7276: }
7278: /*@C
7279: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7281: Collective
7283: Input Parameters:
7284: + mat - the matrix
7285: . n - the number of submatrixes to be extracted
7286: . irow - index set of rows to extract
7287: . icol - index set of columns to extract
7288: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7290: Output Parameter:
7291: . submat - the array of submatrices
7293: Level: advanced
7295: Note:
7296: This is used by `PCGASM`
7298: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7299: @*/
7300: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7301: {
7302: PetscInt i;
7303: PetscBool eq;
7305: PetscFunctionBegin;
7308: if (n) {
7309: PetscAssertPointer(irow, 3);
7311: PetscAssertPointer(icol, 4);
7313: }
7314: PetscAssertPointer(submat, 6);
7315: if (n && scall == MAT_REUSE_MATRIX) {
7316: PetscAssertPointer(*submat, 6);
7318: }
7319: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7320: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7321: MatCheckPreallocated(mat, 1);
7323: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7324: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7325: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7326: for (i = 0; i < n; i++) {
7327: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7328: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7329: }
7330: PetscFunctionReturn(PETSC_SUCCESS);
7331: }
7333: /*@C
7334: MatDestroyMatrices - Destroys an array of matrices
7336: Collective
7338: Input Parameters:
7339: + n - the number of local matrices
7340: - mat - the matrices (this is a pointer to the array of matrices)
7342: Level: advanced
7344: Notes:
7345: Frees not only the matrices, but also the array that contains the matrices
7347: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7349: Fortran Note:
7350: Does not free the `mat` array.
7352: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7353: @*/
7354: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7355: {
7356: PetscInt i;
7358: PetscFunctionBegin;
7359: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7360: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7361: PetscAssertPointer(mat, 2);
7363: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7365: /* memory is allocated even if n = 0 */
7366: PetscCall(PetscFree(*mat));
7367: PetscFunctionReturn(PETSC_SUCCESS);
7368: }
7370: /*@C
7371: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7373: Collective
7375: Input Parameters:
7376: + n - the number of local matrices
7377: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7379: Level: advanced
7381: Note:
7382: Frees not only the matrices, but also the array that contains the matrices
7384: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7385: @*/
7386: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7387: {
7388: Mat mat0;
7390: PetscFunctionBegin;
7391: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7392: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7393: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7394: PetscAssertPointer(mat, 2);
7396: mat0 = (*mat)[0];
7397: if (mat0 && mat0->ops->destroysubmatrices) {
7398: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7399: } else {
7400: PetscCall(MatDestroyMatrices(n, mat));
7401: }
7402: PetscFunctionReturn(PETSC_SUCCESS);
7403: }
7405: /*@
7406: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7408: Collective
7410: Input Parameter:
7411: . mat - the matrix
7413: Output Parameter:
7414: . matstruct - the sequential matrix with the nonzero structure of `mat`
7416: Level: developer
7418: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7419: @*/
7420: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7421: {
7422: PetscFunctionBegin;
7424: PetscAssertPointer(matstruct, 2);
7427: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7428: MatCheckPreallocated(mat, 1);
7430: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7431: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7432: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7433: PetscFunctionReturn(PETSC_SUCCESS);
7434: }
7436: /*@C
7437: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7439: Collective
7441: Input Parameter:
7442: . mat - the matrix
7444: Level: advanced
7446: Note:
7447: This is not needed, one can just call `MatDestroy()`
7449: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7450: @*/
7451: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7452: {
7453: PetscFunctionBegin;
7454: PetscAssertPointer(mat, 1);
7455: PetscCall(MatDestroy(mat));
7456: PetscFunctionReturn(PETSC_SUCCESS);
7457: }
7459: /*@
7460: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7461: replaces the index sets by larger ones that represent submatrices with
7462: additional overlap.
7464: Collective
7466: Input Parameters:
7467: + mat - the matrix
7468: . n - the number of index sets
7469: . is - the array of index sets (these index sets will changed during the call)
7470: - ov - the additional overlap requested
7472: Options Database Key:
7473: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7475: Level: developer
7477: Note:
7478: The computed overlap preserves the matrix block sizes when the blocks are square.
7479: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7480: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7482: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7483: @*/
7484: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7485: {
7486: PetscInt i, bs, cbs;
7488: PetscFunctionBegin;
7492: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7493: if (n) {
7494: PetscAssertPointer(is, 3);
7496: }
7497: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7498: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7499: MatCheckPreallocated(mat, 1);
7501: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7502: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7503: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7504: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7505: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7506: if (bs == cbs) {
7507: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7508: }
7509: PetscFunctionReturn(PETSC_SUCCESS);
7510: }
7512: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7514: /*@
7515: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7516: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7517: additional overlap.
7519: Collective
7521: Input Parameters:
7522: + mat - the matrix
7523: . n - the number of index sets
7524: . is - the array of index sets (these index sets will changed during the call)
7525: - ov - the additional overlap requested
7527: ` Options Database Key:
7528: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7530: Level: developer
7532: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7533: @*/
7534: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7535: {
7536: PetscInt i;
7538: PetscFunctionBegin;
7541: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7542: if (n) {
7543: PetscAssertPointer(is, 3);
7545: }
7546: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7547: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7548: MatCheckPreallocated(mat, 1);
7549: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7550: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7551: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7552: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7553: PetscFunctionReturn(PETSC_SUCCESS);
7554: }
7556: /*@
7557: MatGetBlockSize - Returns the matrix block size.
7559: Not Collective
7561: Input Parameter:
7562: . mat - the matrix
7564: Output Parameter:
7565: . bs - block size
7567: Level: intermediate
7569: Notes:
7570: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7572: If the block size has not been set yet this routine returns 1.
7574: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7575: @*/
7576: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7577: {
7578: PetscFunctionBegin;
7580: PetscAssertPointer(bs, 2);
7581: *bs = PetscAbs(mat->rmap->bs);
7582: PetscFunctionReturn(PETSC_SUCCESS);
7583: }
7585: /*@
7586: MatGetBlockSizes - Returns the matrix block row and column sizes.
7588: Not Collective
7590: Input Parameter:
7591: . mat - the matrix
7593: Output Parameters:
7594: + rbs - row block size
7595: - cbs - column block size
7597: Level: intermediate
7599: Notes:
7600: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7601: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7603: If a block size has not been set yet this routine returns 1.
7605: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7606: @*/
7607: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7608: {
7609: PetscFunctionBegin;
7611: if (rbs) PetscAssertPointer(rbs, 2);
7612: if (cbs) PetscAssertPointer(cbs, 3);
7613: if (rbs) *rbs = PetscAbs(mat->rmap->bs);
7614: if (cbs) *cbs = PetscAbs(mat->cmap->bs);
7615: PetscFunctionReturn(PETSC_SUCCESS);
7616: }
7618: /*@
7619: MatSetBlockSize - Sets the matrix block size.
7621: Logically Collective
7623: Input Parameters:
7624: + mat - the matrix
7625: - bs - block size
7627: Level: intermediate
7629: Notes:
7630: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7631: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7633: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7634: is compatible with the matrix local sizes.
7636: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7637: @*/
7638: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7639: {
7640: PetscFunctionBegin;
7643: PetscCall(MatSetBlockSizes(mat, bs, bs));
7644: PetscFunctionReturn(PETSC_SUCCESS);
7645: }
7647: typedef struct {
7648: PetscInt n;
7649: IS *is;
7650: Mat *mat;
7651: PetscObjectState nonzerostate;
7652: Mat C;
7653: } EnvelopeData;
7655: static PetscErrorCode EnvelopeDataDestroy(void **ptr)
7656: {
7657: EnvelopeData *edata = (EnvelopeData *)*ptr;
7659: PetscFunctionBegin;
7660: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7661: PetscCall(PetscFree(edata->is));
7662: PetscCall(PetscFree(edata));
7663: PetscFunctionReturn(PETSC_SUCCESS);
7664: }
7666: /*@
7667: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7668: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7670: Collective
7672: Input Parameter:
7673: . mat - the matrix
7675: Level: intermediate
7677: Notes:
7678: There can be zeros within the blocks
7680: The blocks can overlap between processes, including laying on more than two processes
7682: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7683: @*/
7684: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7685: {
7686: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7687: PetscInt *diag, *odiag, sc;
7688: VecScatter scatter;
7689: PetscScalar *seqv;
7690: const PetscScalar *parv;
7691: const PetscInt *ia, *ja;
7692: PetscBool set, flag, done;
7693: Mat AA = mat, A;
7694: MPI_Comm comm;
7695: PetscMPIInt rank, size, tag;
7696: MPI_Status status;
7697: PetscContainer container;
7698: EnvelopeData *edata;
7699: Vec seq, par;
7700: IS isglobal;
7702: PetscFunctionBegin;
7704: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7705: if (!set || !flag) {
7706: /* TODO: only needs nonzero structure of transpose */
7707: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7708: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7709: }
7710: PetscCall(MatAIJGetLocalMat(AA, &A));
7711: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7712: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7714: PetscCall(MatGetLocalSize(mat, &n, NULL));
7715: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7716: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7717: PetscCallMPI(MPI_Comm_size(comm, &size));
7718: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7720: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7722: if (rank > 0) {
7723: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7724: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7725: }
7726: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7727: for (i = 0; i < n; i++) {
7728: env = PetscMax(env, ja[ia[i + 1] - 1]);
7729: II = rstart + i;
7730: if (env == II) {
7731: starts[lblocks] = tbs;
7732: sizes[lblocks++] = 1 + II - tbs;
7733: tbs = 1 + II;
7734: }
7735: }
7736: if (rank < size - 1) {
7737: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7738: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7739: }
7741: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7742: if (!set || !flag) PetscCall(MatDestroy(&AA));
7743: PetscCall(MatDestroy(&A));
7745: PetscCall(PetscNew(&edata));
7746: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7747: edata->n = lblocks;
7748: /* create IS needed for extracting blocks from the original matrix */
7749: PetscCall(PetscMalloc1(lblocks, &edata->is));
7750: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7752: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7753: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7754: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7755: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7756: PetscCall(MatSetType(edata->C, MATAIJ));
7758: /* Communicate the start and end of each row, from each block to the correct rank */
7759: /* TODO: Use PetscSF instead of VecScatter */
7760: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7761: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7762: PetscCall(VecGetArrayWrite(seq, &seqv));
7763: for (PetscInt i = 0; i < lblocks; i++) {
7764: for (PetscInt j = 0; j < sizes[i]; j++) {
7765: seqv[cnt] = starts[i];
7766: seqv[cnt + 1] = starts[i] + sizes[i];
7767: cnt += 2;
7768: }
7769: }
7770: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7771: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7772: sc -= cnt;
7773: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7774: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7775: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7776: PetscCall(ISDestroy(&isglobal));
7777: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7778: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7779: PetscCall(VecScatterDestroy(&scatter));
7780: PetscCall(VecDestroy(&seq));
7781: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7782: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7783: PetscCall(VecGetArrayRead(par, &parv));
7784: cnt = 0;
7785: PetscCall(MatGetSize(mat, NULL, &n));
7786: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7787: PetscInt start, end, d = 0, od = 0;
7789: start = (PetscInt)PetscRealPart(parv[cnt]);
7790: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7791: cnt += 2;
7793: if (start < cstart) {
7794: od += cstart - start + n - cend;
7795: d += cend - cstart;
7796: } else if (start < cend) {
7797: od += n - cend;
7798: d += cend - start;
7799: } else od += n - start;
7800: if (end <= cstart) {
7801: od -= cstart - end + n - cend;
7802: d -= cend - cstart;
7803: } else if (end < cend) {
7804: od -= n - cend;
7805: d -= cend - end;
7806: } else od -= n - end;
7808: odiag[i] = od;
7809: diag[i] = d;
7810: }
7811: PetscCall(VecRestoreArrayRead(par, &parv));
7812: PetscCall(VecDestroy(&par));
7813: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7814: PetscCall(PetscFree2(diag, odiag));
7815: PetscCall(PetscFree2(sizes, starts));
7817: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7818: PetscCall(PetscContainerSetPointer(container, edata));
7819: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7820: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7821: PetscCall(PetscObjectDereference((PetscObject)container));
7822: PetscFunctionReturn(PETSC_SUCCESS);
7823: }
7825: /*@
7826: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7828: Collective
7830: Input Parameters:
7831: + A - the matrix
7832: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7834: Output Parameter:
7835: . C - matrix with inverted block diagonal of `A`
7837: Level: advanced
7839: Note:
7840: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7842: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7843: @*/
7844: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7845: {
7846: PetscContainer container;
7847: EnvelopeData *edata;
7848: PetscObjectState nonzerostate;
7850: PetscFunctionBegin;
7851: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7852: if (!container) {
7853: PetscCall(MatComputeVariableBlockEnvelope(A));
7854: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7855: }
7856: PetscCall(PetscContainerGetPointer(container, (void **)&edata));
7857: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7858: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7859: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7861: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7862: *C = edata->C;
7864: for (PetscInt i = 0; i < edata->n; i++) {
7865: Mat D;
7866: PetscScalar *dvalues;
7868: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7869: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7870: PetscCall(MatSeqDenseInvert(D));
7871: PetscCall(MatDenseGetArray(D, &dvalues));
7872: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7873: PetscCall(MatDestroy(&D));
7874: }
7875: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7876: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
7877: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
7878: PetscFunctionReturn(PETSC_SUCCESS);
7879: }
7881: /*@
7882: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
7884: Not Collective
7886: Input Parameters:
7887: + mat - the matrix
7888: . nblocks - the number of blocks on this process, each block can only exist on a single process
7889: - bsizes - the block sizes
7891: Level: intermediate
7893: Notes:
7894: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
7896: Each variable point-block set of degrees of freedom must live on a single MPI process. That is a point block cannot straddle two MPI processes.
7898: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
7899: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
7900: @*/
7901: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
7902: {
7903: PetscInt ncnt = 0, nlocal;
7905: PetscFunctionBegin;
7907: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
7908: 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);
7909: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
7910: 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);
7911: PetscCall(PetscFree(mat->bsizes));
7912: mat->nblocks = nblocks;
7913: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
7914: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
7915: PetscFunctionReturn(PETSC_SUCCESS);
7916: }
7918: /*@C
7919: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
7921: Not Collective; No Fortran Support
7923: Input Parameter:
7924: . mat - the matrix
7926: Output Parameters:
7927: + nblocks - the number of blocks on this process
7928: - bsizes - the block sizes
7930: Level: intermediate
7932: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
7933: @*/
7934: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
7935: {
7936: PetscFunctionBegin;
7938: if (nblocks) *nblocks = mat->nblocks;
7939: if (bsizes) *bsizes = mat->bsizes;
7940: PetscFunctionReturn(PETSC_SUCCESS);
7941: }
7943: /*@
7944: MatSetBlockSizes - Sets the matrix block row and column sizes.
7946: Logically Collective
7948: Input Parameters:
7949: + mat - the matrix
7950: . rbs - row block size
7951: - cbs - column block size
7953: Level: intermediate
7955: Notes:
7956: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
7957: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7958: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7960: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
7961: are compatible with the matrix local sizes.
7963: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
7965: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
7966: @*/
7967: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
7968: {
7969: PetscFunctionBegin;
7973: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
7974: if (mat->rmap->refcnt) {
7975: ISLocalToGlobalMapping l2g = NULL;
7976: PetscLayout nmap = NULL;
7978: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
7979: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
7980: PetscCall(PetscLayoutDestroy(&mat->rmap));
7981: mat->rmap = nmap;
7982: mat->rmap->mapping = l2g;
7983: }
7984: if (mat->cmap->refcnt) {
7985: ISLocalToGlobalMapping l2g = NULL;
7986: PetscLayout nmap = NULL;
7988: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
7989: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
7990: PetscCall(PetscLayoutDestroy(&mat->cmap));
7991: mat->cmap = nmap;
7992: mat->cmap->mapping = l2g;
7993: }
7994: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
7995: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
7996: PetscFunctionReturn(PETSC_SUCCESS);
7997: }
7999: /*@
8000: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8002: Logically Collective
8004: Input Parameters:
8005: + mat - the matrix
8006: . fromRow - matrix from which to copy row block size
8007: - fromCol - matrix from which to copy column block size (can be same as fromRow)
8009: Level: developer
8011: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8012: @*/
8013: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8014: {
8015: PetscFunctionBegin;
8019: if (fromRow->rmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8020: if (fromCol->cmap->bs > 0) PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8021: PetscFunctionReturn(PETSC_SUCCESS);
8022: }
8024: /*@
8025: MatResidual - Default routine to calculate the residual r = b - Ax
8027: Collective
8029: Input Parameters:
8030: + mat - the matrix
8031: . b - the right-hand-side
8032: - x - the approximate solution
8034: Output Parameter:
8035: . r - location to store the residual
8037: Level: developer
8039: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8040: @*/
8041: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8042: {
8043: PetscFunctionBegin;
8049: MatCheckPreallocated(mat, 1);
8050: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8051: if (!mat->ops->residual) {
8052: PetscCall(MatMult(mat, x, r));
8053: PetscCall(VecAYPX(r, -1.0, b));
8054: } else {
8055: PetscUseTypeMethod(mat, residual, b, x, r);
8056: }
8057: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8058: PetscFunctionReturn(PETSC_SUCCESS);
8059: }
8061: /*@C
8062: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8064: Collective
8066: Input Parameters:
8067: + mat - the matrix
8068: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8069: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8070: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8071: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8072: always used.
8074: Output Parameters:
8075: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8076: . 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
8077: . ja - the column indices, use `NULL` if not needed
8078: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8079: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8081: Level: developer
8083: Notes:
8084: You CANNOT change any of the ia[] or ja[] values.
8086: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8088: Fortran Notes:
8089: Use
8090: .vb
8091: PetscInt, pointer :: ia(:),ja(:)
8092: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8093: ! Access the ith and jth entries via ia(i) and ja(j)
8094: .ve
8096: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8097: @*/
8098: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8099: {
8100: PetscFunctionBegin;
8103: if (n) PetscAssertPointer(n, 5);
8104: if (ia) PetscAssertPointer(ia, 6);
8105: if (ja) PetscAssertPointer(ja, 7);
8106: if (done) PetscAssertPointer(done, 8);
8107: MatCheckPreallocated(mat, 1);
8108: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8109: else {
8110: if (done) *done = PETSC_TRUE;
8111: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8112: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8113: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8114: }
8115: PetscFunctionReturn(PETSC_SUCCESS);
8116: }
8118: /*@C
8119: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8121: Collective
8123: Input Parameters:
8124: + mat - the matrix
8125: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8126: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8127: symmetrized
8128: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8129: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8130: always used.
8131: . n - number of columns in the (possibly compressed) matrix
8132: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8133: - ja - the row indices
8135: Output Parameter:
8136: . done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8138: Level: developer
8140: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8141: @*/
8142: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8143: {
8144: PetscFunctionBegin;
8147: PetscAssertPointer(n, 5);
8148: if (ia) PetscAssertPointer(ia, 6);
8149: if (ja) PetscAssertPointer(ja, 7);
8150: PetscAssertPointer(done, 8);
8151: MatCheckPreallocated(mat, 1);
8152: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8153: else {
8154: *done = PETSC_TRUE;
8155: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8156: }
8157: PetscFunctionReturn(PETSC_SUCCESS);
8158: }
8160: /*@C
8161: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8163: Collective
8165: Input Parameters:
8166: + mat - the matrix
8167: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8168: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8169: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8170: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8171: always used.
8172: . n - size of (possibly compressed) matrix
8173: . ia - the row pointers
8174: - ja - the column indices
8176: Output Parameter:
8177: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8179: Level: developer
8181: Note:
8182: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8183: us of the array after it has been restored. If you pass `NULL`, it will
8184: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8186: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8187: @*/
8188: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8189: {
8190: PetscFunctionBegin;
8193: if (ia) PetscAssertPointer(ia, 6);
8194: if (ja) PetscAssertPointer(ja, 7);
8195: if (done) PetscAssertPointer(done, 8);
8196: MatCheckPreallocated(mat, 1);
8198: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8199: else {
8200: if (done) *done = PETSC_TRUE;
8201: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8202: if (n) *n = 0;
8203: if (ia) *ia = NULL;
8204: if (ja) *ja = NULL;
8205: }
8206: PetscFunctionReturn(PETSC_SUCCESS);
8207: }
8209: /*@C
8210: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8212: Collective
8214: Input Parameters:
8215: + mat - the matrix
8216: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8217: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8218: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8219: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8220: always used.
8222: Output Parameters:
8223: + n - size of (possibly compressed) matrix
8224: . ia - the column pointers
8225: . ja - the row indices
8226: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8228: Level: developer
8230: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8231: @*/
8232: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8233: {
8234: PetscFunctionBegin;
8237: if (ia) PetscAssertPointer(ia, 6);
8238: if (ja) PetscAssertPointer(ja, 7);
8239: PetscAssertPointer(done, 8);
8240: MatCheckPreallocated(mat, 1);
8242: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8243: else {
8244: *done = PETSC_TRUE;
8245: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8246: if (n) *n = 0;
8247: if (ia) *ia = NULL;
8248: if (ja) *ja = NULL;
8249: }
8250: PetscFunctionReturn(PETSC_SUCCESS);
8251: }
8253: /*@
8254: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8255: `MatGetColumnIJ()`.
8257: Collective
8259: Input Parameters:
8260: + mat - the matrix
8261: . ncolors - maximum color value
8262: . n - number of entries in colorarray
8263: - colorarray - array indicating color for each column
8265: Output Parameter:
8266: . iscoloring - coloring generated using colorarray information
8268: Level: developer
8270: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8271: @*/
8272: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8273: {
8274: PetscFunctionBegin;
8277: PetscAssertPointer(colorarray, 4);
8278: PetscAssertPointer(iscoloring, 5);
8279: MatCheckPreallocated(mat, 1);
8281: if (!mat->ops->coloringpatch) {
8282: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8283: } else {
8284: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8285: }
8286: PetscFunctionReturn(PETSC_SUCCESS);
8287: }
8289: /*@
8290: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8292: Logically Collective
8294: Input Parameter:
8295: . mat - the factored matrix to be reset
8297: Level: developer
8299: Notes:
8300: This routine should be used only with factored matrices formed by in-place
8301: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8302: format). This option can save memory, for example, when solving nonlinear
8303: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8304: ILU(0) preconditioner.
8306: One can specify in-place ILU(0) factorization by calling
8307: .vb
8308: PCType(pc,PCILU);
8309: PCFactorSeUseInPlace(pc);
8310: .ve
8311: or by using the options -pc_type ilu -pc_factor_in_place
8313: In-place factorization ILU(0) can also be used as a local
8314: solver for the blocks within the block Jacobi or additive Schwarz
8315: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8316: for details on setting local solver options.
8318: Most users should employ the `KSP` interface for linear solvers
8319: instead of working directly with matrix algebra routines such as this.
8320: See, e.g., `KSPCreate()`.
8322: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8323: @*/
8324: PetscErrorCode MatSetUnfactored(Mat mat)
8325: {
8326: PetscFunctionBegin;
8329: MatCheckPreallocated(mat, 1);
8330: mat->factortype = MAT_FACTOR_NONE;
8331: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8332: PetscUseTypeMethod(mat, setunfactored);
8333: PetscFunctionReturn(PETSC_SUCCESS);
8334: }
8336: /*@
8337: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8338: as the original matrix.
8340: Collective
8342: Input Parameters:
8343: + mat - the original matrix
8344: . isrow - parallel `IS` containing the rows this processor should obtain
8345: . 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.
8346: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8348: Output Parameter:
8349: . newmat - the new submatrix, of the same type as the original matrix
8351: Level: advanced
8353: Notes:
8354: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8356: Some matrix types place restrictions on the row and column indices, such
8357: 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;
8358: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8360: The index sets may not have duplicate entries.
8362: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8363: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8364: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8365: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8366: you are finished using it.
8368: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8369: the input matrix.
8371: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8373: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8374: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8376: Example usage:
8377: Consider the following 8x8 matrix with 34 non-zero values, that is
8378: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8379: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8380: as follows
8381: .vb
8382: 1 2 0 | 0 3 0 | 0 4
8383: Proc0 0 5 6 | 7 0 0 | 8 0
8384: 9 0 10 | 11 0 0 | 12 0
8385: -------------------------------------
8386: 13 0 14 | 15 16 17 | 0 0
8387: Proc1 0 18 0 | 19 20 21 | 0 0
8388: 0 0 0 | 22 23 0 | 24 0
8389: -------------------------------------
8390: Proc2 25 26 27 | 0 0 28 | 29 0
8391: 30 0 0 | 31 32 33 | 0 34
8392: .ve
8394: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8396: .vb
8397: 2 0 | 0 3 0 | 0
8398: Proc0 5 6 | 7 0 0 | 8
8399: -------------------------------
8400: Proc1 18 0 | 19 20 21 | 0
8401: -------------------------------
8402: Proc2 26 27 | 0 0 28 | 29
8403: 0 0 | 31 32 33 | 0
8404: .ve
8406: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8407: @*/
8408: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8409: {
8410: PetscMPIInt size;
8411: Mat *local;
8412: IS iscoltmp;
8413: PetscBool flg;
8415: PetscFunctionBegin;
8419: PetscAssertPointer(newmat, 5);
8422: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8423: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8425: MatCheckPreallocated(mat, 1);
8426: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8428: if (!iscol || isrow == iscol) {
8429: PetscBool stride;
8430: PetscMPIInt grabentirematrix = 0, grab;
8431: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8432: if (stride) {
8433: PetscInt first, step, n, rstart, rend;
8434: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8435: if (step == 1) {
8436: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8437: if (rstart == first) {
8438: PetscCall(ISGetLocalSize(isrow, &n));
8439: if (n == rend - rstart) grabentirematrix = 1;
8440: }
8441: }
8442: }
8443: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8444: if (grab) {
8445: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8446: if (cll == MAT_INITIAL_MATRIX) {
8447: *newmat = mat;
8448: PetscCall(PetscObjectReference((PetscObject)mat));
8449: }
8450: PetscFunctionReturn(PETSC_SUCCESS);
8451: }
8452: }
8454: if (!iscol) {
8455: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8456: } else {
8457: iscoltmp = iscol;
8458: }
8460: /* if original matrix is on just one processor then use submatrix generated */
8461: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8462: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8463: goto setproperties;
8464: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8465: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8466: *newmat = *local;
8467: PetscCall(PetscFree(local));
8468: goto setproperties;
8469: } else if (!mat->ops->createsubmatrix) {
8470: /* Create a new matrix type that implements the operation using the full matrix */
8471: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8472: switch (cll) {
8473: case MAT_INITIAL_MATRIX:
8474: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8475: break;
8476: case MAT_REUSE_MATRIX:
8477: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8478: break;
8479: default:
8480: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8481: }
8482: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8483: goto setproperties;
8484: }
8486: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8487: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8488: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8490: setproperties:
8491: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8492: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8493: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8494: }
8495: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8496: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8497: PetscFunctionReturn(PETSC_SUCCESS);
8498: }
8500: /*@
8501: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8503: Not Collective
8505: Input Parameters:
8506: + A - the matrix we wish to propagate options from
8507: - B - the matrix we wish to propagate options to
8509: Level: beginner
8511: Note:
8512: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8514: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8515: @*/
8516: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8517: {
8518: PetscFunctionBegin;
8521: B->symmetry_eternal = A->symmetry_eternal;
8522: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8523: B->symmetric = A->symmetric;
8524: B->structurally_symmetric = A->structurally_symmetric;
8525: B->spd = A->spd;
8526: B->hermitian = A->hermitian;
8527: PetscFunctionReturn(PETSC_SUCCESS);
8528: }
8530: /*@
8531: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8532: used during the assembly process to store values that belong to
8533: other processors.
8535: Not Collective
8537: Input Parameters:
8538: + mat - the matrix
8539: . size - the initial size of the stash.
8540: - bsize - the initial size of the block-stash(if used).
8542: Options Database Keys:
8543: + -matstash_initial_size <size> or <size0,size1,...sizep-1> - set initial size
8544: - -matstash_block_initial_size <bsize> or <bsize0,bsize1,...bsizep-1> - set initial block size
8546: Level: intermediate
8548: Notes:
8549: The block-stash is used for values set with `MatSetValuesBlocked()` while
8550: the stash is used for values set with `MatSetValues()`
8552: Run with the option -info and look for output of the form
8553: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8554: to determine the appropriate value, MM, to use for size and
8555: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8556: to determine the value, BMM to use for bsize
8558: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8559: @*/
8560: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8561: {
8562: PetscFunctionBegin;
8565: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8566: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8567: PetscFunctionReturn(PETSC_SUCCESS);
8568: }
8570: /*@
8571: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8572: the matrix
8574: Neighbor-wise Collective
8576: Input Parameters:
8577: + A - the matrix
8578: . x - the vector to be multiplied by the interpolation operator
8579: - y - the vector to be added to the result
8581: Output Parameter:
8582: . w - the resulting vector
8584: Level: intermediate
8586: Notes:
8587: `w` may be the same vector as `y`.
8589: This allows one to use either the restriction or interpolation (its transpose)
8590: matrix to do the interpolation
8592: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8593: @*/
8594: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8595: {
8596: PetscInt M, N, Ny;
8598: PetscFunctionBegin;
8603: PetscCall(MatGetSize(A, &M, &N));
8604: PetscCall(VecGetSize(y, &Ny));
8605: if (M == Ny) {
8606: PetscCall(MatMultAdd(A, x, y, w));
8607: } else {
8608: PetscCall(MatMultTransposeAdd(A, x, y, w));
8609: }
8610: PetscFunctionReturn(PETSC_SUCCESS);
8611: }
8613: /*@
8614: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8615: the matrix
8617: Neighbor-wise Collective
8619: Input Parameters:
8620: + A - the matrix
8621: - x - the vector to be interpolated
8623: Output Parameter:
8624: . y - the resulting vector
8626: Level: intermediate
8628: Note:
8629: This allows one to use either the restriction or interpolation (its transpose)
8630: matrix to do the interpolation
8632: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8633: @*/
8634: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8635: {
8636: PetscInt M, N, Ny;
8638: PetscFunctionBegin;
8642: PetscCall(MatGetSize(A, &M, &N));
8643: PetscCall(VecGetSize(y, &Ny));
8644: if (M == Ny) {
8645: PetscCall(MatMult(A, x, y));
8646: } else {
8647: PetscCall(MatMultTranspose(A, x, y));
8648: }
8649: PetscFunctionReturn(PETSC_SUCCESS);
8650: }
8652: /*@
8653: MatRestrict - $y = A*x$ or $A^T*x$
8655: Neighbor-wise Collective
8657: Input Parameters:
8658: + A - the matrix
8659: - x - the vector to be restricted
8661: Output Parameter:
8662: . y - the resulting vector
8664: Level: intermediate
8666: Note:
8667: This allows one to use either the restriction or interpolation (its transpose)
8668: matrix to do the restriction
8670: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8671: @*/
8672: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8673: {
8674: PetscInt M, N, Nx;
8676: PetscFunctionBegin;
8680: PetscCall(MatGetSize(A, &M, &N));
8681: PetscCall(VecGetSize(x, &Nx));
8682: if (M == Nx) {
8683: PetscCall(MatMultTranspose(A, x, y));
8684: } else {
8685: PetscCall(MatMult(A, x, y));
8686: }
8687: PetscFunctionReturn(PETSC_SUCCESS);
8688: }
8690: /*@
8691: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8693: Neighbor-wise Collective
8695: Input Parameters:
8696: + A - the matrix
8697: . x - the input dense matrix to be multiplied
8698: - w - the input dense matrix to be added to the result
8700: Output Parameter:
8701: . y - the output dense matrix
8703: Level: intermediate
8705: Note:
8706: This allows one to use either the restriction or interpolation (its transpose)
8707: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8708: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8710: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8711: @*/
8712: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8713: {
8714: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8715: PetscBool trans = PETSC_TRUE;
8716: MatReuse reuse = MAT_INITIAL_MATRIX;
8718: PetscFunctionBegin;
8724: PetscCall(MatGetSize(A, &M, &N));
8725: PetscCall(MatGetSize(x, &Mx, &Nx));
8726: if (N == Mx) trans = PETSC_FALSE;
8727: 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);
8728: Mo = trans ? N : M;
8729: if (*y) {
8730: PetscCall(MatGetSize(*y, &My, &Ny));
8731: if (Mo == My && Nx == Ny) {
8732: reuse = MAT_REUSE_MATRIX;
8733: } else {
8734: 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);
8735: PetscCall(MatDestroy(y));
8736: }
8737: }
8739: if (w && *y == w) { /* this is to minimize changes in PCMG */
8740: PetscBool flg;
8742: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8743: if (w) {
8744: PetscInt My, Ny, Mw, Nw;
8746: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8747: PetscCall(MatGetSize(*y, &My, &Ny));
8748: PetscCall(MatGetSize(w, &Mw, &Nw));
8749: if (!flg || My != Mw || Ny != Nw) w = NULL;
8750: }
8751: if (!w) {
8752: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8753: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8754: PetscCall(PetscObjectDereference((PetscObject)w));
8755: } else {
8756: PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8757: }
8758: }
8759: if (!trans) {
8760: PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8761: } else {
8762: PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8763: }
8764: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8765: PetscFunctionReturn(PETSC_SUCCESS);
8766: }
8768: /*@
8769: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8771: Neighbor-wise Collective
8773: Input Parameters:
8774: + A - the matrix
8775: - x - the input dense matrix
8777: Output Parameter:
8778: . y - the output dense matrix
8780: Level: intermediate
8782: Note:
8783: This allows one to use either the restriction or interpolation (its transpose)
8784: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8785: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8787: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8788: @*/
8789: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8790: {
8791: PetscFunctionBegin;
8792: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8793: PetscFunctionReturn(PETSC_SUCCESS);
8794: }
8796: /*@
8797: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8799: Neighbor-wise Collective
8801: Input Parameters:
8802: + A - the matrix
8803: - x - the input dense matrix
8805: Output Parameter:
8806: . y - the output dense matrix
8808: Level: intermediate
8810: Note:
8811: This allows one to use either the restriction or interpolation (its transpose)
8812: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
8813: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8815: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
8816: @*/
8817: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
8818: {
8819: PetscFunctionBegin;
8820: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8821: PetscFunctionReturn(PETSC_SUCCESS);
8822: }
8824: /*@
8825: MatGetNullSpace - retrieves the null space of a matrix.
8827: Logically Collective
8829: Input Parameters:
8830: + mat - the matrix
8831: - nullsp - the null space object
8833: Level: developer
8835: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
8836: @*/
8837: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
8838: {
8839: PetscFunctionBegin;
8841: PetscAssertPointer(nullsp, 2);
8842: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
8843: PetscFunctionReturn(PETSC_SUCCESS);
8844: }
8846: /*@C
8847: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
8849: Logically Collective
8851: Input Parameters:
8852: + n - the number of matrices
8853: - mat - the array of matrices
8855: Output Parameters:
8856: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
8858: Level: developer
8860: Note:
8861: Call `MatRestoreNullspaces()` to provide these to another array of matrices
8863: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8864: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
8865: @*/
8866: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8867: {
8868: PetscFunctionBegin;
8869: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8870: PetscAssertPointer(mat, 2);
8871: PetscAssertPointer(nullsp, 3);
8873: PetscCall(PetscCalloc1(3 * n, nullsp));
8874: for (PetscInt i = 0; i < n; i++) {
8876: (*nullsp)[i] = mat[i]->nullsp;
8877: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
8878: (*nullsp)[n + i] = mat[i]->nearnullsp;
8879: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
8880: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
8881: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
8882: }
8883: PetscFunctionReturn(PETSC_SUCCESS);
8884: }
8886: /*@C
8887: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
8889: Logically Collective
8891: Input Parameters:
8892: + n - the number of matrices
8893: . mat - the array of matrices
8894: - nullsp - an array of null spaces
8896: Level: developer
8898: Note:
8899: Call `MatGetNullSpaces()` to create `nullsp`
8901: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
8902: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
8903: @*/
8904: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
8905: {
8906: PetscFunctionBegin;
8907: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
8908: PetscAssertPointer(mat, 2);
8909: PetscAssertPointer(nullsp, 3);
8910: PetscAssertPointer(*nullsp, 3);
8912: for (PetscInt i = 0; i < n; i++) {
8914: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
8915: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
8916: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
8917: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
8918: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
8919: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
8920: }
8921: PetscCall(PetscFree(*nullsp));
8922: PetscFunctionReturn(PETSC_SUCCESS);
8923: }
8925: /*@
8926: MatSetNullSpace - attaches a null space to a matrix.
8928: Logically Collective
8930: Input Parameters:
8931: + mat - the matrix
8932: - nullsp - the null space object
8934: Level: advanced
8936: Notes:
8937: This null space is used by the `KSP` linear solvers to solve singular systems.
8939: 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`
8941: 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
8942: to zero but the linear system will still be solved in a least squares sense.
8944: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
8945: the domain of a matrix A (from $R^n$ to $R^m$ (m rows, n columns) $R^n$ = the direct sum of the null space of A, n(A), + the range of $A^T$, $R(A^T)$.
8946: 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
8947: 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
8948: 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($A^T$).
8949: This \hat{b} can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
8951: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one as called
8952: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
8953: routine also automatically calls `MatSetTransposeNullSpace()`.
8955: The user should call `MatNullSpaceDestroy()`.
8957: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
8958: `KSPSetPCSide()`
8959: @*/
8960: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
8961: {
8962: PetscFunctionBegin;
8965: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
8966: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
8967: mat->nullsp = nullsp;
8968: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
8969: PetscFunctionReturn(PETSC_SUCCESS);
8970: }
8972: /*@
8973: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
8975: Logically Collective
8977: Input Parameters:
8978: + mat - the matrix
8979: - nullsp - the null space object
8981: Level: developer
8983: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
8984: @*/
8985: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
8986: {
8987: PetscFunctionBegin;
8990: PetscAssertPointer(nullsp, 2);
8991: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
8992: PetscFunctionReturn(PETSC_SUCCESS);
8993: }
8995: /*@
8996: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
8998: Logically Collective
9000: Input Parameters:
9001: + mat - the matrix
9002: - nullsp - the null space object
9004: Level: advanced
9006: Notes:
9007: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9009: See `MatSetNullSpace()`
9011: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9012: @*/
9013: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9014: {
9015: PetscFunctionBegin;
9018: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9019: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9020: mat->transnullsp = nullsp;
9021: PetscFunctionReturn(PETSC_SUCCESS);
9022: }
9024: /*@
9025: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9026: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9028: Logically Collective
9030: Input Parameters:
9031: + mat - the matrix
9032: - nullsp - the null space object
9034: Level: advanced
9036: Notes:
9037: Overwrites any previous near null space that may have been attached
9039: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9041: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9042: @*/
9043: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9044: {
9045: PetscFunctionBegin;
9049: MatCheckPreallocated(mat, 1);
9050: if (nullsp) PetscCall(PetscObjectReference((PetscObject)nullsp));
9051: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9052: mat->nearnullsp = nullsp;
9053: PetscFunctionReturn(PETSC_SUCCESS);
9054: }
9056: /*@
9057: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9059: Not Collective
9061: Input Parameter:
9062: . mat - the matrix
9064: Output Parameter:
9065: . nullsp - the null space object, `NULL` if not set
9067: Level: advanced
9069: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9070: @*/
9071: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9072: {
9073: PetscFunctionBegin;
9076: PetscAssertPointer(nullsp, 2);
9077: MatCheckPreallocated(mat, 1);
9078: *nullsp = mat->nearnullsp;
9079: PetscFunctionReturn(PETSC_SUCCESS);
9080: }
9082: /*@
9083: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9085: Collective
9087: Input Parameters:
9088: + mat - the matrix
9089: . row - row/column permutation
9090: - info - information on desired factorization process
9092: Level: developer
9094: Notes:
9095: Probably really in-place only when level of fill is zero, otherwise allocates
9096: new space to store factored matrix and deletes previous memory.
9098: Most users should employ the `KSP` interface for linear solvers
9099: instead of working directly with matrix algebra routines such as this.
9100: See, e.g., `KSPCreate()`.
9102: Developer Note:
9103: The Fortran interface is not autogenerated as the
9104: interface definition cannot be generated correctly [due to `MatFactorInfo`]
9106: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9107: @*/
9108: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9109: {
9110: PetscFunctionBegin;
9114: PetscAssertPointer(info, 3);
9115: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9116: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9117: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9118: MatCheckPreallocated(mat, 1);
9119: PetscUseTypeMethod(mat, iccfactor, row, info);
9120: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9121: PetscFunctionReturn(PETSC_SUCCESS);
9122: }
9124: /*@
9125: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9126: ghosted ones.
9128: Not Collective
9130: Input Parameters:
9131: + mat - the matrix
9132: - diag - the diagonal values, including ghost ones
9134: Level: developer
9136: Notes:
9137: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9139: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9141: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9142: @*/
9143: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9144: {
9145: PetscMPIInt size;
9147: PetscFunctionBegin;
9152: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9153: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9154: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9155: if (size == 1) {
9156: PetscInt n, m;
9157: PetscCall(VecGetSize(diag, &n));
9158: PetscCall(MatGetSize(mat, NULL, &m));
9159: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9160: PetscCall(MatDiagonalScale(mat, NULL, diag));
9161: } else {
9162: PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9163: }
9164: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9165: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9166: PetscFunctionReturn(PETSC_SUCCESS);
9167: }
9169: /*@
9170: MatGetInertia - Gets the inertia from a factored matrix
9172: Collective
9174: Input Parameter:
9175: . mat - the matrix
9177: Output Parameters:
9178: + nneg - number of negative eigenvalues
9179: . nzero - number of zero eigenvalues
9180: - npos - number of positive eigenvalues
9182: Level: advanced
9184: Note:
9185: Matrix must have been factored by `MatCholeskyFactor()`
9187: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9188: @*/
9189: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9190: {
9191: PetscFunctionBegin;
9194: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9195: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9196: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9197: PetscFunctionReturn(PETSC_SUCCESS);
9198: }
9200: /*@C
9201: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9203: Neighbor-wise Collective
9205: Input Parameters:
9206: + mat - the factored matrix obtained with `MatGetFactor()`
9207: - b - the right-hand-side vectors
9209: Output Parameter:
9210: . x - the result vectors
9212: Level: developer
9214: Note:
9215: The vectors `b` and `x` cannot be the same. I.e., one cannot
9216: call `MatSolves`(A,x,x).
9218: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9219: @*/
9220: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9221: {
9222: PetscFunctionBegin;
9225: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9226: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9227: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9229: MatCheckPreallocated(mat, 1);
9230: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9231: PetscUseTypeMethod(mat, solves, b, x);
9232: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9233: PetscFunctionReturn(PETSC_SUCCESS);
9234: }
9236: /*@
9237: MatIsSymmetric - Test whether a matrix is symmetric
9239: Collective
9241: Input Parameters:
9242: + A - the matrix to test
9243: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9245: Output Parameter:
9246: . flg - the result
9248: Level: intermediate
9250: Notes:
9251: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9253: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9255: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9256: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9258: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9259: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9260: @*/
9261: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9262: {
9263: PetscFunctionBegin;
9265: PetscAssertPointer(flg, 3);
9266: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9267: else {
9268: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9269: else PetscCall(MatIsTranspose(A, A, tol, flg));
9270: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9271: }
9272: PetscFunctionReturn(PETSC_SUCCESS);
9273: }
9275: /*@
9276: MatIsHermitian - Test whether a matrix is Hermitian
9278: Collective
9280: Input Parameters:
9281: + A - the matrix to test
9282: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9284: Output Parameter:
9285: . flg - the result
9287: Level: intermediate
9289: Notes:
9290: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9292: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9294: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9295: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9297: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9298: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9299: @*/
9300: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9301: {
9302: PetscFunctionBegin;
9304: PetscAssertPointer(flg, 3);
9305: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9306: else {
9307: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9308: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9309: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9310: }
9311: PetscFunctionReturn(PETSC_SUCCESS);
9312: }
9314: /*@
9315: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9317: Not Collective
9319: Input Parameter:
9320: . A - the matrix to check
9322: Output Parameters:
9323: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9324: - flg - the result (only valid if set is `PETSC_TRUE`)
9326: Level: advanced
9328: Notes:
9329: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9330: if you want it explicitly checked
9332: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9333: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9335: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9336: @*/
9337: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9338: {
9339: PetscFunctionBegin;
9341: PetscAssertPointer(set, 2);
9342: PetscAssertPointer(flg, 3);
9343: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9344: *set = PETSC_TRUE;
9345: *flg = PetscBool3ToBool(A->symmetric);
9346: } else {
9347: *set = PETSC_FALSE;
9348: }
9349: PetscFunctionReturn(PETSC_SUCCESS);
9350: }
9352: /*@
9353: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9355: Not Collective
9357: Input Parameter:
9358: . A - the matrix to check
9360: Output Parameters:
9361: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9362: - flg - the result (only valid if set is `PETSC_TRUE`)
9364: Level: advanced
9366: Notes:
9367: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9369: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9370: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9372: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9373: @*/
9374: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9375: {
9376: PetscFunctionBegin;
9378: PetscAssertPointer(set, 2);
9379: PetscAssertPointer(flg, 3);
9380: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9381: *set = PETSC_TRUE;
9382: *flg = PetscBool3ToBool(A->spd);
9383: } else {
9384: *set = PETSC_FALSE;
9385: }
9386: PetscFunctionReturn(PETSC_SUCCESS);
9387: }
9389: /*@
9390: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9392: Not Collective
9394: Input Parameter:
9395: . A - the matrix to check
9397: Output Parameters:
9398: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9399: - flg - the result (only valid if set is `PETSC_TRUE`)
9401: Level: advanced
9403: Notes:
9404: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9405: if you want it explicitly checked
9407: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9408: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9410: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9411: @*/
9412: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9413: {
9414: PetscFunctionBegin;
9416: PetscAssertPointer(set, 2);
9417: PetscAssertPointer(flg, 3);
9418: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9419: *set = PETSC_TRUE;
9420: *flg = PetscBool3ToBool(A->hermitian);
9421: } else {
9422: *set = PETSC_FALSE;
9423: }
9424: PetscFunctionReturn(PETSC_SUCCESS);
9425: }
9427: /*@
9428: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9430: Collective
9432: Input Parameter:
9433: . A - the matrix to test
9435: Output Parameter:
9436: . flg - the result
9438: Level: intermediate
9440: Notes:
9441: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9443: 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
9444: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9446: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9447: @*/
9448: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9449: {
9450: PetscFunctionBegin;
9452: PetscAssertPointer(flg, 2);
9453: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9454: *flg = PetscBool3ToBool(A->structurally_symmetric);
9455: } else {
9456: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9457: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9458: }
9459: PetscFunctionReturn(PETSC_SUCCESS);
9460: }
9462: /*@
9463: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9465: Not Collective
9467: Input Parameter:
9468: . A - the matrix to check
9470: Output Parameters:
9471: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9472: - flg - the result (only valid if set is PETSC_TRUE)
9474: Level: advanced
9476: Notes:
9477: 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
9478: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9480: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9482: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9483: @*/
9484: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9485: {
9486: PetscFunctionBegin;
9488: PetscAssertPointer(set, 2);
9489: PetscAssertPointer(flg, 3);
9490: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9491: *set = PETSC_TRUE;
9492: *flg = PetscBool3ToBool(A->structurally_symmetric);
9493: } else {
9494: *set = PETSC_FALSE;
9495: }
9496: PetscFunctionReturn(PETSC_SUCCESS);
9497: }
9499: /*@
9500: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9501: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9503: Not Collective
9505: Input Parameter:
9506: . mat - the matrix
9508: Output Parameters:
9509: + nstash - the size of the stash
9510: . reallocs - the number of additional mallocs incurred.
9511: . bnstash - the size of the block stash
9512: - breallocs - the number of additional mallocs incurred.in the block stash
9514: Level: advanced
9516: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9517: @*/
9518: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9519: {
9520: PetscFunctionBegin;
9521: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9522: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9523: PetscFunctionReturn(PETSC_SUCCESS);
9524: }
9526: /*@
9527: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9528: parallel layout, `PetscLayout` for rows and columns
9530: Collective
9532: Input Parameter:
9533: . mat - the matrix
9535: Output Parameters:
9536: + right - (optional) vector that the matrix can be multiplied against
9537: - left - (optional) vector that the matrix vector product can be stored in
9539: Level: advanced
9541: Notes:
9542: 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()`.
9544: These are new vectors which are not owned by the mat, they should be destroyed in `VecDestroy()` when no longer needed
9546: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`
9547: @*/
9548: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9549: {
9550: PetscFunctionBegin;
9553: if (mat->ops->getvecs) {
9554: PetscUseTypeMethod(mat, getvecs, right, left);
9555: } else {
9556: if (right) {
9557: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9558: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9559: PetscCall(VecSetType(*right, mat->defaultvectype));
9560: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9561: if (mat->boundtocpu && mat->bindingpropagates) {
9562: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9563: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9564: }
9565: #endif
9566: }
9567: if (left) {
9568: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9569: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9570: PetscCall(VecSetType(*left, mat->defaultvectype));
9571: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9572: if (mat->boundtocpu && mat->bindingpropagates) {
9573: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9574: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9575: }
9576: #endif
9577: }
9578: }
9579: PetscFunctionReturn(PETSC_SUCCESS);
9580: }
9582: /*@
9583: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9584: with default values.
9586: Not Collective
9588: Input Parameter:
9589: . info - the `MatFactorInfo` data structure
9591: Level: developer
9593: Notes:
9594: The solvers are generally used through the `KSP` and `PC` objects, for example
9595: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9597: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9599: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9600: @*/
9601: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9602: {
9603: PetscFunctionBegin;
9604: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9605: PetscFunctionReturn(PETSC_SUCCESS);
9606: }
9608: /*@
9609: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9611: Collective
9613: Input Parameters:
9614: + mat - the factored matrix
9615: - is - the index set defining the Schur indices (0-based)
9617: Level: advanced
9619: Notes:
9620: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9622: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9624: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9626: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9627: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9628: @*/
9629: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9630: {
9631: PetscErrorCode (*f)(Mat, IS);
9633: PetscFunctionBegin;
9638: PetscCheckSameComm(mat, 1, is, 2);
9639: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9640: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9641: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9642: PetscCall(MatDestroy(&mat->schur));
9643: PetscCall((*f)(mat, is));
9644: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9645: PetscFunctionReturn(PETSC_SUCCESS);
9646: }
9648: /*@
9649: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9651: Logically Collective
9653: Input Parameters:
9654: + F - the factored matrix obtained by calling `MatGetFactor()`
9655: . S - location where to return the Schur complement, can be `NULL`
9656: - status - the status of the Schur complement matrix, can be `NULL`
9658: Level: advanced
9660: Notes:
9661: You must call `MatFactorSetSchurIS()` before calling this routine.
9663: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9665: The routine provides a copy of the Schur matrix stored within the solver data structures.
9666: The caller must destroy the object when it is no longer needed.
9667: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9669: 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)
9671: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9673: Developer Note:
9674: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9675: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9677: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9678: @*/
9679: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9680: {
9681: PetscFunctionBegin;
9683: if (S) PetscAssertPointer(S, 2);
9684: if (status) PetscAssertPointer(status, 3);
9685: if (S) {
9686: PetscErrorCode (*f)(Mat, Mat *);
9688: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9689: if (f) {
9690: PetscCall((*f)(F, S));
9691: } else {
9692: PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9693: }
9694: }
9695: if (status) *status = F->schur_status;
9696: PetscFunctionReturn(PETSC_SUCCESS);
9697: }
9699: /*@
9700: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9702: Logically Collective
9704: Input Parameters:
9705: + F - the factored matrix obtained by calling `MatGetFactor()`
9706: . S - location where to return the Schur complement, can be `NULL`
9707: - status - the status of the Schur complement matrix, can be `NULL`
9709: Level: advanced
9711: Notes:
9712: You must call `MatFactorSetSchurIS()` before calling this routine.
9714: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9716: The routine returns a the Schur Complement stored within the data structures of the solver.
9718: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9720: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9722: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9724: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9726: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9727: @*/
9728: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9729: {
9730: PetscFunctionBegin;
9732: if (S) {
9733: PetscAssertPointer(S, 2);
9734: *S = F->schur;
9735: }
9736: if (status) {
9737: PetscAssertPointer(status, 3);
9738: *status = F->schur_status;
9739: }
9740: PetscFunctionReturn(PETSC_SUCCESS);
9741: }
9743: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9744: {
9745: Mat S = F->schur;
9747: PetscFunctionBegin;
9748: switch (F->schur_status) {
9749: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9750: case MAT_FACTOR_SCHUR_INVERTED:
9751: if (S) {
9752: S->ops->solve = NULL;
9753: S->ops->matsolve = NULL;
9754: S->ops->solvetranspose = NULL;
9755: S->ops->matsolvetranspose = NULL;
9756: S->ops->solveadd = NULL;
9757: S->ops->solvetransposeadd = NULL;
9758: S->factortype = MAT_FACTOR_NONE;
9759: PetscCall(PetscFree(S->solvertype));
9760: }
9761: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9762: break;
9763: default:
9764: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9765: }
9766: PetscFunctionReturn(PETSC_SUCCESS);
9767: }
9769: /*@
9770: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9772: Logically Collective
9774: Input Parameters:
9775: + F - the factored matrix obtained by calling `MatGetFactor()`
9776: . S - location where the Schur complement is stored
9777: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9779: Level: advanced
9781: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9782: @*/
9783: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9784: {
9785: PetscFunctionBegin;
9787: if (S) {
9789: *S = NULL;
9790: }
9791: F->schur_status = status;
9792: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9793: PetscFunctionReturn(PETSC_SUCCESS);
9794: }
9796: /*@
9797: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9799: Logically Collective
9801: Input Parameters:
9802: + F - the factored matrix obtained by calling `MatGetFactor()`
9803: . rhs - location where the right-hand side of the Schur complement system is stored
9804: - sol - location where the solution of the Schur complement system has to be returned
9806: Level: advanced
9808: Notes:
9809: The sizes of the vectors should match the size of the Schur complement
9811: Must be called after `MatFactorSetSchurIS()`
9813: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
9814: @*/
9815: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
9816: {
9817: PetscFunctionBegin;
9824: PetscCheckSameComm(F, 1, rhs, 2);
9825: PetscCheckSameComm(F, 1, sol, 3);
9826: PetscCall(MatFactorFactorizeSchurComplement(F));
9827: switch (F->schur_status) {
9828: case MAT_FACTOR_SCHUR_FACTORED:
9829: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
9830: break;
9831: case MAT_FACTOR_SCHUR_INVERTED:
9832: PetscCall(MatMultTranspose(F->schur, rhs, sol));
9833: break;
9834: default:
9835: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9836: }
9837: PetscFunctionReturn(PETSC_SUCCESS);
9838: }
9840: /*@
9841: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
9843: Logically Collective
9845: Input Parameters:
9846: + F - the factored matrix obtained by calling `MatGetFactor()`
9847: . rhs - location where the right-hand side of the Schur complement system is stored
9848: - sol - location where the solution of the Schur complement system has to be returned
9850: Level: advanced
9852: Notes:
9853: The sizes of the vectors should match the size of the Schur complement
9855: Must be called after `MatFactorSetSchurIS()`
9857: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
9858: @*/
9859: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
9860: {
9861: PetscFunctionBegin;
9868: PetscCheckSameComm(F, 1, rhs, 2);
9869: PetscCheckSameComm(F, 1, sol, 3);
9870: PetscCall(MatFactorFactorizeSchurComplement(F));
9871: switch (F->schur_status) {
9872: case MAT_FACTOR_SCHUR_FACTORED:
9873: PetscCall(MatSolve(F->schur, rhs, sol));
9874: break;
9875: case MAT_FACTOR_SCHUR_INVERTED:
9876: PetscCall(MatMult(F->schur, rhs, sol));
9877: break;
9878: default:
9879: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9880: }
9881: PetscFunctionReturn(PETSC_SUCCESS);
9882: }
9884: PETSC_EXTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
9885: #if PetscDefined(HAVE_CUDA)
9886: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
9887: #endif
9889: /* Schur status updated in the interface */
9890: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
9891: {
9892: Mat S = F->schur;
9894: PetscFunctionBegin;
9895: if (S) {
9896: PetscMPIInt size;
9897: PetscBool isdense, isdensecuda;
9899: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
9900: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
9901: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
9902: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
9903: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
9904: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
9905: if (isdense) {
9906: PetscCall(MatSeqDenseInvertFactors_Private(S));
9907: } else if (isdensecuda) {
9908: #if defined(PETSC_HAVE_CUDA)
9909: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
9910: #endif
9911: }
9912: // HIP??????????????
9913: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
9914: }
9915: PetscFunctionReturn(PETSC_SUCCESS);
9916: }
9918: /*@
9919: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
9921: Logically Collective
9923: Input Parameter:
9924: . F - the factored matrix obtained by calling `MatGetFactor()`
9926: Level: advanced
9928: Notes:
9929: Must be called after `MatFactorSetSchurIS()`.
9931: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
9933: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
9934: @*/
9935: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
9936: {
9937: PetscFunctionBegin;
9940: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
9941: PetscCall(MatFactorFactorizeSchurComplement(F));
9942: PetscCall(MatFactorInvertSchurComplement_Private(F));
9943: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
9944: PetscFunctionReturn(PETSC_SUCCESS);
9945: }
9947: /*@
9948: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
9950: Logically Collective
9952: Input Parameter:
9953: . F - the factored matrix obtained by calling `MatGetFactor()`
9955: Level: advanced
9957: Note:
9958: Must be called after `MatFactorSetSchurIS()`
9960: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
9961: @*/
9962: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
9963: {
9964: MatFactorInfo info;
9966: PetscFunctionBegin;
9969: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
9970: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
9971: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
9972: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
9973: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
9974: } else {
9975: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
9976: }
9977: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
9978: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
9979: PetscFunctionReturn(PETSC_SUCCESS);
9980: }
9982: /*@
9983: MatPtAP - Creates the matrix product $C = P^T * A * P$
9985: Neighbor-wise Collective
9987: Input Parameters:
9988: + A - the matrix
9989: . P - the projection matrix
9990: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
9991: - 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
9992: if the result is a dense matrix this is irrelevant
9994: Output Parameter:
9995: . C - the product matrix
9997: Level: intermediate
9999: Notes:
10000: C will be created and must be destroyed by the user with `MatDestroy()`.
10002: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
10004: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10006: Developer Note:
10007: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10009: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10010: @*/
10011: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10012: {
10013: PetscFunctionBegin;
10014: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10015: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10017: if (scall == MAT_INITIAL_MATRIX) {
10018: PetscCall(MatProductCreate(A, P, NULL, C));
10019: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10020: PetscCall(MatProductSetAlgorithm(*C, "default"));
10021: PetscCall(MatProductSetFill(*C, fill));
10023: (*C)->product->api_user = PETSC_TRUE;
10024: PetscCall(MatProductSetFromOptions(*C));
10025: 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);
10026: PetscCall(MatProductSymbolic(*C));
10027: } else { /* scall == MAT_REUSE_MATRIX */
10028: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10029: }
10031: PetscCall(MatProductNumeric(*C));
10032: (*C)->symmetric = A->symmetric;
10033: (*C)->spd = A->spd;
10034: PetscFunctionReturn(PETSC_SUCCESS);
10035: }
10037: /*@
10038: MatRARt - Creates the matrix product $C = R * A * R^T$
10040: Neighbor-wise Collective
10042: Input Parameters:
10043: + A - the matrix
10044: . R - the projection matrix
10045: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10046: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10047: if the result is a dense matrix this is irrelevant
10049: Output Parameter:
10050: . C - the product matrix
10052: Level: intermediate
10054: Notes:
10055: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10057: An alternative approach to this function is to use `MatProductCreate()` and set the desired options before the computation is done
10059: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10060: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10061: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10062: We recommend using `MatPtAP()` when possible.
10064: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10066: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10067: @*/
10068: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10069: {
10070: PetscFunctionBegin;
10071: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10072: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10074: if (scall == MAT_INITIAL_MATRIX) {
10075: PetscCall(MatProductCreate(A, R, NULL, C));
10076: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10077: PetscCall(MatProductSetAlgorithm(*C, "default"));
10078: PetscCall(MatProductSetFill(*C, fill));
10080: (*C)->product->api_user = PETSC_TRUE;
10081: PetscCall(MatProductSetFromOptions(*C));
10082: 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);
10083: PetscCall(MatProductSymbolic(*C));
10084: } else { /* scall == MAT_REUSE_MATRIX */
10085: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10086: }
10088: PetscCall(MatProductNumeric(*C));
10089: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10090: PetscFunctionReturn(PETSC_SUCCESS);
10091: }
10093: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10094: {
10095: PetscBool flg = PETSC_TRUE;
10097: PetscFunctionBegin;
10098: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10099: if (scall == MAT_INITIAL_MATRIX) {
10100: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10101: PetscCall(MatProductCreate(A, B, NULL, C));
10102: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10103: PetscCall(MatProductSetFill(*C, fill));
10104: } else { /* scall == MAT_REUSE_MATRIX */
10105: Mat_Product *product = (*C)->product;
10107: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10108: if (flg && product && product->type != ptype) {
10109: PetscCall(MatProductClear(*C));
10110: product = NULL;
10111: }
10112: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10113: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10114: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10115: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10116: product = (*C)->product;
10117: product->fill = fill;
10118: product->clear = PETSC_TRUE;
10119: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10120: flg = PETSC_FALSE;
10121: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10122: }
10123: }
10124: if (flg) {
10125: (*C)->product->api_user = PETSC_TRUE;
10126: PetscCall(MatProductSetType(*C, ptype));
10127: PetscCall(MatProductSetFromOptions(*C));
10128: PetscCall(MatProductSymbolic(*C));
10129: }
10130: PetscCall(MatProductNumeric(*C));
10131: PetscFunctionReturn(PETSC_SUCCESS);
10132: }
10134: /*@
10135: MatMatMult - Performs matrix-matrix multiplication C=A*B.
10137: Neighbor-wise Collective
10139: Input Parameters:
10140: + A - the left matrix
10141: . B - the right matrix
10142: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10143: - 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
10144: if the result is a dense matrix this is irrelevant
10146: Output Parameter:
10147: . C - the product matrix
10149: Notes:
10150: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10152: `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
10153: call to this function with `MAT_INITIAL_MATRIX`.
10155: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10157: 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`,
10158: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10160: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10162: Example of Usage:
10163: .vb
10164: MatProductCreate(A,B,NULL,&C);
10165: MatProductSetType(C,MATPRODUCT_AB);
10166: MatProductSymbolic(C);
10167: MatProductNumeric(C); // compute C=A * B
10168: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10169: MatProductNumeric(C);
10170: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10171: MatProductNumeric(C);
10172: .ve
10174: Level: intermediate
10176: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10177: @*/
10178: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10179: {
10180: PetscFunctionBegin;
10181: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10182: PetscFunctionReturn(PETSC_SUCCESS);
10183: }
10185: /*@
10186: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10188: Neighbor-wise Collective
10190: Input Parameters:
10191: + A - the left matrix
10192: . B - the right matrix
10193: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10194: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10196: Output Parameter:
10197: . C - the product matrix
10199: Options Database Key:
10200: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10201: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10202: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10204: Level: intermediate
10206: Notes:
10207: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10209: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10211: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10212: actually needed.
10214: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10215: and for pairs of `MATMPIDENSE` matrices.
10217: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABt`
10219: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10221: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()` `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10222: @*/
10223: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10224: {
10225: PetscFunctionBegin;
10226: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10227: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10228: PetscFunctionReturn(PETSC_SUCCESS);
10229: }
10231: /*@
10232: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10234: Neighbor-wise Collective
10236: Input Parameters:
10237: + A - the left matrix
10238: . B - the right matrix
10239: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10240: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10242: Output Parameter:
10243: . C - the product matrix
10245: Level: intermediate
10247: Notes:
10248: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10250: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call.
10252: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_AtB`
10254: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10255: actually needed.
10257: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10258: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10260: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10262: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10263: @*/
10264: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10265: {
10266: PetscFunctionBegin;
10267: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10268: PetscFunctionReturn(PETSC_SUCCESS);
10269: }
10271: /*@
10272: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10274: Neighbor-wise Collective
10276: Input Parameters:
10277: + A - the left matrix
10278: . B - the middle matrix
10279: . C - the right matrix
10280: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10281: - 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
10282: if the result is a dense matrix this is irrelevant
10284: Output Parameter:
10285: . D - the product matrix
10287: Level: intermediate
10289: Notes:
10290: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10292: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10294: This routine is shorthand for using `MatProductCreate()` with the `MatProductType` of `MATPRODUCT_ABC`
10296: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10297: actually needed.
10299: If you have many matrices with the same non-zero structure to multiply, you
10300: should use `MAT_REUSE_MATRIX` in all calls but the first
10302: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10304: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10305: @*/
10306: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10307: {
10308: PetscFunctionBegin;
10309: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10310: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10312: if (scall == MAT_INITIAL_MATRIX) {
10313: PetscCall(MatProductCreate(A, B, C, D));
10314: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10315: PetscCall(MatProductSetAlgorithm(*D, "default"));
10316: PetscCall(MatProductSetFill(*D, fill));
10318: (*D)->product->api_user = PETSC_TRUE;
10319: PetscCall(MatProductSetFromOptions(*D));
10320: 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,
10321: ((PetscObject)C)->type_name);
10322: PetscCall(MatProductSymbolic(*D));
10323: } else { /* user may change input matrices when REUSE */
10324: PetscCall(MatProductReplaceMats(A, B, C, *D));
10325: }
10326: PetscCall(MatProductNumeric(*D));
10327: PetscFunctionReturn(PETSC_SUCCESS);
10328: }
10330: /*@
10331: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10333: Collective
10335: Input Parameters:
10336: + mat - the matrix
10337: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10338: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10339: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10341: Output Parameter:
10342: . matredundant - redundant matrix
10344: Level: advanced
10346: Notes:
10347: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10348: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10350: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10351: calling it.
10353: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10355: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10356: @*/
10357: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10358: {
10359: MPI_Comm comm;
10360: PetscMPIInt size;
10361: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10362: Mat_Redundant *redund = NULL;
10363: PetscSubcomm psubcomm = NULL;
10364: MPI_Comm subcomm_in = subcomm;
10365: Mat *matseq;
10366: IS isrow, iscol;
10367: PetscBool newsubcomm = PETSC_FALSE;
10369: PetscFunctionBegin;
10371: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10372: PetscAssertPointer(*matredundant, 5);
10374: }
10376: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10377: if (size == 1 || nsubcomm == 1) {
10378: if (reuse == MAT_INITIAL_MATRIX) {
10379: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10380: } else {
10381: 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");
10382: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10383: }
10384: PetscFunctionReturn(PETSC_SUCCESS);
10385: }
10387: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10388: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10389: MatCheckPreallocated(mat, 1);
10391: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10392: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10393: /* create psubcomm, then get subcomm */
10394: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10395: PetscCallMPI(MPI_Comm_size(comm, &size));
10396: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10398: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10399: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10400: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10401: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10402: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10403: newsubcomm = PETSC_TRUE;
10404: PetscCall(PetscSubcommDestroy(&psubcomm));
10405: }
10407: /* get isrow, iscol and a local sequential matrix matseq[0] */
10408: if (reuse == MAT_INITIAL_MATRIX) {
10409: mloc_sub = PETSC_DECIDE;
10410: nloc_sub = PETSC_DECIDE;
10411: if (bs < 1) {
10412: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10413: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10414: } else {
10415: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10416: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10417: }
10418: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10419: rstart = rend - mloc_sub;
10420: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10421: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10422: PetscCall(ISSetIdentity(iscol));
10423: } else { /* reuse == MAT_REUSE_MATRIX */
10424: 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");
10425: /* retrieve subcomm */
10426: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10427: redund = (*matredundant)->redundant;
10428: isrow = redund->isrow;
10429: iscol = redund->iscol;
10430: matseq = redund->matseq;
10431: }
10432: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10434: /* get matredundant over subcomm */
10435: if (reuse == MAT_INITIAL_MATRIX) {
10436: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10438: /* create a supporting struct and attach it to C for reuse */
10439: PetscCall(PetscNew(&redund));
10440: (*matredundant)->redundant = redund;
10441: redund->isrow = isrow;
10442: redund->iscol = iscol;
10443: redund->matseq = matseq;
10444: if (newsubcomm) {
10445: redund->subcomm = subcomm;
10446: } else {
10447: redund->subcomm = MPI_COMM_NULL;
10448: }
10449: } else {
10450: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10451: }
10452: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10453: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10454: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10455: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10456: }
10457: #endif
10458: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10459: PetscFunctionReturn(PETSC_SUCCESS);
10460: }
10462: /*@C
10463: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10464: a given `Mat`. Each submatrix can span multiple procs.
10466: Collective
10468: Input Parameters:
10469: + mat - the matrix
10470: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10471: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10473: Output Parameter:
10474: . subMat - parallel sub-matrices each spanning a given `subcomm`
10476: Level: advanced
10478: Notes:
10479: The submatrix partition across processors is dictated by `subComm` a
10480: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10481: is not restricted to be grouped with consecutive original MPI processes.
10483: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10484: map directly to the layout of the original matrix [wrt the local
10485: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10486: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10487: the `subMat`. However the offDiagMat looses some columns - and this is
10488: reconstructed with `MatSetValues()`
10490: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10492: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10493: @*/
10494: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10495: {
10496: PetscMPIInt commsize, subCommSize;
10498: PetscFunctionBegin;
10499: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10500: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10501: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10503: 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");
10504: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10505: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10506: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10507: PetscFunctionReturn(PETSC_SUCCESS);
10508: }
10510: /*@
10511: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10513: Not Collective
10515: Input Parameters:
10516: + mat - matrix to extract local submatrix from
10517: . isrow - local row indices for submatrix
10518: - iscol - local column indices for submatrix
10520: Output Parameter:
10521: . submat - the submatrix
10523: Level: intermediate
10525: Notes:
10526: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10528: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10529: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10531: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10532: `MatSetValuesBlockedLocal()` will also be implemented.
10534: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10535: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10537: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10538: @*/
10539: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10540: {
10541: PetscFunctionBegin;
10545: PetscCheckSameComm(isrow, 2, iscol, 3);
10546: PetscAssertPointer(submat, 4);
10547: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10549: if (mat->ops->getlocalsubmatrix) {
10550: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10551: } else {
10552: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10553: }
10554: PetscFunctionReturn(PETSC_SUCCESS);
10555: }
10557: /*@
10558: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10560: Not Collective
10562: Input Parameters:
10563: + mat - matrix to extract local submatrix from
10564: . isrow - local row indices for submatrix
10565: . iscol - local column indices for submatrix
10566: - submat - the submatrix
10568: Level: intermediate
10570: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10571: @*/
10572: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10573: {
10574: PetscFunctionBegin;
10578: PetscCheckSameComm(isrow, 2, iscol, 3);
10579: PetscAssertPointer(submat, 4);
10582: if (mat->ops->restorelocalsubmatrix) {
10583: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10584: } else {
10585: PetscCall(MatDestroy(submat));
10586: }
10587: *submat = NULL;
10588: PetscFunctionReturn(PETSC_SUCCESS);
10589: }
10591: /*@
10592: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10594: Collective
10596: Input Parameter:
10597: . mat - the matrix
10599: Output Parameter:
10600: . is - if any rows have zero diagonals this contains the list of them
10602: Level: developer
10604: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10605: @*/
10606: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10607: {
10608: PetscFunctionBegin;
10611: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10612: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10614: if (!mat->ops->findzerodiagonals) {
10615: Vec diag;
10616: const PetscScalar *a;
10617: PetscInt *rows;
10618: PetscInt rStart, rEnd, r, nrow = 0;
10620: PetscCall(MatCreateVecs(mat, &diag, NULL));
10621: PetscCall(MatGetDiagonal(mat, diag));
10622: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10623: PetscCall(VecGetArrayRead(diag, &a));
10624: for (r = 0; r < rEnd - rStart; ++r)
10625: if (a[r] == 0.0) ++nrow;
10626: PetscCall(PetscMalloc1(nrow, &rows));
10627: nrow = 0;
10628: for (r = 0; r < rEnd - rStart; ++r)
10629: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10630: PetscCall(VecRestoreArrayRead(diag, &a));
10631: PetscCall(VecDestroy(&diag));
10632: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10633: } else {
10634: PetscUseTypeMethod(mat, findzerodiagonals, is);
10635: }
10636: PetscFunctionReturn(PETSC_SUCCESS);
10637: }
10639: /*@
10640: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10642: Collective
10644: Input Parameter:
10645: . mat - the matrix
10647: Output Parameter:
10648: . is - contains the list of rows with off block diagonal entries
10650: Level: developer
10652: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10653: @*/
10654: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10655: {
10656: PetscFunctionBegin;
10659: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10660: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10662: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10663: PetscFunctionReturn(PETSC_SUCCESS);
10664: }
10666: /*@C
10667: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10669: Collective; No Fortran Support
10671: Input Parameter:
10672: . mat - the matrix
10674: Output Parameter:
10675: . values - the block inverses in column major order (FORTRAN-like)
10677: Level: advanced
10679: Notes:
10680: The size of the blocks is determined by the block size of the matrix.
10682: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10684: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10686: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10687: @*/
10688: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10689: {
10690: PetscFunctionBegin;
10692: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10693: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10694: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10695: PetscFunctionReturn(PETSC_SUCCESS);
10696: }
10698: /*@
10699: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10701: Collective; No Fortran Support
10703: Input Parameters:
10704: + mat - the matrix
10705: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10706: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10708: Output Parameter:
10709: . values - the block inverses in column major order (FORTRAN-like)
10711: Level: advanced
10713: Notes:
10714: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10716: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10718: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10719: @*/
10720: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10721: {
10722: PetscFunctionBegin;
10724: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10725: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10726: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10727: PetscFunctionReturn(PETSC_SUCCESS);
10728: }
10730: /*@
10731: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10733: Collective
10735: Input Parameters:
10736: + A - the matrix
10737: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10739: Level: advanced
10741: Note:
10742: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10744: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10745: @*/
10746: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10747: {
10748: const PetscScalar *vals;
10749: PetscInt *dnnz;
10750: PetscInt m, rstart, rend, bs, i, j;
10752: PetscFunctionBegin;
10753: PetscCall(MatInvertBlockDiagonal(A, &vals));
10754: PetscCall(MatGetBlockSize(A, &bs));
10755: PetscCall(MatGetLocalSize(A, &m, NULL));
10756: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10757: if (A->rmap->bs > 1) PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs)); // mpiaij to A and B
10758: PetscCall(PetscMalloc1(m / bs, &dnnz));
10759: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10760: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10761: PetscCall(PetscFree(dnnz));
10762: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10763: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10764: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10765: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10766: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10767: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10768: PetscFunctionReturn(PETSC_SUCCESS);
10769: }
10771: /*@
10772: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10773: via `MatTransposeColoringCreate()`.
10775: Collective
10777: Input Parameter:
10778: . c - coloring context
10780: Level: intermediate
10782: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10783: @*/
10784: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10785: {
10786: MatTransposeColoring matcolor = *c;
10788: PetscFunctionBegin;
10789: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10790: if (--((PetscObject)matcolor)->refct > 0) {
10791: matcolor = NULL;
10792: PetscFunctionReturn(PETSC_SUCCESS);
10793: }
10795: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10796: PetscCall(PetscFree(matcolor->rows));
10797: PetscCall(PetscFree(matcolor->den2sp));
10798: PetscCall(PetscFree(matcolor->colorforcol));
10799: PetscCall(PetscFree(matcolor->columns));
10800: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
10801: PetscCall(PetscHeaderDestroy(c));
10802: PetscFunctionReturn(PETSC_SUCCESS);
10803: }
10805: /*@
10806: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
10807: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
10808: `MatTransposeColoring` to sparse `B`.
10810: Collective
10812: Input Parameters:
10813: + coloring - coloring context created with `MatTransposeColoringCreate()`
10814: - B - sparse matrix
10816: Output Parameter:
10817: . Btdense - dense matrix $B^T$
10819: Level: developer
10821: Note:
10822: These are used internally for some implementations of `MatRARt()`
10824: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
10825: @*/
10826: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
10827: {
10828: PetscFunctionBegin;
10833: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
10834: PetscFunctionReturn(PETSC_SUCCESS);
10835: }
10837: /*@
10838: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
10839: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
10840: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
10841: $C_{sp}$ from $C_{den}$.
10843: Collective
10845: Input Parameters:
10846: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
10847: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
10849: Output Parameter:
10850: . Csp - sparse matrix
10852: Level: developer
10854: Note:
10855: These are used internally for some implementations of `MatRARt()`
10857: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
10858: @*/
10859: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
10860: {
10861: PetscFunctionBegin;
10866: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
10867: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
10868: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
10869: PetscFunctionReturn(PETSC_SUCCESS);
10870: }
10872: /*@
10873: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
10875: Collective
10877: Input Parameters:
10878: + mat - the matrix product C
10879: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
10881: Output Parameter:
10882: . color - the new coloring context
10884: Level: intermediate
10886: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
10887: `MatTransColoringApplyDenToSp()`
10888: @*/
10889: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
10890: {
10891: MatTransposeColoring c;
10892: MPI_Comm comm;
10894: PetscFunctionBegin;
10895: PetscAssertPointer(color, 3);
10897: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10898: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10899: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
10900: c->ctype = iscoloring->ctype;
10901: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
10902: *color = c;
10903: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
10904: PetscFunctionReturn(PETSC_SUCCESS);
10905: }
10907: /*@
10908: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
10909: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
10911: Not Collective
10913: Input Parameter:
10914: . mat - the matrix
10916: Output Parameter:
10917: . state - the current state
10919: Level: intermediate
10921: Notes:
10922: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
10923: different matrices
10925: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
10927: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
10929: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
10930: @*/
10931: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
10932: {
10933: PetscFunctionBegin;
10935: *state = mat->nonzerostate;
10936: PetscFunctionReturn(PETSC_SUCCESS);
10937: }
10939: /*@
10940: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
10941: matrices from each processor
10943: Collective
10945: Input Parameters:
10946: + comm - the communicators the parallel matrix will live on
10947: . seqmat - the input sequential matrices
10948: . n - number of local columns (or `PETSC_DECIDE`)
10949: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10951: Output Parameter:
10952: . mpimat - the parallel matrix generated
10954: Level: developer
10956: Note:
10957: The number of columns of the matrix in EACH processor MUST be the same.
10959: .seealso: [](ch_matrices), `Mat`
10960: @*/
10961: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
10962: {
10963: PetscMPIInt size;
10965: PetscFunctionBegin;
10966: PetscCallMPI(MPI_Comm_size(comm, &size));
10967: if (size == 1) {
10968: if (reuse == MAT_INITIAL_MATRIX) {
10969: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
10970: } else {
10971: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
10972: }
10973: PetscFunctionReturn(PETSC_SUCCESS);
10974: }
10976: 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");
10978: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
10979: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
10980: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
10981: PetscFunctionReturn(PETSC_SUCCESS);
10982: }
10984: /*@
10985: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
10987: Collective
10989: Input Parameters:
10990: + A - the matrix to create subdomains from
10991: - N - requested number of subdomains
10993: Output Parameters:
10994: + n - number of subdomains resulting on this MPI process
10995: - iss - `IS` list with indices of subdomains on this MPI process
10997: Level: advanced
10999: Note:
11000: The number of subdomains must be smaller than the communicator size
11002: .seealso: [](ch_matrices), `Mat`, `IS`
11003: @*/
11004: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11005: {
11006: MPI_Comm comm, subcomm;
11007: PetscMPIInt size, rank, color;
11008: PetscInt rstart, rend, k;
11010: PetscFunctionBegin;
11011: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11012: PetscCallMPI(MPI_Comm_size(comm, &size));
11013: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11014: 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);
11015: *n = 1;
11016: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11017: color = rank / k;
11018: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11019: PetscCall(PetscMalloc1(1, iss));
11020: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11021: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11022: PetscCallMPI(MPI_Comm_free(&subcomm));
11023: PetscFunctionReturn(PETSC_SUCCESS);
11024: }
11026: /*@
11027: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11029: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11030: If they are not the same, uses `MatMatMatMult()`.
11032: Once the coarse grid problem is constructed, correct for interpolation operators
11033: that are not of full rank, which can legitimately happen in the case of non-nested
11034: geometric multigrid.
11036: Input Parameters:
11037: + restrct - restriction operator
11038: . dA - fine grid matrix
11039: . interpolate - interpolation operator
11040: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11041: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11043: Output Parameter:
11044: . A - the Galerkin coarse matrix
11046: Options Database Key:
11047: . -pc_mg_galerkin <both,pmat,mat,none> - for what matrices the Galerkin process should be used
11049: Level: developer
11051: Note:
11052: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11054: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11055: @*/
11056: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11057: {
11058: IS zerorows;
11059: Vec diag;
11061: PetscFunctionBegin;
11062: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11063: /* Construct the coarse grid matrix */
11064: if (interpolate == restrct) {
11065: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11066: } else {
11067: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11068: }
11070: /* If the interpolation matrix is not of full rank, A will have zero rows.
11071: This can legitimately happen in the case of non-nested geometric multigrid.
11072: In that event, we set the rows of the matrix to the rows of the identity,
11073: ignoring the equations (as the RHS will also be zero). */
11075: PetscCall(MatFindZeroRows(*A, &zerorows));
11077: if (zerorows != NULL) { /* if there are any zero rows */
11078: PetscCall(MatCreateVecs(*A, &diag, NULL));
11079: PetscCall(MatGetDiagonal(*A, diag));
11080: PetscCall(VecISSet(diag, zerorows, 1.0));
11081: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11082: PetscCall(VecDestroy(&diag));
11083: PetscCall(ISDestroy(&zerorows));
11084: }
11085: PetscFunctionReturn(PETSC_SUCCESS);
11086: }
11088: /*@C
11089: MatSetOperation - Allows user to set a matrix operation for any matrix type
11091: Logically Collective
11093: Input Parameters:
11094: + mat - the matrix
11095: . op - the name of the operation
11096: - f - the function that provides the operation
11098: Level: developer
11100: Example Usage:
11101: .vb
11102: extern PetscErrorCode usermult(Mat, Vec, Vec);
11104: PetscCall(MatCreateXXX(comm, ..., &A));
11105: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscVoidFn *)usermult));
11106: .ve
11108: Notes:
11109: See the file `include/petscmat.h` for a complete list of matrix
11110: operations, which all have the form MATOP_<OPERATION>, where
11111: <OPERATION> is the name (in all capital letters) of the
11112: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11114: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11115: sequence as the usual matrix interface routines, since they
11116: are intended to be accessed via the usual matrix interface
11117: routines, e.g.,
11118: .vb
11119: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11120: .ve
11122: In particular each function MUST return `PETSC_SUCCESS` on success and
11123: nonzero on failure.
11125: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11127: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11128: @*/
11129: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, void (*f)(void))
11130: {
11131: PetscFunctionBegin;
11133: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (void (*)(void))mat->ops->view) mat->ops->viewnative = mat->ops->view;
11134: (((void (**)(void))mat->ops)[op]) = f;
11135: PetscFunctionReturn(PETSC_SUCCESS);
11136: }
11138: /*@C
11139: MatGetOperation - Gets a matrix operation for any matrix type.
11141: Not Collective
11143: Input Parameters:
11144: + mat - the matrix
11145: - op - the name of the operation
11147: Output Parameter:
11148: . f - the function that provides the operation
11150: Level: developer
11152: Example Usage:
11153: .vb
11154: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11156: MatGetOperation(A, MATOP_MULT, (void (**)(void))&usermult);
11157: .ve
11159: Notes:
11160: See the file include/petscmat.h for a complete list of matrix
11161: operations, which all have the form MATOP_<OPERATION>, where
11162: <OPERATION> is the name (in all capital letters) of the
11163: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11165: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11167: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11168: @*/
11169: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, void (**f)(void))
11170: {
11171: PetscFunctionBegin;
11173: *f = (((void (**)(void))mat->ops)[op]);
11174: PetscFunctionReturn(PETSC_SUCCESS);
11175: }
11177: /*@
11178: MatHasOperation - Determines whether the given matrix supports the particular operation.
11180: Not Collective
11182: Input Parameters:
11183: + mat - the matrix
11184: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11186: Output Parameter:
11187: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11189: Level: advanced
11191: Note:
11192: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11194: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11195: @*/
11196: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11197: {
11198: PetscFunctionBegin;
11200: PetscAssertPointer(has, 3);
11201: if (mat->ops->hasoperation) {
11202: PetscUseTypeMethod(mat, hasoperation, op, has);
11203: } else {
11204: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11205: else {
11206: *has = PETSC_FALSE;
11207: if (op == MATOP_CREATE_SUBMATRIX) {
11208: PetscMPIInt size;
11210: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11211: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11212: }
11213: }
11214: }
11215: PetscFunctionReturn(PETSC_SUCCESS);
11216: }
11218: /*@
11219: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11221: Collective
11223: Input Parameter:
11224: . mat - the matrix
11226: Output Parameter:
11227: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11229: Level: beginner
11231: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11232: @*/
11233: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11234: {
11235: PetscFunctionBegin;
11238: PetscAssertPointer(cong, 2);
11239: if (!mat->rmap || !mat->cmap) {
11240: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11241: PetscFunctionReturn(PETSC_SUCCESS);
11242: }
11243: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11244: PetscCall(PetscLayoutSetUp(mat->rmap));
11245: PetscCall(PetscLayoutSetUp(mat->cmap));
11246: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11247: if (*cong) mat->congruentlayouts = 1;
11248: else mat->congruentlayouts = 0;
11249: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11250: PetscFunctionReturn(PETSC_SUCCESS);
11251: }
11253: PetscErrorCode MatSetInf(Mat A)
11254: {
11255: PetscFunctionBegin;
11256: PetscUseTypeMethod(A, setinf);
11257: PetscFunctionReturn(PETSC_SUCCESS);
11258: }
11260: /*@
11261: 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
11262: and possibly removes small values from the graph structure.
11264: Collective
11266: Input Parameters:
11267: + A - the matrix
11268: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11269: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11270: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11271: . num_idx - size of 'index' array
11272: - index - array of block indices to use for graph strength of connection weight
11274: Output Parameter:
11275: . graph - the resulting graph
11277: Level: advanced
11279: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11280: @*/
11281: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11282: {
11283: PetscFunctionBegin;
11287: PetscAssertPointer(graph, 7);
11288: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11289: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11290: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11291: PetscFunctionReturn(PETSC_SUCCESS);
11292: }
11294: /*@
11295: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11296: meaning the same memory is used for the matrix, and no new memory is allocated.
11298: Collective
11300: Input Parameters:
11301: + A - the matrix
11302: - 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
11304: Level: intermediate
11306: Developer Note:
11307: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11308: of the arrays in the data structure are unneeded.
11310: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11311: @*/
11312: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11313: {
11314: PetscFunctionBegin;
11316: PetscUseTypeMethod(A, eliminatezeros, keep);
11317: PetscFunctionReturn(PETSC_SUCCESS);
11318: }