Actual source code: matrix.c
1: /*
2: This is where the abstract matrix operations are defined
3: Portions of this code are under:
4: Copyright (c) 2022 Advanced Micro Devices, Inc. All rights reserved.
5: */
7: #include <petsc/private/matimpl.h>
8: #include <petsc/private/isimpl.h>
9: #include <petsc/private/vecimpl.h>
11: /* Logging support */
12: PetscClassId MAT_CLASSID;
13: PetscClassId MAT_COLORING_CLASSID;
14: PetscClassId MAT_FDCOLORING_CLASSID;
15: PetscClassId MAT_TRANSPOSECOLORING_CLASSID;
17: PetscLogEvent MAT_Mult, MAT_MultAdd, MAT_MultTranspose;
18: PetscLogEvent MAT_ADot, MAT_ANorm;
19: PetscLogEvent MAT_MultTransposeAdd, MAT_Solve, MAT_Solves, MAT_SolveAdd, MAT_SolveTranspose, MAT_MatSolve, MAT_MatTrSolve;
20: PetscLogEvent MAT_SolveTransposeAdd, MAT_SOR, MAT_ForwardSolve, MAT_BackwardSolve, MAT_LUFactor, MAT_LUFactorSymbolic;
21: PetscLogEvent MAT_LUFactorNumeric, MAT_CholeskyFactor, MAT_CholeskyFactorSymbolic, MAT_CholeskyFactorNumeric, MAT_ILUFactor;
22: PetscLogEvent MAT_ILUFactorSymbolic, MAT_ICCFactorSymbolic, MAT_Copy, MAT_Convert, MAT_Scale, MAT_AssemblyBegin;
23: PetscLogEvent MAT_QRFactorNumeric, MAT_QRFactorSymbolic, MAT_QRFactor;
24: PetscLogEvent MAT_AssemblyEnd, MAT_SetValues, MAT_GetValues, MAT_GetRow, MAT_GetRowIJ, MAT_CreateSubMats, MAT_GetOrdering, MAT_RedundantMat, MAT_GetSeqNonzeroStructure;
25: PetscLogEvent MAT_IncreaseOverlap, MAT_Partitioning, MAT_PartitioningND, MAT_Coarsen, MAT_ZeroEntries, MAT_Load, MAT_View, MAT_AXPY, MAT_FDColoringCreate;
26: PetscLogEvent MAT_FDColoringSetUp, MAT_FDColoringApply, MAT_Transpose, MAT_FDColoringFunction, MAT_CreateSubMat;
27: PetscLogEvent MAT_TransposeColoringCreate;
28: PetscLogEvent MAT_MatMult, MAT_MatMultSymbolic, MAT_MatMultNumeric;
29: PetscLogEvent MAT_PtAP, MAT_PtAPSymbolic, MAT_PtAPNumeric, MAT_RARt, MAT_RARtSymbolic, MAT_RARtNumeric;
30: PetscLogEvent MAT_MatTransposeMult, MAT_MatTransposeMultSymbolic, MAT_MatTransposeMultNumeric;
31: PetscLogEvent MAT_TransposeMatMult, MAT_TransposeMatMultSymbolic, MAT_TransposeMatMultNumeric;
32: PetscLogEvent MAT_MatMatMult, MAT_MatMatMultSymbolic, MAT_MatMatMultNumeric;
33: PetscLogEvent MAT_MultHermitianTranspose, MAT_MultHermitianTransposeAdd;
34: PetscLogEvent MAT_Getsymtransreduced, MAT_GetBrowsOfAcols;
35: PetscLogEvent MAT_GetBrowsOfAocols, MAT_Getlocalmat, MAT_Getlocalmatcondensed, MAT_Seqstompi, MAT_Seqstompinum, MAT_Seqstompisym;
36: PetscLogEvent MAT_GetMultiProcBlock;
37: PetscLogEvent MAT_CUSPARSECopyToGPU, MAT_CUSPARSECopyFromGPU, MAT_CUSPARSEGenerateTranspose, MAT_CUSPARSESolveAnalysis;
38: PetscLogEvent MAT_HIPSPARSECopyToGPU, MAT_HIPSPARSECopyFromGPU, MAT_HIPSPARSEGenerateTranspose, MAT_HIPSPARSESolveAnalysis;
39: PetscLogEvent MAT_PreallCOO, MAT_SetVCOO;
40: PetscLogEvent MAT_CreateGraph;
41: PetscLogEvent MAT_SetValuesBatch;
42: PetscLogEvent MAT_ViennaCLCopyToGPU;
43: PetscLogEvent MAT_CUDACopyToGPU, MAT_HIPCopyToGPU;
44: PetscLogEvent MAT_DenseCopyToGPU, MAT_DenseCopyFromGPU;
45: PetscLogEvent MAT_Merge, MAT_Residual, MAT_SetRandom;
46: PetscLogEvent MAT_FactorFactS, MAT_FactorInvS;
47: PetscLogEvent MATCOLORING_Apply, MATCOLORING_Comm, MATCOLORING_Local, MATCOLORING_ISCreate, MATCOLORING_SetUp, MATCOLORING_Weights;
48: PetscLogEvent MAT_H2Opus_Build, MAT_H2Opus_Compress, MAT_H2Opus_Orthog, MAT_H2Opus_LR;
50: const char *const MatFactorTypes[] = {"NONE", "LU", "CHOLESKY", "ILU", "ICC", "ILUDT", "QR", "MatFactorType", "MAT_FACTOR_", NULL};
52: /*@
53: MatSetRandom - Sets all components of a matrix to random numbers.
55: Logically Collective
57: Input Parameters:
58: + x - the matrix
59: - rctx - the `PetscRandom` object, formed by `PetscRandomCreate()`, or `NULL` and
60: it will create one internally.
62: Example:
63: .vb
64: PetscRandomCreate(PETSC_COMM_WORLD,&rctx);
65: MatSetRandom(x,rctx);
66: PetscRandomDestroy(rctx);
67: .ve
69: Level: intermediate
71: Notes:
72: For sparse matrices that have been preallocated but not been assembled, it randomly selects appropriate locations,
74: for sparse matrices that already have nonzero locations, it fills the locations with random numbers.
76: It generates an error if used on unassembled sparse matrices that have not been preallocated.
78: .seealso: [](ch_matrices), `Mat`, `PetscRandom`, `PetscRandomCreate()`, `MatZeroEntries()`, `MatSetValues()`, `PetscRandomDestroy()`
79: @*/
80: PetscErrorCode MatSetRandom(Mat x, PetscRandom rctx)
81: {
82: PetscRandom randObj = NULL;
84: PetscFunctionBegin;
88: MatCheckPreallocated(x, 1);
90: if (!rctx) {
91: MPI_Comm comm;
92: PetscCall(PetscObjectGetComm((PetscObject)x, &comm));
93: PetscCall(PetscRandomCreate(comm, &randObj));
94: PetscCall(PetscRandomSetType(randObj, x->defaultrandtype));
95: PetscCall(PetscRandomSetFromOptions(randObj));
96: rctx = randObj;
97: }
98: PetscCall(PetscLogEventBegin(MAT_SetRandom, x, rctx, 0, 0));
99: PetscUseTypeMethod(x, setrandom, rctx);
100: PetscCall(PetscLogEventEnd(MAT_SetRandom, x, rctx, 0, 0));
102: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
103: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
104: PetscCall(PetscRandomDestroy(&randObj));
105: PetscFunctionReturn(PETSC_SUCCESS);
106: }
108: /*@
109: MatCopyHashToXAIJ - copy hash table entries into an XAIJ matrix type
111: Logically Collective
113: Input Parameter:
114: . A - A matrix in unassembled, hash table form
116: Output Parameter:
117: . B - The XAIJ matrix. This can either be `A` or some matrix of equivalent size, e.g. obtained from `A` via `MatDuplicate()`
119: Example:
120: .vb
121: PetscCall(MatDuplicate(A, MAT_DO_NOT_COPY_VALUES, &B));
122: PetscCall(MatCopyHashToXAIJ(A, B));
123: .ve
125: Level: advanced
127: Notes:
128: If `B` is `A`, then the hash table data structure will be destroyed. `B` is assembled
130: .seealso: [](ch_matrices), `Mat`, `MAT_USE_HASH_TABLE`
131: @*/
132: PetscErrorCode MatCopyHashToXAIJ(Mat A, Mat B)
133: {
134: PetscFunctionBegin;
136: PetscUseTypeMethod(A, copyhashtoxaij, B);
137: PetscFunctionReturn(PETSC_SUCCESS);
138: }
140: /*@
141: MatFactorGetErrorZeroPivot - returns the pivot value that was determined to be zero and the row it occurred in
143: Logically Collective
145: Input Parameter:
146: . mat - the factored matrix
148: Output Parameters:
149: + pivot - the pivot value computed
150: - row - the row that the zero pivot occurred. This row value must be interpreted carefully due to row reorderings and which processes
151: the share the matrix
153: Level: advanced
155: Notes:
156: This routine does not work for factorizations done with external packages.
158: This routine should only be called if `MatGetFactorError()` returns a value of `MAT_FACTOR_NUMERIC_ZEROPIVOT`
160: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
162: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`,
163: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorClearError()`,
164: `MAT_FACTOR_NUMERIC_ZEROPIVOT`
165: @*/
166: PetscErrorCode MatFactorGetErrorZeroPivot(Mat mat, PetscReal *pivot, PetscInt *row)
167: {
168: PetscFunctionBegin;
170: PetscAssertPointer(pivot, 2);
171: PetscAssertPointer(row, 3);
172: *pivot = mat->factorerror_zeropivot_value;
173: *row = mat->factorerror_zeropivot_row;
174: PetscFunctionReturn(PETSC_SUCCESS);
175: }
177: /*@
178: MatFactorGetError - gets the error code from a factorization
180: Logically Collective
182: Input Parameter:
183: . mat - the factored matrix
185: Output Parameter:
186: . err - the error code
188: Level: advanced
190: Note:
191: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
193: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`,
194: `MatFactorClearError()`, `MatFactorGetErrorZeroPivot()`, `MatFactorError`
195: @*/
196: PetscErrorCode MatFactorGetError(Mat mat, MatFactorError *err)
197: {
198: PetscFunctionBegin;
200: PetscAssertPointer(err, 2);
201: *err = mat->factorerrortype;
202: PetscFunctionReturn(PETSC_SUCCESS);
203: }
205: /*@
206: MatFactorClearError - clears the error code in a factorization
208: Logically Collective
210: Input Parameter:
211: . mat - the factored matrix
213: Level: developer
215: Note:
216: This can also be called on non-factored matrices that come from, for example, matrices used in SOR.
218: .seealso: [](ch_matrices), `Mat`, `MatZeroEntries()`, `MatFactor()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatFactorGetError()`, `MatFactorGetErrorZeroPivot()`,
219: `MatGetErrorCode()`, `MatFactorError`
220: @*/
221: PetscErrorCode MatFactorClearError(Mat mat)
222: {
223: PetscFunctionBegin;
225: mat->factorerrortype = MAT_FACTOR_NOERROR;
226: mat->factorerror_zeropivot_value = 0.0;
227: mat->factorerror_zeropivot_row = 0;
228: PetscFunctionReturn(PETSC_SUCCESS);
229: }
231: PetscErrorCode MatFindNonzeroRowsOrCols_Basic(Mat mat, PetscBool cols, PetscReal tol, IS *nonzero)
232: {
233: Vec r, l;
234: const PetscScalar *al;
235: PetscInt i, nz, gnz, N, n, st;
237: PetscFunctionBegin;
238: PetscCall(MatCreateVecs(mat, &r, &l));
239: if (!cols) { /* nonzero rows */
240: PetscCall(MatGetOwnershipRange(mat, &st, NULL));
241: PetscCall(MatGetSize(mat, &N, NULL));
242: PetscCall(MatGetLocalSize(mat, &n, NULL));
243: PetscCall(VecSet(l, 0.0));
244: PetscCall(VecSetRandom(r, NULL));
245: PetscCall(MatMult(mat, r, l));
246: PetscCall(VecGetArrayRead(l, &al));
247: } else { /* nonzero columns */
248: PetscCall(MatGetOwnershipRangeColumn(mat, &st, NULL));
249: PetscCall(MatGetSize(mat, NULL, &N));
250: PetscCall(MatGetLocalSize(mat, NULL, &n));
251: PetscCall(VecSet(r, 0.0));
252: PetscCall(VecSetRandom(l, NULL));
253: PetscCall(MatMultTranspose(mat, l, r));
254: PetscCall(VecGetArrayRead(r, &al));
255: }
256: if (tol <= 0.0) {
257: for (i = 0, nz = 0; i < n; i++)
258: if (al[i] != 0.0) nz++;
259: } else {
260: for (i = 0, nz = 0; i < n; i++)
261: if (PetscAbsScalar(al[i]) > tol) nz++;
262: }
263: PetscCallMPI(MPIU_Allreduce(&nz, &gnz, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
264: if (gnz != N) {
265: PetscInt *nzr;
266: PetscCall(PetscMalloc1(nz, &nzr));
267: if (nz) {
268: if (tol < 0) {
269: for (i = 0, nz = 0; i < n; i++)
270: if (al[i] != 0.0) nzr[nz++] = i + st;
271: } else {
272: for (i = 0, nz = 0; i < n; i++)
273: if (PetscAbsScalar(al[i]) > tol) nzr[nz++] = i + st;
274: }
275: }
276: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nz, nzr, PETSC_OWN_POINTER, nonzero));
277: } else *nonzero = NULL;
278: if (!cols) { /* nonzero rows */
279: PetscCall(VecRestoreArrayRead(l, &al));
280: } else {
281: PetscCall(VecRestoreArrayRead(r, &al));
282: }
283: PetscCall(VecDestroy(&l));
284: PetscCall(VecDestroy(&r));
285: PetscFunctionReturn(PETSC_SUCCESS);
286: }
288: /*@
289: MatFindNonzeroRows - Locate all rows that are not completely zero in the matrix
291: Input Parameter:
292: . mat - the matrix
294: Output Parameter:
295: . keptrows - the rows that are not completely zero
297: Level: intermediate
299: Note:
300: `keptrows` is set to `NULL` if all rows are nonzero.
302: Developer Note:
303: If `keptrows` is not `NULL`, it must be sorted.
305: .seealso: [](ch_matrices), `Mat`, `MatFindZeroRows()`
306: @*/
307: PetscErrorCode MatFindNonzeroRows(Mat mat, IS *keptrows)
308: {
309: PetscFunctionBegin;
312: PetscAssertPointer(keptrows, 2);
313: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
314: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
315: if (mat->ops->findnonzerorows) PetscUseTypeMethod(mat, findnonzerorows, keptrows);
316: else PetscCall(MatFindNonzeroRowsOrCols_Basic(mat, PETSC_FALSE, 0.0, keptrows));
317: if (keptrows && *keptrows) PetscCall(ISSetInfo(*keptrows, IS_SORTED, IS_GLOBAL, PETSC_FALSE, PETSC_TRUE));
318: PetscFunctionReturn(PETSC_SUCCESS);
319: }
321: /*@
322: MatFindZeroRows - Locate all rows that are completely zero in the matrix
324: Input Parameter:
325: . mat - the matrix
327: Output Parameter:
328: . zerorows - the rows that are completely zero
330: Level: intermediate
332: Note:
333: `zerorows` is set to `NULL` if no rows are zero.
335: .seealso: [](ch_matrices), `Mat`, `MatFindNonzeroRows()`
336: @*/
337: PetscErrorCode MatFindZeroRows(Mat mat, IS *zerorows)
338: {
339: IS keptrows;
340: PetscInt m, n;
342: PetscFunctionBegin;
345: PetscAssertPointer(zerorows, 2);
346: PetscCall(MatFindNonzeroRows(mat, &keptrows));
347: /* MatFindNonzeroRows sets keptrows to NULL if there are no zero rows.
348: In keeping with this convention, we set zerorows to NULL if there are no zero
349: rows. */
350: if (keptrows == NULL) {
351: *zerorows = NULL;
352: } else {
353: PetscCall(MatGetOwnershipRange(mat, &m, &n));
354: PetscCall(ISComplement(keptrows, m, n, zerorows));
355: PetscCall(ISDestroy(&keptrows));
356: }
357: PetscFunctionReturn(PETSC_SUCCESS);
358: }
360: /*@
361: MatGetDiagonalBlock - Returns the part of the matrix associated with the on-process coupling
363: Not Collective
365: Input Parameter:
366: . A - the matrix
368: Output Parameter:
369: . a - the diagonal part (which is a SEQUENTIAL matrix)
371: Level: advanced
373: Notes:
374: See `MatCreateAIJ()` for more information on the "diagonal part" of the matrix.
376: Use caution, as the reference count on the returned matrix is not incremented and it is used as part of `A`'s normal operation.
378: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MATAIJ`, `MATBAIJ`, `MATSBAIJ`
379: @*/
380: PetscErrorCode MatGetDiagonalBlock(Mat A, Mat *a)
381: {
382: PetscFunctionBegin;
385: PetscAssertPointer(a, 2);
386: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
387: if (A->ops->getdiagonalblock) PetscUseTypeMethod(A, getdiagonalblock, a);
388: else {
389: PetscMPIInt size;
391: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
392: PetscCheck(size == 1, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Not for parallel matrix type %s", ((PetscObject)A)->type_name);
393: *a = A;
394: }
395: PetscFunctionReturn(PETSC_SUCCESS);
396: }
398: /*@
399: MatGetTrace - Gets the trace of a matrix. The sum of the diagonal entries.
401: Collective
403: Input Parameter:
404: . mat - the matrix
406: Output Parameter:
407: . trace - the sum of the diagonal entries
409: Level: advanced
411: .seealso: [](ch_matrices), `Mat`
412: @*/
413: PetscErrorCode MatGetTrace(Mat mat, PetscScalar *trace)
414: {
415: Vec diag;
417: PetscFunctionBegin;
419: PetscAssertPointer(trace, 2);
420: PetscCall(MatCreateVecs(mat, &diag, NULL));
421: PetscCall(MatGetDiagonal(mat, diag));
422: PetscCall(VecSum(diag, trace));
423: PetscCall(VecDestroy(&diag));
424: PetscFunctionReturn(PETSC_SUCCESS);
425: }
427: /*@
428: MatRealPart - Zeros out the imaginary part of the matrix
430: Logically Collective
432: Input Parameter:
433: . mat - the matrix
435: Level: advanced
437: .seealso: [](ch_matrices), `Mat`, `MatImaginaryPart()`
438: @*/
439: PetscErrorCode MatRealPart(Mat mat)
440: {
441: PetscFunctionBegin;
444: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
445: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
446: MatCheckPreallocated(mat, 1);
447: PetscUseTypeMethod(mat, realpart);
448: PetscFunctionReturn(PETSC_SUCCESS);
449: }
451: /*@C
452: MatGetGhosts - Get the global indices of all ghost nodes defined by the sparse matrix
454: Collective
456: Input Parameter:
457: . mat - the matrix
459: Output Parameters:
460: + nghosts - number of ghosts (for `MATBAIJ` and `MATSBAIJ` matrices there is one ghost for each matrix block)
461: - ghosts - the global indices of the ghost points
463: Level: advanced
465: Note:
466: `nghosts` and `ghosts` are suitable to pass into `VecCreateGhost()` or `VecCreateGhostBlock()`
468: .seealso: [](ch_matrices), `Mat`, `VecCreateGhost()`, `VecCreateGhostBlock()`
469: @*/
470: PetscErrorCode MatGetGhosts(Mat mat, PetscInt *nghosts, const PetscInt *ghosts[])
471: {
472: PetscFunctionBegin;
475: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
476: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
477: if (mat->ops->getghosts) PetscUseTypeMethod(mat, getghosts, nghosts, ghosts);
478: else {
479: if (nghosts) *nghosts = 0;
480: if (ghosts) *ghosts = NULL;
481: }
482: PetscFunctionReturn(PETSC_SUCCESS);
483: }
485: /*@
486: MatImaginaryPart - Moves the imaginary part of the matrix to the real part and zeros the imaginary part
488: Logically Collective
490: Input Parameter:
491: . mat - the matrix
493: Level: advanced
495: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`
496: @*/
497: PetscErrorCode MatImaginaryPart(Mat mat)
498: {
499: PetscFunctionBegin;
502: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
503: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
504: MatCheckPreallocated(mat, 1);
505: PetscUseTypeMethod(mat, imaginarypart);
506: PetscFunctionReturn(PETSC_SUCCESS);
507: }
509: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
510: /*@C
511: MatGetRow - Gets a row of a matrix. You MUST call `MatRestoreRow()`
512: for each row that you get to ensure that your application does
513: not bleed memory.
515: Not Collective
517: Input Parameters:
518: + mat - the matrix
519: - row - the row to get
521: Output Parameters:
522: + ncols - if not `NULL`, the number of nonzeros in `row`
523: . cols - if not `NULL`, the column numbers
524: - vals - if not `NULL`, the numerical values
526: Level: advanced
528: Notes:
529: This routine is provided for people who need to have direct access
530: to the structure of a matrix. We hope that we provide enough
531: high-level matrix routines that few users will need it.
533: `MatGetRow()` always returns 0-based column indices, regardless of
534: whether the internal representation is 0-based (default) or 1-based.
536: For better efficiency, set `cols` and/or `vals` to `NULL` if you do
537: not wish to extract these quantities.
539: The user can only examine the values extracted with `MatGetRow()`;
540: the values CANNOT be altered. To change the matrix entries, one
541: must use `MatSetValues()`.
543: You can only have one call to `MatGetRow()` outstanding for a particular
544: matrix at a time, per processor. `MatGetRow()` can only obtain rows
545: associated with the given processor, it cannot get rows from the
546: other processors; for that we suggest using `MatCreateSubMatrices()`, then
547: `MatGetRow()` on the submatrix. The row index passed to `MatGetRow()`
548: is in the global number of rows.
550: Use `MatGetRowIJ()` and `MatRestoreRowIJ()` to access all the local indices of the sparse matrix.
552: Use `MatSeqAIJGetArray()` and similar functions to access the numerical values for certain matrix types directly.
554: Fortran Note:
555: .vb
556: PetscInt, pointer :: cols(:)
557: PetscScalar, pointer :: vals(:)
558: .ve
560: .seealso: [](ch_matrices), `Mat`, `MatRestoreRow()`, `MatSetValues()`, `MatGetValues()`, `MatCreateSubMatrices()`, `MatGetDiagonal()`, `MatGetRowIJ()`, `MatRestoreRowIJ()`
561: @*/
562: PetscErrorCode MatGetRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
563: {
564: PetscInt incols;
566: PetscFunctionBegin;
569: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
570: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
571: MatCheckPreallocated(mat, 1);
572: 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);
573: PetscCall(PetscLogEventBegin(MAT_GetRow, mat, 0, 0, 0));
574: PetscUseTypeMethod(mat, getrow, row, &incols, (PetscInt **)cols, (PetscScalar **)vals);
575: if (ncols) *ncols = incols;
576: PetscCall(PetscLogEventEnd(MAT_GetRow, mat, 0, 0, 0));
577: PetscFunctionReturn(PETSC_SUCCESS);
578: }
580: /*@
581: MatConjugate - replaces the matrix values with their complex conjugates
583: Logically Collective
585: Input Parameter:
586: . mat - the matrix
588: Level: advanced
590: .seealso: [](ch_matrices), `Mat`, `MatRealPart()`, `MatImaginaryPart()`, `VecConjugate()`, `MatTranspose()`
591: @*/
592: PetscErrorCode MatConjugate(Mat mat)
593: {
594: PetscFunctionBegin;
596: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
597: if (PetscDefined(USE_COMPLEX) && !(mat->symmetric == PETSC_BOOL3_TRUE && mat->hermitian == PETSC_BOOL3_TRUE)) {
598: PetscUseTypeMethod(mat, conjugate);
599: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
600: }
601: PetscFunctionReturn(PETSC_SUCCESS);
602: }
604: /*@C
605: MatRestoreRow - Frees any temporary space allocated by `MatGetRow()`.
607: Not Collective
609: Input Parameters:
610: + mat - the matrix
611: . row - the row to get
612: . ncols - the number of nonzeros
613: . cols - the columns of the nonzeros
614: - vals - if nonzero the column values
616: Level: advanced
618: Notes:
619: This routine should be called after you have finished examining the entries.
621: This routine zeros out `ncols`, `cols`, and `vals`. This is to prevent accidental
622: us of the array after it has been restored. If you pass `NULL`, it will
623: not zero the pointers. Use of `cols` or `vals` after `MatRestoreRow()` is invalid.
625: Fortran Note:
626: .vb
627: PetscInt, pointer :: cols(:)
628: PetscScalar, pointer :: vals(:)
629: .ve
631: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`
632: @*/
633: PetscErrorCode MatRestoreRow(Mat mat, PetscInt row, PetscInt *ncols, const PetscInt *cols[], const PetscScalar *vals[])
634: {
635: PetscFunctionBegin;
637: if (ncols) PetscAssertPointer(ncols, 3);
638: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
639: PetscTryTypeMethod(mat, restorerow, row, ncols, (PetscInt **)cols, (PetscScalar **)vals);
640: if (ncols) *ncols = 0;
641: if (cols) *cols = NULL;
642: if (vals) *vals = NULL;
643: PetscFunctionReturn(PETSC_SUCCESS);
644: }
646: /*@
647: MatGetRowUpperTriangular - Sets a flag to enable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
648: You should call `MatRestoreRowUpperTriangular()` after calling` MatGetRow()` and `MatRestoreRow()` to disable the flag.
650: Not Collective
652: Input Parameter:
653: . mat - the matrix
655: Level: advanced
657: Note:
658: 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.
660: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatRestoreRowUpperTriangular()`
661: @*/
662: PetscErrorCode MatGetRowUpperTriangular(Mat mat)
663: {
664: PetscFunctionBegin;
667: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
668: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
669: MatCheckPreallocated(mat, 1);
670: PetscTryTypeMethod(mat, getrowuppertriangular);
671: PetscFunctionReturn(PETSC_SUCCESS);
672: }
674: /*@
675: MatRestoreRowUpperTriangular - Disable calls to `MatGetRow()` for matrix in `MATSBAIJ` format.
677: Not Collective
679: Input Parameter:
680: . mat - the matrix
682: Level: advanced
684: Note:
685: This routine should be called after you have finished calls to `MatGetRow()` and `MatRestoreRow()`.
687: .seealso: [](ch_matrices), `Mat`, `MATSBAIJ`, `MatGetRowUpperTriangular()`
688: @*/
689: PetscErrorCode MatRestoreRowUpperTriangular(Mat mat)
690: {
691: PetscFunctionBegin;
694: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
695: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
696: MatCheckPreallocated(mat, 1);
697: PetscTryTypeMethod(mat, restorerowuppertriangular);
698: PetscFunctionReturn(PETSC_SUCCESS);
699: }
701: /*@
702: MatSetOptionsPrefix - Sets the prefix used for searching for all
703: `Mat` options in the database.
705: Logically Collective
707: Input Parameters:
708: + A - the matrix
709: - prefix - the prefix to prepend to all option names
711: Level: advanced
713: Notes:
714: A hyphen (-) must NOT be given at the beginning of the prefix name.
715: The first character of all runtime options is AUTOMATICALLY the hyphen.
717: This is NOT used for options for the factorization of the matrix. Normally the
718: prefix is automatically passed in from the PC calling the factorization. To set
719: it directly use `MatSetOptionsPrefixFactor()`
721: .seealso: [](ch_matrices), `Mat`, `MatSetFromOptions()`, `MatSetOptionsPrefixFactor()`
722: @*/
723: PetscErrorCode MatSetOptionsPrefix(Mat A, const char prefix[])
724: {
725: PetscFunctionBegin;
727: PetscCall(PetscObjectSetOptionsPrefix((PetscObject)A, prefix));
728: PetscTryMethod(A, "MatSetOptionsPrefix_C", (Mat, const char[]), (A, prefix));
729: PetscFunctionReturn(PETSC_SUCCESS);
730: }
732: /*@
733: MatSetOptionsPrefixFactor - Sets the prefix used for searching for all matrix factor options in the database for
734: for matrices created with `MatGetFactor()`
736: Logically Collective
738: Input Parameters:
739: + A - the matrix
740: - prefix - the prefix to prepend to all option names for the factored matrix
742: Level: developer
744: Notes:
745: A hyphen (-) must NOT be given at the beginning of the prefix name.
746: The first character of all runtime options is AUTOMATICALLY the hyphen.
748: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
749: it directly when not using `KSP`/`PC` use `MatSetOptionsPrefixFactor()`
751: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSetFromOptions()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`
752: @*/
753: PetscErrorCode MatSetOptionsPrefixFactor(Mat A, const char prefix[])
754: {
755: PetscFunctionBegin;
757: if (prefix) {
758: PetscAssertPointer(prefix, 2);
759: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
760: if (prefix != A->factorprefix) {
761: PetscCall(PetscFree(A->factorprefix));
762: PetscCall(PetscStrallocpy(prefix, &A->factorprefix));
763: }
764: } else PetscCall(PetscFree(A->factorprefix));
765: PetscFunctionReturn(PETSC_SUCCESS);
766: }
768: /*@
769: MatAppendOptionsPrefixFactor - Appends to the prefix used for searching for all matrix factor options in the database for
770: for matrices created with `MatGetFactor()`
772: Logically Collective
774: Input Parameters:
775: + A - the matrix
776: - prefix - the prefix to prepend to all option names for the factored matrix
778: Level: developer
780: Notes:
781: A hyphen (-) must NOT be given at the beginning of the prefix name.
782: The first character of all runtime options is AUTOMATICALLY the hyphen.
784: Normally the prefix is automatically passed in from the `PC` calling the factorization. To set
785: it directly when not using `KSP`/`PC` use `MatAppendOptionsPrefixFactor()`
787: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `PetscOptionsCreate()`, `PetscOptionsDestroy()`, `PetscObjectSetOptionsPrefix()`, `PetscObjectPrependOptionsPrefix()`,
788: `PetscObjectGetOptionsPrefix()`, `TSAppendOptionsPrefix()`, `SNESAppendOptionsPrefix()`, `KSPAppendOptionsPrefix()`, `MatSetOptionsPrefixFactor()`,
789: `MatSetOptionsPrefix()`
790: @*/
791: PetscErrorCode MatAppendOptionsPrefixFactor(Mat A, const char prefix[])
792: {
793: size_t len1, len2, new_len;
795: PetscFunctionBegin;
797: if (!prefix) PetscFunctionReturn(PETSC_SUCCESS);
798: if (!A->factorprefix) {
799: PetscCall(MatSetOptionsPrefixFactor(A, prefix));
800: PetscFunctionReturn(PETSC_SUCCESS);
801: }
802: PetscCheck(prefix[0] != '-', PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Options prefix should not begin with a hyphen");
804: PetscCall(PetscStrlen(A->factorprefix, &len1));
805: PetscCall(PetscStrlen(prefix, &len2));
806: new_len = len1 + len2 + 1;
807: PetscCall(PetscRealloc(new_len * sizeof(*A->factorprefix), &A->factorprefix));
808: PetscCall(PetscStrncpy(A->factorprefix + len1, prefix, len2 + 1));
809: PetscFunctionReturn(PETSC_SUCCESS);
810: }
812: /*@
813: MatAppendOptionsPrefix - Appends to the prefix used for searching for all
814: matrix options in the database.
816: Logically Collective
818: Input Parameters:
819: + A - the matrix
820: - prefix - the prefix to prepend to all option names
822: Level: advanced
824: Note:
825: A hyphen (-) must NOT be given at the beginning of the prefix name.
826: The first character of all runtime options is AUTOMATICALLY the hyphen.
828: .seealso: [](ch_matrices), `Mat`, `MatGetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefix()`
829: @*/
830: PetscErrorCode MatAppendOptionsPrefix(Mat A, const char prefix[])
831: {
832: PetscFunctionBegin;
834: PetscCall(PetscObjectAppendOptionsPrefix((PetscObject)A, prefix));
835: PetscTryMethod(A, "MatAppendOptionsPrefix_C", (Mat, const char[]), (A, prefix));
836: PetscFunctionReturn(PETSC_SUCCESS);
837: }
839: /*@
840: MatGetOptionsPrefix - Gets the prefix used for searching for all
841: matrix options in the database.
843: Not Collective
845: Input Parameter:
846: . A - the matrix
848: Output Parameter:
849: . prefix - pointer to the prefix string used
851: Level: advanced
853: .seealso: [](ch_matrices), `Mat`, `MatAppendOptionsPrefix()`, `MatSetOptionsPrefix()`, `MatAppendOptionsPrefixFactor()`, `MatSetOptionsPrefixFactor()`
854: @*/
855: PetscErrorCode MatGetOptionsPrefix(Mat A, const char *prefix[])
856: {
857: PetscFunctionBegin;
859: PetscAssertPointer(prefix, 2);
860: PetscCall(PetscObjectGetOptionsPrefix((PetscObject)A, prefix));
861: PetscFunctionReturn(PETSC_SUCCESS);
862: }
864: /*@
865: MatGetState - Gets the state of a `Mat`. Same value as returned by `PetscObjectStateGet()`
867: Not Collective
869: Input Parameter:
870: . A - the matrix
872: Output Parameter:
873: . state - the object state
875: Level: advanced
877: Note:
878: Object state is an integer which gets increased every time
879: the object is changed. By saving and later querying the object state
880: one can determine whether information about the object is still current.
882: See `MatGetNonzeroState()` to determine if the nonzero structure of the matrix has changed.
884: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PetscObjectStateGet()`, `MatGetNonzeroState()`
885: @*/
886: PetscErrorCode MatGetState(Mat A, PetscObjectState *state)
887: {
888: PetscFunctionBegin;
890: PetscAssertPointer(state, 2);
891: PetscCall(PetscObjectStateGet((PetscObject)A, state));
892: PetscFunctionReturn(PETSC_SUCCESS);
893: }
895: /*@
896: MatResetPreallocation - Reset matrix to use the original preallocation values provided by the user, for example with `MatXAIJSetPreallocation()`
898: Collective
900: Input Parameter:
901: . A - the matrix
903: Level: beginner
905: Notes:
906: After calling `MatAssemblyBegin()` and `MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY` the matrix data structures represent the nonzeros assigned to the
907: matrix. If that space is less than the preallocated space that extra preallocated space is no longer available to take on new values. `MatResetPreallocation()`
908: makes all of the preallocation space available
910: Current values in the matrix are lost in this call
912: Currently only supported for `MATAIJ` matrices.
914: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJSetPreallocation()`, `MatMPIAIJSetPreallocation()`, `MatXAIJSetPreallocation()`
915: @*/
916: PetscErrorCode MatResetPreallocation(Mat A)
917: {
918: PetscFunctionBegin;
921: PetscUseMethod(A, "MatResetPreallocation_C", (Mat), (A));
922: PetscFunctionReturn(PETSC_SUCCESS);
923: }
925: /*@
926: MatResetHash - Reset the matrix so that it will use a hash table for the next round of `MatSetValues()` and `MatAssemblyBegin()`/`MatAssemblyEnd()`.
928: Collective
930: Input Parameter:
931: . A - the matrix
933: Level: intermediate
935: Notes:
936: The matrix will again delete the hash table data structures after following calls to `MatAssemblyBegin()`/`MatAssemblyEnd()` with `MAT_FINAL_ASSEMBLY`.
938: Currently only supported for `MATAIJ` matrices.
940: .seealso: [](ch_matrices), `Mat`, `MatResetPreallocation()`
941: @*/
942: PetscErrorCode MatResetHash(Mat A)
943: {
944: PetscFunctionBegin;
947: 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()");
948: if (A->num_ass == 0) PetscFunctionReturn(PETSC_SUCCESS);
949: PetscUseMethod(A, "MatResetHash_C", (Mat), (A));
950: /* These flags are used to determine whether certain setups occur */
951: A->was_assembled = PETSC_FALSE;
952: A->assembled = PETSC_FALSE;
953: /* Log that the state of this object has changed; this will help guarantee that preconditioners get re-setup */
954: PetscCall(PetscObjectStateIncrease((PetscObject)A));
955: PetscFunctionReturn(PETSC_SUCCESS);
956: }
958: /*@
959: MatSetUp - Sets up the internal matrix data structures for later use by the matrix
961: Collective
963: Input Parameter:
964: . A - the matrix
966: Level: advanced
968: Notes:
969: If the user has not set preallocation for this matrix then an efficient algorithm will be used for the first round of
970: setting values in the matrix.
972: This routine is called internally by other `Mat` functions when needed so rarely needs to be called by users
974: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatCreate()`, `MatDestroy()`, `MatXAIJSetPreallocation()`
975: @*/
976: PetscErrorCode MatSetUp(Mat A)
977: {
978: PetscFunctionBegin;
980: if (!((PetscObject)A)->type_name) {
981: PetscMPIInt size;
983: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)A), &size));
984: PetscCall(MatSetType(A, size == 1 ? MATSEQAIJ : MATMPIAIJ));
985: }
986: if (!A->preallocated) PetscTryTypeMethod(A, setup);
987: PetscCall(PetscLayoutSetUp(A->rmap));
988: PetscCall(PetscLayoutSetUp(A->cmap));
989: A->preallocated = PETSC_TRUE;
990: PetscFunctionReturn(PETSC_SUCCESS);
991: }
993: #if defined(PETSC_HAVE_SAWS)
994: #include <petscviewersaws.h>
995: #endif
997: /*
998: If threadsafety is on extraneous matrices may be printed
1000: This flag cannot be stored in the matrix because the original matrix in MatView() may assemble a new matrix which is passed into MatViewFromOptions()
1001: */
1002: #if !defined(PETSC_HAVE_THREADSAFETY)
1003: static PetscInt insidematview = 0;
1004: #endif
1006: /*@
1007: MatViewFromOptions - View properties of the matrix based on options set in the options database
1009: Collective
1011: Input Parameters:
1012: + A - the matrix
1013: . obj - optional additional object that provides the options prefix to use
1014: - name - command line option
1016: Options Database Key:
1017: . -name [viewertype][:...] - option name and values. See `PetscObjectViewFromOptions()` for the possible arguments
1019: Level: intermediate
1021: .seealso: [](ch_matrices), `Mat`, `MatView()`, `PetscObjectViewFromOptions()`, `MatCreate()`
1022: @*/
1023: PetscErrorCode MatViewFromOptions(Mat A, PetscObject obj, const char name[])
1024: {
1025: PetscFunctionBegin;
1027: #if !defined(PETSC_HAVE_THREADSAFETY)
1028: if (insidematview) PetscFunctionReturn(PETSC_SUCCESS);
1029: #endif
1030: PetscCall(PetscObjectViewFromOptions((PetscObject)A, obj, name));
1031: PetscFunctionReturn(PETSC_SUCCESS);
1032: }
1034: /*@
1035: MatView - display information about a matrix in a variety ways
1037: Collective on viewer
1039: Input Parameters:
1040: + mat - the matrix
1041: - viewer - visualization context
1043: Options Database Keys:
1044: + -mat_view ::ascii_info - Prints info on matrix at conclusion of `MatAssemblyEnd()`
1045: . -mat_view ::ascii_info_detail - Prints more detailed info
1046: . -mat_view - Prints matrix in ASCII format
1047: . -mat_view ::ascii_matlab - Prints matrix in MATLAB format
1048: . -mat_view draw - PetscDraws nonzero structure of matrix, using `MatView()` and `PetscDrawOpenX()`.
1049: . -display name - Sets display name (default is host)
1050: . -draw_pause sec - Sets number of seconds to pause after display
1051: . -mat_view socket - Sends matrix to socket, can be accessed from MATLAB (see Users-Manual: ch_matlab for details)
1052: . -viewer_socket_machine machine - -
1053: . -viewer_socket_port port - -
1054: . -mat_view binary - save matrix to file in binary format
1055: - -viewer_binary_filename name - -
1057: Level: beginner
1059: Notes:
1060: The available visualization contexts include
1061: + `PETSC_VIEWER_STDOUT_SELF` - for sequential matrices
1062: . `PETSC_VIEWER_STDOUT_WORLD` - for parallel matrices created on `PETSC_COMM_WORLD`
1063: . `PETSC_VIEWER_STDOUT_`(comm) - for matrices created on MPI communicator comm
1064: - `PETSC_VIEWER_DRAW_WORLD` - graphical display of nonzero structure
1066: The user can open alternative visualization contexts with
1067: + `PetscViewerASCIIOpen()` - Outputs matrix to a specified file
1068: . `PetscViewerBinaryOpen()` - Outputs matrix in binary to a specified file; corresponding input uses `MatLoad()`
1069: . `PetscViewerDrawOpen()` - Outputs nonzero matrix nonzero structure to an X window display
1070: - `PetscViewerSocketOpen()` - Outputs matrix to Socket viewer, `PETSCVIEWERSOCKET`. Only the `MATSEQDENSE` and `MATAIJ` types support this viewer.
1072: The user can call `PetscViewerPushFormat()` to specify the output
1073: format of ASCII printed objects (when using `PETSC_VIEWER_STDOUT_SELF`,
1074: `PETSC_VIEWER_STDOUT_WORLD` and `PetscViewerASCIIOpen()`). Available formats include
1075: + `PETSC_VIEWER_DEFAULT` - default, prints matrix contents
1076: . `PETSC_VIEWER_ASCII_MATLAB` - prints matrix contents in MATLAB format
1077: . `PETSC_VIEWER_ASCII_DENSE` - prints entire matrix including zeros
1078: . `PETSC_VIEWER_ASCII_COMMON` - prints matrix contents, using a sparse format common among all matrix types
1079: . `PETSC_VIEWER_ASCII_IMPL` - prints matrix contents, using an implementation-specific format (which is in many cases the same as the default)
1080: . `PETSC_VIEWER_ASCII_INFO` - prints basic information about the matrix size and structure (not the matrix entries)
1081: - `PETSC_VIEWER_ASCII_INFO_DETAIL` - prints more detailed information about the matrix nonzero structure (still not vector or matrix entries)
1083: The ASCII viewers are only recommended for small matrices on at most a moderate number of processes,
1084: the program will seemingly hang and take hours for larger matrices, for larger matrices one should use the binary format.
1086: In the debugger you can do "call MatView(mat,0)" to display the matrix. (The same holds for any PETSc object viewer).
1088: See the manual page for `MatLoad()` for the exact format of the binary file when the binary
1089: viewer is used.
1091: See share/petsc/matlab/PetscBinaryRead.m for a MATLAB code that can read in the binary file when the binary
1092: viewer is used and lib/petsc/bin/PetscBinaryIO.py for loading them into Python.
1094: One can use '-mat_view draw -draw_pause -1' to pause the graphical display of matrix nonzero structure,
1095: and then use the following mouse functions.
1096: .vb
1097: left mouse: zoom in
1098: middle mouse: zoom out
1099: right mouse: continue with the simulation
1100: .ve
1102: .seealso: [](ch_matrices), `Mat`, `PetscViewerPushFormat()`, `PetscViewerASCIIOpen()`, `PetscViewerDrawOpen()`, `PetscViewer`,
1103: `PetscViewerSocketOpen()`, `PetscViewerBinaryOpen()`, `MatLoad()`, `MatViewFromOptions()`
1104: @*/
1105: PetscErrorCode MatView(Mat mat, PetscViewer viewer)
1106: {
1107: PetscInt rows, cols, rbs, cbs;
1108: PetscBool isascii, isstring, issaws;
1109: PetscViewerFormat format;
1110: PetscMPIInt size;
1112: PetscFunctionBegin;
1115: if (!viewer) PetscCall(PetscViewerASCIIGetStdout(PetscObjectComm((PetscObject)mat), &viewer));
1118: PetscCall(PetscViewerGetFormat(viewer, &format));
1119: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)viewer), &size));
1120: if (size == 1 && format == PETSC_VIEWER_LOAD_BALANCE) PetscFunctionReturn(PETSC_SUCCESS);
1122: #if !defined(PETSC_HAVE_THREADSAFETY)
1123: insidematview++;
1124: #endif
1125: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSTRING, &isstring));
1126: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &isascii));
1127: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERSAWS, &issaws));
1128: 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");
1130: PetscCall(PetscLogEventBegin(MAT_View, mat, viewer, 0, 0));
1131: if (isascii) {
1132: if (!mat->preallocated) {
1133: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been preallocated yet\n"));
1134: #if !defined(PETSC_HAVE_THREADSAFETY)
1135: insidematview--;
1136: #endif
1137: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1138: PetscFunctionReturn(PETSC_SUCCESS);
1139: }
1140: if (!mat->assembled) {
1141: PetscCall(PetscViewerASCIIPrintf(viewer, "Matrix has not been assembled yet\n"));
1142: #if !defined(PETSC_HAVE_THREADSAFETY)
1143: insidematview--;
1144: #endif
1145: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1146: PetscFunctionReturn(PETSC_SUCCESS);
1147: }
1148: PetscCall(PetscObjectPrintClassNamePrefixType((PetscObject)mat, viewer));
1149: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1150: MatNullSpace nullsp, transnullsp;
1152: PetscCall(PetscViewerASCIIPushTab(viewer));
1153: PetscCall(MatGetSize(mat, &rows, &cols));
1154: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
1155: if (rbs != 1 || cbs != 1) {
1156: 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" : ""));
1157: else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT ", bs=%" PetscInt_FMT "%s\n", rows, cols, rbs, mat->bsizes ? " variable blocks set" : ""));
1158: } else PetscCall(PetscViewerASCIIPrintf(viewer, "rows=%" PetscInt_FMT ", cols=%" PetscInt_FMT "\n", rows, cols));
1159: if (mat->factortype) {
1160: MatSolverType solver;
1161: PetscCall(MatFactorGetSolverType(mat, &solver));
1162: PetscCall(PetscViewerASCIIPrintf(viewer, "package used to perform factorization: %s\n", solver));
1163: }
1164: if (mat->ops->getinfo) {
1165: PetscBool is_constant_or_diagonal;
1167: // Don't print nonzero information for constant or diagonal matrices, it just adds noise to the output
1168: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &is_constant_or_diagonal, MATCONSTANTDIAGONAL, MATDIAGONAL, ""));
1169: if (!is_constant_or_diagonal) {
1170: MatInfo info;
1172: PetscCall(MatGetInfo(mat, MAT_GLOBAL_SUM, &info));
1173: PetscCall(PetscViewerASCIIPrintf(viewer, "total: nonzeros=%.f, allocated nonzeros=%.f\n", info.nz_used, info.nz_allocated));
1174: if (!mat->factortype) PetscCall(PetscViewerASCIIPrintf(viewer, "total number of mallocs used during MatSetValues calls=%" PetscInt_FMT "\n", (PetscInt)info.mallocs));
1175: }
1176: }
1177: PetscCall(MatGetNullSpace(mat, &nullsp));
1178: PetscCall(MatGetTransposeNullSpace(mat, &transnullsp));
1179: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached null space\n"));
1180: if (transnullsp && transnullsp != nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached transposed null space\n"));
1181: PetscCall(MatGetNearNullSpace(mat, &nullsp));
1182: if (nullsp) PetscCall(PetscViewerASCIIPrintf(viewer, " has attached near null space\n"));
1183: PetscCall(PetscViewerASCIIPushTab(viewer));
1184: PetscCall(MatProductView(mat, viewer));
1185: PetscCall(PetscViewerASCIIPopTab(viewer));
1186: if (mat->bsizes && format == PETSC_VIEWER_ASCII_INFO_DETAIL) {
1187: IS tmp;
1189: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)viewer), mat->nblocks, mat->bsizes, PETSC_USE_POINTER, &tmp));
1190: PetscCall(PetscObjectSetName((PetscObject)tmp, "Block Sizes"));
1191: PetscCall(PetscViewerASCIIPushTab(viewer));
1192: PetscCall(ISView(tmp, viewer));
1193: PetscCall(PetscViewerASCIIPopTab(viewer));
1194: PetscCall(ISDestroy(&tmp));
1195: }
1196: }
1197: } else if (issaws) {
1198: #if defined(PETSC_HAVE_SAWS)
1199: PetscMPIInt rank;
1201: PetscCall(PetscObjectName((PetscObject)mat));
1202: PetscCallMPI(MPI_Comm_rank(PETSC_COMM_WORLD, &rank));
1203: if (!((PetscObject)mat)->amsmem && rank == 0) PetscCall(PetscObjectViewSAWs((PetscObject)mat, viewer));
1204: #endif
1205: } else if (isstring) {
1206: const char *type;
1207: PetscCall(MatGetType(mat, &type));
1208: PetscCall(PetscViewerStringSPrintf(viewer, " MatType: %-7.7s", type));
1209: PetscTryTypeMethod(mat, view, viewer);
1210: }
1211: if ((format == PETSC_VIEWER_NATIVE || format == PETSC_VIEWER_LOAD_BALANCE) && mat->ops->viewnative) {
1212: PetscCall(PetscViewerASCIIPushTab(viewer));
1213: PetscUseTypeMethod(mat, viewnative, viewer);
1214: PetscCall(PetscViewerASCIIPopTab(viewer));
1215: } else if (mat->ops->view) {
1216: PetscCall(PetscViewerASCIIPushTab(viewer));
1217: PetscUseTypeMethod(mat, view, viewer);
1218: PetscCall(PetscViewerASCIIPopTab(viewer));
1219: }
1220: if (isascii) {
1221: PetscCall(PetscViewerGetFormat(viewer, &format));
1222: if (format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscCall(PetscViewerASCIIPopTab(viewer));
1223: }
1224: PetscCall(PetscLogEventEnd(MAT_View, mat, viewer, 0, 0));
1225: #if !defined(PETSC_HAVE_THREADSAFETY)
1226: insidematview--;
1227: #endif
1228: PetscFunctionReturn(PETSC_SUCCESS);
1229: }
1231: #if defined(PETSC_USE_DEBUG)
1232: #include <../src/sys/totalview/tv_data_display.h>
1233: PETSC_UNUSED static int TV_display_type(const struct _p_Mat *mat)
1234: {
1235: TV_add_row("Local rows", "int", &mat->rmap->n);
1236: TV_add_row("Local columns", "int", &mat->cmap->n);
1237: TV_add_row("Global rows", "int", &mat->rmap->N);
1238: TV_add_row("Global columns", "int", &mat->cmap->N);
1239: TV_add_row("Typename", TV_ascii_string_type, ((PetscObject)mat)->type_name);
1240: return TV_format_OK;
1241: }
1242: #endif
1244: /*@
1245: MatLoad - Loads a matrix that has been stored in binary/HDF5 format
1246: with `MatView()`. The matrix format is determined from the options database.
1247: Generates a parallel MPI matrix if the communicator has more than one
1248: processor. The default matrix type is `MATAIJ`.
1250: Collective
1252: Input Parameters:
1253: + mat - the newly loaded matrix, this needs to have been created with `MatCreate()`
1254: or some related function before a call to `MatLoad()`
1255: - viewer - `PETSCVIEWERBINARY`/`PETSCVIEWERHDF5` file viewer
1257: Options Database Key:
1258: . -matload_block_size bs - set block size
1260: Level: beginner
1262: Notes:
1263: If the `Mat` type has not yet been given then `MATAIJ` is used, call `MatSetFromOptions()` on the
1264: `Mat` before calling this routine if you wish to set it from the options database.
1266: `MatLoad()` automatically loads into the options database any options
1267: given in the file filename.info where filename is the name of the file
1268: that was passed to the `PetscViewerBinaryOpen()`. The options in the info
1269: file will be ignored if you use the -viewer_binary_skip_info option.
1271: If the type or size of mat is not set before a call to `MatLoad()`, PETSc
1272: sets the default matrix type AIJ and sets the local and global sizes.
1273: If type and/or size is already set, then the same are used.
1275: In parallel, each processor can load a subset of rows (or the
1276: entire matrix). This routine is especially useful when a large
1277: matrix is stored on disk and only part of it is desired on each
1278: processor. For example, a parallel solver may access only some of
1279: the rows from each processor. The algorithm used here reads
1280: relatively small blocks of data rather than reading the entire
1281: matrix and then subsetting it.
1283: Viewer's `PetscViewerType` must be either `PETSCVIEWERBINARY` or `PETSCVIEWERHDF5`.
1284: Such viewer can be created using `PetscViewerBinaryOpen()` or `PetscViewerHDF5Open()`,
1285: or the sequence like
1286: .vb
1287: `PetscViewer` v;
1288: `PetscViewerCreate`(`PETSC_COMM_WORLD`,&v);
1289: `PetscViewerSetType`(v,`PETSCVIEWERBINARY`);
1290: `PetscViewerSetFromOptions`(v);
1291: `PetscViewerFileSetMode`(v,`FILE_MODE_READ`);
1292: `PetscViewerFileSetName`(v,"datafile");
1293: .ve
1294: The optional `PetscViewerSetFromOptions()` call allows overriding `PetscViewerSetType()` using the option
1295: .vb
1296: -viewer_type {binary, hdf5}
1297: .ve
1299: See the example src/ksp/ksp/tutorials/ex27.c with the first approach,
1300: and src/mat/tutorials/ex10.c with the second approach.
1302: In case of `PETSCVIEWERBINARY`, a native PETSc binary format is used. Each of the blocks
1303: is read onto MPI rank 0 and then shipped to its destination MPI rank, one after another.
1304: Multiple objects, both matrices and vectors, can be stored within the same file.
1305: Their `PetscObject` name is ignored; they are loaded in the order of their storage.
1307: Most users should not need to know the details of the binary storage
1308: format, since `MatLoad()` and `MatView()` completely hide these details.
1309: But for anyone who is interested, the standard binary matrix storage
1310: format is
1312: .vb
1313: PetscInt MAT_FILE_CLASSID
1314: PetscInt number of rows
1315: PetscInt number of columns
1316: PetscInt total number of nonzeros
1317: PetscInt *number nonzeros in each row
1318: PetscInt *column indices of all nonzeros (starting index is zero)
1319: PetscScalar *values of all nonzeros
1320: .ve
1321: If PETSc was not configured with `--with-64-bit-indices` then only `MATMPIAIJ` matrices with more than `PETSC_INT_MAX` non-zeros can be
1322: 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
1323: case will not fit in a (32-bit) `PetscInt` the value `PETSC_INT_MAX` is used for the header entry `total number of nonzeros`.
1325: PETSc automatically does the byte swapping for
1326: machines that store the bytes reversed. Thus if you write your own binary
1327: read/write routines you have to swap the bytes; see `PetscBinaryRead()`
1328: and `PetscBinaryWrite()` to see how this may be done.
1330: In case of `PETSCVIEWERHDF5`, a parallel HDF5 reader is used.
1331: Each processor's chunk is loaded independently by its owning MPI process.
1332: Multiple objects, both matrices and vectors, can be stored within the same file.
1333: They are looked up by their PetscObject name.
1335: As the MATLAB MAT-File Version 7.3 format is also a HDF5 flavor, we decided to use
1336: by default the same structure and naming of the AIJ arrays and column count
1337: within the HDF5 file. This means that a MAT file saved with -v7.3 flag, e.g.
1338: .vb
1339: save example.mat A b -v7.3
1340: .ve
1341: can be directly read by this routine (see Reference 1 for details).
1343: Depending on your MATLAB version, this format might be a default,
1344: otherwise you can set it as default in Preferences.
1346: Unless -nocompression flag is used to save the file in MATLAB,
1347: PETSc must be configured with ZLIB package.
1349: See also examples src/mat/tutorials/ex10.c and src/ksp/ksp/tutorials/ex27.c
1351: This reader currently supports only real `MATSEQAIJ`, `MATMPIAIJ`, `MATSEQDENSE` and `MATMPIDENSE` matrices for `PETSCVIEWERHDF5`
1353: Corresponding `MatView()` is not yet implemented.
1355: The loaded matrix is actually a transpose of the original one in MATLAB,
1356: unless you push `PETSC_VIEWER_HDF5_MAT` format (see examples above).
1357: With this format, matrix is automatically transposed by PETSc,
1358: unless the matrix is marked as SPD or symmetric
1359: (see `MatSetOption()`, `MAT_SPD`, `MAT_SYMMETRIC`).
1361: See MATLAB Documentation on `save()`, <https://www.mathworks.com/help/matlab/ref/save.html#btox10b-1-version>
1363: .seealso: [](ch_matrices), `Mat`, `PetscViewerBinaryOpen()`, `PetscViewerSetType()`, `MatView()`, `VecLoad()`
1364: @*/
1365: PetscErrorCode MatLoad(Mat mat, PetscViewer viewer)
1366: {
1367: PetscBool flg;
1369: PetscFunctionBegin;
1373: if (!((PetscObject)mat)->type_name) PetscCall(MatSetType(mat, MATAIJ));
1375: flg = PETSC_FALSE;
1376: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_symmetric", &flg, NULL));
1377: if (flg) {
1378: PetscCall(MatSetOption(mat, MAT_SYMMETRIC, PETSC_TRUE));
1379: PetscCall(MatSetOption(mat, MAT_SYMMETRY_ETERNAL, PETSC_TRUE));
1380: }
1381: flg = PETSC_FALSE;
1382: PetscCall(PetscOptionsGetBool(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matload_spd", &flg, NULL));
1383: if (flg) PetscCall(MatSetOption(mat, MAT_SPD, PETSC_TRUE));
1385: PetscCall(PetscLogEventBegin(MAT_Load, mat, viewer, 0, 0));
1386: PetscUseTypeMethod(mat, load, viewer);
1387: PetscCall(PetscLogEventEnd(MAT_Load, mat, viewer, 0, 0));
1388: PetscFunctionReturn(PETSC_SUCCESS);
1389: }
1391: static PetscErrorCode MatDestroy_Redundant(Mat_Redundant **redundant)
1392: {
1393: Mat_Redundant *redund = *redundant;
1395: PetscFunctionBegin;
1396: if (redund) {
1397: if (redund->matseq) { /* via MatCreateSubMatrices() */
1398: PetscCall(ISDestroy(&redund->isrow));
1399: PetscCall(ISDestroy(&redund->iscol));
1400: PetscCall(MatDestroySubMatrices(1, &redund->matseq));
1401: } else {
1402: PetscCall(PetscFree2(redund->send_rank, redund->recv_rank));
1403: PetscCall(PetscFree(redund->sbuf_j));
1404: PetscCall(PetscFree(redund->sbuf_a));
1405: for (PetscInt i = 0; i < redund->nrecvs; i++) {
1406: PetscCall(PetscFree(redund->rbuf_j[i]));
1407: PetscCall(PetscFree(redund->rbuf_a[i]));
1408: }
1409: PetscCall(PetscFree4(redund->sbuf_nz, redund->rbuf_nz, redund->rbuf_j, redund->rbuf_a));
1410: }
1412: PetscCall(PetscCommDestroy(&redund->subcomm));
1413: PetscCall(PetscFree(redund));
1414: }
1415: PetscFunctionReturn(PETSC_SUCCESS);
1416: }
1418: /*@
1419: MatDestroy - Frees space taken by a matrix.
1421: Collective
1423: Input Parameter:
1424: . A - the matrix
1426: Level: beginner
1428: Developer Note:
1429: Some special arrays of matrices are not destroyed in this routine but instead by the routines called by
1430: `MatDestroySubMatrices()`. Thus one must be sure that any changes here must also be made in those routines.
1431: `MatHeaderMerge()` and `MatHeaderReplace()` also manipulate the data in the `Mat` object and likely need changes
1432: if changes are needed here.
1434: .seealso: [](ch_matrices), `Mat`, `MatCreate()`
1435: @*/
1436: PetscErrorCode MatDestroy(Mat *A)
1437: {
1438: PetscFunctionBegin;
1439: if (!*A) PetscFunctionReturn(PETSC_SUCCESS);
1441: if (--((PetscObject)*A)->refct > 0) {
1442: *A = NULL;
1443: PetscFunctionReturn(PETSC_SUCCESS);
1444: }
1446: /* if memory was published with SAWs then destroy it */
1447: PetscCall(PetscObjectSAWsViewOff((PetscObject)*A));
1448: PetscTryTypeMethod(*A, destroy);
1450: PetscCall(PetscFree((*A)->factorprefix));
1451: PetscCall(PetscFree((*A)->defaultvectype));
1452: PetscCall(PetscFree((*A)->defaultrandtype));
1453: PetscCall(PetscFree((*A)->bsizes));
1454: PetscCall(PetscFree((*A)->solvertype));
1455: for (PetscInt i = 0; i < MAT_FACTOR_NUM_TYPES; i++) PetscCall(PetscFree((*A)->preferredordering[i]));
1456: if ((*A)->redundant && (*A)->redundant->matseq[0] == *A) (*A)->redundant->matseq[0] = NULL;
1457: PetscCall(MatDestroy_Redundant(&(*A)->redundant));
1458: PetscCall(MatProductClear(*A));
1459: PetscCall(MatNullSpaceDestroy(&(*A)->nullsp));
1460: PetscCall(MatNullSpaceDestroy(&(*A)->transnullsp));
1461: PetscCall(MatNullSpaceDestroy(&(*A)->nearnullsp));
1462: PetscCall(MatDestroy(&(*A)->schur));
1463: PetscCall(VecDestroy(&(*A)->dot_vec));
1464: PetscCall(PetscLayoutDestroy(&(*A)->rmap));
1465: PetscCall(PetscLayoutDestroy(&(*A)->cmap));
1466: PetscCall(PetscHeaderDestroy(A));
1467: PetscFunctionReturn(PETSC_SUCCESS);
1468: }
1470: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1471: /*@
1472: MatSetValues - Inserts or adds a block of values into a matrix.
1473: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1474: MUST be called after all calls to `MatSetValues()` have been completed.
1476: Not Collective
1478: Input Parameters:
1479: + mat - the matrix
1480: . m - the number of rows
1481: . idxm - the global indices of the rows
1482: . n - the number of columns
1483: . idxn - the global indices of the columns
1484: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1485: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1486: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1488: Level: beginner
1490: Notes:
1491: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1492: options cannot be mixed without intervening calls to the assembly
1493: routines.
1495: `MatSetValues()` uses 0-based row and column numbers in Fortran
1496: as well as in C.
1498: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1499: simply ignored. This allows easily inserting element stiffness matrices
1500: with homogeneous Dirichlet boundary conditions that you don't want represented
1501: in the matrix.
1503: Efficiency Alert:
1504: The routine `MatSetValuesBlocked()` may offer much better efficiency
1505: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1507: Fortran Notes:
1508: If any of `idxm`, `idxn`, and `v` are scalars pass them using, for example,
1509: .vb
1510: call MatSetValues(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
1511: .ve
1513: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1515: Developer Note:
1516: This is labeled with C so does not automatically generate Fortran stubs and interfaces
1517: because it requires multiple Fortran interfaces depending on which arguments are scalar or arrays.
1519: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1520: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1521: @*/
1522: PetscErrorCode MatSetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
1523: {
1524: PetscFunctionBeginHot;
1527: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1528: PetscAssertPointer(idxm, 3);
1529: PetscAssertPointer(idxn, 5);
1530: MatCheckPreallocated(mat, 1);
1532: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
1533: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
1535: if (PetscDefined(USE_DEBUG)) {
1536: PetscInt i, j;
1538: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1539: if (v) {
1540: for (i = 0; i < m; i++) {
1541: for (j = 0; j < n; j++) {
1542: if (mat->erroriffailure && PetscIsInfOrNanScalar(v[i * n + j]))
1543: #if defined(PETSC_USE_COMPLEX)
1544: 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]);
1545: #else
1546: 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]);
1547: #endif
1548: }
1549: }
1550: }
1551: 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);
1552: 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);
1553: }
1555: if (mat->assembled) {
1556: mat->was_assembled = PETSC_TRUE;
1557: mat->assembled = PETSC_FALSE;
1558: }
1559: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1560: PetscUseTypeMethod(mat, setvalues, m, idxm, n, idxn, v, addv);
1561: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1562: PetscFunctionReturn(PETSC_SUCCESS);
1563: }
1565: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1566: /*@
1567: MatSetValuesIS - Inserts or adds a block of values into a matrix using an `IS` to indicate the rows and columns
1568: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
1569: MUST be called after all calls to `MatSetValues()` have been completed.
1571: Not Collective
1573: Input Parameters:
1574: + mat - the matrix
1575: . ism - the rows to provide
1576: . isn - the columns to provide
1577: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1578: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1579: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
1581: Level: beginner
1583: Notes:
1584: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1586: Calls to `MatSetValues()` with the `INSERT_VALUES` and `ADD_VALUES`
1587: options cannot be mixed without intervening calls to the assembly
1588: routines.
1590: `MatSetValues()` uses 0-based row and column numbers in Fortran
1591: as well as in C.
1593: Negative indices may be passed in `ism` and `isn`, these rows and columns are
1594: simply ignored. This allows easily inserting element stiffness matrices
1595: with homogeneous Dirichlet boundary conditions that you don't want represented
1596: in the matrix.
1598: Fortran Note:
1599: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1601: Efficiency Alert:
1602: The routine `MatSetValuesBlocked()` may offer much better efficiency
1603: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1605: This is currently not optimized for any particular `ISType`
1607: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatSetValues()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1608: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1609: @*/
1610: PetscErrorCode MatSetValuesIS(Mat mat, IS ism, IS isn, const PetscScalar v[], InsertMode addv)
1611: {
1612: PetscInt m, n;
1613: const PetscInt *rows, *cols;
1615: PetscFunctionBeginHot;
1617: PetscCall(ISGetIndices(ism, &rows));
1618: PetscCall(ISGetIndices(isn, &cols));
1619: PetscCall(ISGetLocalSize(ism, &m));
1620: PetscCall(ISGetLocalSize(isn, &n));
1621: PetscCall(MatSetValues(mat, m, rows, n, cols, v, addv));
1622: PetscCall(ISRestoreIndices(ism, &rows));
1623: PetscCall(ISRestoreIndices(isn, &cols));
1624: PetscFunctionReturn(PETSC_SUCCESS);
1625: }
1627: /*@
1628: MatSetValuesRowLocal - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1629: values into a matrix
1631: Not Collective
1633: Input Parameters:
1634: + mat - the matrix
1635: . row - the (block) row to set
1636: - v - a one-dimensional array that contains the values. For `MATBAIJ` they are implicitly stored as a two-dimensional array, by default in row-major order.
1637: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1639: Level: intermediate
1641: Notes:
1642: The values, `v`, are column-oriented (for the block version) and sorted
1644: All the nonzero values in `row` must be provided
1646: The matrix must have previously had its column indices set, likely by having been assembled.
1648: `row` must belong to this MPI process
1650: Fortran Note:
1651: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1653: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1654: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetValuesRow()`, `MatSetLocalToGlobalMapping()`
1655: @*/
1656: PetscErrorCode MatSetValuesRowLocal(Mat mat, PetscInt row, const PetscScalar v[])
1657: {
1658: PetscInt globalrow;
1660: PetscFunctionBegin;
1663: PetscAssertPointer(v, 3);
1664: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, 1, &row, &globalrow));
1665: PetscCall(MatSetValuesRow(mat, globalrow, v));
1666: PetscFunctionReturn(PETSC_SUCCESS);
1667: }
1669: /*@
1670: MatSetValuesRow - Inserts a row (block row for `MATBAIJ` matrices) of nonzero
1671: values into a matrix
1673: Not Collective
1675: Input Parameters:
1676: + mat - the matrix
1677: . row - the (block) row to set
1678: - 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
1680: Level: advanced
1682: Notes:
1683: The values, `v`, are column-oriented for the block version.
1685: All the nonzeros in `row` must be provided
1687: THE MATRIX MUST HAVE PREVIOUSLY HAD ITS COLUMN INDICES SET. IT IS RARE THAT THIS ROUTINE IS USED, usually `MatSetValues()` is used.
1689: `row` must belong to this process
1691: .seealso: [](ch_matrices), `Mat`, `MatSetValues()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1692: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`
1693: @*/
1694: PetscErrorCode MatSetValuesRow(Mat mat, PetscInt row, const PetscScalar v[])
1695: {
1696: PetscFunctionBeginHot;
1699: MatCheckPreallocated(mat, 1);
1700: PetscAssertPointer(v, 3);
1701: PetscCheck(mat->insertmode != ADD_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add and insert values");
1702: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
1703: mat->insertmode = INSERT_VALUES;
1705: if (mat->assembled) {
1706: mat->was_assembled = PETSC_TRUE;
1707: mat->assembled = PETSC_FALSE;
1708: }
1709: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
1710: PetscUseTypeMethod(mat, setvaluesrow, row, v);
1711: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
1712: PetscFunctionReturn(PETSC_SUCCESS);
1713: }
1715: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
1716: /*@
1717: MatSetValuesStencil - Inserts or adds a block of values into a matrix.
1718: Using structured grid indexing
1720: Not Collective
1722: Input Parameters:
1723: + mat - the matrix
1724: . m - number of rows being entered
1725: . idxm - grid coordinates (and component number when dof > 1) for matrix rows being entered
1726: . n - number of columns being entered
1727: . idxn - grid coordinates (and component number when dof > 1) for matrix columns being entered
1728: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1729: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1730: - addv - either `ADD_VALUES` to add to existing entries at that location or `INSERT_VALUES` to replace existing entries with new values
1732: Level: beginner
1734: Notes:
1735: By default the values, `v`, are row-oriented. See `MatSetOption()` for other options.
1737: Calls to `MatSetValuesStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1738: options cannot be mixed without intervening calls to the assembly
1739: routines.
1741: The grid coordinates are across the entire grid, not just the local portion
1743: `MatSetValuesStencil()` uses 0-based row and column numbers in Fortran
1744: as well as in C.
1746: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1748: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1749: or call `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1751: The columns and rows in the stencil passed in MUST be contained within the
1752: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1753: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1754: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1755: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1757: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
1758: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
1759: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
1760: `DM_BOUNDARY_PERIODIC` boundary type.
1762: 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
1763: a single value per point) you can skip filling those indices.
1765: Inspired by the structured grid interface to the HYPRE package
1766: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1768: Fortran Note:
1769: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1771: Efficiency Alert:
1772: The routine `MatSetValuesBlockedStencil()` may offer much better efficiency
1773: for users of block sparse formats (`MATSEQBAIJ` and `MATMPIBAIJ`).
1775: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1776: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`
1777: @*/
1778: PetscErrorCode MatSetValuesStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1779: {
1780: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1781: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1782: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1784: PetscFunctionBegin;
1785: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1788: PetscAssertPointer(idxm, 3);
1789: PetscAssertPointer(idxn, 5);
1791: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1792: jdxm = buf;
1793: jdxn = buf + m;
1794: } else {
1795: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1796: jdxm = bufm;
1797: jdxn = bufn;
1798: }
1799: for (i = 0; i < m; i++) {
1800: for (j = 0; j < 3 - sdim; j++) dxm++;
1801: tmp = *dxm++ - starts[0];
1802: for (j = 0; j < dim - 1; j++) {
1803: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1804: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1805: }
1806: if (mat->stencil.noc) dxm++;
1807: jdxm[i] = tmp;
1808: }
1809: for (i = 0; i < n; i++) {
1810: for (j = 0; j < 3 - sdim; j++) dxn++;
1811: tmp = *dxn++ - starts[0];
1812: for (j = 0; j < dim - 1; j++) {
1813: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1814: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1815: }
1816: if (mat->stencil.noc) dxn++;
1817: jdxn[i] = tmp;
1818: }
1819: PetscCall(MatSetValuesLocal(mat, m, jdxm, n, jdxn, v, addv));
1820: PetscCall(PetscFree2(bufm, bufn));
1821: PetscFunctionReturn(PETSC_SUCCESS);
1822: }
1824: /*@
1825: MatSetValuesBlockedStencil - Inserts or adds a block of values into a matrix.
1826: Using structured grid indexing
1828: Not Collective
1830: Input Parameters:
1831: + mat - the matrix
1832: . m - number of rows being entered
1833: . idxm - grid coordinates for matrix rows being entered
1834: . n - number of columns being entered
1835: . idxn - grid coordinates for matrix columns being entered
1836: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1837: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1838: - addv - either `ADD_VALUES` to add to existing entries or `INSERT_VALUES` to replace existing entries with new values
1840: Level: beginner
1842: Notes:
1843: By default the values, `v`, are row-oriented and unsorted.
1844: See `MatSetOption()` for other options.
1846: Calls to `MatSetValuesBlockedStencil()` with the `INSERT_VALUES` and `ADD_VALUES`
1847: options cannot be mixed without intervening calls to the assembly
1848: routines.
1850: The grid coordinates are across the entire grid, not just the local portion
1852: `MatSetValuesBlockedStencil()` uses 0-based row and column numbers in Fortran
1853: as well as in C.
1855: For setting/accessing vector values via array coordinates you can use the `DMDAVecGetArray()` routine
1857: In order to use this routine you must either obtain the matrix with `DMCreateMatrix()`
1858: or call `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()` and `MatSetStencil()` first.
1860: The columns and rows in the stencil passed in MUST be contained within the
1861: ghost region of the given process as set with DMDACreateXXX() or `MatSetStencil()`. For example,
1862: if you create a `DMDA` with an overlap of one grid level and on a particular process its first
1863: local nonghost x logical coordinate is 6 (so its first ghost x logical coordinate is 5) the
1864: first i index you can use in your column and row indices in `MatSetStencil()` is 5.
1866: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
1867: simply ignored. This allows easily inserting element stiffness matrices
1868: with homogeneous Dirichlet boundary conditions that you don't want represented
1869: in the matrix.
1871: Inspired by the structured grid interface to the HYPRE package
1872: (https://computation.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods)
1874: Fortran Notes:
1875: `idxm` and `idxn` should be declared as
1876: .vb
1877: MatStencil idxm(4,m),idxn(4,n)
1878: .ve
1879: and the values inserted using
1880: .vb
1881: idxm(MatStencil_i,1) = i
1882: idxm(MatStencil_j,1) = j
1883: idxm(MatStencil_k,1) = k
1884: etc
1885: .ve
1887: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
1889: .seealso: [](ch_matrices), `Mat`, `DMDA`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1890: `MatSetValues()`, `MatSetValuesStencil()`, `MatSetStencil()`, `DMCreateMatrix()`, `DMDAVecGetArray()`, `MatStencil`,
1891: `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`
1892: @*/
1893: PetscErrorCode MatSetValuesBlockedStencil(Mat mat, PetscInt m, const MatStencil idxm[], PetscInt n, const MatStencil idxn[], const PetscScalar v[], InsertMode addv)
1894: {
1895: PetscInt buf[8192], *bufm = NULL, *bufn = NULL, *jdxm, *jdxn;
1896: PetscInt j, i, dim = mat->stencil.dim, *dims = mat->stencil.dims + 1, tmp;
1897: PetscInt *starts = mat->stencil.starts, *dxm = (PetscInt *)idxm, *dxn = (PetscInt *)idxn, sdim = dim - (1 - (PetscInt)mat->stencil.noc);
1899: PetscFunctionBegin;
1900: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
1903: PetscAssertPointer(idxm, 3);
1904: PetscAssertPointer(idxn, 5);
1905: PetscAssertPointer(v, 6);
1907: if ((m + n) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
1908: jdxm = buf;
1909: jdxn = buf + m;
1910: } else {
1911: PetscCall(PetscMalloc2(m, &bufm, n, &bufn));
1912: jdxm = bufm;
1913: jdxn = bufn;
1914: }
1915: for (i = 0; i < m; i++) {
1916: for (j = 0; j < 3 - sdim; j++) dxm++;
1917: tmp = *dxm++ - starts[0];
1918: for (j = 0; j < sdim - 1; j++) {
1919: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1920: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
1921: }
1922: dxm++;
1923: jdxm[i] = tmp;
1924: }
1925: for (i = 0; i < n; i++) {
1926: for (j = 0; j < 3 - sdim; j++) dxn++;
1927: tmp = *dxn++ - starts[0];
1928: for (j = 0; j < sdim - 1; j++) {
1929: if ((*dxn++ - starts[j + 1]) < 0 || tmp < 0) tmp = -1;
1930: else tmp = tmp * dims[j] + *(dxn - 1) - starts[j + 1];
1931: }
1932: dxn++;
1933: jdxn[i] = tmp;
1934: }
1935: PetscCall(MatSetValuesBlockedLocal(mat, m, jdxm, n, jdxn, v, addv));
1936: PetscCall(PetscFree2(bufm, bufn));
1937: PetscFunctionReturn(PETSC_SUCCESS);
1938: }
1940: /*@
1941: MatSetStencil - Sets the grid information for setting values into a matrix via
1942: `MatSetValuesStencil()`
1944: Not Collective
1946: Input Parameters:
1947: + mat - the matrix
1948: . dim - dimension of the grid 1, 2, or 3
1949: . dims - number of grid points in x, y, and z direction, including ghost points on your processor
1950: . starts - starting point of ghost nodes on your processor in x, y, and z direction
1951: - dof - number of degrees of freedom per node
1953: Level: beginner
1955: Notes:
1956: Inspired by the structured grid interface to the HYPRE package
1957: (www.llnl.gov/CASC/hyper)
1959: For matrices generated with `DMCreateMatrix()` this routine is automatically called and so not needed by the
1960: user.
1962: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
1963: `MatSetValues()`, `MatSetValuesBlockedStencil()`, `MatSetValuesStencil()`
1964: @*/
1965: PetscErrorCode MatSetStencil(Mat mat, PetscInt dim, const PetscInt dims[], const PetscInt starts[], PetscInt dof)
1966: {
1967: PetscFunctionBegin;
1969: PetscAssertPointer(dims, 3);
1970: PetscAssertPointer(starts, 4);
1972: mat->stencil.dim = dim + (dof > 1);
1973: for (PetscInt i = 0; i < dim; i++) {
1974: mat->stencil.dims[i] = dims[dim - i - 1]; /* copy the values in backwards */
1975: mat->stencil.starts[i] = starts[dim - i - 1];
1976: }
1977: mat->stencil.dims[dim] = dof;
1978: mat->stencil.starts[dim] = 0;
1979: mat->stencil.noc = (PetscBool)(dof == 1);
1980: PetscFunctionReturn(PETSC_SUCCESS);
1981: }
1983: /*@
1984: MatSetValuesBlocked - Inserts or adds a block of values into a matrix.
1986: Not Collective
1988: Input Parameters:
1989: + mat - the matrix
1990: . m - the number of block rows
1991: . idxm - the global block indices
1992: . n - the number of block columns
1993: . idxn - the global block indices
1994: . v - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
1995: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
1996: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` replaces existing entries with new values
1998: Level: intermediate
2000: Notes:
2001: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call
2002: MatXXXXSetPreallocation() or `MatSetUp()` before using this routine.
2004: The `m` and `n` count the NUMBER of blocks in the row direction and column direction,
2005: NOT the total number of rows/columns; for example, if the block size is 2 and
2006: you are passing in values for rows 2,3,4,5 then `m` would be 2 (not 4).
2007: The values in `idxm` would be 1 2; that is the first index for each block divided by
2008: the block size.
2010: You must call `MatSetBlockSize()` when constructing this matrix (before
2011: preallocating it).
2013: By default, the values, `v`, are stored in row-major order. See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2015: Calls to `MatSetValuesBlocked()` with the `INSERT_VALUES` and `ADD_VALUES`
2016: options cannot be mixed without intervening calls to the assembly
2017: routines.
2019: `MatSetValuesBlocked()` uses 0-based row and column numbers in Fortran
2020: as well as in C.
2022: Negative indices may be passed in `idxm` and `idxn`, these rows and columns are
2023: simply ignored. This allows easily inserting element stiffness matrices
2024: with homogeneous Dirichlet boundary conditions that you don't want represented
2025: in the matrix.
2027: Each time an entry is set within a sparse matrix via `MatSetValues()`,
2028: internal searching must be done to determine where to place the
2029: data in the matrix storage space. By instead inserting blocks of
2030: entries via `MatSetValuesBlocked()`, the overhead of matrix assembly is
2031: reduced.
2033: Example:
2034: .vb
2035: Suppose m=n=2 and block size(bs) = 2 The array is
2037: 1 2 | 3 4
2038: 5 6 | 7 8
2039: - - - | - - -
2040: 9 10 | 11 12
2041: 13 14 | 15 16
2043: v[] should be passed in like
2044: v[] = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
2046: If you are not using row-oriented storage of v (that is you called MatSetOption(mat,MAT_ROW_ORIENTED,PETSC_FALSE)) then
2047: v[] = [1,5,9,13,2,6,10,14,3,7,11,15,4,8,12,16]
2048: .ve
2050: Fortran Notes:
2051: If any of `idmx`, `idxn`, and `v` are scalars pass them using, for example,
2052: .vb
2053: call MatSetValuesBlocked(mat, one, [idxm], one, [idxn], [v], INSERT_VALUES, ierr)
2054: .ve
2056: If `v` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2058: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesBlockedLocal()`
2059: @*/
2060: PetscErrorCode MatSetValuesBlocked(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], const PetscScalar v[], InsertMode addv)
2061: {
2062: PetscFunctionBeginHot;
2065: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2066: PetscAssertPointer(idxm, 3);
2067: PetscAssertPointer(idxn, 5);
2068: MatCheckPreallocated(mat, 1);
2069: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2070: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2071: if (PetscDefined(USE_DEBUG)) {
2072: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2073: PetscCheck(mat->ops->setvaluesblocked || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2074: }
2075: if (PetscDefined(USE_DEBUG)) {
2076: PetscInt rbs, cbs, M, N, i;
2077: PetscCall(MatGetBlockSizes(mat, &rbs, &cbs));
2078: PetscCall(MatGetSize(mat, &M, &N));
2079: 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);
2080: for (i = 0; i < n; i++)
2081: 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);
2082: }
2083: if (mat->assembled) {
2084: mat->was_assembled = PETSC_TRUE;
2085: mat->assembled = PETSC_FALSE;
2086: }
2087: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2088: if (mat->ops->setvaluesblocked) PetscUseTypeMethod(mat, setvaluesblocked, m, idxm, n, idxn, v, addv);
2089: else {
2090: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *iidxm, *iidxn;
2091: PetscInt i, j, bs, cbs;
2093: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
2094: if ((m * bs + n * cbs) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2095: iidxm = buf;
2096: iidxn = buf + m * bs;
2097: } else {
2098: PetscCall(PetscMalloc2(m * bs, &bufr, n * cbs, &bufc));
2099: iidxm = bufr;
2100: iidxn = bufc;
2101: }
2102: for (i = 0; i < m; i++) {
2103: for (j = 0; j < bs; j++) iidxm[i * bs + j] = bs * idxm[i] + j;
2104: }
2105: if (m != n || bs != cbs || idxm != idxn) {
2106: for (i = 0; i < n; i++) {
2107: for (j = 0; j < cbs; j++) iidxn[i * cbs + j] = cbs * idxn[i] + j;
2108: }
2109: } else iidxn = iidxm;
2110: PetscCall(MatSetValues(mat, m * bs, iidxm, n * cbs, iidxn, v, addv));
2111: PetscCall(PetscFree2(bufr, bufc));
2112: }
2113: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2114: PetscFunctionReturn(PETSC_SUCCESS);
2115: }
2117: /*@
2118: MatGetValues - Gets a block of local values from a matrix.
2120: Not Collective; can only return values that are owned by the give process
2122: Input Parameters:
2123: + mat - the matrix
2124: . v - a logically two-dimensional array for storing the values
2125: . m - the number of rows
2126: . idxm - the global indices of the rows
2127: . n - the number of columns
2128: - idxn - the global indices of the columns
2130: Level: advanced
2132: Notes:
2133: The user must allocate space (m*n `PetscScalar`s) for the values, `v`.
2134: The values, `v`, are then returned in a row-oriented format,
2135: analogous to that used by default in `MatSetValues()`.
2137: `MatGetValues()` uses 0-based row and column numbers in
2138: Fortran as well as in C.
2140: `MatGetValues()` requires that the matrix has been assembled
2141: with `MatAssemblyBegin()`/`MatAssemblyEnd()`. Thus, calls to
2142: `MatSetValues()` and `MatGetValues()` CANNOT be made in succession
2143: without intermediate matrix assembly.
2145: Negative row or column indices will be ignored and those locations in `v` will be
2146: left unchanged.
2148: For the standard row-based matrix formats, `idxm` can only contain rows owned by the requesting MPI process.
2149: That is, rows with global index greater than or equal to rstart and less than rend where rstart and rend are obtainable
2150: from `MatGetOwnershipRange`(mat,&rstart,&rend).
2152: .seealso: [](ch_matrices), `Mat`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatSetValues()`, `MatGetOwnershipRange()`, `MatGetValuesLocal()`, `MatGetValue()`
2153: @*/
2154: PetscErrorCode MatGetValues(Mat mat, PetscInt m, const PetscInt idxm[], PetscInt n, const PetscInt idxn[], PetscScalar v[])
2155: {
2156: PetscFunctionBegin;
2159: if (!m || !n) PetscFunctionReturn(PETSC_SUCCESS);
2160: PetscAssertPointer(idxm, 3);
2161: PetscAssertPointer(idxn, 5);
2162: PetscAssertPointer(v, 6);
2163: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2164: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2165: MatCheckPreallocated(mat, 1);
2167: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2168: PetscUseTypeMethod(mat, getvalues, m, idxm, n, idxn, v);
2169: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2170: PetscFunctionReturn(PETSC_SUCCESS);
2171: }
2173: /*@
2174: MatGetValuesLocal - retrieves values from certain locations in a matrix using the local numbering of the indices
2175: defined previously by `MatSetLocalToGlobalMapping()`
2177: Not Collective
2179: Input Parameters:
2180: + mat - the matrix
2181: . nrow - number of rows
2182: . irow - the row local indices
2183: . ncol - number of columns
2184: - icol - the column local indices
2186: Output Parameter:
2187: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2188: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2190: Level: advanced
2192: Notes:
2193: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine.
2195: 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,
2196: are greater than or equal to rstart and less than rend where rstart and rend are obtainable from `MatGetOwnershipRange`(mat,&rstart,&rend). One can
2197: determine if the resulting global row associated with the local row r is owned by the requesting MPI process by applying the `ISLocalToGlobalMapping` set
2198: with `MatSetLocalToGlobalMapping()`.
2200: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2201: `MatSetValuesLocal()`, `MatGetValues()`
2202: @*/
2203: PetscErrorCode MatGetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], PetscScalar y[])
2204: {
2205: PetscFunctionBeginHot;
2208: MatCheckPreallocated(mat, 1);
2209: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to retrieve */
2210: PetscAssertPointer(irow, 3);
2211: PetscAssertPointer(icol, 5);
2212: if (PetscDefined(USE_DEBUG)) {
2213: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2214: PetscCheck(mat->ops->getvalueslocal || mat->ops->getvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2215: }
2216: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2217: PetscCall(PetscLogEventBegin(MAT_GetValues, mat, 0, 0, 0));
2218: if (mat->ops->getvalueslocal) PetscUseTypeMethod(mat, getvalueslocal, nrow, irow, ncol, icol, y);
2219: else {
2220: PetscInt buf[8192], *bufr = NULL, *bufc = NULL, *irowm, *icolm;
2221: if ((nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2222: irowm = buf;
2223: icolm = buf + nrow;
2224: } else {
2225: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2226: irowm = bufr;
2227: icolm = bufc;
2228: }
2229: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global row mapping (See MatSetLocalToGlobalMapping()).");
2230: PetscCheck(mat->cmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MatGetValuesLocal() cannot proceed without local-to-global column mapping (See MatSetLocalToGlobalMapping()).");
2231: PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, irowm));
2232: PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, icolm));
2233: PetscCall(MatGetValues(mat, nrow, irowm, ncol, icolm, y));
2234: PetscCall(PetscFree2(bufr, bufc));
2235: }
2236: PetscCall(PetscLogEventEnd(MAT_GetValues, mat, 0, 0, 0));
2237: PetscFunctionReturn(PETSC_SUCCESS);
2238: }
2240: /*@
2241: MatSetValuesBatch - Adds (`ADD_VALUES`) many blocks of values into a matrix at once. The blocks must all be square and
2242: the same size. Currently, this can only be called once and creates the given matrix.
2244: Not Collective
2246: Input Parameters:
2247: + mat - the matrix
2248: . nb - the number of blocks
2249: . bs - the number of rows (and columns) in each block
2250: . rows - a concatenation of the rows for each block
2251: - v - a concatenation of logically two-dimensional arrays of values
2253: Level: advanced
2255: Notes:
2256: `MatSetPreallocationCOO()` and `MatSetValuesCOO()` may be a better way to provide the values
2258: In the future, we will extend this routine to handle rectangular blocks, and to allow multiple calls for a given matrix.
2260: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValuesBlocked()`, `MatSetValuesLocal()`,
2261: `InsertMode`, `INSERT_VALUES`, `ADD_VALUES`, `MatSetValues()`, `MatSetPreallocationCOO()`, `MatSetValuesCOO()`
2262: @*/
2263: PetscErrorCode MatSetValuesBatch(Mat mat, PetscInt nb, PetscInt bs, PetscInt rows[], const PetscScalar v[])
2264: {
2265: PetscFunctionBegin;
2268: PetscAssertPointer(rows, 4);
2269: PetscAssertPointer(v, 5);
2270: PetscAssert(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2272: PetscCall(PetscLogEventBegin(MAT_SetValuesBatch, mat, 0, 0, 0));
2273: for (PetscInt b = 0; b < nb; ++b) PetscCall(MatSetValues(mat, bs, &rows[b * bs], bs, &rows[b * bs], &v[b * bs * bs], ADD_VALUES));
2274: PetscCall(PetscLogEventEnd(MAT_SetValuesBatch, mat, 0, 0, 0));
2275: PetscFunctionReturn(PETSC_SUCCESS);
2276: }
2278: /*@
2279: MatSetLocalToGlobalMapping - Sets a local-to-global numbering for use by
2280: the routine `MatSetValuesLocal()` to allow users to insert matrix entries
2281: using a local (per-processor) numbering.
2283: Not Collective
2285: Input Parameters:
2286: + x - the matrix
2287: . rmapping - row mapping created with `ISLocalToGlobalMappingCreate()` or `ISLocalToGlobalMappingCreateIS()`
2288: - cmapping - column mapping
2290: Level: intermediate
2292: Note:
2293: If the matrix is obtained with `DMCreateMatrix()` then this may already have been called on the matrix
2295: .seealso: [](ch_matrices), `Mat`, `DM`, `DMCreateMatrix()`, `MatGetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetValuesLocal()`, `MatGetValuesLocal()`
2296: @*/
2297: PetscErrorCode MatSetLocalToGlobalMapping(Mat x, ISLocalToGlobalMapping rmapping, ISLocalToGlobalMapping cmapping)
2298: {
2299: PetscFunctionBegin;
2304: if (x->ops->setlocaltoglobalmapping) PetscUseTypeMethod(x, setlocaltoglobalmapping, rmapping, cmapping);
2305: else {
2306: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->rmap, rmapping));
2307: PetscCall(PetscLayoutSetISLocalToGlobalMapping(x->cmap, cmapping));
2308: }
2309: PetscFunctionReturn(PETSC_SUCCESS);
2310: }
2312: /*@
2313: MatGetLocalToGlobalMapping - Gets the local-to-global numbering set by `MatSetLocalToGlobalMapping()`
2315: Not Collective
2317: Input Parameter:
2318: . A - the matrix
2320: Output Parameters:
2321: + rmapping - row mapping
2322: - cmapping - column mapping
2324: Level: advanced
2326: .seealso: [](ch_matrices), `Mat`, `MatSetLocalToGlobalMapping()`, `MatSetValuesLocal()`
2327: @*/
2328: PetscErrorCode MatGetLocalToGlobalMapping(Mat A, ISLocalToGlobalMapping *rmapping, ISLocalToGlobalMapping *cmapping)
2329: {
2330: PetscFunctionBegin;
2333: if (rmapping) {
2334: PetscAssertPointer(rmapping, 2);
2335: *rmapping = A->rmap->mapping;
2336: }
2337: if (cmapping) {
2338: PetscAssertPointer(cmapping, 3);
2339: *cmapping = A->cmap->mapping;
2340: }
2341: PetscFunctionReturn(PETSC_SUCCESS);
2342: }
2344: /*@
2345: MatSetLayouts - Sets the `PetscLayout` objects for rows and columns of a matrix
2347: Logically Collective
2349: Input Parameters:
2350: + A - the matrix
2351: . rmap - row layout
2352: - cmap - column layout
2354: Level: advanced
2356: Note:
2357: The `PetscLayout` objects are usually created automatically for the matrix so this routine rarely needs to be called.
2359: .seealso: [](ch_matrices), `Mat`, `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatGetLayouts()`
2360: @*/
2361: PetscErrorCode MatSetLayouts(Mat A, PetscLayout rmap, PetscLayout cmap)
2362: {
2363: PetscFunctionBegin;
2365: PetscCall(PetscLayoutReference(rmap, &A->rmap));
2366: PetscCall(PetscLayoutReference(cmap, &A->cmap));
2367: PetscFunctionReturn(PETSC_SUCCESS);
2368: }
2370: /*@
2371: MatGetLayouts - Gets the `PetscLayout` objects for rows and columns
2373: Not Collective
2375: Input Parameter:
2376: . A - the matrix
2378: Output Parameters:
2379: + rmap - row layout
2380: - cmap - column layout
2382: Level: advanced
2384: .seealso: [](ch_matrices), `Mat`, [Matrix Layouts](sec_matlayout), `PetscLayout`, `MatCreateVecs()`, `MatGetLocalToGlobalMapping()`, `MatSetLayouts()`
2385: @*/
2386: PetscErrorCode MatGetLayouts(Mat A, PetscLayout *rmap, PetscLayout *cmap)
2387: {
2388: PetscFunctionBegin;
2391: if (rmap) {
2392: PetscAssertPointer(rmap, 2);
2393: *rmap = A->rmap;
2394: }
2395: if (cmap) {
2396: PetscAssertPointer(cmap, 3);
2397: *cmap = A->cmap;
2398: }
2399: PetscFunctionReturn(PETSC_SUCCESS);
2400: }
2402: /*@
2403: MatSetValuesLocal - Inserts or adds values into certain locations of a matrix,
2404: using a local numbering of the rows and columns.
2406: Not Collective
2408: Input Parameters:
2409: + mat - the matrix
2410: . nrow - number of rows
2411: . irow - the row local indices
2412: . ncol - number of columns
2413: . icol - the column local indices
2414: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2415: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2416: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2418: Level: intermediate
2420: Notes:
2421: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetLocalToGlobalMapping()` before using this routine
2423: Calls to `MatSetValuesLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2424: options cannot be mixed without intervening calls to the assembly
2425: routines.
2427: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2428: MUST be called after all calls to `MatSetValuesLocal()` have been completed.
2430: Fortran Notes:
2431: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2432: .vb
2433: call MatSetValuesLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2434: .ve
2436: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2438: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatAssemblyEnd()`, `MatSetValues()`, `MatSetLocalToGlobalMapping()`,
2439: `MatGetValuesLocal()`
2440: @*/
2441: PetscErrorCode MatSetValuesLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2442: {
2443: PetscFunctionBeginHot;
2446: MatCheckPreallocated(mat, 1);
2447: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2448: PetscAssertPointer(irow, 3);
2449: PetscAssertPointer(icol, 5);
2450: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2451: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2452: if (PetscDefined(USE_DEBUG)) {
2453: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2454: PetscCheck(mat->ops->setvalueslocal || mat->ops->setvalues, PETSC_COMM_SELF, PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2455: }
2457: if (mat->assembled) {
2458: mat->was_assembled = PETSC_TRUE;
2459: mat->assembled = PETSC_FALSE;
2460: }
2461: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2462: if (mat->ops->setvalueslocal) PetscUseTypeMethod(mat, setvalueslocal, nrow, irow, ncol, icol, y, addv);
2463: else {
2464: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2465: const PetscInt *irowm, *icolm;
2467: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= (PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf)) {
2468: bufr = buf;
2469: bufc = buf + nrow;
2470: irowm = bufr;
2471: icolm = bufc;
2472: } else {
2473: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2474: irowm = bufr;
2475: icolm = bufc;
2476: }
2477: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApply(mat->rmap->mapping, nrow, irow, bufr));
2478: else irowm = irow;
2479: if (mat->cmap->mapping) {
2480: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApply(mat->cmap->mapping, ncol, icol, bufc));
2481: else icolm = irowm;
2482: } else icolm = icol;
2483: PetscCall(MatSetValues(mat, nrow, irowm, ncol, icolm, y, addv));
2484: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2485: }
2486: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2487: PetscFunctionReturn(PETSC_SUCCESS);
2488: }
2490: /*@
2491: MatSetValuesBlockedLocal - Inserts or adds values into certain locations of a matrix,
2492: using a local ordering of the nodes a block at a time.
2494: Not Collective
2496: Input Parameters:
2497: + mat - the matrix
2498: . nrow - number of rows
2499: . irow - the row local indices
2500: . ncol - number of columns
2501: . icol - the column local indices
2502: . y - a one-dimensional array that contains the values implicitly stored as a two-dimensional array, by default in row-major order.
2503: See `MAT_ROW_ORIENTED` in `MatSetOption()` for how to use column-major order.
2504: - addv - either `ADD_VALUES` to add values to any existing entries, or `INSERT_VALUES` to replace existing entries with new values
2506: Level: intermediate
2508: Notes:
2509: If you create the matrix yourself (that is not with a call to `DMCreateMatrix()`) then you MUST call `MatSetBlockSize()` and `MatSetLocalToGlobalMapping()`
2510: before using this routineBefore calling `MatSetValuesLocal()`, the user must first set the
2512: Calls to `MatSetValuesBlockedLocal()` with the `INSERT_VALUES` and `ADD_VALUES`
2513: options cannot be mixed without intervening calls to the assembly
2514: routines.
2516: These values may be cached, so `MatAssemblyBegin()` and `MatAssemblyEnd()`
2517: MUST be called after all calls to `MatSetValuesBlockedLocal()` have been completed.
2519: Fortran Notes:
2520: If any of `irow`, `icol`, and `y` are scalars pass them using, for example,
2521: .vb
2522: call MatSetValuesBlockedLocal(mat, one, [irow], one, [icol], [y], INSERT_VALUES, ierr)
2523: .ve
2525: If `y` is a two-dimensional array use `reshape()` to pass it as a one dimensional array
2527: .seealso: [](ch_matrices), `Mat`, `MatSetBlockSize()`, `MatSetLocalToGlobalMapping()`, `MatAssemblyBegin()`, `MatAssemblyEnd()`,
2528: `MatSetValuesLocal()`, `MatSetValuesBlocked()`
2529: @*/
2530: PetscErrorCode MatSetValuesBlockedLocal(Mat mat, PetscInt nrow, const PetscInt irow[], PetscInt ncol, const PetscInt icol[], const PetscScalar y[], InsertMode addv)
2531: {
2532: PetscFunctionBeginHot;
2535: MatCheckPreallocated(mat, 1);
2536: if (!nrow || !ncol) PetscFunctionReturn(PETSC_SUCCESS); /* no values to insert */
2537: PetscAssertPointer(irow, 3);
2538: PetscAssertPointer(icol, 5);
2539: if (mat->insertmode == NOT_SET_VALUES) mat->insertmode = addv;
2540: else PetscCheck(mat->insertmode == addv, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot mix add values and insert values");
2541: if (PetscDefined(USE_DEBUG)) {
2542: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2543: 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);
2544: }
2546: if (mat->assembled) {
2547: mat->was_assembled = PETSC_TRUE;
2548: mat->assembled = PETSC_FALSE;
2549: }
2550: if (PetscUnlikelyDebug(mat->rmap->mapping)) { /* Condition on the mapping existing, because MatSetValuesBlockedLocal_IS does not require it to be set. */
2551: PetscInt irbs, rbs;
2552: PetscCall(MatGetBlockSizes(mat, &rbs, NULL));
2553: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->rmap->mapping, &irbs));
2554: PetscCheck(rbs == irbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different row block sizes! mat %" PetscInt_FMT ", row l2g map %" PetscInt_FMT, rbs, irbs);
2555: }
2556: if (PetscUnlikelyDebug(mat->cmap->mapping)) {
2557: PetscInt icbs, cbs;
2558: PetscCall(MatGetBlockSizes(mat, NULL, &cbs));
2559: PetscCall(ISLocalToGlobalMappingGetBlockSize(mat->cmap->mapping, &icbs));
2560: PetscCheck(cbs == icbs, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Different col block sizes! mat %" PetscInt_FMT ", col l2g map %" PetscInt_FMT, cbs, icbs);
2561: }
2562: PetscCall(PetscLogEventBegin(MAT_SetValues, mat, 0, 0, 0));
2563: if (mat->ops->setvaluesblockedlocal) PetscUseTypeMethod(mat, setvaluesblockedlocal, nrow, irow, ncol, icol, y, addv);
2564: else {
2565: PetscInt buf[8192], *bufr = NULL, *bufc = NULL;
2566: const PetscInt *irowm, *icolm;
2568: if ((!mat->rmap->mapping && !mat->cmap->mapping) || (nrow + ncol) <= ((PetscInt)PETSC_STATIC_ARRAY_LENGTH(buf))) {
2569: bufr = buf;
2570: bufc = buf + nrow;
2571: irowm = bufr;
2572: icolm = bufc;
2573: } else {
2574: PetscCall(PetscMalloc2(nrow, &bufr, ncol, &bufc));
2575: irowm = bufr;
2576: icolm = bufc;
2577: }
2578: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->rmap->mapping, nrow, irow, bufr));
2579: else irowm = irow;
2580: if (mat->cmap->mapping) {
2581: if (mat->cmap->mapping != mat->rmap->mapping || ncol != nrow || icol != irow) PetscCall(ISLocalToGlobalMappingApplyBlock(mat->cmap->mapping, ncol, icol, bufc));
2582: else icolm = irowm;
2583: } else icolm = icol;
2584: PetscCall(MatSetValuesBlocked(mat, nrow, irowm, ncol, icolm, y, addv));
2585: if (bufr != buf) PetscCall(PetscFree2(bufr, bufc));
2586: }
2587: PetscCall(PetscLogEventEnd(MAT_SetValues, mat, 0, 0, 0));
2588: PetscFunctionReturn(PETSC_SUCCESS);
2589: }
2591: /*@
2592: MatMultDiagonalBlock - Computes the matrix-vector product, $y = Dx$. Where `D` is defined by the inode or block structure of the diagonal
2594: Collective
2596: Input Parameters:
2597: + mat - the matrix
2598: - x - the vector to be multiplied
2600: Output Parameter:
2601: . y - the result
2603: Level: developer
2605: Note:
2606: The vectors `x` and `y` cannot be the same. I.e., one cannot
2607: call `MatMultDiagonalBlock`(A,y,y).
2609: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2610: @*/
2611: PetscErrorCode MatMultDiagonalBlock(Mat mat, Vec x, Vec y)
2612: {
2613: PetscFunctionBegin;
2619: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2620: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2621: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2622: MatCheckPreallocated(mat, 1);
2624: PetscUseTypeMethod(mat, multdiagonalblock, x, y);
2625: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2626: PetscFunctionReturn(PETSC_SUCCESS);
2627: }
2629: /*@
2630: MatMult - Computes the matrix-vector product, $y = Ax$.
2632: Neighbor-wise Collective
2634: Input Parameters:
2635: + mat - the matrix
2636: - x - the vector to be multiplied
2638: Output Parameter:
2639: . y - the result
2641: Level: beginner
2643: Note:
2644: The vectors `x` and `y` cannot be the same. I.e., one cannot
2645: call `MatMult`(A,y,y).
2647: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
2648: @*/
2649: PetscErrorCode MatMult(Mat mat, Vec x, Vec y)
2650: {
2651: PetscFunctionBegin;
2655: VecCheckAssembled(x);
2657: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2658: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2659: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2660: 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);
2661: 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);
2662: 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);
2663: 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);
2664: PetscCall(VecSetErrorIfLocked(y, 3));
2665: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2666: MatCheckPreallocated(mat, 1);
2668: PetscCall(VecLockReadPush(x));
2669: PetscCall(PetscLogEventBegin(MAT_Mult, mat, x, y, 0));
2670: PetscUseTypeMethod(mat, mult, x, y);
2671: PetscCall(PetscLogEventEnd(MAT_Mult, mat, x, y, 0));
2672: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2673: PetscCall(VecLockReadPop(x));
2674: PetscFunctionReturn(PETSC_SUCCESS);
2675: }
2677: /*@
2678: MatMultTranspose - Computes matrix transpose times a vector $y = A^T * x$.
2680: Neighbor-wise Collective
2682: Input Parameters:
2683: + mat - the matrix
2684: - x - the vector to be multiplied
2686: Output Parameter:
2687: . y - the result
2689: Level: beginner
2691: Notes:
2692: The vectors `x` and `y` cannot be the same. I.e., one cannot
2693: call `MatMultTranspose`(A,y,y).
2695: For complex numbers this does NOT compute the Hermitian (complex conjugate) transpose multiple,
2696: use `MatMultHermitianTranspose()`
2698: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatMultHermitianTranspose()`, `MatTranspose()`
2699: @*/
2700: PetscErrorCode MatMultTranspose(Mat mat, Vec x, Vec y)
2701: {
2702: PetscErrorCode (*op)(Mat, Vec, Vec) = NULL;
2704: PetscFunctionBegin;
2708: VecCheckAssembled(x);
2711: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2712: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2713: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2714: 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);
2715: 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);
2716: 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);
2717: 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);
2718: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
2719: MatCheckPreallocated(mat, 1);
2721: if (!mat->ops->multtranspose) {
2722: if (mat->symmetric == PETSC_BOOL3_TRUE && mat->ops->mult) op = mat->ops->mult;
2723: 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);
2724: } else op = mat->ops->multtranspose;
2725: PetscCall(PetscLogEventBegin(MAT_MultTranspose, mat, x, y, 0));
2726: PetscCall(VecLockReadPush(x));
2727: PetscCall((*op)(mat, x, y));
2728: PetscCall(VecLockReadPop(x));
2729: PetscCall(PetscLogEventEnd(MAT_MultTranspose, mat, x, y, 0));
2730: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2731: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_FALSE));
2732: PetscFunctionReturn(PETSC_SUCCESS);
2733: }
2735: /*@
2736: MatMultHermitianTranspose - Computes matrix Hermitian-transpose times a vector $y = A^H * x$.
2738: Neighbor-wise Collective
2740: Input Parameters:
2741: + mat - the matrix
2742: - x - the vector to be multiplied
2744: Output Parameter:
2745: . y - the result
2747: Level: beginner
2749: Notes:
2750: The vectors `x` and `y` cannot be the same. I.e., one cannot
2751: call `MatMultHermitianTranspose`(A,y,y).
2753: Also called the conjugate transpose, complex conjugate transpose, or adjoint.
2755: For real numbers `MatMultTranspose()` and `MatMultHermitianTranspose()` are identical.
2757: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `MatMultHermitianTransposeAdd()`, `MatMultTranspose()`
2758: @*/
2759: PetscErrorCode MatMultHermitianTranspose(Mat mat, Vec x, Vec y)
2760: {
2761: PetscFunctionBegin;
2767: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2768: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2769: PetscCheck(x != y, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "x and y must be different vectors");
2770: 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);
2771: 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);
2772: 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);
2773: 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);
2774: MatCheckPreallocated(mat, 1);
2776: PetscCall(PetscLogEventBegin(MAT_MultHermitianTranspose, mat, x, y, 0));
2777: #if defined(PETSC_USE_COMPLEX)
2778: if (mat->ops->multhermitiantranspose || (mat->hermitian == PETSC_BOOL3_TRUE && mat->ops->mult)) {
2779: PetscCall(VecLockReadPush(x));
2780: if (mat->ops->multhermitiantranspose) PetscUseTypeMethod(mat, multhermitiantranspose, x, y);
2781: else PetscUseTypeMethod(mat, mult, x, y);
2782: PetscCall(VecLockReadPop(x));
2783: } else {
2784: Vec w;
2785: PetscCall(VecDuplicate(x, &w));
2786: PetscCall(VecCopy(x, w));
2787: PetscCall(VecConjugate(w));
2788: PetscCall(MatMultTranspose(mat, w, y));
2789: PetscCall(VecDestroy(&w));
2790: PetscCall(VecConjugate(y));
2791: }
2792: PetscCall(PetscObjectStateIncrease((PetscObject)y));
2793: #else
2794: PetscCall(MatMultTranspose(mat, x, y));
2795: #endif
2796: PetscCall(PetscLogEventEnd(MAT_MultHermitianTranspose, mat, x, y, 0));
2797: PetscFunctionReturn(PETSC_SUCCESS);
2798: }
2800: /*@
2801: MatMultAdd - Computes $v3 = v2 + A * v1$.
2803: Neighbor-wise Collective
2805: Input Parameters:
2806: + mat - the matrix
2807: . v1 - the vector to be multiplied by `mat`
2808: - v2 - the vector to be added to the result
2810: Output Parameter:
2811: . v3 - the result
2813: Level: beginner
2815: Note:
2816: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2817: call `MatMultAdd`(A,v1,v2,v1).
2819: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMult()`, `MatMultTransposeAdd()`
2820: @*/
2821: PetscErrorCode MatMultAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2822: {
2823: PetscFunctionBegin;
2830: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2831: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2832: 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);
2833: /* 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);
2834: 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); */
2835: 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);
2836: 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);
2837: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2838: MatCheckPreallocated(mat, 1);
2840: PetscCall(PetscLogEventBegin(MAT_MultAdd, mat, v1, v2, v3));
2841: PetscCall(VecLockReadPush(v1));
2842: PetscUseTypeMethod(mat, multadd, v1, v2, v3);
2843: PetscCall(VecLockReadPop(v1));
2844: PetscCall(PetscLogEventEnd(MAT_MultAdd, mat, v1, v2, v3));
2845: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2846: PetscFunctionReturn(PETSC_SUCCESS);
2847: }
2849: /*@
2850: MatMultTransposeAdd - Computes $v3 = v2 + A^T * v1$.
2852: Neighbor-wise Collective
2854: Input Parameters:
2855: + mat - the matrix
2856: . v1 - the vector to be multiplied by the transpose of the matrix
2857: - v2 - the vector to be added to the result
2859: Output Parameter:
2860: . v3 - the result
2862: Level: beginner
2864: Note:
2865: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2866: call `MatMultTransposeAdd`(A,v1,v2,v1).
2868: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2869: @*/
2870: PetscErrorCode MatMultTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2871: {
2872: PetscErrorCode (*op)(Mat, Vec, Vec, Vec) = (!mat->ops->multtransposeadd && mat->symmetric) ? mat->ops->multadd : mat->ops->multtransposeadd;
2874: PetscFunctionBegin;
2881: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2882: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2883: 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);
2884: 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);
2885: 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);
2886: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2887: PetscCheck(op, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)mat)->type_name);
2888: MatCheckPreallocated(mat, 1);
2890: PetscCall(PetscLogEventBegin(MAT_MultTransposeAdd, mat, v1, v2, v3));
2891: PetscCall(VecLockReadPush(v1));
2892: PetscCall((*op)(mat, v1, v2, v3));
2893: PetscCall(VecLockReadPop(v1));
2894: PetscCall(PetscLogEventEnd(MAT_MultTransposeAdd, mat, v1, v2, v3));
2895: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2896: PetscFunctionReturn(PETSC_SUCCESS);
2897: }
2899: /*@
2900: MatMultHermitianTransposeAdd - Computes $v3 = v2 + A^H * v1$.
2902: Neighbor-wise Collective
2904: Input Parameters:
2905: + mat - the matrix
2906: . v1 - the vector to be multiplied by the Hermitian transpose
2907: - v2 - the vector to be added to the result
2909: Output Parameter:
2910: . v3 - the result
2912: Level: beginner
2914: Note:
2915: The vectors `v1` and `v3` cannot be the same. I.e., one cannot
2916: call `MatMultHermitianTransposeAdd`(A,v1,v2,v1).
2918: .seealso: [](ch_matrices), `Mat`, `MatMultHermitianTranspose()`, `MatMultTranspose()`, `MatMultAdd()`, `MatMult()`
2919: @*/
2920: PetscErrorCode MatMultHermitianTransposeAdd(Mat mat, Vec v1, Vec v2, Vec v3)
2921: {
2922: PetscFunctionBegin;
2929: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
2930: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
2931: PetscCheck(v1 != v3, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "v1 and v3 must be different vectors");
2932: 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);
2933: 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);
2934: 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);
2935: MatCheckPreallocated(mat, 1);
2937: PetscCall(PetscLogEventBegin(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2938: PetscCall(VecLockReadPush(v1));
2939: if (mat->ops->multhermitiantransposeadd) PetscUseTypeMethod(mat, multhermitiantransposeadd, v1, v2, v3);
2940: else {
2941: Vec w, z;
2942: PetscCall(VecDuplicate(v1, &w));
2943: PetscCall(VecCopy(v1, w));
2944: PetscCall(VecConjugate(w));
2945: PetscCall(VecDuplicate(v3, &z));
2946: PetscCall(MatMultTranspose(mat, w, z));
2947: PetscCall(VecDestroy(&w));
2948: PetscCall(VecConjugate(z));
2949: if (v2 != v3) PetscCall(VecWAXPY(v3, 1.0, v2, z));
2950: else PetscCall(VecAXPY(v3, 1.0, z));
2951: PetscCall(VecDestroy(&z));
2952: }
2953: PetscCall(VecLockReadPop(v1));
2954: PetscCall(PetscLogEventEnd(MAT_MultHermitianTransposeAdd, mat, v1, v2, v3));
2955: PetscCall(PetscObjectStateIncrease((PetscObject)v3));
2956: PetscFunctionReturn(PETSC_SUCCESS);
2957: }
2959: PetscErrorCode MatADot_Default(Mat mat, Vec x, Vec y, PetscScalar *val)
2960: {
2961: PetscFunctionBegin;
2962: if (!mat->dot_vec) PetscCall(MatCreateVecs(mat, &mat->dot_vec, NULL));
2963: PetscCall(MatMult(mat, x, mat->dot_vec));
2964: PetscCall(VecDot(mat->dot_vec, y, val));
2965: PetscFunctionReturn(PETSC_SUCCESS);
2966: }
2968: PetscErrorCode MatANorm_Default(Mat mat, Vec x, PetscReal *val)
2969: {
2970: PetscScalar sval;
2972: PetscFunctionBegin;
2973: PetscCall(MatADot_Default(mat, x, x, &sval));
2974: PetscCheck(PetscRealPart(sval) >= 0.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not positive definite");
2975: PetscCheck(PetscAbsReal(PetscImaginaryPart(sval)) < 100 * PETSC_MACHINE_EPSILON, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix argument is not Hermitian");
2976: *val = PetscSqrtReal(PetscRealPart(sval));
2977: PetscFunctionReturn(PETSC_SUCCESS);
2978: }
2980: /*@
2981: MatADot - Computes the inner product with respect to a matrix, i.e., $(x, y)_A = y^H A x$ where $A$ is symmetric (Hermitian when using complex)
2982: positive definite.
2984: Collective
2986: Input Parameters:
2987: + mat - matrix used to define the inner product
2988: . x - first vector
2989: - y - second vector
2991: Output Parameter:
2992: . val - the dot product with respect to `A`
2994: Level: intermediate
2996: Note:
2997: For complex vectors, `MatADot()` computes
2998: $$
2999: val = (x,y)_A = y^H A x,
3000: $$
3001: where $y^H$ denotes the conjugate transpose of `y`. Note that this corresponds to the "mathematicians" complex
3002: inner product where the SECOND argument gets the complex conjugate.
3004: .seealso: [](ch_matrices), `Mat`, `MatANorm()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3005: @*/
3006: PetscErrorCode MatADot(Mat mat, Vec x, Vec y, PetscScalar *val)
3007: {
3008: PetscFunctionBegin;
3012: VecCheckAssembled(x);
3014: VecCheckAssembled(y);
3017: PetscAssertPointer(val, 4);
3018: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3019: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3020: 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);
3021: 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);
3022: 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);
3023: 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);
3024: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3025: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(y, 3, PETSC_TRUE));
3026: MatCheckPreallocated(mat, 1);
3028: PetscCall(VecLockReadPush(x));
3029: PetscCall(VecLockReadPush(y));
3030: PetscCall(PetscLogEventBegin(MAT_ADot, mat, x, y, 0));
3031: PetscUseTypeMethod(mat, adot, x, y, val);
3032: PetscCall(PetscLogEventEnd(MAT_ADot, mat, x, y, 0));
3033: PetscCall(VecLockReadPop(y));
3034: PetscCall(VecLockReadPop(x));
3035: PetscFunctionReturn(PETSC_SUCCESS);
3036: }
3038: /*@
3039: MatANorm - Computes the norm with respect to a matrix, i.e., $(x, x)_A^{1/2} = (x^H A x)^{1/2}$ where $A$ is symmetric (Hermitian when using complex)
3040: positive definite.
3042: Collective
3044: Input Parameters:
3045: + mat - matrix used to define norm
3046: - x - the vector to compute the norm of
3048: Output Parameter:
3049: . val - the norm with respect to `A`
3051: Level: intermediate
3053: Note:
3054: For complex vectors, `MatANorm()` computes
3055: $$
3056: val = (x,x)_A^{1/2} = (x^H A x)^{1/2},
3057: $$
3058: where $x^H$ denotes the conjugate transpose of `x`.
3060: .seealso: [](ch_matrices), `Mat`, `MatADot()`, `VecDot()`, `VecNorm()`, `MatMult()`, `MatMultAdd()`, `MatMultTransposeAdd()`
3061: @*/
3062: PetscErrorCode MatANorm(Mat mat, Vec x, PetscReal *val)
3063: {
3064: PetscFunctionBegin;
3068: VecCheckAssembled(x);
3070: PetscAssertPointer(val, 3);
3071: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3072: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3073: 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);
3074: 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);
3075: 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);
3076: 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);
3077: if (mat->erroriffailure) PetscCall(VecValidValues_Internal(x, 2, PETSC_TRUE));
3078: MatCheckPreallocated(mat, 1);
3080: PetscCall(VecLockReadPush(x));
3081: PetscCall(PetscLogEventBegin(MAT_ANorm, mat, x, 0, 0));
3082: PetscUseTypeMethod(mat, anorm, x, val);
3083: PetscCall(PetscLogEventEnd(MAT_ANorm, mat, x, 0, 0));
3084: PetscCall(VecLockReadPop(x));
3085: PetscFunctionReturn(PETSC_SUCCESS);
3086: }
3088: /*@
3089: MatGetFactorType - gets the type of factorization a matrix is
3091: Not Collective
3093: Input Parameter:
3094: . mat - the matrix
3096: Output Parameter:
3097: . 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`
3099: Level: intermediate
3101: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatSetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3102: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3103: @*/
3104: PetscErrorCode MatGetFactorType(Mat mat, MatFactorType *t)
3105: {
3106: PetscFunctionBegin;
3109: PetscAssertPointer(t, 2);
3110: *t = mat->factortype;
3111: PetscFunctionReturn(PETSC_SUCCESS);
3112: }
3114: /*@
3115: MatSetFactorType - sets the type of factorization a matrix is
3117: Logically Collective
3119: Input Parameters:
3120: + mat - the matrix
3121: - 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`
3123: Level: intermediate
3125: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatGetFactor()`, `MatGetFactorType()`, `MAT_FACTOR_NONE`, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ILU`,
3126: `MAT_FACTOR_ICC`, `MAT_FACTOR_ILUDT`, `MAT_FACTOR_QR`
3127: @*/
3128: PetscErrorCode MatSetFactorType(Mat mat, MatFactorType t)
3129: {
3130: PetscFunctionBegin;
3133: mat->factortype = t;
3134: PetscFunctionReturn(PETSC_SUCCESS);
3135: }
3137: /*@
3138: MatGetInfo - Returns information about matrix storage (number of
3139: nonzeros, memory, etc.).
3141: Collective if `MAT_GLOBAL_MAX` or `MAT_GLOBAL_SUM` is used as the flag
3143: Input Parameters:
3144: + mat - the matrix
3145: - 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)
3147: Output Parameter:
3148: . info - matrix information context
3150: Options Database Key:
3151: . -mat_view ::ascii_info - print matrix info to `PETSC_STDOUT`
3153: Level: intermediate
3155: Notes:
3156: The `MatInfo` context contains a variety of matrix data, including
3157: number of nonzeros allocated and used, number of mallocs during
3158: matrix assembly, etc. Additional information for factored matrices
3159: is provided (such as the fill ratio, number of mallocs during
3160: factorization, etc.).
3162: Example:
3163: See the file ${PETSC_DIR}/include/petscmat.h for a complete list of
3164: data within the `MatInfo` context. For example,
3165: .vb
3166: MatInfo info;
3167: Mat A;
3168: double mal, nz_a, nz_u;
3170: MatGetInfo(A, MAT_LOCAL, &info);
3171: mal = info.mallocs;
3172: nz_a = info.nz_allocated;
3173: .ve
3175: .seealso: [](ch_matrices), `Mat`, `MatInfo`, `MatStashGetInfo()`
3176: @*/
3177: PetscErrorCode MatGetInfo(Mat mat, MatInfoType flag, MatInfo *info)
3178: {
3179: PetscFunctionBegin;
3182: PetscAssertPointer(info, 3);
3183: MatCheckPreallocated(mat, 1);
3184: PetscUseTypeMethod(mat, getinfo, flag, info);
3185: PetscFunctionReturn(PETSC_SUCCESS);
3186: }
3188: /*
3189: This is used by external packages where it is not easy to get the info from the actual
3190: matrix factorization.
3191: */
3192: PetscErrorCode MatGetInfo_External(Mat A, MatInfoType flag, MatInfo *info)
3193: {
3194: PetscFunctionBegin;
3195: PetscCall(PetscMemzero(info, sizeof(MatInfo)));
3196: PetscFunctionReturn(PETSC_SUCCESS);
3197: }
3199: /*@
3200: MatLUFactor - Performs in-place LU factorization of matrix.
3202: Collective
3204: Input Parameters:
3205: + mat - the matrix
3206: . row - row permutation
3207: . col - column permutation
3208: - info - options for factorization, includes
3209: .vb
3210: fill - expected fill as ratio of original fill.
3211: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3212: Run with the option -info to determine an optimal value to use
3213: .ve
3215: Level: developer
3217: Notes:
3218: Most users should employ the `KSP` interface for linear solvers
3219: instead of working directly with matrix algebra routines such as this.
3220: See, e.g., `KSPCreate()`.
3222: This changes the state of the matrix to a factored matrix; it cannot be used
3223: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3225: This is really in-place only for dense matrices, the preferred approach is to use `MatGetFactor()`, `MatLUFactorSymbolic()`, and `MatLUFactorNumeric()`
3226: when not using `KSP`.
3228: Fortran Note:
3229: A valid (non-null) `info` argument must be provided
3231: .seealso: [](ch_matrices), [Matrix Factorization](sec_matfactor), `Mat`, `MatFactorType`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
3232: `MatGetOrdering()`, `MatSetUnfactored()`, `MatFactorInfo`, `MatGetFactor()`
3233: @*/
3234: PetscErrorCode MatLUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3235: {
3236: MatFactorInfo tinfo;
3238: PetscFunctionBegin;
3242: if (info) PetscAssertPointer(info, 4);
3244: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3245: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3246: MatCheckPreallocated(mat, 1);
3247: if (!info) {
3248: PetscCall(MatFactorInfoInitialize(&tinfo));
3249: info = &tinfo;
3250: }
3252: PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, row, col, 0));
3253: PetscUseTypeMethod(mat, lufactor, row, col, info);
3254: PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, row, col, 0));
3255: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3256: PetscFunctionReturn(PETSC_SUCCESS);
3257: }
3259: /*@
3260: MatILUFactor - Performs in-place ILU factorization of matrix.
3262: Collective
3264: Input Parameters:
3265: + mat - the matrix
3266: . row - row permutation
3267: . col - column permutation
3268: - info - structure containing
3269: .vb
3270: levels - number of levels of fill.
3271: expected fill - as ratio of original fill.
3272: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
3273: missing diagonal entries)
3274: .ve
3276: Level: developer
3278: Notes:
3279: Most users should employ the `KSP` interface for linear solvers
3280: instead of working directly with matrix algebra routines such as this.
3281: See, e.g., `KSPCreate()`.
3283: Probably really in-place only when level of fill is zero, otherwise allocates
3284: new space to store factored matrix and deletes previous memory. The preferred approach is to use `MatGetFactor()`, `MatILUFactorSymbolic()`, and `MatILUFactorNumeric()`
3285: when not using `KSP`.
3287: Fortran Note:
3288: A valid (non-null) `info` argument must be provided
3290: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatILUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
3291: @*/
3292: PetscErrorCode MatILUFactor(Mat mat, IS row, IS col, const MatFactorInfo *info)
3293: {
3294: PetscFunctionBegin;
3298: PetscAssertPointer(info, 4);
3300: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
3301: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3302: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3303: MatCheckPreallocated(mat, 1);
3305: PetscCall(PetscLogEventBegin(MAT_ILUFactor, mat, row, col, 0));
3306: PetscUseTypeMethod(mat, ilufactor, row, col, info);
3307: PetscCall(PetscLogEventEnd(MAT_ILUFactor, mat, row, col, 0));
3308: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3309: PetscFunctionReturn(PETSC_SUCCESS);
3310: }
3312: /*@
3313: MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
3314: Call this routine before calling `MatLUFactorNumeric()` and after `MatGetFactor()`.
3316: Collective
3318: Input Parameters:
3319: + fact - the factor matrix obtained with `MatGetFactor()`
3320: . mat - the matrix
3321: . row - the row permutation
3322: . col - the column permutation
3323: - info - options for factorization, includes
3324: .vb
3325: fill - expected fill as ratio of original fill. Run with the option -info to determine an optimal value to use
3326: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3327: .ve
3329: Level: developer
3331: Notes:
3332: See [Matrix Factorization](sec_matfactor) for additional information about factorizations
3334: Most users should employ the simplified `KSP` interface for linear solvers
3335: instead of working directly with matrix algebra routines such as this.
3336: See, e.g., `KSPCreate()`.
3338: Fortran Note:
3339: A valid (non-null) `info` argument must be provided
3341: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`, `MatFactorInfoInitialize()`
3342: @*/
3343: PetscErrorCode MatLUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
3344: {
3345: MatFactorInfo tinfo;
3347: PetscFunctionBegin;
3352: if (info) PetscAssertPointer(info, 5);
3355: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3356: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3357: MatCheckPreallocated(mat, 2);
3358: if (!info) {
3359: PetscCall(MatFactorInfoInitialize(&tinfo));
3360: info = &tinfo;
3361: }
3363: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorSymbolic, mat, row, col, 0));
3364: PetscUseTypeMethod(fact, lufactorsymbolic, mat, row, col, info);
3365: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorSymbolic, mat, row, col, 0));
3366: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3367: PetscFunctionReturn(PETSC_SUCCESS);
3368: }
3370: /*@
3371: MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
3372: Call this routine after first calling `MatLUFactorSymbolic()` and `MatGetFactor()`.
3374: Collective
3376: Input Parameters:
3377: + fact - the factor matrix obtained with `MatGetFactor()`
3378: . mat - the matrix
3379: - info - options for factorization
3381: Level: developer
3383: Notes:
3384: See `MatLUFactor()` for in-place factorization. See
3385: `MatCholeskyFactorNumeric()` for the symmetric, positive definite case.
3387: Most users should employ the `KSP` interface for linear solvers
3388: instead of working directly with matrix algebra routines such as this.
3389: See, e.g., `KSPCreate()`.
3391: Fortran Note:
3392: A valid (non-null) `info` argument must be provided
3394: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactorSymbolic()`, `MatLUFactor()`, `MatCholeskyFactor()`
3395: @*/
3396: PetscErrorCode MatLUFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3397: {
3398: MatFactorInfo tinfo;
3400: PetscFunctionBegin;
3405: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3406: 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,
3407: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3409: MatCheckPreallocated(mat, 2);
3410: if (!info) {
3411: PetscCall(MatFactorInfoInitialize(&tinfo));
3412: info = &tinfo;
3413: }
3415: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_LUFactorNumeric, mat, fact, 0, 0));
3416: else PetscCall(PetscLogEventBegin(MAT_LUFactor, mat, fact, 0, 0));
3417: PetscUseTypeMethod(fact, lufactornumeric, mat, info);
3418: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_LUFactorNumeric, mat, fact, 0, 0));
3419: else PetscCall(PetscLogEventEnd(MAT_LUFactor, mat, fact, 0, 0));
3420: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3421: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3422: PetscFunctionReturn(PETSC_SUCCESS);
3423: }
3425: /*@
3426: MatCholeskyFactor - Performs in-place Cholesky factorization of a
3427: symmetric matrix.
3429: Collective
3431: Input Parameters:
3432: + mat - the matrix
3433: . perm - row and column permutations
3434: - info - expected fill as ratio of original fill
3436: Level: developer
3438: Notes:
3439: See `MatLUFactor()` for the nonsymmetric case. See also `MatGetFactor()`,
3440: `MatCholeskyFactorSymbolic()`, and `MatCholeskyFactorNumeric()`.
3442: Most users should employ the `KSP` interface for linear solvers
3443: instead of working directly with matrix algebra routines such as this.
3444: See, e.g., `KSPCreate()`.
3446: Fortran Note:
3447: A valid (non-null) `info` argument must be provided
3449: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatLUFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactorNumeric()`,
3450: `MatGetOrdering()`
3451: @*/
3452: PetscErrorCode MatCholeskyFactor(Mat mat, IS perm, const MatFactorInfo *info)
3453: {
3454: MatFactorInfo tinfo;
3456: PetscFunctionBegin;
3459: if (info) PetscAssertPointer(info, 3);
3461: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3462: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3463: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3464: MatCheckPreallocated(mat, 1);
3465: if (!info) {
3466: PetscCall(MatFactorInfoInitialize(&tinfo));
3467: info = &tinfo;
3468: }
3470: PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, perm, 0, 0));
3471: PetscUseTypeMethod(mat, choleskyfactor, perm, info);
3472: PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, perm, 0, 0));
3473: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3474: PetscFunctionReturn(PETSC_SUCCESS);
3475: }
3477: /*@
3478: MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
3479: of a symmetric matrix.
3481: Collective
3483: Input Parameters:
3484: + fact - the factor matrix obtained with `MatGetFactor()`
3485: . mat - the matrix
3486: . perm - row and column permutations
3487: - info - options for factorization, includes
3488: .vb
3489: fill - expected fill as ratio of original fill.
3490: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3491: Run with the option -info to determine an optimal value to use
3492: .ve
3494: Level: developer
3496: Notes:
3497: See `MatLUFactorSymbolic()` for the nonsymmetric case. See also
3498: `MatCholeskyFactor()` and `MatCholeskyFactorNumeric()`.
3500: Most users should employ the `KSP` interface for linear solvers
3501: instead of working directly with matrix algebra routines such as this.
3502: See, e.g., `KSPCreate()`.
3504: Fortran Note:
3505: A valid (non-null) `info` argument must be provided
3507: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactor()`, `MatCholeskyFactorNumeric()`,
3508: `MatGetOrdering()`
3509: @*/
3510: PetscErrorCode MatCholeskyFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
3511: {
3512: MatFactorInfo tinfo;
3514: PetscFunctionBegin;
3518: if (info) PetscAssertPointer(info, 4);
3521: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "Matrix must be square");
3522: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3523: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3524: MatCheckPreallocated(mat, 2);
3525: if (!info) {
3526: PetscCall(MatFactorInfoInitialize(&tinfo));
3527: info = &tinfo;
3528: }
3530: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3531: PetscUseTypeMethod(fact, choleskyfactorsymbolic, mat, perm, info);
3532: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorSymbolic, mat, perm, 0, 0));
3533: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3534: PetscFunctionReturn(PETSC_SUCCESS);
3535: }
3537: /*@
3538: MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
3539: of a symmetric matrix. Call this routine after first calling `MatGetFactor()` and
3540: `MatCholeskyFactorSymbolic()`.
3542: Collective
3544: Input Parameters:
3545: + fact - the factor matrix obtained with `MatGetFactor()`, where the factored values are stored
3546: . mat - the initial matrix that is to be factored
3547: - info - options for factorization
3549: Level: developer
3551: Note:
3552: Most users should employ the `KSP` interface for linear solvers
3553: instead of working directly with matrix algebra routines such as this.
3554: See, e.g., `KSPCreate()`.
3556: Fortran Note:
3557: A valid (non-null) `info` argument must be provided
3559: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatCholeskyFactorSymbolic()`, `MatCholeskyFactor()`, `MatLUFactorNumeric()`
3560: @*/
3561: PetscErrorCode MatCholeskyFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3562: {
3563: MatFactorInfo tinfo;
3565: PetscFunctionBegin;
3570: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3571: 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,
3572: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3573: MatCheckPreallocated(mat, 2);
3574: if (!info) {
3575: PetscCall(MatFactorInfoInitialize(&tinfo));
3576: info = &tinfo;
3577: }
3579: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3580: else PetscCall(PetscLogEventBegin(MAT_CholeskyFactor, mat, fact, 0, 0));
3581: PetscUseTypeMethod(fact, choleskyfactornumeric, mat, info);
3582: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_CholeskyFactorNumeric, mat, fact, 0, 0));
3583: else PetscCall(PetscLogEventEnd(MAT_CholeskyFactor, mat, fact, 0, 0));
3584: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3585: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3586: PetscFunctionReturn(PETSC_SUCCESS);
3587: }
3589: /*@
3590: MatQRFactor - Performs in-place QR factorization of matrix.
3592: Collective
3594: Input Parameters:
3595: + mat - the matrix
3596: . col - column permutation
3597: - info - options for factorization, includes
3598: .vb
3599: fill - expected fill as ratio of original fill.
3600: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3601: Run with the option -info to determine an optimal value to use
3602: .ve
3604: Level: developer
3606: Notes:
3607: Most users should employ the `KSP` interface for linear solvers
3608: instead of working directly with matrix algebra routines such as this.
3609: See, e.g., `KSPCreate()`.
3611: This changes the state of the matrix to a factored matrix; it cannot be used
3612: for example with `MatSetValues()` unless one first calls `MatSetUnfactored()`.
3614: Fortran Note:
3615: A valid (non-null) `info` argument must be provided
3617: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactorSymbolic()`, `MatQRFactorNumeric()`, `MatLUFactor()`,
3618: `MatSetUnfactored()`
3619: @*/
3620: PetscErrorCode MatQRFactor(Mat mat, IS col, const MatFactorInfo *info)
3621: {
3622: PetscFunctionBegin;
3625: if (info) PetscAssertPointer(info, 3);
3627: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3628: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3629: MatCheckPreallocated(mat, 1);
3630: PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, col, 0, 0));
3631: PetscUseMethod(mat, "MatQRFactor_C", (Mat, IS, const MatFactorInfo *), (mat, col, info));
3632: PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, col, 0, 0));
3633: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
3634: PetscFunctionReturn(PETSC_SUCCESS);
3635: }
3637: /*@
3638: MatQRFactorSymbolic - Performs symbolic QR factorization of matrix.
3639: Call this routine after `MatGetFactor()` but before calling `MatQRFactorNumeric()`.
3641: Collective
3643: Input Parameters:
3644: + fact - the factor matrix obtained with `MatGetFactor()`
3645: . mat - the matrix
3646: . col - column permutation
3647: - info - options for factorization, includes
3648: .vb
3649: fill - expected fill as ratio of original fill.
3650: dtcol - pivot tolerance (0 no pivot, 1 full column pivoting)
3651: Run with the option -info to determine an optimal value to use
3652: .ve
3654: Level: developer
3656: Note:
3657: Most users should employ the `KSP` interface for linear solvers
3658: instead of working directly with matrix algebra routines such as this.
3659: See, e.g., `KSPCreate()`.
3661: Fortran Note:
3662: A valid (non-null) `info` argument must be provided
3664: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatFactorInfo`, `MatQRFactor()`, `MatQRFactorNumeric()`, `MatLUFactor()`, `MatFactorInfoInitialize()`
3665: @*/
3666: PetscErrorCode MatQRFactorSymbolic(Mat fact, Mat mat, IS col, const MatFactorInfo *info)
3667: {
3668: MatFactorInfo tinfo;
3670: PetscFunctionBegin;
3674: if (info) PetscAssertPointer(info, 4);
3677: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3678: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3679: MatCheckPreallocated(mat, 2);
3680: if (!info) {
3681: PetscCall(MatFactorInfoInitialize(&tinfo));
3682: info = &tinfo;
3683: }
3685: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorSymbolic, fact, mat, col, 0));
3686: PetscUseMethod(fact, "MatQRFactorSymbolic_C", (Mat, Mat, IS, const MatFactorInfo *), (fact, mat, col, info));
3687: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorSymbolic, fact, mat, col, 0));
3688: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3689: PetscFunctionReturn(PETSC_SUCCESS);
3690: }
3692: /*@
3693: MatQRFactorNumeric - Performs numeric QR factorization of a matrix.
3694: Call this routine after first calling `MatGetFactor()`, and `MatQRFactorSymbolic()`.
3696: Collective
3698: Input Parameters:
3699: + fact - the factor matrix obtained with `MatGetFactor()`
3700: . mat - the matrix
3701: - info - options for factorization
3703: Level: developer
3705: Notes:
3706: See `MatQRFactor()` for in-place factorization.
3708: Most users should employ the `KSP` interface for linear solvers
3709: instead of working directly with matrix algebra routines such as this.
3710: See, e.g., `KSPCreate()`.
3712: Fortran Note:
3713: A valid (non-null) `info` argument must be provided
3715: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorInfo`, `MatGetFactor()`, `MatQRFactor()`, `MatQRFactorSymbolic()`, `MatLUFactor()`
3716: @*/
3717: PetscErrorCode MatQRFactorNumeric(Mat fact, Mat mat, const MatFactorInfo *info)
3718: {
3719: MatFactorInfo tinfo;
3721: PetscFunctionBegin;
3726: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3727: 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,
3728: mat->rmap->N, (fact)->rmap->N, mat->cmap->N, (fact)->cmap->N);
3730: MatCheckPreallocated(mat, 2);
3731: if (!info) {
3732: PetscCall(MatFactorInfoInitialize(&tinfo));
3733: info = &tinfo;
3734: }
3736: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_QRFactorNumeric, mat, fact, 0, 0));
3737: else PetscCall(PetscLogEventBegin(MAT_QRFactor, mat, fact, 0, 0));
3738: PetscUseMethod(fact, "MatQRFactorNumeric_C", (Mat, Mat, const MatFactorInfo *), (fact, mat, info));
3739: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_QRFactorNumeric, mat, fact, 0, 0));
3740: else PetscCall(PetscLogEventEnd(MAT_QRFactor, mat, fact, 0, 0));
3741: PetscCall(MatViewFromOptions(fact, NULL, "-mat_factor_view"));
3742: PetscCall(PetscObjectStateIncrease((PetscObject)fact));
3743: PetscFunctionReturn(PETSC_SUCCESS);
3744: }
3746: /*@
3747: MatSolve - Solves $A x = b$, given a factored matrix.
3749: Neighbor-wise Collective
3751: Input Parameters:
3752: + mat - the factored matrix
3753: - b - the right-hand-side vector
3755: Output Parameter:
3756: . x - the result vector
3758: Level: developer
3760: Notes:
3761: The vectors `b` and `x` cannot be the same. I.e., one cannot
3762: call `MatSolve`(A,x,x).
3764: Most users should employ the `KSP` interface for linear solvers
3765: instead of working directly with matrix algebra routines such as this.
3766: See, e.g., `KSPCreate()`.
3768: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactor()`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
3769: @*/
3770: PetscErrorCode MatSolve(Mat mat, Vec b, Vec x)
3771: {
3772: PetscFunctionBegin;
3777: PetscCheckSameComm(mat, 1, b, 2);
3778: PetscCheckSameComm(mat, 1, x, 3);
3779: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
3780: 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);
3781: 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);
3782: 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);
3783: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3784: MatCheckPreallocated(mat, 1);
3786: PetscCall(PetscLogEventBegin(MAT_Solve, mat, b, x, 0));
3787: PetscCall(VecFlag(x, mat->factorerrortype));
3788: if (mat->factorerrortype) PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
3789: else PetscUseTypeMethod(mat, solve, b, x);
3790: PetscCall(PetscLogEventEnd(MAT_Solve, mat, b, x, 0));
3791: PetscCall(PetscObjectStateIncrease((PetscObject)x));
3792: PetscFunctionReturn(PETSC_SUCCESS);
3793: }
3795: static PetscErrorCode MatMatSolve_Basic(Mat A, Mat B, Mat X, PetscBool trans)
3796: {
3797: Vec b, x;
3798: PetscInt N, i;
3799: PetscErrorCode (*f)(Mat, Vec, Vec);
3800: PetscBool Abound, Bneedconv = PETSC_FALSE, Xneedconv = PETSC_FALSE;
3802: PetscFunctionBegin;
3803: if (A->factorerrortype) {
3804: PetscCall(PetscInfo(A, "MatFactorError %d\n", A->factorerrortype));
3805: PetscCall(MatSetInf(X));
3806: PetscFunctionReturn(PETSC_SUCCESS);
3807: }
3808: f = (!trans || (!A->ops->solvetranspose && A->symmetric)) ? A->ops->solve : A->ops->solvetranspose;
3809: PetscCheck(f, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Mat type %s", ((PetscObject)A)->type_name);
3810: PetscCall(MatBoundToCPU(A, &Abound));
3811: if (!Abound) {
3812: PetscCall(PetscObjectTypeCompareAny((PetscObject)B, &Bneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3813: PetscCall(PetscObjectTypeCompareAny((PetscObject)X, &Xneedconv, MATSEQDENSE, MATMPIDENSE, ""));
3814: }
3815: #if PetscDefined(HAVE_CUDA)
3816: if (Bneedconv) PetscCall(MatConvert(B, MATDENSECUDA, MAT_INPLACE_MATRIX, &B));
3817: if (Xneedconv) PetscCall(MatConvert(X, MATDENSECUDA, MAT_INPLACE_MATRIX, &X));
3818: #elif PetscDefined(HAVE_HIP)
3819: if (Bneedconv) PetscCall(MatConvert(B, MATDENSEHIP, MAT_INPLACE_MATRIX, &B));
3820: if (Xneedconv) PetscCall(MatConvert(X, MATDENSEHIP, MAT_INPLACE_MATRIX, &X));
3821: #endif
3822: PetscCall(MatGetSize(B, NULL, &N));
3823: for (i = 0; i < N; i++) {
3824: PetscCall(MatDenseGetColumnVecRead(B, i, &b));
3825: PetscCall(MatDenseGetColumnVecWrite(X, i, &x));
3826: PetscCall((*f)(A, b, x));
3827: PetscCall(MatDenseRestoreColumnVecWrite(X, i, &x));
3828: PetscCall(MatDenseRestoreColumnVecRead(B, i, &b));
3829: }
3830: if (Bneedconv) PetscCall(MatConvert(B, MATDENSE, MAT_INPLACE_MATRIX, &B));
3831: if (Xneedconv) PetscCall(MatConvert(X, MATDENSE, MAT_INPLACE_MATRIX, &X));
3832: PetscFunctionReturn(PETSC_SUCCESS);
3833: }
3835: /*@
3836: MatMatSolve - Solves $A X = B$, given a factored matrix.
3838: Neighbor-wise Collective
3840: Input Parameters:
3841: + A - the factored matrix
3842: - B - the right-hand-side matrix `MATDENSE` (or sparse `MATAIJ`-- when using MUMPS)
3844: Output Parameter:
3845: . X - the result matrix (dense matrix)
3847: Level: developer
3849: Note:
3850: If `B` is a `MATDENSE` matrix then one can call `MatMatSolve`(A,B,B) except with `MATSOLVERMKL_CPARDISO`;
3851: otherwise, `B` and `X` cannot be the same.
3853: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3854: @*/
3855: PetscErrorCode MatMatSolve(Mat A, Mat B, Mat X)
3856: {
3857: PetscFunctionBegin;
3862: PetscCheckSameComm(A, 1, B, 2);
3863: PetscCheckSameComm(A, 1, X, 3);
3864: 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);
3865: 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);
3866: 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");
3867: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3868: MatCheckPreallocated(A, 1);
3870: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3871: if (!A->ops->matsolve) {
3872: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolve\n", ((PetscObject)A)->type_name));
3873: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_FALSE));
3874: } else PetscUseTypeMethod(A, matsolve, B, X);
3875: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3876: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3877: PetscFunctionReturn(PETSC_SUCCESS);
3878: }
3880: /*@
3881: MatMatSolveTranspose - Solves $A^T X = B $, given a factored matrix.
3883: Neighbor-wise Collective
3885: Input Parameters:
3886: + A - the factored matrix
3887: - B - the right-hand-side matrix (`MATDENSE` matrix)
3889: Output Parameter:
3890: . X - the result matrix (dense matrix)
3892: Level: developer
3894: Note:
3895: The matrices `B` and `X` cannot be the same. I.e., one cannot
3896: call `MatMatSolveTranspose`(A,X,X).
3898: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolveTranspose()`, `MatMatSolve()`, `MatLUFactor()`, `MatCholeskyFactor()`
3899: @*/
3900: PetscErrorCode MatMatSolveTranspose(Mat A, Mat B, Mat X)
3901: {
3902: PetscFunctionBegin;
3907: PetscCheckSameComm(A, 1, B, 2);
3908: PetscCheckSameComm(A, 1, X, 3);
3909: PetscCheck(X != B, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3910: 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);
3911: 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);
3912: 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);
3913: 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");
3914: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3915: MatCheckPreallocated(A, 1);
3917: PetscCall(PetscLogEventBegin(MAT_MatSolve, A, B, X, 0));
3918: if (!A->ops->matsolvetranspose) {
3919: PetscCall(PetscInfo(A, "Mat type %s using basic MatMatSolveTranspose\n", ((PetscObject)A)->type_name));
3920: PetscCall(MatMatSolve_Basic(A, B, X, PETSC_TRUE));
3921: } else PetscUseTypeMethod(A, matsolvetranspose, B, X);
3922: PetscCall(PetscLogEventEnd(MAT_MatSolve, A, B, X, 0));
3923: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3924: PetscFunctionReturn(PETSC_SUCCESS);
3925: }
3927: /*@
3928: MatMatTransposeSolve - Solves $A X = B^T$, given a factored matrix.
3930: Neighbor-wise Collective
3932: Input Parameters:
3933: + A - the factored matrix
3934: - Bt - the transpose of right-hand-side matrix as a `MATDENSE`
3936: Output Parameter:
3937: . X - the result matrix (dense matrix)
3939: Level: developer
3941: Note:
3942: 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
3943: format on the host processor and call `MatMatTransposeSolve()` to implement MUMPS' `MatMatSolve()`.
3945: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatMatSolve()`, `MatMatSolveTranspose()`, `MatLUFactor()`, `MatCholeskyFactor()`
3946: @*/
3947: PetscErrorCode MatMatTransposeSolve(Mat A, Mat Bt, Mat X)
3948: {
3949: PetscFunctionBegin;
3954: PetscCheckSameComm(A, 1, Bt, 2);
3955: PetscCheckSameComm(A, 1, X, 3);
3957: PetscCheck(X != Bt, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_IDN, "X and B must be different matrices");
3958: 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);
3959: 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);
3960: 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");
3961: if (!A->rmap->N && !A->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
3962: PetscCheck(A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
3963: MatCheckPreallocated(A, 1);
3965: PetscCall(PetscLogEventBegin(MAT_MatTrSolve, A, Bt, X, 0));
3966: PetscUseTypeMethod(A, mattransposesolve, Bt, X);
3967: PetscCall(PetscLogEventEnd(MAT_MatTrSolve, A, Bt, X, 0));
3968: PetscCall(PetscObjectStateIncrease((PetscObject)X));
3969: PetscFunctionReturn(PETSC_SUCCESS);
3970: }
3972: /*@
3973: MatForwardSolve - Solves $ L x = b $, given a factored matrix, $A = LU $, or
3974: $U^T*D^(1/2) x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
3976: Neighbor-wise Collective
3978: Input Parameters:
3979: + mat - the factored matrix
3980: - b - the right-hand-side vector
3982: Output Parameter:
3983: . x - the result vector
3985: Level: developer
3987: Notes:
3988: `MatSolve()` should be used for most applications, as it performs
3989: a forward solve followed by a backward solve.
3991: The vectors `b` and `x` cannot be the same, i.e., one cannot
3992: call `MatForwardSolve`(A,x,x).
3994: For matrix in `MATSEQBAIJ` format with block size larger than 1,
3995: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
3996: `MatForwardSolve()` solves $U^T*D y = b$, and
3997: `MatBackwardSolve()` solves $U x = y$.
3998: Thus they do not provide a symmetric preconditioner.
4000: .seealso: [](ch_matrices), `Mat`, `MatBackwardSolve()`, `MatGetFactor()`, `MatSolve()`
4001: @*/
4002: PetscErrorCode MatForwardSolve(Mat mat, Vec b, Vec x)
4003: {
4004: PetscFunctionBegin;
4009: PetscCheckSameComm(mat, 1, b, 2);
4010: PetscCheckSameComm(mat, 1, x, 3);
4011: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4012: 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);
4013: 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);
4014: 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);
4015: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4016: MatCheckPreallocated(mat, 1);
4018: PetscCall(PetscLogEventBegin(MAT_ForwardSolve, mat, b, x, 0));
4019: PetscUseTypeMethod(mat, forwardsolve, b, x);
4020: PetscCall(PetscLogEventEnd(MAT_ForwardSolve, mat, b, x, 0));
4021: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4022: PetscFunctionReturn(PETSC_SUCCESS);
4023: }
4025: /*@
4026: MatBackwardSolve - Solves $U x = b$, given a factored matrix, $A = LU$.
4027: $D^(1/2) U x = b$, given a factored symmetric matrix, $A = U^T*D*U$,
4029: Neighbor-wise Collective
4031: Input Parameters:
4032: + mat - the factored matrix
4033: - b - the right-hand-side vector
4035: Output Parameter:
4036: . x - the result vector
4038: Level: developer
4040: Notes:
4041: `MatSolve()` should be used for most applications, as it performs
4042: a forward solve followed by a backward solve.
4044: The vectors `b` and `x` cannot be the same. I.e., one cannot
4045: call `MatBackwardSolve`(A,x,x).
4047: For matrix in `MATSEQBAIJ` format with block size larger than 1,
4048: the diagonal blocks are not implemented as $D = D^(1/2) * D^(1/2)$ yet.
4049: `MatForwardSolve()` solves $U^T*D y = b$, and
4050: `MatBackwardSolve()` solves $U x = y$.
4051: Thus they do not provide a symmetric preconditioner.
4053: .seealso: [](ch_matrices), `Mat`, `MatForwardSolve()`, `MatGetFactor()`, `MatSolve()`
4054: @*/
4055: PetscErrorCode MatBackwardSolve(Mat mat, Vec b, Vec x)
4056: {
4057: PetscFunctionBegin;
4062: PetscCheckSameComm(mat, 1, b, 2);
4063: PetscCheckSameComm(mat, 1, x, 3);
4064: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4065: 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);
4066: 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);
4067: 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);
4068: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4069: MatCheckPreallocated(mat, 1);
4071: PetscCall(PetscLogEventBegin(MAT_BackwardSolve, mat, b, x, 0));
4072: PetscUseTypeMethod(mat, backwardsolve, b, x);
4073: PetscCall(PetscLogEventEnd(MAT_BackwardSolve, mat, b, x, 0));
4074: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4075: PetscFunctionReturn(PETSC_SUCCESS);
4076: }
4078: /*@
4079: MatSolveAdd - Computes $x = y + A^{-1}*b$, given a factored matrix.
4081: Neighbor-wise Collective
4083: Input Parameters:
4084: + mat - the factored matrix
4085: . b - the right-hand-side vector
4086: - y - the vector to be added to
4088: Output Parameter:
4089: . x - the result vector
4091: Level: developer
4093: Note:
4094: The vectors `b` and `x` cannot be the same. I.e., one cannot
4095: call `MatSolveAdd`(A,x,y,x).
4097: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolve()`, `MatGetFactor()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`
4098: @*/
4099: PetscErrorCode MatSolveAdd(Mat mat, Vec b, Vec y, Vec x)
4100: {
4101: PetscScalar one = 1.0;
4102: Vec tmp;
4104: PetscFunctionBegin;
4110: PetscCheckSameComm(mat, 1, b, 2);
4111: PetscCheckSameComm(mat, 1, y, 3);
4112: PetscCheckSameComm(mat, 1, x, 4);
4113: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4114: 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);
4115: 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);
4116: 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);
4117: 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);
4118: 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);
4119: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4120: MatCheckPreallocated(mat, 1);
4122: PetscCall(PetscLogEventBegin(MAT_SolveAdd, mat, b, x, y));
4123: PetscCall(VecFlag(x, mat->factorerrortype));
4124: if (mat->factorerrortype) {
4125: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4126: } else if (mat->ops->solveadd) {
4127: PetscUseTypeMethod(mat, solveadd, b, y, x);
4128: } else {
4129: /* do the solve then the add manually */
4130: if (x != y) {
4131: PetscCall(MatSolve(mat, b, x));
4132: PetscCall(VecAXPY(x, one, y));
4133: } else {
4134: PetscCall(VecDuplicate(x, &tmp));
4135: PetscCall(VecCopy(x, tmp));
4136: PetscCall(MatSolve(mat, b, x));
4137: PetscCall(VecAXPY(x, one, tmp));
4138: PetscCall(VecDestroy(&tmp));
4139: }
4140: }
4141: PetscCall(PetscLogEventEnd(MAT_SolveAdd, mat, b, x, y));
4142: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4143: PetscFunctionReturn(PETSC_SUCCESS);
4144: }
4146: /*@
4147: MatSolveTranspose - Solves $A^T x = b$, given a factored matrix.
4149: Neighbor-wise Collective
4151: Input Parameters:
4152: + mat - the factored matrix
4153: - b - the right-hand-side vector
4155: Output Parameter:
4156: . x - the result vector
4158: Level: developer
4160: Notes:
4161: The vectors `b` and `x` cannot be the same. I.e., one cannot
4162: call `MatSolveTranspose`(A,x,x).
4164: Most users should employ the `KSP` interface for linear solvers
4165: instead of working directly with matrix algebra routines such as this.
4166: See, e.g., `KSPCreate()`.
4168: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `KSP`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTransposeAdd()`
4169: @*/
4170: PetscErrorCode MatSolveTranspose(Mat mat, Vec b, Vec x)
4171: {
4172: PetscErrorCode (*f)(Mat, Vec, Vec) = (!mat->ops->solvetranspose && mat->symmetric) ? mat->ops->solve : mat->ops->solvetranspose;
4174: PetscFunctionBegin;
4179: PetscCheckSameComm(mat, 1, b, 2);
4180: PetscCheckSameComm(mat, 1, x, 3);
4181: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4182: 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);
4183: 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);
4184: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4185: MatCheckPreallocated(mat, 1);
4186: PetscCall(PetscLogEventBegin(MAT_SolveTranspose, mat, b, x, 0));
4187: PetscCall(VecFlag(x, mat->factorerrortype));
4188: if (mat->factorerrortype) {
4189: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4190: } else {
4191: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Matrix type %s", ((PetscObject)mat)->type_name);
4192: PetscCall((*f)(mat, b, x));
4193: }
4194: PetscCall(PetscLogEventEnd(MAT_SolveTranspose, mat, b, x, 0));
4195: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4196: PetscFunctionReturn(PETSC_SUCCESS);
4197: }
4199: /*@
4200: MatSolveTransposeAdd - Computes $x = y + A^{-T} b$
4201: factored matrix.
4203: Neighbor-wise Collective
4205: Input Parameters:
4206: + mat - the factored matrix
4207: . b - the right-hand-side vector
4208: - y - the vector to be added to
4210: Output Parameter:
4211: . x - the result vector
4213: Level: developer
4215: Note:
4216: The vectors `b` and `x` cannot be the same. I.e., one cannot
4217: call `MatSolveTransposeAdd`(A,x,y,x).
4219: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatSolve()`, `MatSolveAdd()`, `MatSolveTranspose()`
4220: @*/
4221: PetscErrorCode MatSolveTransposeAdd(Mat mat, Vec b, Vec y, Vec x)
4222: {
4223: PetscScalar one = 1.0;
4224: Vec tmp;
4225: PetscErrorCode (*f)(Mat, Vec, Vec, Vec) = (!mat->ops->solvetransposeadd && mat->symmetric) ? mat->ops->solveadd : mat->ops->solvetransposeadd;
4227: PetscFunctionBegin;
4233: PetscCheckSameComm(mat, 1, b, 2);
4234: PetscCheckSameComm(mat, 1, y, 3);
4235: PetscCheckSameComm(mat, 1, x, 4);
4236: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
4237: 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);
4238: 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);
4239: 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);
4240: 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);
4241: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
4242: MatCheckPreallocated(mat, 1);
4244: PetscCall(PetscLogEventBegin(MAT_SolveTransposeAdd, mat, b, x, y));
4245: PetscCall(VecFlag(x, mat->factorerrortype));
4246: if (mat->factorerrortype) {
4247: PetscCall(PetscInfo(mat, "MatFactorError %d\n", mat->factorerrortype));
4248: } else if (f) {
4249: PetscCall((*f)(mat, b, y, x));
4250: } else {
4251: /* do the solve then the add manually */
4252: if (x != y) {
4253: PetscCall(MatSolveTranspose(mat, b, x));
4254: PetscCall(VecAXPY(x, one, y));
4255: } else {
4256: PetscCall(VecDuplicate(x, &tmp));
4257: PetscCall(VecCopy(x, tmp));
4258: PetscCall(MatSolveTranspose(mat, b, x));
4259: PetscCall(VecAXPY(x, one, tmp));
4260: PetscCall(VecDestroy(&tmp));
4261: }
4262: }
4263: PetscCall(PetscLogEventEnd(MAT_SolveTransposeAdd, mat, b, x, y));
4264: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4265: PetscFunctionReturn(PETSC_SUCCESS);
4266: }
4268: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
4269: /*@
4270: MatSOR - Computes relaxation (SOR, Gauss-Seidel) sweeps.
4272: Neighbor-wise Collective
4274: Input Parameters:
4275: + mat - the matrix
4276: . b - the right-hand side
4277: . omega - the relaxation factor
4278: . flag - flag indicating the type of SOR (see below)
4279: . shift - diagonal shift
4280: . its - the number of iterations
4281: - lits - the number of local iterations
4283: Output Parameter:
4284: . x - the solution (can contain an initial guess, use option `SOR_ZERO_INITIAL_GUESS` to indicate no guess)
4286: SOR Flags:
4287: + `SOR_FORWARD_SWEEP` - forward SOR
4288: . `SOR_BACKWARD_SWEEP` - backward SOR
4289: . `SOR_SYMMETRIC_SWEEP` - SSOR (symmetric SOR)
4290: . `SOR_LOCAL_FORWARD_SWEEP` - local forward SOR
4291: . `SOR_LOCAL_BACKWARD_SWEEP` - local forward SOR
4292: . `SOR_LOCAL_SYMMETRIC_SWEEP` - local SSOR
4293: . `SOR_EISENSTAT` - SOR with Eisenstat trick
4294: . `SOR_APPLY_UPPER`, `SOR_APPLY_LOWER` - applies upper/lower triangular part of matrix to vector (with `omega`)
4295: - `SOR_ZERO_INITIAL_GUESS` - zero initial guess
4297: Level: developer
4299: Notes:
4300: `SOR_LOCAL_FORWARD_SWEEP`, `SOR_LOCAL_BACKWARD_SWEEP`, and
4301: `SOR_LOCAL_SYMMETRIC_SWEEP` perform separate independent smoothings
4302: on each processor.
4304: Application programmers will not generally use `MatSOR()` directly,
4305: but instead will employ `PCSOR` or `PCEISENSTAT`
4307: For `MATBAIJ`, `MATSBAIJ`, and `MATAIJ` matrices with inodes, this does a block SOR smoothing, otherwise it does a pointwise smoothing.
4308: For `MATAIJ` matrices with inodes, the block sizes are determined by the inode sizes, not the block size set with `MatSetBlockSize()`
4310: Vectors `x` and `b` CANNOT be the same
4312: The flags are implemented as bitwise inclusive or operations.
4313: For example, use (`SOR_ZERO_INITIAL_GUESS` | `SOR_SYMMETRIC_SWEEP`)
4314: to specify a zero initial guess for SSOR.
4316: Developer Note:
4317: We should add block SOR support for `MATAIJ` matrices with block size set to greater than one and no inodes
4319: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `KSP`, `PC`, `MatGetFactor()`
4320: @*/
4321: PetscErrorCode MatSOR(Mat mat, Vec b, PetscReal omega, MatSORType flag, PetscReal shift, PetscInt its, PetscInt lits, Vec x)
4322: {
4323: PetscFunctionBegin;
4328: PetscCheckSameComm(mat, 1, b, 2);
4329: PetscCheckSameComm(mat, 1, x, 8);
4330: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4331: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4332: 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);
4333: 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);
4334: 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);
4335: PetscCheck(its > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires global its %" PetscInt_FMT " positive", its);
4336: PetscCheck(lits > 0, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Relaxation requires local its %" PetscInt_FMT " positive", lits);
4337: PetscCheck(b != x, PETSC_COMM_SELF, PETSC_ERR_ARG_IDN, "b and x vector cannot be the same");
4339: MatCheckPreallocated(mat, 1);
4340: PetscCall(PetscLogEventBegin(MAT_SOR, mat, b, x, 0));
4341: PetscUseTypeMethod(mat, sor, b, omega, flag, shift, its, lits, x);
4342: PetscCall(PetscLogEventEnd(MAT_SOR, mat, b, x, 0));
4343: PetscCall(PetscObjectStateIncrease((PetscObject)x));
4344: PetscFunctionReturn(PETSC_SUCCESS);
4345: }
4347: /*
4348: Default matrix copy routine.
4349: */
4350: PetscErrorCode MatCopy_Basic(Mat A, Mat B, MatStructure str)
4351: {
4352: PetscInt i, rstart = 0, rend = 0, nz;
4353: const PetscInt *cwork;
4354: const PetscScalar *vwork;
4356: PetscFunctionBegin;
4357: if (B->assembled) PetscCall(MatZeroEntries(B));
4358: if (str == SAME_NONZERO_PATTERN) {
4359: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
4360: for (i = rstart; i < rend; i++) {
4361: PetscCall(MatGetRow(A, i, &nz, &cwork, &vwork));
4362: PetscCall(MatSetValues(B, 1, &i, nz, cwork, vwork, INSERT_VALUES));
4363: PetscCall(MatRestoreRow(A, i, &nz, &cwork, &vwork));
4364: }
4365: } else {
4366: PetscCall(MatAYPX(B, 0.0, A, str));
4367: }
4368: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4369: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4370: PetscFunctionReturn(PETSC_SUCCESS);
4371: }
4373: /*@
4374: MatCopy - Copies a matrix to another matrix.
4376: Collective
4378: Input Parameters:
4379: + A - the matrix
4380: - str - `SAME_NONZERO_PATTERN` or `DIFFERENT_NONZERO_PATTERN`
4382: Output Parameter:
4383: . B - where the copy is put
4385: Level: intermediate
4387: Notes:
4388: If you use `SAME_NONZERO_PATTERN`, then the two matrices must have the same nonzero pattern or the routine will crash.
4390: `MatCopy()` copies the matrix entries of a matrix to another existing
4391: matrix (after first zeroing the second matrix). A related routine is
4392: `MatConvert()`, which first creates a new matrix and then copies the data.
4394: .seealso: [](ch_matrices), `Mat`, `MatConvert()`, `MatDuplicate()`
4395: @*/
4396: PetscErrorCode MatCopy(Mat A, Mat B, MatStructure str)
4397: {
4398: PetscInt i;
4400: PetscFunctionBegin;
4405: PetscCheckSameComm(A, 1, B, 2);
4406: MatCheckPreallocated(B, 2);
4407: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4408: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4409: 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,
4410: A->cmap->N, B->cmap->N);
4411: MatCheckPreallocated(A, 1);
4412: if (A == B) PetscFunctionReturn(PETSC_SUCCESS);
4414: PetscCall(PetscLogEventBegin(MAT_Copy, A, B, 0, 0));
4415: if (A->ops->copy) PetscUseTypeMethod(A, copy, B, str);
4416: else PetscCall(MatCopy_Basic(A, B, str));
4418: B->stencil.dim = A->stencil.dim;
4419: B->stencil.noc = A->stencil.noc;
4420: for (i = 0; i <= A->stencil.dim + (A->stencil.noc ? 0 : -1); i++) {
4421: B->stencil.dims[i] = A->stencil.dims[i];
4422: B->stencil.starts[i] = A->stencil.starts[i];
4423: }
4425: PetscCall(PetscLogEventEnd(MAT_Copy, A, B, 0, 0));
4426: PetscCall(PetscObjectStateIncrease((PetscObject)B));
4427: PetscFunctionReturn(PETSC_SUCCESS);
4428: }
4430: /*@
4431: MatConvert - Converts a matrix to another matrix, either of the same
4432: or different type.
4434: Collective
4436: Input Parameters:
4437: + mat - the matrix
4438: . newtype - new matrix type. Use `MATSAME` to create a new matrix of the
4439: same type as the original matrix.
4440: - reuse - denotes if the destination matrix is to be created or reused.
4441: 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
4442: `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).
4444: Output Parameter:
4445: . M - pointer to place new matrix
4447: Level: intermediate
4449: Notes:
4450: `MatConvert()` first creates a new matrix and then copies the data from
4451: the first matrix. A related routine is `MatCopy()`, which copies the matrix
4452: entries of one matrix to another already existing matrix context.
4454: Cannot be used to convert a sequential matrix to parallel or parallel to sequential,
4455: the MPI communicator of the generated matrix is always the same as the communicator
4456: of the input matrix.
4458: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatDuplicate()`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
4459: @*/
4460: PetscErrorCode MatConvert(Mat mat, MatType newtype, MatReuse reuse, Mat *M)
4461: {
4462: PetscBool sametype, issame, flg;
4463: PetscBool3 issymmetric, ishermitian, isspd;
4464: char convname[256], mtype[256];
4465: Mat B;
4467: PetscFunctionBegin;
4470: PetscAssertPointer(M, 4);
4471: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4472: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4473: MatCheckPreallocated(mat, 1);
4475: PetscCall(PetscOptionsGetString(((PetscObject)mat)->options, ((PetscObject)mat)->prefix, "-matconvert_type", mtype, sizeof(mtype), &flg));
4476: if (flg) newtype = mtype;
4478: PetscCall(PetscObjectTypeCompare((PetscObject)mat, newtype, &sametype));
4479: PetscCall(PetscStrcmp(newtype, "same", &issame));
4480: PetscCheck(!(reuse == MAT_INPLACE_MATRIX) || !(mat != *M), PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires same input and output matrix");
4481: if (reuse == MAT_REUSE_MATRIX) {
4483: PetscCheck(mat != *M, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_REUSE_MATRIX means reuse matrix in final argument, perhaps you mean MAT_INPLACE_MATRIX");
4484: }
4486: if ((reuse == MAT_INPLACE_MATRIX) && (issame || sametype)) {
4487: PetscCall(PetscInfo(mat, "Early return for inplace %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4488: PetscFunctionReturn(PETSC_SUCCESS);
4489: }
4491: /* Cache Mat options because some converters use MatHeaderReplace() */
4492: issymmetric = mat->symmetric;
4493: ishermitian = mat->hermitian;
4494: isspd = mat->spd;
4496: if ((sametype || issame) && (reuse == MAT_INITIAL_MATRIX) && mat->ops->duplicate) {
4497: PetscCall(PetscInfo(mat, "Calling duplicate for initial matrix %s %d %d\n", ((PetscObject)mat)->type_name, sametype, issame));
4498: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4499: } else {
4500: PetscErrorCode (*conv)(Mat, MatType, MatReuse, Mat *) = NULL;
4501: const char *prefix[3] = {"seq", "mpi", ""};
4502: PetscInt i;
4503: /*
4504: Order of precedence:
4505: 0) See if newtype is a superclass of the current matrix.
4506: 1) See if a specialized converter is known to the current matrix.
4507: 2) See if a specialized converter is known to the desired matrix class.
4508: 3) See if a good general converter is registered for the desired class
4509: (as of 6/27/03 only MATMPIADJ falls into this category).
4510: 4) See if a good general converter is known for the current matrix.
4511: 5) Use a really basic converter.
4512: */
4514: /* 0) See if newtype is a superclass of the current matrix.
4515: i.e mat is mpiaij and newtype is aij */
4516: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4517: PetscCall(PetscStrncpy(convname, prefix[i], sizeof(convname)));
4518: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4519: PetscCall(PetscStrcmp(convname, ((PetscObject)mat)->type_name, &flg));
4520: PetscCall(PetscInfo(mat, "Check superclass %s %s -> %d\n", convname, ((PetscObject)mat)->type_name, flg));
4521: if (flg) {
4522: if (reuse == MAT_INPLACE_MATRIX) {
4523: PetscCall(PetscInfo(mat, "Early return\n"));
4524: PetscFunctionReturn(PETSC_SUCCESS);
4525: } else if (reuse == MAT_INITIAL_MATRIX && mat->ops->duplicate) {
4526: PetscCall(PetscInfo(mat, "Calling MatDuplicate\n"));
4527: PetscUseTypeMethod(mat, duplicate, MAT_COPY_VALUES, M);
4528: PetscFunctionReturn(PETSC_SUCCESS);
4529: } else if (reuse == MAT_REUSE_MATRIX && mat->ops->copy) {
4530: PetscCall(PetscInfo(mat, "Calling MatCopy\n"));
4531: PetscCall(MatCopy(mat, *M, SAME_NONZERO_PATTERN));
4532: PetscFunctionReturn(PETSC_SUCCESS);
4533: }
4534: }
4535: }
4536: /* 1) See if a specialized converter is known to the current matrix and the desired class */
4537: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4538: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4539: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4540: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4541: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4542: PetscCall(PetscStrlcat(convname, issame ? ((PetscObject)mat)->type_name : newtype, sizeof(convname)));
4543: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4544: PetscCall(PetscObjectQueryFunction((PetscObject)mat, convname, &conv));
4545: PetscCall(PetscInfo(mat, "Check specialized (1) %s (%s) -> %d\n", convname, ((PetscObject)mat)->type_name, !!conv));
4546: if (conv) goto foundconv;
4547: }
4549: /* 2) See if a specialized converter is known to the desired matrix class. */
4550: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &B));
4551: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
4552: PetscCall(MatSetType(B, newtype));
4553: for (i = 0; i < (PetscInt)PETSC_STATIC_ARRAY_LENGTH(prefix); i++) {
4554: PetscCall(PetscStrncpy(convname, "MatConvert_", sizeof(convname)));
4555: PetscCall(PetscStrlcat(convname, ((PetscObject)mat)->type_name, sizeof(convname)));
4556: PetscCall(PetscStrlcat(convname, "_", sizeof(convname)));
4557: PetscCall(PetscStrlcat(convname, prefix[i], sizeof(convname)));
4558: PetscCall(PetscStrlcat(convname, newtype, sizeof(convname)));
4559: PetscCall(PetscStrlcat(convname, "_C", sizeof(convname)));
4560: PetscCall(PetscObjectQueryFunction((PetscObject)B, convname, &conv));
4561: PetscCall(PetscInfo(mat, "Check specialized (2) %s (%s) -> %d\n", convname, ((PetscObject)B)->type_name, !!conv));
4562: if (conv) {
4563: PetscCall(MatDestroy(&B));
4564: goto foundconv;
4565: }
4566: }
4568: /* 3) See if a good general converter is registered for the desired class */
4569: conv = B->ops->convertfrom;
4570: PetscCall(PetscInfo(mat, "Check convertfrom (%s) -> %d\n", ((PetscObject)B)->type_name, !!conv));
4571: PetscCall(MatDestroy(&B));
4572: if (conv) goto foundconv;
4574: /* 4) See if a good general converter is known for the current matrix */
4575: if (mat->ops->convert) conv = mat->ops->convert;
4576: PetscCall(PetscInfo(mat, "Check general convert (%s) -> %d\n", ((PetscObject)mat)->type_name, !!conv));
4577: if (conv) goto foundconv;
4579: /* 5) Use a really basic converter. */
4580: PetscCall(PetscInfo(mat, "Using MatConvert_Basic\n"));
4581: conv = MatConvert_Basic;
4583: foundconv:
4584: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
4585: PetscCall((*conv)(mat, newtype, reuse, M));
4586: if (mat->rmap->mapping && mat->cmap->mapping && !(*M)->rmap->mapping && !(*M)->cmap->mapping) {
4587: /* the block sizes must be same if the mappings are copied over */
4588: (*M)->rmap->bs = mat->rmap->bs;
4589: (*M)->cmap->bs = mat->cmap->bs;
4590: PetscCall(PetscObjectReference((PetscObject)mat->rmap->mapping));
4591: PetscCall(PetscObjectReference((PetscObject)mat->cmap->mapping));
4592: (*M)->rmap->mapping = mat->rmap->mapping;
4593: (*M)->cmap->mapping = mat->cmap->mapping;
4594: }
4595: (*M)->stencil.dim = mat->stencil.dim;
4596: (*M)->stencil.noc = mat->stencil.noc;
4597: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
4598: (*M)->stencil.dims[i] = mat->stencil.dims[i];
4599: (*M)->stencil.starts[i] = mat->stencil.starts[i];
4600: }
4601: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
4602: }
4603: PetscCall(PetscObjectStateIncrease((PetscObject)*M));
4605: /* Reset Mat options */
4606: if (issymmetric != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SYMMETRIC, PetscBool3ToBool(issymmetric)));
4607: if (ishermitian != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_HERMITIAN, PetscBool3ToBool(ishermitian)));
4608: if (isspd != PETSC_BOOL3_UNKNOWN) PetscCall(MatSetOption(*M, MAT_SPD, PetscBool3ToBool(isspd)));
4609: PetscFunctionReturn(PETSC_SUCCESS);
4610: }
4612: /*@
4613: MatFactorGetSolverType - Returns name of the package providing the factorization routines
4615: Not Collective
4617: Input Parameter:
4618: . mat - the matrix, must be a factored matrix
4620: Output Parameter:
4621: . type - the string name of the package (do not free this string)
4623: Level: intermediate
4625: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatSolverType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`
4626: @*/
4627: PetscErrorCode MatFactorGetSolverType(Mat mat, MatSolverType *type)
4628: {
4629: PetscErrorCode (*conv)(Mat, MatSolverType *);
4631: PetscFunctionBegin;
4634: PetscAssertPointer(type, 2);
4635: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
4636: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorGetSolverType_C", &conv));
4637: if (conv) PetscCall((*conv)(mat, type));
4638: else *type = MATSOLVERPETSC;
4639: PetscFunctionReturn(PETSC_SUCCESS);
4640: }
4642: typedef struct _MatSolverTypeForSpecifcType *MatSolverTypeForSpecifcType;
4643: struct _MatSolverTypeForSpecifcType {
4644: MatType mtype;
4645: /* no entry for MAT_FACTOR_NONE */
4646: PetscErrorCode (*createfactor[MAT_FACTOR_NUM_TYPES - 1])(Mat, MatFactorType, Mat *);
4647: MatSolverTypeForSpecifcType next;
4648: };
4650: typedef struct _MatSolverTypeHolder *MatSolverTypeHolder;
4651: struct _MatSolverTypeHolder {
4652: char *name;
4653: MatSolverTypeForSpecifcType handlers;
4654: MatSolverTypeHolder next;
4655: };
4657: static MatSolverTypeHolder MatSolverTypeHolders = NULL;
4659: /*@C
4660: MatSolverTypeRegister - Registers a `MatSolverType` that works for a particular matrix type
4662: Logically Collective, No Fortran Support
4664: Input Parameters:
4665: + package - name of the package, for example `petsc` or `superlu`
4666: . mtype - the matrix type that works with this package
4667: . ftype - the type of factorization supported by the package
4668: - createfactor - routine that will create the factored matrix ready to be used
4670: Level: developer
4672: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorGetSolverType()`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`,
4673: `MatGetFactor()`
4674: @*/
4675: PetscErrorCode MatSolverTypeRegister(MatSolverType package, MatType mtype, MatFactorType ftype, PetscErrorCode (*createfactor)(Mat, MatFactorType, Mat *))
4676: {
4677: MatSolverTypeHolder next = MatSolverTypeHolders, prev = NULL;
4678: PetscBool flg;
4679: MatSolverTypeForSpecifcType inext, iprev = NULL;
4681: PetscFunctionBegin;
4682: PetscCall(MatInitializePackage());
4683: if (!next) {
4684: PetscCall(PetscNew(&MatSolverTypeHolders));
4685: PetscCall(PetscStrallocpy(package, &MatSolverTypeHolders->name));
4686: PetscCall(PetscNew(&MatSolverTypeHolders->handlers));
4687: PetscCall(PetscStrallocpy(mtype, (char **)&MatSolverTypeHolders->handlers->mtype));
4688: MatSolverTypeHolders->handlers->createfactor[(int)ftype - 1] = createfactor;
4689: PetscFunctionReturn(PETSC_SUCCESS);
4690: }
4691: while (next) {
4692: PetscCall(PetscStrcasecmp(package, next->name, &flg));
4693: if (flg) {
4694: PetscCheck(next->handlers, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatSolverTypeHolder is missing handlers");
4695: inext = next->handlers;
4696: while (inext) {
4697: PetscCall(PetscStrcasecmp(mtype, inext->mtype, &flg));
4698: if (flg) {
4699: inext->createfactor[(int)ftype - 1] = createfactor;
4700: PetscFunctionReturn(PETSC_SUCCESS);
4701: }
4702: iprev = inext;
4703: inext = inext->next;
4704: }
4705: PetscCall(PetscNew(&iprev->next));
4706: PetscCall(PetscStrallocpy(mtype, (char **)&iprev->next->mtype));
4707: iprev->next->createfactor[(int)ftype - 1] = createfactor;
4708: PetscFunctionReturn(PETSC_SUCCESS);
4709: }
4710: prev = next;
4711: next = next->next;
4712: }
4713: PetscCall(PetscNew(&prev->next));
4714: PetscCall(PetscStrallocpy(package, &prev->next->name));
4715: PetscCall(PetscNew(&prev->next->handlers));
4716: PetscCall(PetscStrallocpy(mtype, (char **)&prev->next->handlers->mtype));
4717: prev->next->handlers->createfactor[(int)ftype - 1] = createfactor;
4718: PetscFunctionReturn(PETSC_SUCCESS);
4719: }
4721: /*@C
4722: MatSolverTypeGet - Gets the function that creates the factor matrix if it exist
4724: Input Parameters:
4725: + 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
4726: . ftype - the type of factorization supported by the type
4727: - mtype - the matrix type that works with this type
4729: Output Parameters:
4730: + foundtype - `PETSC_TRUE` if the type was registered
4731: . foundmtype - `PETSC_TRUE` if the type supports the requested mtype
4732: - createfactor - routine that will create the factored matrix ready to be used or `NULL` if not found
4734: Calling sequence of `createfactor`:
4735: + A - the matrix providing the factor matrix
4736: . ftype - the `MatFactorType` of the factor requested
4737: - B - the new factor matrix that responds to MatXXFactorSymbolic,Numeric() functions, such as `MatLUFactorSymbolic()`
4739: Level: developer
4741: Note:
4742: When `type` is `NULL` the available functions are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4743: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4744: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4746: .seealso: [](ch_matrices), `Mat`, `MatFactorType`, `MatType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatSolverTypeRegister()`, `MatGetFactor()`,
4747: `MatInitializePackage()`
4748: @*/
4749: PetscErrorCode MatSolverTypeGet(MatSolverType type, MatType mtype, MatFactorType ftype, PetscBool *foundtype, PetscBool *foundmtype, PetscErrorCode (**createfactor)(Mat A, MatFactorType ftype, Mat *B))
4750: {
4751: MatSolverTypeHolder next = MatSolverTypeHolders;
4752: PetscBool flg;
4753: MatSolverTypeForSpecifcType inext;
4755: PetscFunctionBegin;
4756: if (foundtype) *foundtype = PETSC_FALSE;
4757: if (foundmtype) *foundmtype = PETSC_FALSE;
4758: if (createfactor) *createfactor = NULL;
4760: if (type) {
4761: while (next) {
4762: PetscCall(PetscStrcasecmp(type, next->name, &flg));
4763: if (flg) {
4764: if (foundtype) *foundtype = PETSC_TRUE;
4765: inext = next->handlers;
4766: while (inext) {
4767: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4768: if (flg) {
4769: if (foundmtype) *foundmtype = PETSC_TRUE;
4770: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4771: PetscFunctionReturn(PETSC_SUCCESS);
4772: }
4773: inext = inext->next;
4774: }
4775: }
4776: next = next->next;
4777: }
4778: } else {
4779: while (next) {
4780: inext = next->handlers;
4781: while (inext) {
4782: PetscCall(PetscStrcmp(mtype, inext->mtype, &flg));
4783: if (flg && inext->createfactor[(int)ftype - 1]) {
4784: if (foundtype) *foundtype = PETSC_TRUE;
4785: if (foundmtype) *foundmtype = PETSC_TRUE;
4786: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4787: PetscFunctionReturn(PETSC_SUCCESS);
4788: }
4789: inext = inext->next;
4790: }
4791: next = next->next;
4792: }
4793: /* try with base classes inext->mtype */
4794: next = MatSolverTypeHolders;
4795: while (next) {
4796: inext = next->handlers;
4797: while (inext) {
4798: PetscCall(PetscStrbeginswith(mtype, inext->mtype, &flg));
4799: if (flg && inext->createfactor[(int)ftype - 1]) {
4800: if (foundtype) *foundtype = PETSC_TRUE;
4801: if (foundmtype) *foundmtype = PETSC_TRUE;
4802: if (createfactor) *createfactor = inext->createfactor[(int)ftype - 1];
4803: PetscFunctionReturn(PETSC_SUCCESS);
4804: }
4805: inext = inext->next;
4806: }
4807: next = next->next;
4808: }
4809: }
4810: PetscFunctionReturn(PETSC_SUCCESS);
4811: }
4813: PetscErrorCode MatSolverTypeDestroy(void)
4814: {
4815: MatSolverTypeHolder next = MatSolverTypeHolders, prev;
4816: MatSolverTypeForSpecifcType inext, iprev;
4818: PetscFunctionBegin;
4819: while (next) {
4820: PetscCall(PetscFree(next->name));
4821: inext = next->handlers;
4822: while (inext) {
4823: PetscCall(PetscFree(inext->mtype));
4824: iprev = inext;
4825: inext = inext->next;
4826: PetscCall(PetscFree(iprev));
4827: }
4828: prev = next;
4829: next = next->next;
4830: PetscCall(PetscFree(prev));
4831: }
4832: MatSolverTypeHolders = NULL;
4833: PetscFunctionReturn(PETSC_SUCCESS);
4834: }
4836: /*@
4837: MatFactorGetCanUseOrdering - Indicates if the factorization can use the ordering provided in `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4839: Logically Collective
4841: Input Parameter:
4842: . mat - the matrix
4844: Output Parameter:
4845: . flg - `PETSC_TRUE` if uses the ordering
4847: Level: developer
4849: Note:
4850: Most internal PETSc factorizations use the ordering passed to the factorization routine but external
4851: packages do not, thus we want to skip generating the ordering when it is not needed or used.
4853: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4854: @*/
4855: PetscErrorCode MatFactorGetCanUseOrdering(Mat mat, PetscBool *flg)
4856: {
4857: PetscFunctionBegin;
4858: *flg = mat->canuseordering;
4859: PetscFunctionReturn(PETSC_SUCCESS);
4860: }
4862: /*@
4863: MatFactorGetPreferredOrdering - The preferred ordering for a particular matrix factor object
4865: Logically Collective
4867: Input Parameters:
4868: + mat - the matrix obtained with `MatGetFactor()`
4869: - ftype - the factorization type to be used
4871: Output Parameter:
4872: . otype - the preferred ordering type
4874: Level: developer
4876: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatFactorType`, `MatOrderingType`, `MatCopy()`, `MatDuplicate()`, `MatGetFactorAvailable()`, `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`
4877: @*/
4878: PetscErrorCode MatFactorGetPreferredOrdering(Mat mat, MatFactorType ftype, MatOrderingType *otype)
4879: {
4880: PetscFunctionBegin;
4881: *otype = mat->preferredordering[ftype];
4882: PetscCheck(*otype, PETSC_COMM_SELF, PETSC_ERR_PLIB, "MatFactor did not have a preferred ordering");
4883: PetscFunctionReturn(PETSC_SUCCESS);
4884: }
4886: /*@
4887: MatGetFactor - Returns a matrix suitable to calls to routines such as `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
4888: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactorNumeric()`, `MatILUFactorNumeric()`, and
4889: `MatICCFactorNumeric()`
4891: Collective
4893: Input Parameters:
4894: + mat - the matrix
4895: . type - name of solver type, for example, `superlu_dist`, `petsc` (to use PETSc's solver if it is available), if this is 'NULL', then the first result that satisfies
4896: the other criteria is returned
4897: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4899: Output Parameter:
4900: . f - the factor matrix used with MatXXFactorSymbolic,Numeric() calls. Can be `NULL` in some cases, see notes below.
4902: Options Database Keys:
4903: + -pc_factor_mat_solver_type type - choose the type at run time. When using `KSP` solvers
4904: . -pc_factor_mat_factor_on_host (true|false) - do matrix factorization on host (with device matrices). Default is doing it on device
4905: - -pc_factor_mat_solve_on_host (true|false) - do matrix solve on host (with device matrices). Default is doing it on device
4907: Level: intermediate
4909: Notes:
4910: Some of the packages, such as MUMPS, have options for controlling the factorization, these are in the form `-prefix_mat_packagename_packageoption`
4911: (for example, `-mat_mumps_icntl_6 1`) where `prefix` is normally set automatically from the calling `KSP`/`PC`. If `MatGetFactor()` is called directly,
4912: without using a `PC`, one can set the prefix by
4913: calling `MatSetOptionsPrefixFactor()` on the originating matrix or `MatSetOptionsPrefix()` on the resulting factor matrix.
4915: Some PETSc matrix formats have alternative solvers available that are provided by alternative packages
4916: such as PaStiX, SuperLU_DIST, MUMPS etc. PETSc must have been configured to use the external solver,
4917: using the corresponding `./configure` option such as `--download-package` or `--with-package-dir`.
4919: When `type` is `NULL` the available results are searched for based on the order of the calls to `MatSolverTypeRegister()` in `MatInitializePackage()`.
4920: Since different PETSc configurations may have different external solvers, seemingly identical runs with different PETSc configurations may use a different solver.
4921: For example if one configuration had `--download-mumps` while a different one had `--download-superlu_dist`.
4923: The return matrix can be `NULL` if the requested factorization is not available, since some combinations of matrix types and factorization
4924: types registered with `MatSolverTypeRegister()` cannot be fully tested if not at runtime.
4926: Developer Note:
4927: This should actually be called `MatCreateFactor()` since it creates a new factor object
4929: The `MatGetFactor()` implementations should not be accessing the PETSc options database or making other decisions about solver options,
4930: that should be delayed until the later operations. This is to ensure the correct options prefix has been set in the factor matrix.
4932: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `KSP`, `MatSolverType`, `MatFactorType`, `MatCopy()`, `MatDuplicate()`,
4933: `MatGetFactorAvailable()`, `MatFactorGetCanUseOrdering()`, `MatSolverTypeRegister()`, `MatSolverTypeGet()`,
4934: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatInitializePackage()`,
4935: `MatLUFactorSymbolic()`, `MatCholeskyFactorSymbolic()`, `MatILUFactorSymbolic()`,
4936: `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactorNumeric()`, `MatILUFactorNumeric()`,
4937: `MatICCFactorNumeric()`
4938: @*/
4939: PetscErrorCode MatGetFactor(Mat mat, MatSolverType type, MatFactorType ftype, Mat *f)
4940: {
4941: PetscBool foundtype, foundmtype, shell, hasop = PETSC_FALSE;
4942: PetscErrorCode (*conv)(Mat, MatFactorType, Mat *);
4944: PetscFunctionBegin;
4948: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4949: MatCheckPreallocated(mat, 1);
4951: PetscCall(MatIsShell(mat, &shell));
4952: if (shell) PetscCall(MatHasOperation(mat, MATOP_GET_FACTOR, &hasop));
4953: if (hasop) {
4954: PetscUseTypeMethod(mat, getfactor, type, ftype, f);
4955: PetscFunctionReturn(PETSC_SUCCESS);
4956: }
4958: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, &foundtype, &foundmtype, &conv));
4959: if (!foundtype) {
4960: if (type) {
4961: 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],
4962: ((PetscObject)mat)->type_name, type);
4963: } else {
4964: 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);
4965: }
4966: }
4967: PetscCheck(foundmtype, PetscObjectComm((PetscObject)mat), PETSC_ERR_MISSING_FACTOR, "MatSolverType %s does not support matrix type %s", type, ((PetscObject)mat)->type_name);
4968: 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);
4970: PetscCall((*conv)(mat, ftype, f));
4971: if (mat->factorprefix) PetscCall(MatSetOptionsPrefix(*f, mat->factorprefix));
4972: PetscFunctionReturn(PETSC_SUCCESS);
4973: }
4975: /*@
4976: MatGetFactorAvailable - Returns a flag if matrix supports particular type and factor type
4978: Not Collective
4980: Input Parameters:
4981: + mat - the matrix
4982: . type - name of solver type, for example, `superlu`, `petsc` (to use PETSc's default)
4983: - ftype - factor type, `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`
4985: Output Parameter:
4986: . flg - PETSC_TRUE if the factorization is available
4988: Level: intermediate
4990: Notes:
4991: Some PETSc matrix formats have alternative solvers available that are contained in alternative packages
4992: such as pastix, superlu, mumps etc.
4994: PETSc must have been ./configure to use the external solver, using the option --download-package
4996: Developer Note:
4997: This should actually be called `MatCreateFactorAvailable()` since `MatGetFactor()` creates a new factor object
4999: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatSolverType`, `MatFactorType`, `MatGetFactor()`, `MatCopy()`, `MatDuplicate()`, `MatSolverTypeRegister()`,
5000: `MAT_FACTOR_LU`, `MAT_FACTOR_CHOLESKY`, `MAT_FACTOR_ICC`, `MAT_FACTOR_ILU`, `MAT_FACTOR_QR`, `MatSolverTypeGet()`
5001: @*/
5002: PetscErrorCode MatGetFactorAvailable(Mat mat, MatSolverType type, MatFactorType ftype, PetscBool *flg)
5003: {
5004: PetscErrorCode (*gconv)(Mat, MatFactorType, Mat *);
5006: PetscFunctionBegin;
5008: PetscAssertPointer(flg, 4);
5010: *flg = PETSC_FALSE;
5011: if (!((PetscObject)mat)->type_name) PetscFunctionReturn(PETSC_SUCCESS);
5013: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5014: MatCheckPreallocated(mat, 1);
5016: PetscCall(MatSolverTypeGet(type, ((PetscObject)mat)->type_name, ftype, NULL, NULL, &gconv));
5017: *flg = gconv ? PETSC_TRUE : PETSC_FALSE;
5018: PetscFunctionReturn(PETSC_SUCCESS);
5019: }
5021: /*@
5022: MatDuplicate - Duplicates a matrix including the non-zero structure.
5024: Collective
5026: Input Parameters:
5027: + mat - the matrix
5028: - op - One of `MAT_DO_NOT_COPY_VALUES`, `MAT_COPY_VALUES`, or `MAT_SHARE_NONZERO_PATTERN`.
5029: See the manual page for `MatDuplicateOption()` for an explanation of these options.
5031: Output Parameter:
5032: . M - pointer to place new matrix
5034: Level: intermediate
5036: Notes:
5037: You cannot change the nonzero pattern for the parent or child matrix later if you use `MAT_SHARE_NONZERO_PATTERN`.
5039: If `op` is not `MAT_COPY_VALUES` the numerical values in the new matrix are zeroed.
5041: 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.
5043: When original mat is a product of matrix operation, e.g., an output of `MatMatMult()` or `MatCreateSubMatrix()`, only the matrix data structure of `mat`
5044: is duplicated and the internal data structures created for the reuse of previous matrix operations are not duplicated.
5045: User should not use `MatDuplicate()` to create new matrix `M` if `M` is intended to be reused as the product of matrix operation.
5047: .seealso: [](ch_matrices), `Mat`, `MatCopy()`, `MatConvert()`, `MatDuplicateOption`
5048: @*/
5049: PetscErrorCode MatDuplicate(Mat mat, MatDuplicateOption op, Mat *M)
5050: {
5051: Mat B;
5052: VecType vtype;
5053: PetscInt i;
5054: PetscObject dm, container_h, container_d;
5055: PetscErrorCodeFn *viewf;
5057: PetscFunctionBegin;
5060: PetscAssertPointer(M, 3);
5061: PetscCheck(op != MAT_COPY_VALUES || mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "MAT_COPY_VALUES not allowed for unassembled matrix");
5062: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5063: MatCheckPreallocated(mat, 1);
5065: PetscCall(PetscLogEventBegin(MAT_Convert, mat, 0, 0, 0));
5066: PetscUseTypeMethod(mat, duplicate, op, M);
5067: PetscCall(PetscLogEventEnd(MAT_Convert, mat, 0, 0, 0));
5068: B = *M;
5070: PetscCall(MatGetOperation(mat, MATOP_VIEW, &viewf));
5071: if (viewf) PetscCall(MatSetOperation(B, MATOP_VIEW, viewf));
5072: PetscCall(MatGetVecType(mat, &vtype));
5073: PetscCall(MatSetVecType(B, vtype));
5075: B->stencil.dim = mat->stencil.dim;
5076: B->stencil.noc = mat->stencil.noc;
5077: for (i = 0; i <= mat->stencil.dim + (mat->stencil.noc ? 0 : -1); i++) {
5078: B->stencil.dims[i] = mat->stencil.dims[i];
5079: B->stencil.starts[i] = mat->stencil.starts[i];
5080: }
5082: B->nooffproczerorows = mat->nooffproczerorows;
5083: B->nooffprocentries = mat->nooffprocentries;
5085: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_dm", &dm));
5086: if (dm) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_dm", dm));
5087: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Host", &container_h));
5088: if (container_h) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Host", container_h));
5089: PetscCall(PetscObjectQuery((PetscObject)mat, "__PETSc_MatCOOStruct_Device", &container_d));
5090: if (container_d) PetscCall(PetscObjectCompose((PetscObject)B, "__PETSc_MatCOOStruct_Device", container_d));
5091: if (op == MAT_COPY_VALUES) PetscCall(MatPropagateSymmetryOptions(mat, B));
5092: PetscCall(PetscObjectStateIncrease((PetscObject)B));
5093: PetscFunctionReturn(PETSC_SUCCESS);
5094: }
5096: /*@
5097: MatGetDiagonal - Gets the diagonal of a matrix as a `Vec`
5099: Logically Collective
5101: Input Parameter:
5102: . mat - the matrix
5104: Output Parameter:
5105: . v - the diagonal of the matrix
5107: Level: intermediate
5109: Note:
5110: If `mat` has local sizes `n` x `m`, this routine fills the first `ndiag = min(n, m)` entries
5111: of `v` with the diagonal values. Thus `v` must have local size of at least `ndiag`. If `v`
5112: is larger than `ndiag`, the values of the remaining entries are unspecified.
5114: Currently only correct in parallel for square matrices.
5116: .seealso: [](ch_matrices), `Mat`, `Vec`, `MatGetRow()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`
5117: @*/
5118: PetscErrorCode MatGetDiagonal(Mat mat, Vec v)
5119: {
5120: PetscFunctionBegin;
5124: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5125: MatCheckPreallocated(mat, 1);
5126: if (PetscDefined(USE_DEBUG)) {
5127: PetscInt nv, row, col, ndiag;
5129: PetscCall(VecGetLocalSize(v, &nv));
5130: PetscCall(MatGetLocalSize(mat, &row, &col));
5131: ndiag = PetscMin(row, col);
5132: 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);
5133: }
5135: PetscUseTypeMethod(mat, getdiagonal, v);
5136: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5137: PetscFunctionReturn(PETSC_SUCCESS);
5138: }
5140: /*@
5141: MatGetRowMin - Gets the minimum value (of the real part) of each
5142: row of the matrix
5144: Logically Collective
5146: Input Parameter:
5147: . mat - the matrix
5149: Output Parameters:
5150: + v - the vector for storing the maximums
5151: - idx - the indices of the column found for each row (optional, pass `NULL` if not needed)
5153: Level: intermediate
5155: Note:
5156: The result of this call are the same as if one converted the matrix to dense format
5157: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5159: This code is only implemented for a couple of matrix formats.
5161: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`,
5162: `MatGetRowMax()`
5163: @*/
5164: PetscErrorCode MatGetRowMin(Mat mat, Vec v, PetscInt idx[])
5165: {
5166: PetscFunctionBegin;
5170: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5172: if (!mat->cmap->N) {
5173: PetscCall(VecSet(v, PETSC_MAX_REAL));
5174: if (idx) {
5175: PetscInt i, m = mat->rmap->n;
5176: for (i = 0; i < m; i++) idx[i] = -1;
5177: }
5178: } else {
5179: MatCheckPreallocated(mat, 1);
5180: }
5181: PetscUseTypeMethod(mat, getrowmin, v, idx);
5182: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5183: PetscFunctionReturn(PETSC_SUCCESS);
5184: }
5186: /*@
5187: MatGetRowMinAbs - Gets the minimum value (in absolute value) of each
5188: row of the matrix
5190: Logically Collective
5192: Input Parameter:
5193: . mat - the matrix
5195: Output Parameters:
5196: + v - the vector for storing the minimums
5197: - idx - the indices of the column found for each row (or `NULL` if not needed)
5199: Level: intermediate
5201: Notes:
5202: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5203: row is 0 (the first column).
5205: This code is only implemented for a couple of matrix formats.
5207: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`
5208: @*/
5209: PetscErrorCode MatGetRowMinAbs(Mat mat, Vec v, PetscInt idx[])
5210: {
5211: PetscFunctionBegin;
5215: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5216: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5218: if (!mat->cmap->N) {
5219: PetscCall(VecSet(v, 0.0));
5220: if (idx) {
5221: PetscInt i, m = mat->rmap->n;
5222: for (i = 0; i < m; i++) idx[i] = -1;
5223: }
5224: } else {
5225: MatCheckPreallocated(mat, 1);
5226: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5227: PetscUseTypeMethod(mat, getrowminabs, v, idx);
5228: }
5229: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5230: PetscFunctionReturn(PETSC_SUCCESS);
5231: }
5233: /*@
5234: MatGetRowMax - Gets the maximum value (of the real part) of each
5235: row of the matrix
5237: Logically Collective
5239: Input Parameter:
5240: . mat - the matrix
5242: Output Parameters:
5243: + v - the vector for storing the maximums
5244: - idx - the indices of the column found for each row (optional, otherwise pass `NULL`)
5246: Level: intermediate
5248: Notes:
5249: The result of this call are the same as if one converted the matrix to dense format
5250: and found the minimum value in each row (i.e. the implicit zeros are counted as zeros).
5252: This code is only implemented for a couple of matrix formats.
5254: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMaxAbs()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5255: @*/
5256: PetscErrorCode MatGetRowMax(Mat mat, Vec v, PetscInt idx[])
5257: {
5258: PetscFunctionBegin;
5262: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5264: if (!mat->cmap->N) {
5265: PetscCall(VecSet(v, PETSC_MIN_REAL));
5266: if (idx) {
5267: PetscInt i, m = mat->rmap->n;
5268: for (i = 0; i < m; i++) idx[i] = -1;
5269: }
5270: } else {
5271: MatCheckPreallocated(mat, 1);
5272: PetscUseTypeMethod(mat, getrowmax, v, idx);
5273: }
5274: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5275: PetscFunctionReturn(PETSC_SUCCESS);
5276: }
5278: /*@
5279: MatGetRowMaxAbs - Gets the maximum value (in absolute value) of each
5280: row of the matrix
5282: Logically Collective
5284: Input Parameter:
5285: . mat - the matrix
5287: Output Parameters:
5288: + v - the vector for storing the maximums
5289: - idx - the indices of the column found for each row (or `NULL` if not needed)
5291: Level: intermediate
5293: Notes:
5294: if a row is completely empty or has only 0.0 values, then the `idx` value for that
5295: row is 0 (the first column).
5297: This code is only implemented for a couple of matrix formats.
5299: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowSum()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5300: @*/
5301: PetscErrorCode MatGetRowMaxAbs(Mat mat, Vec v, PetscInt idx[])
5302: {
5303: PetscFunctionBegin;
5307: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5309: if (!mat->cmap->N) {
5310: PetscCall(VecSet(v, 0.0));
5311: if (idx) {
5312: PetscInt i, m = mat->rmap->n;
5313: for (i = 0; i < m; i++) idx[i] = -1;
5314: }
5315: } else {
5316: MatCheckPreallocated(mat, 1);
5317: if (idx) PetscCall(PetscArrayzero(idx, mat->rmap->n));
5318: PetscUseTypeMethod(mat, getrowmaxabs, v, idx);
5319: }
5320: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5321: PetscFunctionReturn(PETSC_SUCCESS);
5322: }
5324: /*@
5325: MatGetRowSumAbs - Gets the sum value (in absolute value) of each row of the matrix
5327: Logically Collective
5329: Input Parameter:
5330: . mat - the matrix
5332: Output Parameter:
5333: . v - the vector for storing the sum
5335: Level: intermediate
5337: This code is only implemented for a couple of matrix formats.
5339: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMinAbs()`
5340: @*/
5341: PetscErrorCode MatGetRowSumAbs(Mat mat, Vec v)
5342: {
5343: PetscFunctionBegin;
5347: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5349: if (!mat->cmap->N) PetscCall(VecSet(v, 0.0));
5350: else {
5351: MatCheckPreallocated(mat, 1);
5352: PetscUseTypeMethod(mat, getrowsumabs, v);
5353: }
5354: PetscCall(PetscObjectStateIncrease((PetscObject)v));
5355: PetscFunctionReturn(PETSC_SUCCESS);
5356: }
5358: /*@
5359: MatGetRowSum - Gets the sum of each row of the matrix
5361: Logically or Neighborhood Collective
5363: Input Parameter:
5364: . mat - the matrix
5366: Output Parameter:
5367: . v - the vector for storing the sum of rows
5369: Level: intermediate
5371: Note:
5372: This code is slow since it is not currently specialized for different formats
5374: .seealso: [](ch_matrices), `Mat`, `MatGetDiagonal()`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRowMax()`, `MatGetRowMin()`, `MatGetRowMaxAbs()`, `MatGetRowMinAbs()`, `MatGetRowSumAbs()`
5375: @*/
5376: PetscErrorCode MatGetRowSum(Mat mat, Vec v)
5377: {
5378: Vec ones;
5380: PetscFunctionBegin;
5384: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5385: MatCheckPreallocated(mat, 1);
5386: PetscCall(MatCreateVecs(mat, &ones, NULL));
5387: PetscCall(VecSet(ones, 1.));
5388: PetscCall(MatMult(mat, ones, v));
5389: PetscCall(VecDestroy(&ones));
5390: PetscFunctionReturn(PETSC_SUCCESS);
5391: }
5393: /*@
5394: MatTransposeSetPrecursor - Set the matrix from which the second matrix will receive numerical transpose data with a call to `MatTranspose`(A,`MAT_REUSE_MATRIX`,&B)
5395: when B was not obtained with `MatTranspose`(A,`MAT_INITIAL_MATRIX`,&B)
5397: Collective
5399: Input Parameter:
5400: . mat - the matrix to provide the transpose
5402: Output Parameter:
5403: . 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
5405: Level: advanced
5407: Note:
5408: 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
5409: routine allows bypassing that call.
5411: .seealso: [](ch_matrices), `Mat`, `MatTransposeSymbolic()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5412: @*/
5413: PetscErrorCode MatTransposeSetPrecursor(Mat mat, Mat B)
5414: {
5415: MatParentState *rb = NULL;
5417: PetscFunctionBegin;
5418: PetscCall(PetscNew(&rb));
5419: rb->id = ((PetscObject)mat)->id;
5420: rb->state = 0;
5421: PetscCall(MatGetNonzeroState(mat, &rb->nonzerostate));
5422: PetscCall(PetscObjectContainerCompose((PetscObject)B, "MatTransposeParent", rb, PetscCtxDestroyDefault));
5423: PetscFunctionReturn(PETSC_SUCCESS);
5424: }
5426: static PetscErrorCode MatTranspose_Private(Mat mat, MatReuse reuse, Mat *B, PetscBool conjugate)
5427: {
5428: PetscContainer rB = NULL;
5429: MatParentState *rb = NULL;
5430: PetscErrorCode (*f)(Mat, MatReuse, Mat *) = NULL;
5432: PetscFunctionBegin;
5435: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5436: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5437: PetscCheck(reuse != MAT_INPLACE_MATRIX || mat == *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX requires last matrix to match first");
5438: PetscCheck(reuse != MAT_REUSE_MATRIX || mat != *B, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Perhaps you mean MAT_INPLACE_MATRIX");
5439: MatCheckPreallocated(mat, 1);
5440: if (reuse == MAT_REUSE_MATRIX) {
5441: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5442: PetscCheck(rB, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose(). Suggest MatTransposeSetPrecursor().");
5443: PetscCall(PetscContainerGetPointer(rB, &rb));
5444: PetscCheck(rb->id == ((PetscObject)mat)->id, PetscObjectComm((PetscObject)*B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5445: if (rb->state == ((PetscObject)mat)->state) PetscFunctionReturn(PETSC_SUCCESS);
5446: }
5448: if (conjugate) {
5449: f = mat->ops->hermitiantranspose;
5450: if (f) PetscCall((*f)(mat, reuse, B));
5451: }
5452: if (!f && !(reuse == MAT_INPLACE_MATRIX && mat->hermitian == PETSC_BOOL3_TRUE && conjugate)) {
5453: PetscCall(PetscLogEventBegin(MAT_Transpose, mat, 0, 0, 0));
5454: if (reuse != MAT_INPLACE_MATRIX || mat->symmetric != PETSC_BOOL3_TRUE) {
5455: PetscUseTypeMethod(mat, transpose, reuse, B);
5456: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5457: }
5458: PetscCall(PetscLogEventEnd(MAT_Transpose, mat, 0, 0, 0));
5459: if (conjugate) PetscCall(MatConjugate(*B));
5460: }
5462: if (reuse == MAT_INITIAL_MATRIX) PetscCall(MatTransposeSetPrecursor(mat, *B));
5463: if (reuse != MAT_INPLACE_MATRIX) {
5464: PetscCall(PetscObjectQuery((PetscObject)*B, "MatTransposeParent", (PetscObject *)&rB));
5465: PetscCall(PetscContainerGetPointer(rB, &rb));
5466: rb->state = ((PetscObject)mat)->state;
5467: rb->nonzerostate = mat->nonzerostate;
5468: }
5469: PetscFunctionReturn(PETSC_SUCCESS);
5470: }
5472: /*@
5473: MatTranspose - Computes the transpose of a matrix, either in-place or out-of-place.
5475: Collective
5477: Input Parameters:
5478: + mat - the matrix to transpose
5479: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5481: Output Parameter:
5482: . B - the transpose of the matrix
5484: Level: intermediate
5486: Notes:
5487: If you use `MAT_INPLACE_MATRIX` then you must pass in `&mat` for `B`
5489: `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
5490: transpose, call `MatTransposeSetPrecursor(mat, B)` before calling this routine.
5492: 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.
5494: Consider using `MatCreateTranspose()` instead if you only need a matrix that behaves like the transpose but don't need the storage to be changed.
5495: For example, the result of `MatCreateTranspose()` will compute the transpose of the given matrix times a vector for matrix-vector products computed with `MatMult()`.
5497: If `mat` is unchanged from the last call this function returns immediately without recomputing the result
5499: If you only need the symbolic transpose of a matrix, and not the numerical values, use `MatTransposeSymbolic()`
5501: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`,
5502: `MatTransposeSymbolic()`, `MatCreateTranspose()`
5503: @*/
5504: PetscErrorCode MatTranspose(Mat mat, MatReuse reuse, Mat *B)
5505: {
5506: PetscFunctionBegin;
5507: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_FALSE));
5508: PetscFunctionReturn(PETSC_SUCCESS);
5509: }
5511: /*@
5512: MatTransposeSymbolic - Computes the symbolic part of the transpose of a matrix.
5514: Collective
5516: Input Parameter:
5517: . A - the matrix to transpose
5519: Output Parameter:
5520: . 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
5521: numerical portion.
5523: Level: intermediate
5525: Note:
5526: This is not supported for many matrix types, use `MatTranspose()` in those cases
5528: .seealso: [](ch_matrices), `Mat`, `MatTransposeSetPrecursor()`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`, `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, `MAT_INPLACE_MATRIX`
5529: @*/
5530: PetscErrorCode MatTransposeSymbolic(Mat A, Mat *B)
5531: {
5532: PetscFunctionBegin;
5535: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5536: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5537: PetscCall(PetscLogEventBegin(MAT_Transpose, A, 0, 0, 0));
5538: PetscUseTypeMethod(A, transposesymbolic, B);
5539: PetscCall(PetscLogEventEnd(MAT_Transpose, A, 0, 0, 0));
5541: PetscCall(MatTransposeSetPrecursor(A, *B));
5542: PetscFunctionReturn(PETSC_SUCCESS);
5543: }
5545: PetscErrorCode MatTransposeCheckNonzeroState_Private(Mat A, Mat B)
5546: {
5547: PetscContainer rB;
5548: MatParentState *rb;
5550: PetscFunctionBegin;
5553: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5554: PetscCheck(!A->factortype, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5555: PetscCall(PetscObjectQuery((PetscObject)B, "MatTransposeParent", (PetscObject *)&rB));
5556: PetscCheck(rB, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from call to MatTranspose()");
5557: PetscCall(PetscContainerGetPointer(rB, &rb));
5558: PetscCheck(rb->id == ((PetscObject)A)->id, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONG, "Reuse matrix used was not generated from input matrix");
5559: PetscCheck(rb->nonzerostate == A->nonzerostate, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Reuse matrix has changed nonzero structure");
5560: PetscFunctionReturn(PETSC_SUCCESS);
5561: }
5563: /*@
5564: MatIsTranspose - Test whether a matrix is another one's transpose,
5565: or its own, in which case it tests symmetry.
5567: Collective
5569: Input Parameters:
5570: + A - the matrix to test
5571: . B - the matrix to test against, this can equal the first parameter
5572: - tol - tolerance, differences between entries smaller than this are counted as zero
5574: Output Parameter:
5575: . flg - the result
5577: Level: intermediate
5579: Notes:
5580: The sequential algorithm has a running time of the order of the number of nonzeros; the parallel
5581: test involves parallel copies of the block off-diagonal parts of the matrix.
5583: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`
5584: @*/
5585: PetscErrorCode MatIsTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5586: {
5587: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5589: PetscFunctionBegin;
5592: PetscAssertPointer(flg, 4);
5593: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsTranspose_C", &f));
5594: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsTranspose_C", &g));
5595: *flg = PETSC_FALSE;
5596: if (f && g) {
5597: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for symmetry test");
5598: PetscCall((*f)(A, B, tol, flg));
5599: } else {
5600: MatType mattype;
5602: PetscCall(MatGetType(f ? B : A, &mattype));
5603: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for transpose", mattype);
5604: }
5605: PetscFunctionReturn(PETSC_SUCCESS);
5606: }
5608: /*@
5609: MatHermitianTranspose - Computes an in-place or out-of-place Hermitian transpose of a matrix in complex conjugate.
5611: Collective
5613: Input Parameters:
5614: + mat - the matrix to transpose and complex conjugate
5615: - reuse - either `MAT_INITIAL_MATRIX`, `MAT_REUSE_MATRIX`, or `MAT_INPLACE_MATRIX`
5617: Output Parameter:
5618: . B - the Hermitian transpose
5620: Level: intermediate
5622: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatMultTranspose()`, `MatMultTransposeAdd()`, `MatIsTranspose()`, `MatReuse`
5623: @*/
5624: PetscErrorCode MatHermitianTranspose(Mat mat, MatReuse reuse, Mat *B)
5625: {
5626: PetscFunctionBegin;
5627: PetscCall(MatTranspose_Private(mat, reuse, B, PETSC_TRUE));
5628: PetscFunctionReturn(PETSC_SUCCESS);
5629: }
5631: /*@
5632: MatIsHermitianTranspose - Test whether a matrix is another one's Hermitian transpose,
5634: Collective
5636: Input Parameters:
5637: + A - the matrix to test
5638: . B - the matrix to test against, this can equal the first parameter
5639: - tol - tolerance, differences between entries smaller than this are counted as zero
5641: Output Parameter:
5642: . flg - the result
5644: Level: intermediate
5646: Notes:
5647: Only available for `MATAIJ` matrices.
5649: The sequential algorithm
5650: has a running time of the order of the number of nonzeros; the parallel
5651: test involves parallel copies of the block off-diagonal parts of the matrix.
5653: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsTranspose()`
5654: @*/
5655: PetscErrorCode MatIsHermitianTranspose(Mat A, Mat B, PetscReal tol, PetscBool *flg)
5656: {
5657: PetscErrorCode (*f)(Mat, Mat, PetscReal, PetscBool *), (*g)(Mat, Mat, PetscReal, PetscBool *);
5659: PetscFunctionBegin;
5662: PetscAssertPointer(flg, 4);
5663: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatIsHermitianTranspose_C", &f));
5664: PetscCall(PetscObjectQueryFunction((PetscObject)B, "MatIsHermitianTranspose_C", &g));
5665: if (f && g) {
5666: PetscCheck(f == g, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_NOTSAMETYPE, "Matrices do not have the same comparator for Hermitian test");
5667: PetscCall((*f)(A, B, tol, flg));
5668: } else {
5669: MatType mattype;
5671: PetscCall(MatGetType(f ? B : A, &mattype));
5672: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "Matrix of type %s does not support checking for Hermitian transpose", mattype);
5673: }
5674: PetscFunctionReturn(PETSC_SUCCESS);
5675: }
5677: /*@
5678: MatPermute - Creates a new matrix with rows and columns permuted from the
5679: original.
5681: Collective
5683: Input Parameters:
5684: + mat - the matrix to permute
5685: . row - row permutation, each processor supplies only the permutation for its rows
5686: - col - column permutation, each processor supplies only the permutation for its columns
5688: Output Parameter:
5689: . B - the permuted matrix
5691: Level: advanced
5693: Note:
5694: The index sets map from row/col of permuted matrix to row/col of original matrix.
5695: The index sets should be on the same communicator as mat and have the same local sizes.
5697: Developer Note:
5698: If you want to implement `MatPermute()` for a matrix type, and your approach doesn't
5699: exploit the fact that row and col are permutations, consider implementing the
5700: more general `MatCreateSubMatrix()` instead.
5702: .seealso: [](ch_matrices), `Mat`, `MatGetOrdering()`, `ISAllGather()`, `MatCreateSubMatrix()`
5703: @*/
5704: PetscErrorCode MatPermute(Mat mat, IS row, IS col, Mat *B)
5705: {
5706: PetscFunctionBegin;
5711: PetscAssertPointer(B, 4);
5712: PetscCheckSameComm(mat, 1, row, 2);
5713: if (row != col) PetscCheckSameComm(row, 2, col, 3);
5714: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5715: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5716: PetscCheck(mat->ops->permute || mat->ops->createsubmatrix, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatPermute not available for Mat type %s", ((PetscObject)mat)->type_name);
5717: MatCheckPreallocated(mat, 1);
5719: if (mat->ops->permute) {
5720: PetscUseTypeMethod(mat, permute, row, col, B);
5721: PetscCall(PetscObjectStateIncrease((PetscObject)*B));
5722: } else {
5723: PetscCall(MatCreateSubMatrix(mat, row, col, MAT_INITIAL_MATRIX, B));
5724: }
5725: PetscFunctionReturn(PETSC_SUCCESS);
5726: }
5728: /*@
5729: MatEqual - Compares two matrices.
5731: Collective
5733: Input Parameters:
5734: + A - the first matrix
5735: - B - the second matrix
5737: Output Parameter:
5738: . flg - `PETSC_TRUE` if the matrices are equal; `PETSC_FALSE` otherwise.
5740: Level: intermediate
5742: Note:
5743: If either of the matrix is "matrix-free", meaning the matrix entries are not stored explicitly then equality is determined by comparing
5744: the results of several matrix-vector product using randomly created vectors, see `MatMultEqual()`.
5746: .seealso: [](ch_matrices), `Mat`, `MatMultEqual()`
5747: @*/
5748: PetscErrorCode MatEqual(Mat A, Mat B, PetscBool *flg)
5749: {
5750: PetscFunctionBegin;
5755: PetscAssertPointer(flg, 3);
5756: PetscCheckSameComm(A, 1, B, 2);
5757: MatCheckPreallocated(A, 1);
5758: MatCheckPreallocated(B, 2);
5759: PetscCheck(A->assembled, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5760: PetscCheck(B->assembled, PetscObjectComm((PetscObject)B), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5761: 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,
5762: B->cmap->N);
5763: if (A->ops->equal && A->ops->equal == B->ops->equal) PetscUseTypeMethod(A, equal, B, flg);
5764: else PetscCall(MatMultEqual(A, B, 10, flg));
5765: PetscFunctionReturn(PETSC_SUCCESS);
5766: }
5768: /*@
5769: MatDiagonalScale - Scales a matrix on the left and right by diagonal
5770: matrices that are stored as vectors. Either of the two scaling
5771: matrices can be `NULL`.
5773: Collective
5775: Input Parameters:
5776: + mat - the matrix to be scaled
5777: . l - the left scaling vector (or `NULL`)
5778: - r - the right scaling vector (or `NULL`)
5780: Level: intermediate
5782: Note:
5783: `MatDiagonalScale()` computes $A = LAR$, where
5784: L = a diagonal matrix (stored as a vector), R = a diagonal matrix (stored as a vector)
5785: The L scales the rows of the matrix, the R scales the columns of the matrix.
5787: .seealso: [](ch_matrices), `Mat`, `MatScale()`, `MatShift()`, `MatDiagonalSet()`
5788: @*/
5789: PetscErrorCode MatDiagonalScale(Mat mat, Vec l, Vec r)
5790: {
5791: PetscBool flg = PETSC_FALSE;
5793: PetscFunctionBegin;
5796: if (l) {
5798: PetscCheckSameComm(mat, 1, l, 2);
5799: }
5800: if (r) {
5802: PetscCheckSameComm(mat, 1, r, 3);
5803: }
5804: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5805: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5806: MatCheckPreallocated(mat, 1);
5807: if (!l && !r) PetscFunctionReturn(PETSC_SUCCESS);
5809: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5810: PetscUseTypeMethod(mat, diagonalscale, l, r);
5811: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5812: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5813: if (l != r && (PetscBool3ToBool(mat->symmetric) || PetscBool3ToBool(mat->hermitian))) {
5814: if (!PetscDefined(USE_COMPLEX) || PetscBool3ToBool(mat->symmetric)) {
5815: if (l && r) PetscCall(VecEqual(l, r, &flg));
5816: if (!flg) {
5817: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5818: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format, left and right scaling vectors must be the same");
5819: mat->symmetric = mat->spd = PETSC_BOOL3_FALSE;
5820: if (!PetscDefined(USE_COMPLEX)) mat->hermitian = PETSC_BOOL3_FALSE;
5821: else mat->hermitian = PETSC_BOOL3_UNKNOWN;
5822: }
5823: }
5824: if (PetscDefined(USE_COMPLEX) && PetscBool3ToBool(mat->hermitian)) {
5825: flg = PETSC_FALSE;
5826: if (l && r) {
5827: Vec conjugate;
5829: PetscCall(VecDuplicate(l, &conjugate));
5830: PetscCall(VecCopy(l, conjugate));
5831: PetscCall(VecConjugate(conjugate));
5832: PetscCall(VecEqual(conjugate, r, &flg));
5833: PetscCall(VecDestroy(&conjugate));
5834: }
5835: if (!flg) {
5836: PetscCall(PetscObjectTypeCompareAny((PetscObject)mat, &flg, MATSEQSBAIJ, MATMPISBAIJ, ""));
5837: PetscCheck(!flg, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "For symmetric format and Hermitian matrix, left and right scaling vectors must be conjugate one of the other");
5838: mat->hermitian = PETSC_BOOL3_FALSE;
5839: mat->symmetric = mat->spd = PETSC_BOOL3_UNKNOWN;
5840: }
5841: }
5842: }
5843: PetscFunctionReturn(PETSC_SUCCESS);
5844: }
5846: /*@
5847: MatScale - Scales all elements of a matrix by a given number.
5849: Logically Collective
5851: Input Parameters:
5852: + mat - the matrix to be scaled
5853: - a - the scaling value
5855: Level: intermediate
5857: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
5858: @*/
5859: PetscErrorCode MatScale(Mat mat, PetscScalar a)
5860: {
5861: PetscFunctionBegin;
5864: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5865: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5867: MatCheckPreallocated(mat, 1);
5869: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
5870: if (a != (PetscScalar)1.0) {
5871: PetscUseTypeMethod(mat, scale, a);
5872: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
5873: }
5874: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
5875: PetscFunctionReturn(PETSC_SUCCESS);
5876: }
5878: /*@
5879: MatNorm - Calculates various norms of a matrix.
5881: Collective
5883: Input Parameters:
5884: + mat - the matrix
5885: - type - the type of norm, `NORM_1`, `NORM_FROBENIUS`, `NORM_INFINITY`
5887: Output Parameter:
5888: . nrm - the resulting norm
5890: Level: intermediate
5892: .seealso: [](ch_matrices), `Mat`
5893: @*/
5894: PetscErrorCode MatNorm(Mat mat, NormType type, PetscReal *nrm)
5895: {
5896: PetscFunctionBegin;
5899: PetscAssertPointer(nrm, 3);
5901: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
5902: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
5903: MatCheckPreallocated(mat, 1);
5905: PetscUseTypeMethod(mat, norm, type, nrm);
5906: PetscFunctionReturn(PETSC_SUCCESS);
5907: }
5909: /*
5910: This variable is used to prevent counting of MatAssemblyBegin() that
5911: are called from within a MatAssemblyEnd().
5912: */
5913: static PetscInt MatAssemblyEnd_InUse = 0;
5914: /*@
5915: MatAssemblyBegin - Begins assembling the matrix. This routine should
5916: be called after completing all calls to `MatSetValues()`.
5918: Collective
5920: Input Parameters:
5921: + mat - the matrix
5922: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5924: Level: beginner
5926: Notes:
5927: `MatSetValues()` generally caches the values that belong to other MPI processes. The matrix is ready to
5928: use only after `MatAssemblyBegin()` and `MatAssemblyEnd()` have been called.
5930: Use `MAT_FLUSH_ASSEMBLY` when switching between `ADD_VALUES` and `INSERT_VALUES`
5931: in `MatSetValues()`; use `MAT_FINAL_ASSEMBLY` for the final assembly before
5932: using the matrix.
5934: ALL processes that share a matrix MUST call `MatAssemblyBegin()` and `MatAssemblyEnd()` the SAME NUMBER of times, and each time with the
5935: 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
5936: a global collective operation requiring all processes that share the matrix.
5938: Space for preallocated nonzeros that is not filled by a call to `MatSetValues()` or a related routine are compressed
5939: out by assembly. If you intend to use that extra space on a subsequent assembly, be sure to insert explicit zeros
5940: before `MAT_FINAL_ASSEMBLY` so the space is not compressed out.
5942: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssembled()`
5943: @*/
5944: PetscErrorCode MatAssemblyBegin(Mat mat, MatAssemblyType type)
5945: {
5946: PetscFunctionBegin;
5949: MatCheckPreallocated(mat, 1);
5950: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix. Did you forget to call MatSetUnfactored()?");
5951: if (mat->assembled) {
5952: mat->was_assembled = PETSC_TRUE;
5953: mat->assembled = PETSC_FALSE;
5954: }
5956: if (!MatAssemblyEnd_InUse) {
5957: PetscCall(PetscLogEventBegin(MAT_AssemblyBegin, mat, 0, 0, 0));
5958: PetscTryTypeMethod(mat, assemblybegin, type);
5959: PetscCall(PetscLogEventEnd(MAT_AssemblyBegin, mat, 0, 0, 0));
5960: } else PetscTryTypeMethod(mat, assemblybegin, type);
5961: PetscFunctionReturn(PETSC_SUCCESS);
5962: }
5964: /*@
5965: MatAssembled - Indicates if a matrix has been assembled and is ready for
5966: use; for example, in matrix-vector product.
5968: Not Collective
5970: Input Parameter:
5971: . mat - the matrix
5973: Output Parameter:
5974: . assembled - `PETSC_TRUE` or `PETSC_FALSE`
5976: Level: advanced
5978: .seealso: [](ch_matrices), `Mat`, `MatAssemblyEnd()`, `MatSetValues()`, `MatAssemblyBegin()`
5979: @*/
5980: PetscErrorCode MatAssembled(Mat mat, PetscBool *assembled)
5981: {
5982: PetscFunctionBegin;
5984: PetscAssertPointer(assembled, 2);
5985: *assembled = mat->assembled;
5986: PetscFunctionReturn(PETSC_SUCCESS);
5987: }
5989: /*@
5990: MatAssemblyEnd - Completes assembling the matrix. This routine should
5991: be called after `MatAssemblyBegin()`.
5993: Collective
5995: Input Parameters:
5996: + mat - the matrix
5997: - type - type of assembly, either `MAT_FLUSH_ASSEMBLY` or `MAT_FINAL_ASSEMBLY`
5999: Options Database Key:
6000: . -mat_view [viewertype][:...] - option name and values. See `MatViewFromOptions()`/`PetscObjectViewFromOptions()` for the possible arguments
6002: Level: beginner
6004: .seealso: [](ch_matrices), `Mat`, `MatAssemblyBegin()`, `MatSetValues()`, `PetscDrawOpenX()`, `PetscDrawCreate()`, `MatView()`, `MatAssembled()`, `PetscViewerSocketOpen()`,
6005: `MatViewFromOptions()`, `PetscObjectViewFromOptions()`
6006: @*/
6007: PetscErrorCode MatAssemblyEnd(Mat mat, MatAssemblyType type)
6008: {
6009: static PetscInt inassm = 0;
6010: PetscBool flg = PETSC_FALSE;
6012: PetscFunctionBegin;
6016: inassm++;
6017: MatAssemblyEnd_InUse++;
6018: if (MatAssemblyEnd_InUse == 1) { /* Do the logging only the first time through */
6019: PetscCall(PetscLogEventBegin(MAT_AssemblyEnd, mat, 0, 0, 0));
6020: PetscTryTypeMethod(mat, assemblyend, type);
6021: PetscCall(PetscLogEventEnd(MAT_AssemblyEnd, mat, 0, 0, 0));
6022: } else PetscTryTypeMethod(mat, assemblyend, type);
6024: /* Flush assembly is not a true assembly */
6025: if (type != MAT_FLUSH_ASSEMBLY) {
6026: if (mat->num_ass) {
6027: if (!mat->symmetry_eternal) {
6028: mat->symmetric = PETSC_BOOL3_UNKNOWN;
6029: mat->hermitian = PETSC_BOOL3_UNKNOWN;
6030: }
6031: if (!mat->structural_symmetry_eternal && mat->ass_nonzerostate != mat->nonzerostate) mat->structurally_symmetric = PETSC_BOOL3_UNKNOWN;
6032: if (!mat->spd_eternal) mat->spd = PETSC_BOOL3_UNKNOWN;
6033: }
6034: mat->num_ass++;
6035: mat->assembled = PETSC_TRUE;
6036: mat->ass_nonzerostate = mat->nonzerostate;
6037: }
6039: mat->insertmode = NOT_SET_VALUES;
6040: MatAssemblyEnd_InUse--;
6041: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6042: if (inassm == 1 && type != MAT_FLUSH_ASSEMBLY) {
6043: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6045: if (mat->checksymmetryonassembly) {
6046: PetscCall(MatIsSymmetric(mat, mat->checksymmetrytol, &flg));
6047: if (flg) {
6048: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6049: } else {
6050: PetscCall(PetscPrintf(PetscObjectComm((PetscObject)mat), "Matrix is not symmetric (tolerance %g)\n", (double)mat->checksymmetrytol));
6051: }
6052: }
6053: if (mat->nullsp && mat->checknullspaceonassembly) PetscCall(MatNullSpaceTest(mat->nullsp, mat, NULL));
6054: }
6055: inassm--;
6056: PetscFunctionReturn(PETSC_SUCCESS);
6057: }
6059: // PetscClangLinter pragma disable: -fdoc-section-header-unknown
6060: /*@
6061: MatSetOption - Sets a parameter option for a matrix. Some options
6062: may be specific to certain storage formats. Some options
6063: determine how values will be inserted (or added). Sorted,
6064: row-oriented input will generally assemble the fastest. The default
6065: is row-oriented.
6067: Logically Collective for certain operations, such as `MAT_SPD`, not collective for `MAT_ROW_ORIENTED`, see `MatOption`
6069: Input Parameters:
6070: + mat - the matrix
6071: . op - the option, one of those listed below (and possibly others),
6072: - flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6074: Options Describing Matrix Structure:
6075: + `MAT_SPD` - symmetric positive definite
6076: . `MAT_SYMMETRIC` - symmetric in terms of both structure and value
6077: . `MAT_HERMITIAN` - transpose is the complex conjugation
6078: . `MAT_STRUCTURALLY_SYMMETRIC` - symmetric nonzero structure
6079: . `MAT_SYMMETRY_ETERNAL` - indicates the symmetry (or Hermitian structure) or its absence will persist through any changes to the matrix
6080: . `MAT_STRUCTURAL_SYMMETRY_ETERNAL` - indicates the structural symmetry or its absence will persist through any changes to the matrix
6081: . `MAT_SPD_ETERNAL` - indicates the value of `MAT_SPD` (true or false) will persist through any changes to the matrix
6083: These are not really options of the matrix, they are knowledge about the structure of the matrix that users may provide so that they
6084: do not need to be computed (usually at a high cost)
6086: Options For Use with `MatSetValues()`:
6087: Insert a logically dense subblock, which can be
6088: . `MAT_ROW_ORIENTED` - row-oriented (default)
6090: These options reflect the data you pass in with `MatSetValues()`; it has
6091: nothing to do with how the data is stored internally in the matrix
6092: data structure.
6094: When (re)assembling a matrix, we can restrict the input for
6095: efficiency/debugging purposes. These options include
6096: . `MAT_NEW_NONZERO_LOCATIONS` - additional insertions will be allowed if they generate a new nonzero (slow)
6097: . `MAT_FORCE_DIAGONAL_ENTRIES` - forces diagonal entries to be allocated
6098: . `MAT_IGNORE_OFF_PROC_ENTRIES` - drops off-processor entries
6099: . `MAT_NEW_NONZERO_LOCATION_ERR` - generates an error for new matrix entry
6100: . `MAT_USE_HASH_TABLE` - uses a hash table to speed up matrix assembly
6101: . `MAT_NO_OFF_PROC_ENTRIES` - you know each process will only set values for its own rows, will generate an error if
6102: any process sets values for another process. This avoids all reductions in the MatAssembly routines and thus improves
6103: performance for very large process counts.
6104: - `MAT_SUBSET_OFF_PROC_ENTRIES` - you know that the first assembly after setting this flag will set a superset
6105: of the off-process entries required for all subsequent assemblies. This avoids a rendezvous step in the MatAssembly
6106: functions, instead sending only neighbor messages.
6108: Level: intermediate
6110: Notes:
6111: Except for `MAT_UNUSED_NONZERO_LOCATION_ERR` and `MAT_ROW_ORIENTED` all processes that share the matrix must pass the same value in flg!
6113: Some options are relevant only for particular matrix types and
6114: are thus ignored by others. Other options are not supported by
6115: certain matrix types and will generate an error message if set.
6117: If using Fortran to compute a matrix, one may need to
6118: use the column-oriented option (or convert to the row-oriented
6119: format).
6121: `MAT_NEW_NONZERO_LOCATIONS` set to `PETSC_FALSE` indicates that any add or insertion
6122: that would generate a new entry in the nonzero structure is instead
6123: ignored. Thus, if memory has not already been allocated for this particular
6124: data, then the insertion is ignored. For dense matrices, in which
6125: the entire array is allocated, no entries are ever ignored.
6126: Set after the first `MatAssemblyEnd()`. If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6128: `MAT_NEW_NONZERO_LOCATION_ERR` set to PETSC_TRUE indicates that any add or insertion
6129: that would generate a new entry in the nonzero structure instead produces
6130: 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
6132: `MAT_NEW_NONZERO_ALLOCATION_ERR` set to `PETSC_TRUE` indicates that any add or insertion
6133: that would generate a new entry that has not been preallocated will
6134: instead produce an error. (Currently supported for `MATAIJ` and `MATBAIJ` formats
6135: only.) This is a useful flag when debugging matrix memory preallocation.
6136: If this option is set, then the `MatAssemblyBegin()`/`MatAssemblyEnd()` processes has one less global reduction
6138: `MAT_IGNORE_OFF_PROC_ENTRIES` set to `PETSC_TRUE` indicates entries destined for
6139: other processors should be dropped, rather than stashed.
6140: This is useful if you know that the "owning" processor is also
6141: always generating the correct matrix entries, so that PETSc need
6142: not transfer duplicate entries generated on another processor.
6144: `MAT_USE_HASH_TABLE` indicates that a hash table be used to improve the
6145: searches during matrix assembly. When this flag is set, the hash table
6146: is created during the first matrix assembly. This hash table is
6147: used the next time through, during `MatSetValues()`/`MatSetValuesBlocked()`
6148: to improve the searching of indices. `MAT_NEW_NONZERO_LOCATIONS` flag
6149: should be used with `MAT_USE_HASH_TABLE` flag. This option is currently
6150: supported by `MATMPIBAIJ` format only.
6152: `MAT_KEEP_NONZERO_PATTERN` indicates when `MatZeroRows()` is called the zeroed entries
6153: are kept in the nonzero structure. This flag is not used for `MatZeroRowsColumns()`
6155: `MAT_IGNORE_ZERO_ENTRIES` - for `MATAIJ` and `MATIS` matrices this will stop zero values from creating
6156: a zero location in the matrix
6158: `MAT_USE_INODES` - indicates using inode version of the code - works with `MATAIJ` matrix types
6160: `MAT_NO_OFF_PROC_ZERO_ROWS` - you know each process will only zero its own rows. This avoids all reductions in the
6161: zero row routines and thus improves performance for very large process counts.
6163: `MAT_IGNORE_LOWER_TRIANGULAR` - For `MATSBAIJ` matrices will ignore any insertions you make in the lower triangular
6164: part of the matrix (since they should match the upper triangular part).
6166: `MAT_SORTED_FULL` - each process provides exactly its local rows; all column indices for a given row are passed in a
6167: single call to `MatSetValues()`, preallocation is perfect, row-oriented, `INSERT_VALUES` is used. Common
6168: with finite difference schemes with non-periodic boundary conditions.
6170: Developer Note:
6171: `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, and `MAT_SPD_ETERNAL` are used by `MatAssemblyEnd()` and in other
6172: places where otherwise the value of `MAT_SYMMETRIC`, `MAT_STRUCTURALLY_SYMMETRIC` or `MAT_SPD` would need to be changed back
6173: to `PETSC_BOOL3_UNKNOWN` because the matrix values had changed so the code cannot be certain that the related property had
6174: not changed.
6176: .seealso: [](ch_matrices), `MatOption`, `Mat`, `MatGetOption()`
6177: @*/
6178: PetscErrorCode MatSetOption(Mat mat, MatOption op, PetscBool flg)
6179: {
6180: PetscFunctionBegin;
6182: if (op > 0) {
6185: }
6187: 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);
6189: switch (op) {
6190: case MAT_FORCE_DIAGONAL_ENTRIES:
6191: mat->force_diagonals = flg;
6192: PetscFunctionReturn(PETSC_SUCCESS);
6193: case MAT_NO_OFF_PROC_ENTRIES:
6194: mat->nooffprocentries = flg;
6195: PetscFunctionReturn(PETSC_SUCCESS);
6196: case MAT_SUBSET_OFF_PROC_ENTRIES:
6197: mat->assembly_subset = flg;
6198: if (!mat->assembly_subset) { /* See the same logic in VecAssembly wrt VEC_SUBSET_OFF_PROC_ENTRIES */
6199: #if !defined(PETSC_HAVE_MPIUNI)
6200: PetscCall(MatStashScatterDestroy_BTS(&mat->stash));
6201: #endif
6202: mat->stash.first_assembly_done = PETSC_FALSE;
6203: }
6204: PetscFunctionReturn(PETSC_SUCCESS);
6205: case MAT_NO_OFF_PROC_ZERO_ROWS:
6206: mat->nooffproczerorows = flg;
6207: PetscFunctionReturn(PETSC_SUCCESS);
6208: case MAT_SPD:
6209: if (flg) {
6210: mat->spd = PETSC_BOOL3_TRUE;
6211: mat->symmetric = PETSC_BOOL3_TRUE;
6212: mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6213: #if !defined(PETSC_USE_COMPLEX)
6214: mat->hermitian = PETSC_BOOL3_TRUE;
6215: #endif
6216: } else {
6217: mat->spd = PETSC_BOOL3_FALSE;
6218: }
6219: break;
6220: case MAT_SYMMETRIC:
6221: mat->symmetric = PetscBoolToBool3(flg);
6222: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6223: #if !defined(PETSC_USE_COMPLEX)
6224: mat->hermitian = PetscBoolToBool3(flg);
6225: #endif
6226: break;
6227: case MAT_HERMITIAN:
6228: mat->hermitian = PetscBoolToBool3(flg);
6229: if (flg) mat->structurally_symmetric = PETSC_BOOL3_TRUE;
6230: #if !defined(PETSC_USE_COMPLEX)
6231: mat->symmetric = PetscBoolToBool3(flg);
6232: #endif
6233: break;
6234: case MAT_STRUCTURALLY_SYMMETRIC:
6235: mat->structurally_symmetric = PetscBoolToBool3(flg);
6236: break;
6237: case MAT_SYMMETRY_ETERNAL:
6238: 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");
6239: mat->symmetry_eternal = flg;
6240: if (flg) mat->structural_symmetry_eternal = PETSC_TRUE;
6241: break;
6242: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6243: 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");
6244: mat->structural_symmetry_eternal = flg;
6245: break;
6246: case MAT_SPD_ETERNAL:
6247: 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");
6248: mat->spd_eternal = flg;
6249: if (flg) {
6250: mat->structural_symmetry_eternal = PETSC_TRUE;
6251: mat->symmetry_eternal = PETSC_TRUE;
6252: }
6253: break;
6254: case MAT_STRUCTURE_ONLY:
6255: mat->structure_only = flg;
6256: break;
6257: case MAT_SORTED_FULL:
6258: mat->sortedfull = flg;
6259: break;
6260: default:
6261: break;
6262: }
6263: PetscTryTypeMethod(mat, setoption, op, flg);
6264: PetscFunctionReturn(PETSC_SUCCESS);
6265: }
6267: /*@
6268: MatGetOption - Gets a parameter option that has been set for a matrix.
6270: Logically Collective
6272: Input Parameters:
6273: + mat - the matrix
6274: - op - the option, this only responds to certain options, check the code for which ones
6276: Output Parameter:
6277: . flg - turn the option on (`PETSC_TRUE`) or off (`PETSC_FALSE`)
6279: Level: intermediate
6281: Notes:
6282: Can only be called after `MatSetSizes()` and `MatSetType()` have been set.
6284: Certain option values may be unknown, for those use the routines `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, or
6285: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6287: .seealso: [](ch_matrices), `Mat`, `MatOption`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`,
6288: `MatIsSymmetricKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
6289: @*/
6290: PetscErrorCode MatGetOption(Mat mat, MatOption op, PetscBool *flg)
6291: {
6292: PetscFunctionBegin;
6296: 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);
6297: 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()");
6299: switch (op) {
6300: case MAT_NO_OFF_PROC_ENTRIES:
6301: *flg = mat->nooffprocentries;
6302: break;
6303: case MAT_NO_OFF_PROC_ZERO_ROWS:
6304: *flg = mat->nooffproczerorows;
6305: break;
6306: case MAT_SYMMETRIC:
6307: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSymmetric() or MatIsSymmetricKnown()");
6308: break;
6309: case MAT_HERMITIAN:
6310: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsHermitian() or MatIsHermitianKnown()");
6311: break;
6312: case MAT_STRUCTURALLY_SYMMETRIC:
6313: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsStructurallySymmetric() or MatIsStructurallySymmetricKnown()");
6314: break;
6315: case MAT_SPD:
6316: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "Use MatIsSPDKnown()");
6317: break;
6318: case MAT_SYMMETRY_ETERNAL:
6319: *flg = mat->symmetry_eternal;
6320: break;
6321: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
6322: *flg = mat->symmetry_eternal;
6323: break;
6324: default:
6325: break;
6326: }
6327: PetscFunctionReturn(PETSC_SUCCESS);
6328: }
6330: /*@
6331: MatZeroEntries - Zeros all entries of a matrix. For sparse matrices
6332: this routine retains the old nonzero structure.
6334: Logically Collective
6336: Input Parameter:
6337: . mat - the matrix
6339: Level: intermediate
6341: Note:
6342: 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.
6343: See the Performance chapter of the users manual for information on preallocating matrices.
6345: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`
6346: @*/
6347: PetscErrorCode MatZeroEntries(Mat mat)
6348: {
6349: PetscFunctionBegin;
6352: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6353: 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");
6354: MatCheckPreallocated(mat, 1);
6356: PetscCall(PetscLogEventBegin(MAT_ZeroEntries, mat, 0, 0, 0));
6357: PetscUseTypeMethod(mat, zeroentries);
6358: PetscCall(PetscLogEventEnd(MAT_ZeroEntries, mat, 0, 0, 0));
6359: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6360: PetscFunctionReturn(PETSC_SUCCESS);
6361: }
6363: /*@
6364: MatZeroRowsColumns - Zeros all entries (except possibly the main diagonal)
6365: of a set of rows and columns of a matrix.
6367: Collective
6369: Input Parameters:
6370: + mat - the matrix
6371: . numRows - the number of rows/columns to zero
6372: . rows - the global row indices
6373: . diag - value put in the diagonal of the eliminated rows
6374: . x - optional vector of the solution for zeroed rows (other entries in vector are not used), these must be set before this call
6375: - b - optional vector of the right-hand side, that will be adjusted by provided solution entries
6377: Level: intermediate
6379: Notes:
6380: This routine, along with `MatZeroRows()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6382: For each zeroed row, the value of the corresponding `b` is set to diag times the value of the corresponding `x`.
6383: 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
6385: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6386: Krylov method to take advantage of the known solution on the zeroed rows.
6388: For the parallel case, all processes that share the matrix (i.e.,
6389: those in the communicator used for matrix creation) MUST call this
6390: routine, regardless of whether any rows being zeroed are owned by
6391: them.
6393: Unlike `MatZeroRows()`, this ignores the `MAT_KEEP_NONZERO_PATTERN` option value set with `MatSetOption()`, it merely zeros those entries in the matrix, but never
6394: 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
6395: missing.
6397: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6398: list only rows local to itself).
6400: The option `MAT_NO_OFF_PROC_ZERO_ROWS` does not apply to this routine.
6402: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRows()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6403: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6404: @*/
6405: PetscErrorCode MatZeroRowsColumns(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6406: {
6407: PetscFunctionBegin;
6410: if (numRows) PetscAssertPointer(rows, 3);
6411: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6412: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6413: MatCheckPreallocated(mat, 1);
6415: PetscUseTypeMethod(mat, zerorowscolumns, numRows, rows, diag, x, b);
6416: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6417: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6418: PetscFunctionReturn(PETSC_SUCCESS);
6419: }
6421: /*@
6422: MatZeroRowsColumnsIS - Zeros all entries (except possibly the main diagonal)
6423: of a set of rows and columns of a matrix.
6425: Collective
6427: Input Parameters:
6428: + mat - the matrix
6429: . is - the rows to zero
6430: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6431: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6432: - b - optional vector of right-hand side, that will be adjusted by provided solution
6434: Level: intermediate
6436: Note:
6437: See `MatZeroRowsColumns()` for details on how this routine operates.
6439: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6440: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRows()`, `MatZeroRowsColumnsStencil()`
6441: @*/
6442: PetscErrorCode MatZeroRowsColumnsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6443: {
6444: PetscInt numRows;
6445: const PetscInt *rows;
6447: PetscFunctionBegin;
6452: PetscCall(ISGetLocalSize(is, &numRows));
6453: PetscCall(ISGetIndices(is, &rows));
6454: PetscCall(MatZeroRowsColumns(mat, numRows, rows, diag, x, b));
6455: PetscCall(ISRestoreIndices(is, &rows));
6456: PetscFunctionReturn(PETSC_SUCCESS);
6457: }
6459: /*@
6460: MatZeroRows - Zeros all entries (except possibly the main diagonal)
6461: of a set of rows of a matrix.
6463: Collective
6465: Input Parameters:
6466: + mat - the matrix
6467: . numRows - the number of rows to zero
6468: . rows - the global row indices
6469: . diag - value put in the diagonal of the zeroed rows
6470: . x - optional vector of solutions for zeroed rows (other entries in vector are not used), these must be set before this call
6471: - b - optional vector of right-hand side, that will be adjusted by provided solution entries
6473: Level: intermediate
6475: Notes:
6476: This routine, along with `MatZeroRowsColumns()`, is typically used to eliminate known Dirichlet boundary conditions from a linear system.
6478: For each zeroed row, the value of the corresponding `b` is set to `diag` times the value of the corresponding `x`.
6480: If the resulting linear system is to be solved with `KSP` then one can (but does not have to) call `KSPSetInitialGuessNonzero()` to allow the
6481: Krylov method to take advantage of the known solution on the zeroed rows.
6483: 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)
6484: from the matrix.
6486: Unlike `MatZeroRowsColumns()` for the `MATAIJ` and `MATBAIJ` matrix formats this removes the old nonzero structure, from the eliminated rows of the matrix
6487: but does not release memory. Because of this removal matrix-vector products with the adjusted matrix will be a bit faster. For the dense
6488: formats this does not alter the nonzero structure.
6490: If the option `MatSetOption`(mat,`MAT_KEEP_NONZERO_PATTERN`,`PETSC_TRUE`) the nonzero structure
6491: of the matrix is not changed the values are
6492: merely zeroed.
6494: The user can set a value in the diagonal entry (or for the `MATAIJ` format
6495: formats can optionally remove the main diagonal entry from the
6496: nonzero structure as well, by passing 0.0 as the final argument).
6498: For the parallel case, all processes that share the matrix (i.e.,
6499: those in the communicator used for matrix creation) MUST call this
6500: routine, regardless of whether any rows being zeroed are owned by
6501: them.
6503: Each processor can indicate any rows in the entire matrix to be zeroed (i.e. each process does NOT have to
6504: list only rows local to itself).
6506: You can call `MatSetOption`(mat,`MAT_NO_OFF_PROC_ZERO_ROWS`,`PETSC_TRUE`) if each process indicates only rows it
6507: owns that are to be zeroed. This saves a global synchronization in the implementation.
6509: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6510: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `PCREDISTRIBUTE`, `MAT_KEEP_NONZERO_PATTERN`
6511: @*/
6512: PetscErrorCode MatZeroRows(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6513: {
6514: PetscFunctionBegin;
6517: if (numRows) PetscAssertPointer(rows, 3);
6518: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6519: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6520: MatCheckPreallocated(mat, 1);
6522: PetscUseTypeMethod(mat, zerorows, numRows, rows, diag, x, b);
6523: PetscCall(MatViewFromOptions(mat, NULL, "-mat_view"));
6524: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6525: PetscFunctionReturn(PETSC_SUCCESS);
6526: }
6528: /*@
6529: MatZeroRowsIS - Zeros all entries (except possibly the main diagonal)
6530: of a set of rows of a matrix indicated by an `IS`
6532: Collective
6534: Input Parameters:
6535: + mat - the matrix
6536: . is - index set, `IS`, of rows to remove (if `NULL` then no row is removed)
6537: . diag - value put in all diagonals of eliminated rows
6538: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6539: - b - optional vector of right-hand side, that will be adjusted by provided solution
6541: Level: intermediate
6543: Note:
6544: See `MatZeroRows()` for details on how this routine operates.
6546: .seealso: [](ch_matrices), `Mat`, `MatZeroRows()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6547: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`, `IS`
6548: @*/
6549: PetscErrorCode MatZeroRowsIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6550: {
6551: PetscInt numRows = 0;
6552: const PetscInt *rows = NULL;
6554: PetscFunctionBegin;
6557: if (is) {
6559: PetscCall(ISGetLocalSize(is, &numRows));
6560: PetscCall(ISGetIndices(is, &rows));
6561: }
6562: PetscCall(MatZeroRows(mat, numRows, rows, diag, x, b));
6563: if (is) PetscCall(ISRestoreIndices(is, &rows));
6564: PetscFunctionReturn(PETSC_SUCCESS);
6565: }
6567: /*@
6568: MatZeroRowsStencil - Zeros all entries (except possibly the main diagonal)
6569: of a set of rows of a matrix indicated by a `MatStencil`. These rows must be local to the process.
6571: Collective
6573: Input Parameters:
6574: + mat - the matrix
6575: . numRows - the number of rows to remove
6576: . rows - the grid coordinates (and component number when dof > 1) for matrix rows indicated by an array of `MatStencil`
6577: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6578: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6579: - b - optional vector of right-hand side, that will be adjusted by provided solution
6581: Level: intermediate
6583: Notes:
6584: See `MatZeroRows()` for details on how this routine operates.
6586: The grid coordinates are across the entire grid, not just the local portion
6588: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6589: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6590: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6591: `DM_BOUNDARY_PERIODIC` boundary type.
6593: 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
6594: a single value per point) you can skip filling those indices.
6596: Fortran Note:
6597: `idxm` and `idxn` should be declared as
6598: .vb
6599: MatStencil idxm(4, m)
6600: .ve
6601: and the values inserted using
6602: .vb
6603: idxm(MatStencil_i, 1) = i
6604: idxm(MatStencil_j, 1) = j
6605: idxm(MatStencil_k, 1) = k
6606: idxm(MatStencil_c, 1) = c
6607: etc
6608: .ve
6610: .seealso: [](ch_matrices), `Mat`, `MatStencil`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRows()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6611: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6612: @*/
6613: PetscErrorCode MatZeroRowsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6614: {
6615: PetscInt dim = mat->stencil.dim;
6616: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6617: PetscInt *dims = mat->stencil.dims + 1;
6618: PetscInt *starts = mat->stencil.starts;
6619: PetscInt *dxm = (PetscInt *)rows;
6620: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6622: PetscFunctionBegin;
6625: if (numRows) PetscAssertPointer(rows, 3);
6627: PetscCall(PetscMalloc1(numRows, &jdxm));
6628: for (i = 0; i < numRows; ++i) {
6629: /* Skip unused dimensions (they are ordered k, j, i, c) */
6630: for (j = 0; j < 3 - sdim; ++j) dxm++;
6631: /* Local index in X dir */
6632: tmp = *dxm++ - starts[0];
6633: /* Loop over remaining dimensions */
6634: for (j = 0; j < dim - 1; ++j) {
6635: /* If nonlocal, set index to be negative */
6636: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6637: /* Update local index */
6638: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6639: }
6640: /* Skip component slot if necessary */
6641: if (mat->stencil.noc) dxm++;
6642: /* Local row number */
6643: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6644: }
6645: PetscCall(MatZeroRowsLocal(mat, numNewRows, jdxm, diag, x, b));
6646: PetscCall(PetscFree(jdxm));
6647: PetscFunctionReturn(PETSC_SUCCESS);
6648: }
6650: /*@
6651: MatZeroRowsColumnsStencil - Zeros all row and column entries (except possibly the main diagonal)
6652: of a set of rows and columns of a matrix.
6654: Collective
6656: Input Parameters:
6657: + mat - the matrix
6658: . numRows - the number of rows/columns to remove
6659: . rows - the grid coordinates (and component number when dof > 1) for matrix rows
6660: . diag - value put in all diagonals of eliminated rows (0.0 will even eliminate diagonal entry)
6661: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6662: - b - optional vector of right-hand side, that will be adjusted by provided solution
6664: Level: intermediate
6666: Notes:
6667: See `MatZeroRowsColumns()` for details on how this routine operates.
6669: The grid coordinates are across the entire grid, not just the local portion
6671: For periodic boundary conditions use negative indices for values to the left (below 0; that are to be
6672: obtained by wrapping values from right edge). For values to the right of the last entry using that index plus one
6673: etc to obtain values that obtained by wrapping the values from the left edge. This does not work for anything but the
6674: `DM_BOUNDARY_PERIODIC` boundary type.
6676: 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
6677: a single value per point) you can skip filling those indices.
6679: Fortran Note:
6680: `idxm` and `idxn` should be declared as
6681: .vb
6682: MatStencil idxm(4, m)
6683: .ve
6684: and the values inserted using
6685: .vb
6686: idxm(MatStencil_i, 1) = i
6687: idxm(MatStencil_j, 1) = j
6688: idxm(MatStencil_k, 1) = k
6689: idxm(MatStencil_c, 1) = c
6690: etc
6691: .ve
6693: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6694: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRows()`
6695: @*/
6696: PetscErrorCode MatZeroRowsColumnsStencil(Mat mat, PetscInt numRows, const MatStencil rows[], PetscScalar diag, Vec x, Vec b)
6697: {
6698: PetscInt dim = mat->stencil.dim;
6699: PetscInt sdim = dim - (1 - (PetscInt)mat->stencil.noc);
6700: PetscInt *dims = mat->stencil.dims + 1;
6701: PetscInt *starts = mat->stencil.starts;
6702: PetscInt *dxm = (PetscInt *)rows;
6703: PetscInt *jdxm, i, j, tmp, numNewRows = 0;
6705: PetscFunctionBegin;
6708: if (numRows) PetscAssertPointer(rows, 3);
6710: PetscCall(PetscMalloc1(numRows, &jdxm));
6711: for (i = 0; i < numRows; ++i) {
6712: /* Skip unused dimensions (they are ordered k, j, i, c) */
6713: for (j = 0; j < 3 - sdim; ++j) dxm++;
6714: /* Local index in X dir */
6715: tmp = *dxm++ - starts[0];
6716: /* Loop over remaining dimensions */
6717: for (j = 0; j < dim - 1; ++j) {
6718: /* If nonlocal, set index to be negative */
6719: if ((*dxm++ - starts[j + 1]) < 0 || tmp < 0) tmp = PETSC_INT_MIN;
6720: /* Update local index */
6721: else tmp = tmp * dims[j] + *(dxm - 1) - starts[j + 1];
6722: }
6723: /* Skip component slot if necessary */
6724: if (mat->stencil.noc) dxm++;
6725: /* Local row number */
6726: if (tmp >= 0) jdxm[numNewRows++] = tmp;
6727: }
6728: PetscCall(MatZeroRowsColumnsLocal(mat, numNewRows, jdxm, diag, x, b));
6729: PetscCall(PetscFree(jdxm));
6730: PetscFunctionReturn(PETSC_SUCCESS);
6731: }
6733: /*@
6734: MatZeroRowsLocal - Zeros all entries (except possibly the main diagonal)
6735: of a set of rows of a matrix; using local numbering of rows.
6737: Collective
6739: Input Parameters:
6740: + mat - the matrix
6741: . numRows - the number of rows to remove
6742: . rows - the local row indices
6743: . diag - value put in all diagonals of eliminated rows
6744: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6745: - b - optional vector of right-hand side, that will be adjusted by provided solution
6747: Level: intermediate
6749: Notes:
6750: Before calling `MatZeroRowsLocal()`, the user must first set the
6751: local-to-global mapping by calling MatSetLocalToGlobalMapping(), this is often already set for matrices obtained with `DMCreateMatrix()`.
6753: See `MatZeroRows()` for details on how this routine operates.
6755: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRows()`, `MatSetOption()`,
6756: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6757: @*/
6758: PetscErrorCode MatZeroRowsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6759: {
6760: PetscFunctionBegin;
6763: if (numRows) PetscAssertPointer(rows, 3);
6764: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6765: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6766: MatCheckPreallocated(mat, 1);
6768: if (mat->ops->zerorowslocal) {
6769: PetscUseTypeMethod(mat, zerorowslocal, numRows, rows, diag, x, b);
6770: } else {
6771: IS is, newis;
6772: PetscInt *newRows, nl = 0;
6774: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6775: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6776: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6777: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6778: for (PetscInt i = 0; i < numRows; i++)
6779: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6780: PetscUseTypeMethod(mat, zerorows, nl, newRows, diag, x, b);
6781: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6782: PetscCall(ISDestroy(&newis));
6783: PetscCall(ISDestroy(&is));
6784: }
6785: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6786: PetscFunctionReturn(PETSC_SUCCESS);
6787: }
6789: /*@
6790: MatZeroRowsLocalIS - Zeros all entries (except possibly the main diagonal)
6791: of a set of rows of a matrix; using local numbering of rows.
6793: Collective
6795: Input Parameters:
6796: + mat - the matrix
6797: . is - index set of rows to remove
6798: . diag - value put in all diagonals of eliminated rows
6799: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6800: - b - optional vector of right-hand side, that will be adjusted by provided solution
6802: Level: intermediate
6804: Notes:
6805: Before calling `MatZeroRowsLocalIS()`, the user must first set the
6806: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6808: See `MatZeroRows()` for details on how this routine operates.
6810: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRows()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6811: `MatZeroRowsColumnsLocal()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6812: @*/
6813: PetscErrorCode MatZeroRowsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6814: {
6815: PetscInt numRows;
6816: const PetscInt *rows;
6818: PetscFunctionBegin;
6822: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6823: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6824: MatCheckPreallocated(mat, 1);
6826: PetscCall(ISGetLocalSize(is, &numRows));
6827: PetscCall(ISGetIndices(is, &rows));
6828: PetscCall(MatZeroRowsLocal(mat, numRows, rows, diag, x, b));
6829: PetscCall(ISRestoreIndices(is, &rows));
6830: PetscFunctionReturn(PETSC_SUCCESS);
6831: }
6833: /*@
6834: MatZeroRowsColumnsLocal - Zeros all entries (except possibly the main diagonal)
6835: of a set of rows and columns of a matrix; using local numbering of rows.
6837: Collective
6839: Input Parameters:
6840: + mat - the matrix
6841: . numRows - the number of rows to remove
6842: . rows - the global row indices
6843: . diag - value put in all diagonals of eliminated rows
6844: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6845: - b - optional vector of right-hand side, that will be adjusted by provided solution
6847: Level: intermediate
6849: Notes:
6850: Before calling `MatZeroRowsColumnsLocal()`, the user must first set the
6851: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6853: See `MatZeroRowsColumns()` for details on how this routine operates.
6855: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6856: `MatZeroRows()`, `MatZeroRowsColumnsLocalIS()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6857: @*/
6858: PetscErrorCode MatZeroRowsColumnsLocal(Mat mat, PetscInt numRows, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
6859: {
6860: PetscFunctionBegin;
6863: if (numRows) PetscAssertPointer(rows, 3);
6864: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6865: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6866: MatCheckPreallocated(mat, 1);
6868: if (mat->ops->zerorowscolumnslocal) {
6869: PetscUseTypeMethod(mat, zerorowscolumnslocal, numRows, rows, diag, x, b);
6870: } else {
6871: IS is, newis;
6872: PetscInt *newRows, nl = 0;
6874: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Need to provide local to global mapping to matrix first");
6875: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, numRows, rows, PETSC_USE_POINTER, &is));
6876: PetscCall(ISLocalToGlobalMappingApplyIS(mat->rmap->mapping, is, &newis));
6877: PetscCall(ISGetIndices(newis, (const PetscInt **)&newRows));
6878: for (PetscInt i = 0; i < numRows; i++)
6879: if (newRows[i] > -1) newRows[nl++] = newRows[i];
6880: PetscUseTypeMethod(mat, zerorowscolumns, nl, newRows, diag, x, b);
6881: PetscCall(ISRestoreIndices(newis, (const PetscInt **)&newRows));
6882: PetscCall(ISDestroy(&newis));
6883: PetscCall(ISDestroy(&is));
6884: }
6885: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
6886: PetscFunctionReturn(PETSC_SUCCESS);
6887: }
6889: /*@
6890: MatZeroRowsColumnsLocalIS - Zeros all entries (except possibly the main diagonal)
6891: of a set of rows and columns of a matrix; using local numbering of rows.
6893: Collective
6895: Input Parameters:
6896: + mat - the matrix
6897: . is - index set of rows to remove
6898: . diag - value put in all diagonals of eliminated rows
6899: . x - optional vector of solutions for zeroed rows (other entries in vector are not used)
6900: - b - optional vector of right-hand side, that will be adjusted by provided solution
6902: Level: intermediate
6904: Notes:
6905: Before calling `MatZeroRowsColumnsLocalIS()`, the user must first set the
6906: local-to-global mapping by calling `MatSetLocalToGlobalMapping()`, this is often already set for matrices obtained with `DMCreateMatrix()`.
6908: See `MatZeroRowsColumns()` for details on how this routine operates.
6910: .seealso: [](ch_matrices), `Mat`, `MatZeroRowsIS()`, `MatZeroRowsColumns()`, `MatZeroRowsLocalIS()`, `MatZeroRowsStencil()`, `MatZeroEntries()`, `MatZeroRowsLocal()`, `MatSetOption()`,
6911: `MatZeroRowsColumnsLocal()`, `MatZeroRows()`, `MatZeroRowsColumnsIS()`, `MatZeroRowsColumnsStencil()`
6912: @*/
6913: PetscErrorCode MatZeroRowsColumnsLocalIS(Mat mat, IS is, PetscScalar diag, Vec x, Vec b)
6914: {
6915: PetscInt numRows;
6916: const PetscInt *rows;
6918: PetscFunctionBegin;
6922: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
6923: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
6924: MatCheckPreallocated(mat, 1);
6926: PetscCall(ISGetLocalSize(is, &numRows));
6927: PetscCall(ISGetIndices(is, &rows));
6928: PetscCall(MatZeroRowsColumnsLocal(mat, numRows, rows, diag, x, b));
6929: PetscCall(ISRestoreIndices(is, &rows));
6930: PetscFunctionReturn(PETSC_SUCCESS);
6931: }
6933: /*@
6934: MatGetSize - Returns the numbers of rows and columns in a matrix.
6936: Not Collective
6938: Input Parameter:
6939: . mat - the matrix
6941: Output Parameters:
6942: + m - the number of global rows
6943: - n - the number of global columns
6945: Level: beginner
6947: Note:
6948: Both output parameters can be `NULL` on input.
6950: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetLocalSize()`
6951: @*/
6952: PetscErrorCode MatGetSize(Mat mat, PetscInt *m, PetscInt *n)
6953: {
6954: PetscFunctionBegin;
6956: if (m) *m = mat->rmap->N;
6957: if (n) *n = mat->cmap->N;
6958: PetscFunctionReturn(PETSC_SUCCESS);
6959: }
6961: /*@
6962: MatGetLocalSize - For most matrix formats, excluding `MATELEMENTAL` and `MATSCALAPACK`, Returns the number of local rows and local columns
6963: of a matrix. For all matrices this is the local size of the left and right vectors as returned by `MatCreateVecs()`.
6965: Not Collective
6967: Input Parameter:
6968: . mat - the matrix
6970: Output Parameters:
6971: + m - the number of local rows, use `NULL` to not obtain this value
6972: - n - the number of local columns, use `NULL` to not obtain this value
6974: Level: beginner
6976: .seealso: [](ch_matrices), `Mat`, `MatSetSizes()`, `MatGetSize()`
6977: @*/
6978: PetscErrorCode MatGetLocalSize(Mat mat, PetscInt *m, PetscInt *n)
6979: {
6980: PetscFunctionBegin;
6982: if (m) PetscAssertPointer(m, 2);
6983: if (n) PetscAssertPointer(n, 3);
6984: if (m) *m = mat->rmap->n;
6985: if (n) *n = mat->cmap->n;
6986: PetscFunctionReturn(PETSC_SUCCESS);
6987: }
6989: /*@
6990: MatGetOwnershipRangeColumn - Returns the range of matrix columns associated with rows of a
6991: vector one multiplies this matrix by that are owned by this processor.
6993: Not Collective, unless matrix has not been allocated, then collective
6995: Input Parameter:
6996: . mat - the matrix
6998: Output Parameters:
6999: + m - the global index of the first local column, use `NULL` to not obtain this value
7000: - n - one more than the global index of the last local column, use `NULL` to not obtain this value
7002: Level: developer
7004: Notes:
7005: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7007: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7008: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7010: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7011: the local values in the matrix.
7013: Returns the columns of the "diagonal block" for most sparse matrix formats. See [Matrix
7014: Layouts](sec_matlayout) for details on matrix layouts.
7016: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7017: `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7018: @*/
7019: PetscErrorCode MatGetOwnershipRangeColumn(Mat mat, PetscInt *m, PetscInt *n)
7020: {
7021: PetscFunctionBegin;
7024: if (m) PetscAssertPointer(m, 2);
7025: if (n) PetscAssertPointer(n, 3);
7026: MatCheckPreallocated(mat, 1);
7027: if (m) *m = mat->cmap->rstart;
7028: if (n) *n = mat->cmap->rend;
7029: PetscFunctionReturn(PETSC_SUCCESS);
7030: }
7032: /*@
7033: MatGetOwnershipRange - For matrices that own values by row, excludes `MATELEMENTAL` and `MATSCALAPACK`, returns the range of matrix rows owned by
7034: this MPI process.
7036: Not Collective
7038: Input Parameter:
7039: . mat - the matrix
7041: Output Parameters:
7042: + m - the global index of the first local row, use `NULL` to not obtain this value
7043: - n - one more than the global index of the last local row, use `NULL` to not obtain this value
7045: Level: beginner
7047: Notes:
7048: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7050: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7051: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7053: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7054: the local values in the matrix.
7056: The high argument is one more than the last element stored locally.
7058: For all matrices it returns the range of matrix rows associated with rows of a vector that
7059: would contain the result of a matrix vector product with this matrix. See [Matrix
7060: Layouts](sec_matlayout) for details on matrix layouts.
7062: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRanges()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscSplitOwnership()`,
7063: `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`, `DMDAGetGhostCorners()`, `DM`
7064: @*/
7065: PetscErrorCode MatGetOwnershipRange(Mat mat, PetscInt *m, PetscInt *n)
7066: {
7067: PetscFunctionBegin;
7070: if (m) PetscAssertPointer(m, 2);
7071: if (n) PetscAssertPointer(n, 3);
7072: MatCheckPreallocated(mat, 1);
7073: if (m) *m = mat->rmap->rstart;
7074: if (n) *n = mat->rmap->rend;
7075: PetscFunctionReturn(PETSC_SUCCESS);
7076: }
7078: /*@C
7079: MatGetOwnershipRanges - For matrices that own values by row, excludes `MATELEMENTAL` and
7080: `MATSCALAPACK`, returns the range of matrix rows owned by each process.
7082: Not Collective, unless matrix has not been allocated
7084: Input Parameter:
7085: . mat - the matrix
7087: Output Parameter:
7088: . ranges - start of each processors portion plus one more than the total length at the end, of length `size` + 1
7089: where `size` is the number of MPI processes used by `mat`
7091: Level: beginner
7093: Notes:
7094: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7096: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7097: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7099: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7100: the local values in the matrix.
7102: For all matrices it returns the ranges of matrix rows associated with rows of a vector that
7103: would contain the result of a matrix vector product with this matrix. See [Matrix
7104: Layouts](sec_matlayout) for details on matrix layouts.
7106: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRangesColumn()`, `PetscLayout`,
7107: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `MatSetSizes()`, `MatCreateAIJ()`,
7108: `DMDAGetGhostCorners()`, `DM`
7109: @*/
7110: PetscErrorCode MatGetOwnershipRanges(Mat mat, const PetscInt *ranges[])
7111: {
7112: PetscFunctionBegin;
7115: MatCheckPreallocated(mat, 1);
7116: PetscCall(PetscLayoutGetRanges(mat->rmap, ranges));
7117: PetscFunctionReturn(PETSC_SUCCESS);
7118: }
7120: /*@C
7121: MatGetOwnershipRangesColumn - Returns the ranges of matrix columns associated with rows of a
7122: vector one multiplies this vector by that are owned by each processor.
7124: Not Collective, unless matrix has not been allocated
7126: Input Parameter:
7127: . mat - the matrix
7129: Output Parameter:
7130: . ranges - start of each processors portion plus one more than the total length at the end
7132: Level: beginner
7134: Notes:
7135: If the `Mat` was obtained from a `DM` with `DMCreateMatrix()`, then the range values are determined by the specific `DM`.
7137: If the `Mat` was created directly the range values are determined by the local size passed to `MatSetSizes()` or `MatCreateAIJ()`.
7138: If `PETSC_DECIDE` was passed as the local size, then the vector uses default values for the range using `PetscSplitOwnership()`.
7140: For certain `DM`, such as `DMDA`, it is better to use `DM` specific routines, such as `DMDAGetGhostCorners()`, to determine
7141: the local values in the matrix.
7143: Returns the columns of the "diagonal blocks", for most sparse matrix formats. See [Matrix
7144: Layouts](sec_matlayout) for details on matrix layouts.
7146: .seealso: [](ch_matrices), `Mat`, `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`, `MatGetOwnershipRanges()`,
7147: `PetscSplitOwnership()`, `PetscSplitOwnershipBlock()`, `PetscLayout`, `MatSetSizes()`, `MatCreateAIJ()`,
7148: `DMDAGetGhostCorners()`, `DM`
7149: @*/
7150: PetscErrorCode MatGetOwnershipRangesColumn(Mat mat, const PetscInt *ranges[])
7151: {
7152: PetscFunctionBegin;
7155: MatCheckPreallocated(mat, 1);
7156: PetscCall(PetscLayoutGetRanges(mat->cmap, ranges));
7157: PetscFunctionReturn(PETSC_SUCCESS);
7158: }
7160: /*@
7161: MatGetOwnershipIS - Get row and column ownership of a matrices' values as index sets.
7163: Not Collective
7165: Input Parameter:
7166: . A - matrix
7168: Output Parameters:
7169: + rows - rows in which this process owns elements, , use `NULL` to not obtain this value
7170: - cols - columns in which this process owns elements, use `NULL` to not obtain this value
7172: Level: intermediate
7174: Note:
7175: You should call `ISDestroy()` on the returned `IS`
7177: For most matrices, excluding `MATELEMENTAL` and `MATSCALAPACK`, this corresponds to values
7178: returned by `MatGetOwnershipRange()`, `MatGetOwnershipRangeColumn()`. For `MATELEMENTAL` and
7179: `MATSCALAPACK` the ownership is more complicated. See [Matrix Layouts](sec_matlayout) for
7180: details on matrix layouts.
7182: .seealso: [](ch_matrices), `IS`, `Mat`, `MatGetOwnershipRanges()`, `MatSetValues()`, `MATELEMENTAL`, `MATSCALAPACK`
7183: @*/
7184: PetscErrorCode MatGetOwnershipIS(Mat A, IS *rows, IS *cols)
7185: {
7186: PetscErrorCode (*f)(Mat, IS *, IS *);
7188: PetscFunctionBegin;
7191: MatCheckPreallocated(A, 1);
7192: PetscCall(PetscObjectQueryFunction((PetscObject)A, "MatGetOwnershipIS_C", &f));
7193: if (f) {
7194: PetscCall((*f)(A, rows, cols));
7195: } else { /* Create a standard row-based partition, each process is responsible for ALL columns in their row block */
7196: if (rows) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->rmap->n, A->rmap->rstart, 1, rows));
7197: if (cols) PetscCall(ISCreateStride(PETSC_COMM_SELF, A->cmap->N, 0, 1, cols));
7198: }
7199: PetscFunctionReturn(PETSC_SUCCESS);
7200: }
7202: /*@
7203: MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix obtained with `MatGetFactor()`
7204: Uses levels of fill only, not drop tolerance. Use `MatLUFactorNumeric()`
7205: to complete the factorization.
7207: Collective
7209: Input Parameters:
7210: + fact - the factorized matrix obtained with `MatGetFactor()`
7211: . mat - the matrix
7212: . row - row permutation
7213: . col - column permutation
7214: - info - structure containing
7215: .vb
7216: levels - number of levels of fill.
7217: expected fill - as ratio of original fill.
7218: 1 or 0 - indicating force fill on diagonal (improves robustness for matrices
7219: missing diagonal entries)
7220: .ve
7222: Level: developer
7224: Notes:
7225: See [Matrix Factorization](sec_matfactor) for additional information.
7227: Most users should employ the `KSP` interface for linear solvers
7228: instead of working directly with matrix algebra routines such as this.
7229: See, e.g., `KSPCreate()`.
7231: Uses the definition of level of fill as in Y. Saad, {cite}`saad2003`
7233: Fortran Note:
7234: A valid (non-null) `info` argument must be provided
7236: .seealso: [](ch_matrices), `Mat`, [Matrix Factorization](sec_matfactor), `MatGetFactor()`, `MatLUFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`,
7237: `MatGetOrdering()`, `MatFactorInfo`
7238: @*/
7239: PetscErrorCode MatILUFactorSymbolic(Mat fact, Mat mat, IS row, IS col, const MatFactorInfo *info)
7240: {
7241: PetscFunctionBegin;
7246: PetscAssertPointer(info, 5);
7247: PetscAssertPointer(fact, 1);
7248: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels of fill negative %" PetscInt_FMT, (PetscInt)info->levels);
7249: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7250: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7251: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7252: MatCheckPreallocated(mat, 2);
7254: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ILUFactorSymbolic, mat, row, col, 0));
7255: PetscUseTypeMethod(fact, ilufactorsymbolic, mat, row, col, info);
7256: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ILUFactorSymbolic, mat, row, col, 0));
7257: PetscFunctionReturn(PETSC_SUCCESS);
7258: }
7260: /*@
7261: MatICCFactorSymbolic - Performs symbolic incomplete
7262: Cholesky factorization for a symmetric matrix. Use
7263: `MatCholeskyFactorNumeric()` to complete the factorization.
7265: Collective
7267: Input Parameters:
7268: + fact - the factorized matrix obtained with `MatGetFactor()`
7269: . mat - the matrix to be factored
7270: . perm - row and column permutation
7271: - info - structure containing
7272: .vb
7273: levels - number of levels of fill.
7274: expected fill - as ratio of original fill.
7275: .ve
7277: Level: developer
7279: Notes:
7280: Most users should employ the `KSP` interface for linear solvers
7281: instead of working directly with matrix algebra routines such as this.
7282: See, e.g., `KSPCreate()`.
7284: This uses the definition of level of fill as in Y. Saad {cite}`saad2003`
7286: Fortran Note:
7287: A valid (non-null) `info` argument must be provided
7289: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactorNumeric()`, `MatCholeskyFactor()`, `MatFactorInfo`
7290: @*/
7291: PetscErrorCode MatICCFactorSymbolic(Mat fact, Mat mat, IS perm, const MatFactorInfo *info)
7292: {
7293: PetscFunctionBegin;
7297: PetscAssertPointer(info, 4);
7298: PetscAssertPointer(fact, 1);
7299: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7300: PetscCheck(info->levels >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Levels negative %" PetscInt_FMT, (PetscInt)info->levels);
7301: PetscCheck(info->fill >= 1.0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Expected fill less than 1.0 %g", (double)info->fill);
7302: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7303: MatCheckPreallocated(mat, 2);
7305: if (!fact->trivialsymbolic) PetscCall(PetscLogEventBegin(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7306: PetscUseTypeMethod(fact, iccfactorsymbolic, mat, perm, info);
7307: if (!fact->trivialsymbolic) PetscCall(PetscLogEventEnd(MAT_ICCFactorSymbolic, mat, perm, 0, 0));
7308: PetscFunctionReturn(PETSC_SUCCESS);
7309: }
7311: /*@C
7312: MatCreateSubMatrices - Extracts several submatrices from a matrix. If submat
7313: points to an array of valid matrices, they may be reused to store the new
7314: submatrices.
7316: Collective
7318: Input Parameters:
7319: + mat - the matrix
7320: . n - the number of submatrixes to be extracted (on this processor, may be zero)
7321: . irow - index set of rows to extract
7322: . icol - index set of columns to extract
7323: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7325: Output Parameter:
7326: . submat - the array of submatrices
7328: Level: advanced
7330: Notes:
7331: `MatCreateSubMatrices()` can extract ONLY sequential submatrices
7332: (from both sequential and parallel matrices). Use `MatCreateSubMatrix()`
7333: to extract a parallel submatrix.
7335: Some matrix types place restrictions on the row and column
7336: indices, such as that they be sorted or that they be equal to each other.
7338: The index sets may not have duplicate entries.
7340: When extracting submatrices from a parallel matrix, each processor can
7341: form a different submatrix by setting the rows and columns of its
7342: individual index sets according to the local submatrix desired.
7344: When finished using the submatrices, the user should destroy
7345: them with `MatDestroySubMatrices()`.
7347: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
7348: original matrix has not changed from that last call to `MatCreateSubMatrices()`.
7350: This routine creates the matrices in submat; you should NOT create them before
7351: calling it. It also allocates the array of matrix pointers submat.
7353: For `MATBAIJ` matrices the index sets must respect the block structure, that is if they
7354: request one row/column in a block, they must request all rows/columns that are in
7355: that block. For example, if the block size is 2 you cannot request just row 0 and
7356: column 0.
7358: Fortran Note:
7359: .vb
7360: Mat, pointer :: submat(:)
7361: .ve
7363: .seealso: [](ch_matrices), `Mat`, `MatDestroySubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7364: @*/
7365: PetscErrorCode MatCreateSubMatrices(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7366: {
7367: PetscInt i;
7368: PetscBool eq;
7370: PetscFunctionBegin;
7373: if (n) {
7374: PetscAssertPointer(irow, 3);
7376: PetscAssertPointer(icol, 4);
7378: }
7379: PetscAssertPointer(submat, 6);
7380: if (n && scall == MAT_REUSE_MATRIX) {
7381: PetscAssertPointer(*submat, 6);
7383: }
7384: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7385: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7386: MatCheckPreallocated(mat, 1);
7387: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7388: PetscUseTypeMethod(mat, createsubmatrices, n, irow, icol, scall, submat);
7389: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7390: for (i = 0; i < n; i++) {
7391: (*submat)[i]->factortype = MAT_FACTOR_NONE; /* in case in place factorization was previously done on submatrix */
7392: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7393: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7394: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
7395: if (mat->boundtocpu && mat->bindingpropagates) {
7396: PetscCall(MatBindToCPU((*submat)[i], PETSC_TRUE));
7397: PetscCall(MatSetBindingPropagates((*submat)[i], PETSC_TRUE));
7398: }
7399: #endif
7400: }
7401: PetscFunctionReturn(PETSC_SUCCESS);
7402: }
7404: /*@C
7405: MatCreateSubMatricesMPI - Extracts MPI submatrices across a sub communicator of `mat` (by pairs of `IS` that may live on subcomms).
7407: Collective
7409: Input Parameters:
7410: + mat - the matrix
7411: . n - the number of submatrixes to be extracted
7412: . irow - index set of rows to extract
7413: . icol - index set of columns to extract
7414: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
7416: Output Parameter:
7417: . submat - the array of submatrices
7419: Level: advanced
7421: Note:
7422: This is used by `PCGASM`
7424: .seealso: [](ch_matrices), `Mat`, `PCGASM`, `MatCreateSubMatrices()`, `MatCreateSubMatrix()`, `MatGetRow()`, `MatGetDiagonal()`, `MatReuse`
7425: @*/
7426: PetscErrorCode MatCreateSubMatricesMPI(Mat mat, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *submat[])
7427: {
7428: PetscInt i;
7429: PetscBool eq;
7431: PetscFunctionBegin;
7434: if (n) {
7435: PetscAssertPointer(irow, 3);
7437: PetscAssertPointer(icol, 4);
7439: }
7440: PetscAssertPointer(submat, 6);
7441: if (n && scall == MAT_REUSE_MATRIX) {
7442: PetscAssertPointer(*submat, 6);
7444: }
7445: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7446: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7447: MatCheckPreallocated(mat, 1);
7449: PetscCall(PetscLogEventBegin(MAT_CreateSubMats, mat, 0, 0, 0));
7450: PetscUseTypeMethod(mat, createsubmatricesmpi, n, irow, icol, scall, submat);
7451: PetscCall(PetscLogEventEnd(MAT_CreateSubMats, mat, 0, 0, 0));
7452: for (i = 0; i < n; i++) {
7453: PetscCall(ISEqualUnsorted(irow[i], icol[i], &eq));
7454: if (eq) PetscCall(MatPropagateSymmetryOptions(mat, (*submat)[i]));
7455: }
7456: PetscFunctionReturn(PETSC_SUCCESS);
7457: }
7459: /*@C
7460: MatDestroyMatrices - Destroys an array of matrices
7462: Collective
7464: Input Parameters:
7465: + n - the number of local matrices
7466: - mat - the matrices (this is a pointer to the array of matrices)
7468: Level: advanced
7470: Notes:
7471: Frees not only the matrices, but also the array that contains the matrices
7473: For matrices obtained with `MatCreateSubMatrices()` use `MatDestroySubMatrices()`
7475: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroySubMatrices()`
7476: @*/
7477: PetscErrorCode MatDestroyMatrices(PetscInt n, Mat *mat[])
7478: {
7479: PetscInt i;
7481: PetscFunctionBegin;
7482: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7483: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7484: PetscAssertPointer(mat, 2);
7486: for (i = 0; i < n; i++) PetscCall(MatDestroy(&(*mat)[i]));
7488: /* memory is allocated even if n = 0 */
7489: PetscCall(PetscFree(*mat));
7490: PetscFunctionReturn(PETSC_SUCCESS);
7491: }
7493: /*@C
7494: MatDestroySubMatrices - Destroys a set of matrices obtained with `MatCreateSubMatrices()`.
7496: Collective
7498: Input Parameters:
7499: + n - the number of local matrices
7500: - mat - the matrices (this is a pointer to the array of matrices, to match the calling sequence of `MatCreateSubMatrices()`)
7502: Level: advanced
7504: Note:
7505: Frees not only the matrices, but also the array that contains the matrices
7507: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7508: @*/
7509: PetscErrorCode MatDestroySubMatrices(PetscInt n, Mat *mat[])
7510: {
7511: Mat mat0;
7513: PetscFunctionBegin;
7514: if (!*mat) PetscFunctionReturn(PETSC_SUCCESS);
7515: /* mat[] is an array of length n+1, see MatCreateSubMatrices_xxx() */
7516: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Trying to destroy negative number of matrices %" PetscInt_FMT, n);
7517: PetscAssertPointer(mat, 2);
7519: mat0 = (*mat)[0];
7520: if (mat0 && mat0->ops->destroysubmatrices) {
7521: PetscCall((*mat0->ops->destroysubmatrices)(n, mat));
7522: } else {
7523: PetscCall(MatDestroyMatrices(n, mat));
7524: }
7525: PetscFunctionReturn(PETSC_SUCCESS);
7526: }
7528: /*@
7529: MatGetSeqNonzeroStructure - Extracts the nonzero structure from a matrix and stores it, in its entirety, on each process
7531: Collective
7533: Input Parameter:
7534: . mat - the matrix
7536: Output Parameter:
7537: . matstruct - the sequential matrix with the nonzero structure of `mat`
7539: Level: developer
7541: .seealso: [](ch_matrices), `Mat`, `MatDestroySeqNonzeroStructure()`, `MatCreateSubMatrices()`, `MatDestroyMatrices()`
7542: @*/
7543: PetscErrorCode MatGetSeqNonzeroStructure(Mat mat, Mat *matstruct)
7544: {
7545: PetscFunctionBegin;
7547: PetscAssertPointer(matstruct, 2);
7550: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7551: MatCheckPreallocated(mat, 1);
7553: PetscCall(PetscLogEventBegin(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7554: PetscUseTypeMethod(mat, getseqnonzerostructure, matstruct);
7555: PetscCall(PetscLogEventEnd(MAT_GetSeqNonzeroStructure, mat, 0, 0, 0));
7556: PetscFunctionReturn(PETSC_SUCCESS);
7557: }
7559: /*@C
7560: MatDestroySeqNonzeroStructure - Destroys matrix obtained with `MatGetSeqNonzeroStructure()`.
7562: Collective
7564: Input Parameter:
7565: . mat - the matrix
7567: Level: advanced
7569: Note:
7570: This is not needed, one can just call `MatDestroy()`
7572: .seealso: [](ch_matrices), `Mat`, `MatGetSeqNonzeroStructure()`
7573: @*/
7574: PetscErrorCode MatDestroySeqNonzeroStructure(Mat *mat)
7575: {
7576: PetscFunctionBegin;
7577: PetscAssertPointer(mat, 1);
7578: PetscCall(MatDestroy(mat));
7579: PetscFunctionReturn(PETSC_SUCCESS);
7580: }
7582: /*@
7583: MatIncreaseOverlap - Given a set of submatrices indicated by index sets,
7584: replaces the index sets by larger ones that represent submatrices with
7585: additional overlap.
7587: Collective
7589: Input Parameters:
7590: + mat - the matrix
7591: . n - the number of index sets
7592: . is - the array of index sets (these index sets will changed during the call)
7593: - ov - the additional overlap requested
7595: Options Database Key:
7596: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7598: Level: developer
7600: Note:
7601: The computed overlap preserves the matrix block sizes when the blocks are square.
7602: That is: if a matrix nonzero for a given block would increase the overlap all columns associated with
7603: that block are included in the overlap regardless of whether each specific column would increase the overlap.
7605: .seealso: [](ch_matrices), `Mat`, `PCASM`, `MatSetBlockSize()`, `MatIncreaseOverlapSplit()`, `MatCreateSubMatrices()`
7606: @*/
7607: PetscErrorCode MatIncreaseOverlap(Mat mat, PetscInt n, IS is[], PetscInt ov)
7608: {
7609: PetscInt i, bs, cbs;
7611: PetscFunctionBegin;
7615: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7616: if (n) {
7617: PetscAssertPointer(is, 3);
7619: }
7620: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7621: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7622: MatCheckPreallocated(mat, 1);
7624: if (!ov || !n) PetscFunctionReturn(PETSC_SUCCESS);
7625: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7626: PetscUseTypeMethod(mat, increaseoverlap, n, is, ov);
7627: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7628: PetscCall(MatGetBlockSizes(mat, &bs, &cbs));
7629: if (bs == cbs) {
7630: for (i = 0; i < n; i++) PetscCall(ISSetBlockSize(is[i], bs));
7631: }
7632: PetscFunctionReturn(PETSC_SUCCESS);
7633: }
7635: PetscErrorCode MatIncreaseOverlapSplit_Single(Mat, IS *, PetscInt);
7637: /*@
7638: MatIncreaseOverlapSplit - Given a set of submatrices indicated by index sets across
7639: a sub communicator, replaces the index sets by larger ones that represent submatrices with
7640: additional overlap.
7642: Collective
7644: Input Parameters:
7645: + mat - the matrix
7646: . n - the number of index sets
7647: . is - the array of index sets (these index sets will changed during the call)
7648: - ov - the additional overlap requested
7650: ` Options Database Key:
7651: . -mat_increase_overlap_scalable - use a scalable algorithm to compute the overlap (supported by MPIAIJ matrix)
7653: Level: developer
7655: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatIncreaseOverlap()`
7656: @*/
7657: PetscErrorCode MatIncreaseOverlapSplit(Mat mat, PetscInt n, IS is[], PetscInt ov)
7658: {
7659: PetscInt i;
7661: PetscFunctionBegin;
7664: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Must have one or more domains, you have %" PetscInt_FMT, n);
7665: if (n) {
7666: PetscAssertPointer(is, 3);
7668: }
7669: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
7670: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
7671: MatCheckPreallocated(mat, 1);
7672: if (!ov) PetscFunctionReturn(PETSC_SUCCESS);
7673: PetscCall(PetscLogEventBegin(MAT_IncreaseOverlap, mat, 0, 0, 0));
7674: for (i = 0; i < n; i++) PetscCall(MatIncreaseOverlapSplit_Single(mat, &is[i], ov));
7675: PetscCall(PetscLogEventEnd(MAT_IncreaseOverlap, mat, 0, 0, 0));
7676: PetscFunctionReturn(PETSC_SUCCESS);
7677: }
7679: /*@
7680: MatGetBlockSize - Returns the matrix block size.
7682: Not Collective
7684: Input Parameter:
7685: . mat - the matrix
7687: Output Parameter:
7688: . bs - block size
7690: Level: intermediate
7692: Notes:
7693: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7695: If the block size has not been set yet this routine returns 1.
7697: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSizes()`
7698: @*/
7699: PetscErrorCode MatGetBlockSize(Mat mat, PetscInt *bs)
7700: {
7701: PetscFunctionBegin;
7703: PetscAssertPointer(bs, 2);
7704: *bs = mat->rmap->bs;
7705: PetscFunctionReturn(PETSC_SUCCESS);
7706: }
7708: /*@
7709: MatGetBlockSizes - Returns the matrix block row and column sizes.
7711: Not Collective
7713: Input Parameter:
7714: . mat - the matrix
7716: Output Parameters:
7717: + rbs - row block size
7718: - cbs - column block size
7720: Level: intermediate
7722: Notes:
7723: Block row formats are `MATBAIJ` and `MATSBAIJ` ALWAYS have square block storage in the matrix.
7724: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
7726: If a block size has not been set yet this routine returns 1.
7728: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatSetBlockSizes()`
7729: @*/
7730: PetscErrorCode MatGetBlockSizes(Mat mat, PetscInt *rbs, PetscInt *cbs)
7731: {
7732: PetscFunctionBegin;
7734: if (rbs) PetscAssertPointer(rbs, 2);
7735: if (cbs) PetscAssertPointer(cbs, 3);
7736: if (rbs) *rbs = mat->rmap->bs;
7737: if (cbs) *cbs = mat->cmap->bs;
7738: PetscFunctionReturn(PETSC_SUCCESS);
7739: }
7741: /*@
7742: MatSetBlockSize - Sets the matrix block size.
7744: Logically Collective
7746: Input Parameters:
7747: + mat - the matrix
7748: - bs - block size
7750: Level: intermediate
7752: Notes:
7753: Block row formats are `MATBAIJ` and `MATSBAIJ` formats ALWAYS have square block storage in the matrix.
7754: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
7756: For `MATAIJ` matrix format, this function can be called at a later stage, provided that the specified block size
7757: is compatible with the matrix local sizes.
7759: .seealso: [](ch_matrices), `Mat`, `MATBAIJ`, `MATSBAIJ`, `MATAIJ`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`
7760: @*/
7761: PetscErrorCode MatSetBlockSize(Mat mat, PetscInt bs)
7762: {
7763: PetscFunctionBegin;
7766: PetscCall(MatSetBlockSizes(mat, bs, bs));
7767: PetscFunctionReturn(PETSC_SUCCESS);
7768: }
7770: typedef struct {
7771: PetscInt n;
7772: IS *is;
7773: Mat *mat;
7774: PetscObjectState nonzerostate;
7775: Mat C;
7776: } EnvelopeData;
7778: static PetscErrorCode EnvelopeDataDestroy(PetscCtxRt ptr)
7779: {
7780: EnvelopeData *edata = *(EnvelopeData **)ptr;
7782: PetscFunctionBegin;
7783: for (PetscInt i = 0; i < edata->n; i++) PetscCall(ISDestroy(&edata->is[i]));
7784: PetscCall(PetscFree(edata->is));
7785: PetscCall(PetscFree(edata));
7786: PetscFunctionReturn(PETSC_SUCCESS);
7787: }
7789: /*@
7790: MatComputeVariableBlockEnvelope - Given a matrix whose nonzeros are in blocks along the diagonal this computes and stores
7791: the sizes of these blocks in the matrix. An individual block may lie over several processes.
7793: Collective
7795: Input Parameter:
7796: . mat - the matrix
7798: Level: intermediate
7800: Notes:
7801: There can be zeros within the blocks
7803: The blocks can overlap between processes, including laying on more than two processes
7805: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatSetVariableBlockSizes()`
7806: @*/
7807: PetscErrorCode MatComputeVariableBlockEnvelope(Mat mat)
7808: {
7809: PetscInt n, *sizes, *starts, i = 0, env = 0, tbs = 0, lblocks = 0, rstart, II, ln = 0, cnt = 0, cstart, cend;
7810: PetscInt *diag, *odiag, sc;
7811: VecScatter scatter;
7812: PetscScalar *seqv;
7813: const PetscScalar *parv;
7814: const PetscInt *ia, *ja;
7815: PetscBool set, flag, done;
7816: Mat AA = mat, A;
7817: MPI_Comm comm;
7818: PetscMPIInt rank, size, tag;
7819: MPI_Status status;
7820: PetscContainer container;
7821: EnvelopeData *edata;
7822: Vec seq, par;
7823: IS isglobal;
7825: PetscFunctionBegin;
7827: PetscCall(MatIsSymmetricKnown(mat, &set, &flag));
7828: if (!set || !flag) {
7829: /* TODO: only needs nonzero structure of transpose */
7830: PetscCall(MatTranspose(mat, MAT_INITIAL_MATRIX, &AA));
7831: PetscCall(MatAXPY(AA, 1.0, mat, DIFFERENT_NONZERO_PATTERN));
7832: }
7833: PetscCall(MatAIJGetLocalMat(AA, &A));
7834: PetscCall(MatGetRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7835: PetscCheck(done, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Unable to get IJ structure from matrix");
7837: PetscCall(MatGetLocalSize(mat, &n, NULL));
7838: PetscCall(PetscObjectGetNewTag((PetscObject)mat, &tag));
7839: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
7840: PetscCallMPI(MPI_Comm_size(comm, &size));
7841: PetscCallMPI(MPI_Comm_rank(comm, &rank));
7843: PetscCall(PetscMalloc2(n, &sizes, n, &starts));
7845: if (rank > 0) {
7846: PetscCallMPI(MPI_Recv(&env, 1, MPIU_INT, rank - 1, tag, comm, &status));
7847: PetscCallMPI(MPI_Recv(&tbs, 1, MPIU_INT, rank - 1, tag, comm, &status));
7848: }
7849: PetscCall(MatGetOwnershipRange(mat, &rstart, NULL));
7850: for (i = 0; i < n; i++) {
7851: env = PetscMax(env, ja[ia[i + 1] - 1]);
7852: II = rstart + i;
7853: if (env == II) {
7854: starts[lblocks] = tbs;
7855: sizes[lblocks++] = 1 + II - tbs;
7856: tbs = 1 + II;
7857: }
7858: }
7859: if (rank < size - 1) {
7860: PetscCallMPI(MPI_Send(&env, 1, MPIU_INT, rank + 1, tag, comm));
7861: PetscCallMPI(MPI_Send(&tbs, 1, MPIU_INT, rank + 1, tag, comm));
7862: }
7864: PetscCall(MatRestoreRowIJ(A, 0, PETSC_FALSE, PETSC_FALSE, &n, &ia, &ja, &done));
7865: if (!set || !flag) PetscCall(MatDestroy(&AA));
7866: PetscCall(MatDestroy(&A));
7868: PetscCall(PetscNew(&edata));
7869: PetscCall(MatGetNonzeroState(mat, &edata->nonzerostate));
7870: edata->n = lblocks;
7871: /* create IS needed for extracting blocks from the original matrix */
7872: PetscCall(PetscMalloc1(lblocks, &edata->is));
7873: for (PetscInt i = 0; i < lblocks; i++) PetscCall(ISCreateStride(PETSC_COMM_SELF, sizes[i], starts[i], 1, &edata->is[i]));
7875: /* Create the resulting inverse matrix nonzero structure with preallocation information */
7876: PetscCall(MatCreate(PetscObjectComm((PetscObject)mat), &edata->C));
7877: PetscCall(MatSetSizes(edata->C, mat->rmap->n, mat->cmap->n, mat->rmap->N, mat->cmap->N));
7878: PetscCall(MatSetBlockSizesFromMats(edata->C, mat, mat));
7879: PetscCall(MatSetType(edata->C, MATAIJ));
7881: /* Communicate the start and end of each row, from each block to the correct rank */
7882: /* TODO: Use PetscSF instead of VecScatter */
7883: for (PetscInt i = 0; i < lblocks; i++) ln += sizes[i];
7884: PetscCall(VecCreateSeq(PETSC_COMM_SELF, 2 * ln, &seq));
7885: PetscCall(VecGetArrayWrite(seq, &seqv));
7886: for (PetscInt i = 0; i < lblocks; i++) {
7887: for (PetscInt j = 0; j < sizes[i]; j++) {
7888: seqv[cnt] = starts[i];
7889: seqv[cnt + 1] = starts[i] + sizes[i];
7890: cnt += 2;
7891: }
7892: }
7893: PetscCall(VecRestoreArrayWrite(seq, &seqv));
7894: PetscCallMPI(MPI_Scan(&cnt, &sc, 1, MPIU_INT, MPI_SUM, PetscObjectComm((PetscObject)mat)));
7895: sc -= cnt;
7896: PetscCall(VecCreateMPI(PetscObjectComm((PetscObject)mat), 2 * mat->rmap->n, 2 * mat->rmap->N, &par));
7897: PetscCall(ISCreateStride(PETSC_COMM_SELF, cnt, sc, 1, &isglobal));
7898: PetscCall(VecScatterCreate(seq, NULL, par, isglobal, &scatter));
7899: PetscCall(ISDestroy(&isglobal));
7900: PetscCall(VecScatterBegin(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7901: PetscCall(VecScatterEnd(scatter, seq, par, INSERT_VALUES, SCATTER_FORWARD));
7902: PetscCall(VecScatterDestroy(&scatter));
7903: PetscCall(VecDestroy(&seq));
7904: PetscCall(MatGetOwnershipRangeColumn(mat, &cstart, &cend));
7905: PetscCall(PetscMalloc2(mat->rmap->n, &diag, mat->rmap->n, &odiag));
7906: PetscCall(VecGetArrayRead(par, &parv));
7907: cnt = 0;
7908: PetscCall(MatGetSize(mat, NULL, &n));
7909: for (PetscInt i = 0; i < mat->rmap->n; i++) {
7910: PetscInt start, end, d = 0, od = 0;
7912: start = (PetscInt)PetscRealPart(parv[cnt]);
7913: end = (PetscInt)PetscRealPart(parv[cnt + 1]);
7914: cnt += 2;
7916: if (start < cstart) {
7917: od += cstart - start + n - cend;
7918: d += cend - cstart;
7919: } else if (start < cend) {
7920: od += n - cend;
7921: d += cend - start;
7922: } else od += n - start;
7923: if (end <= cstart) {
7924: od -= cstart - end + n - cend;
7925: d -= cend - cstart;
7926: } else if (end < cend) {
7927: od -= n - cend;
7928: d -= cend - end;
7929: } else od -= n - end;
7931: odiag[i] = od;
7932: diag[i] = d;
7933: }
7934: PetscCall(VecRestoreArrayRead(par, &parv));
7935: PetscCall(VecDestroy(&par));
7936: PetscCall(MatXAIJSetPreallocation(edata->C, mat->rmap->bs, diag, odiag, NULL, NULL));
7937: PetscCall(PetscFree2(diag, odiag));
7938: PetscCall(PetscFree2(sizes, starts));
7940: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
7941: PetscCall(PetscContainerSetPointer(container, edata));
7942: PetscCall(PetscContainerSetCtxDestroy(container, EnvelopeDataDestroy));
7943: PetscCall(PetscObjectCompose((PetscObject)mat, "EnvelopeData", (PetscObject)container));
7944: PetscCall(PetscObjectDereference((PetscObject)container));
7945: PetscFunctionReturn(PETSC_SUCCESS);
7946: }
7948: /*@
7949: MatInvertVariableBlockEnvelope - set matrix C to be the inverted block diagonal of matrix A
7951: Collective
7953: Input Parameters:
7954: + A - the matrix
7955: - reuse - indicates if the `C` matrix was obtained from a previous call to this routine
7957: Output Parameter:
7958: . C - matrix with inverted block diagonal of `A`
7960: Level: advanced
7962: Note:
7963: For efficiency the matrix `A` should have all the nonzero entries clustered in smallish blocks along the diagonal.
7965: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatComputeBlockDiagonal()`
7966: @*/
7967: PetscErrorCode MatInvertVariableBlockEnvelope(Mat A, MatReuse reuse, Mat *C)
7968: {
7969: PetscContainer container;
7970: EnvelopeData *edata;
7971: PetscObjectState nonzerostate;
7973: PetscFunctionBegin;
7974: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7975: if (!container) {
7976: PetscCall(MatComputeVariableBlockEnvelope(A));
7977: PetscCall(PetscObjectQuery((PetscObject)A, "EnvelopeData", (PetscObject *)&container));
7978: }
7979: PetscCall(PetscContainerGetPointer(container, &edata));
7980: PetscCall(MatGetNonzeroState(A, &nonzerostate));
7981: PetscCheck(nonzerostate <= edata->nonzerostate, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Cannot handle changes to matrix nonzero structure");
7982: PetscCheck(reuse != MAT_REUSE_MATRIX || *C == edata->C, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "C matrix must be the same as previously output");
7984: PetscCall(MatCreateSubMatrices(A, edata->n, edata->is, edata->is, MAT_INITIAL_MATRIX, &edata->mat));
7985: *C = edata->C;
7987: for (PetscInt i = 0; i < edata->n; i++) {
7988: Mat D;
7989: PetscScalar *dvalues;
7991: PetscCall(MatConvert(edata->mat[i], MATSEQDENSE, MAT_INITIAL_MATRIX, &D));
7992: PetscCall(MatSetOption(*C, MAT_ROW_ORIENTED, PETSC_FALSE));
7993: PetscCall(MatSeqDenseInvert(D));
7994: PetscCall(MatDenseGetArray(D, &dvalues));
7995: PetscCall(MatSetValuesIS(*C, edata->is[i], edata->is[i], dvalues, INSERT_VALUES));
7996: PetscCall(MatDestroy(&D));
7997: }
7998: PetscCall(MatDestroySubMatrices(edata->n, &edata->mat));
7999: PetscCall(MatAssemblyBegin(*C, MAT_FINAL_ASSEMBLY));
8000: PetscCall(MatAssemblyEnd(*C, MAT_FINAL_ASSEMBLY));
8001: PetscFunctionReturn(PETSC_SUCCESS);
8002: }
8004: /*@
8005: MatSetVariableBlockSizes - Sets diagonal point-blocks of the matrix that need not be of the same size
8007: Not Collective
8009: Input Parameters:
8010: + mat - the matrix
8011: . nblocks - the number of blocks on this process, each block can only exist on a single process
8012: - bsizes - the block sizes
8014: Level: intermediate
8016: Notes:
8017: Currently used by `PCVPBJACOBI` for `MATAIJ` matrices
8019: 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.
8021: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatGetVariableBlockSizes()`,
8022: `MatComputeVariableBlockEnvelope()`, `PCVPBJACOBI`
8023: @*/
8024: PetscErrorCode MatSetVariableBlockSizes(Mat mat, PetscInt nblocks, const PetscInt bsizes[])
8025: {
8026: PetscInt ncnt = 0, nlocal;
8028: PetscFunctionBegin;
8030: PetscCall(MatGetLocalSize(mat, &nlocal, NULL));
8031: 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);
8032: for (PetscInt i = 0; i < nblocks; i++) ncnt += bsizes[i];
8033: 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);
8034: PetscCall(PetscFree(mat->bsizes));
8035: mat->nblocks = nblocks;
8036: PetscCall(PetscMalloc1(nblocks, &mat->bsizes));
8037: PetscCall(PetscArraycpy(mat->bsizes, bsizes, nblocks));
8038: PetscFunctionReturn(PETSC_SUCCESS);
8039: }
8041: /*@C
8042: MatGetVariableBlockSizes - Gets a diagonal blocks of the matrix that need not be of the same size
8044: Not Collective; No Fortran Support
8046: Input Parameter:
8047: . mat - the matrix
8049: Output Parameters:
8050: + nblocks - the number of blocks on this process
8051: - bsizes - the block sizes
8053: Level: intermediate
8055: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`, `MatGetBlockSizes()`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8056: @*/
8057: PetscErrorCode MatGetVariableBlockSizes(Mat mat, PetscInt *nblocks, const PetscInt *bsizes[])
8058: {
8059: PetscFunctionBegin;
8061: if (nblocks) *nblocks = mat->nblocks;
8062: if (bsizes) *bsizes = mat->bsizes;
8063: PetscFunctionReturn(PETSC_SUCCESS);
8064: }
8066: /*@
8067: MatSelectVariableBlockSizes - When creating a submatrix, pass on the variable block sizes
8069: Not Collective
8071: Input Parameter:
8072: + subA - the submatrix
8073: . A - the original matrix
8074: - isrow - The `IS` of selected rows for the submatrix, must be sorted
8076: Level: developer
8078: Notes:
8079: If the index set is not sorted or contains off-process entries, this function will do nothing.
8081: .seealso: [](ch_matrices), `Mat`, `MatSetVariableBlockSizes()`, `MatComputeVariableBlockEnvelope()`
8082: @*/
8083: PetscErrorCode MatSelectVariableBlockSizes(Mat subA, Mat A, IS isrow)
8084: {
8085: const PetscInt *rows;
8086: PetscInt n, rStart, rEnd, Nb = 0;
8087: PetscBool flg = A->bsizes ? PETSC_TRUE : PETSC_FALSE;
8089: PetscFunctionBegin;
8090: // The code for block size extraction does not support an unsorted IS
8091: if (flg) PetscCall(ISSorted(isrow, &flg));
8092: // We don't support originally off-diagonal blocks
8093: if (flg) {
8094: PetscCall(MatGetOwnershipRange(A, &rStart, &rEnd));
8095: PetscCall(ISGetLocalSize(isrow, &n));
8096: PetscCall(ISGetIndices(isrow, &rows));
8097: for (PetscInt i = 0; i < n && flg; ++i) {
8098: if (rows[i] < rStart || rows[i] >= rEnd) flg = PETSC_FALSE;
8099: }
8100: PetscCall(ISRestoreIndices(isrow, &rows));
8101: }
8102: // quiet return if we can't extract block size
8103: PetscCallMPI(MPIU_Allreduce(MPI_IN_PLACE, &flg, 1, MPI_C_BOOL, MPI_LAND, PetscObjectComm((PetscObject)subA)));
8104: if (!flg) PetscFunctionReturn(PETSC_SUCCESS);
8106: // extract block sizes
8107: PetscCall(ISGetIndices(isrow, &rows));
8108: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8109: PetscBool occupied = PETSC_FALSE;
8111: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8112: const PetscInt row = gr + br;
8114: if (i == n) break;
8115: if (rows[i] == row) {
8116: occupied = PETSC_TRUE;
8117: ++i;
8118: }
8119: while (i < n && rows[i] < row) ++i;
8120: }
8121: gr += A->bsizes[b];
8122: if (occupied) ++Nb;
8123: }
8124: subA->nblocks = Nb;
8125: PetscCall(PetscFree(subA->bsizes));
8126: PetscCall(PetscMalloc1(subA->nblocks, &subA->bsizes));
8127: PetscInt sb = 0;
8128: for (PetscInt b = 0, gr = rStart, i = 0; b < A->nblocks; ++b) {
8129: if (sb < subA->nblocks) subA->bsizes[sb] = 0;
8130: for (PetscInt br = 0; br < A->bsizes[b]; ++br) {
8131: const PetscInt row = gr + br;
8133: if (i == n) break;
8134: if (rows[i] == row) {
8135: ++subA->bsizes[sb];
8136: ++i;
8137: }
8138: while (i < n && rows[i] < row) ++i;
8139: }
8140: gr += A->bsizes[b];
8141: if (sb < subA->nblocks && subA->bsizes[sb]) ++sb;
8142: }
8143: PetscCheck(sb == subA->nblocks, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Invalid number of blocks %" PetscInt_FMT " != %" PetscInt_FMT, sb, subA->nblocks);
8144: PetscInt nlocal, ncnt = 0;
8145: PetscCall(MatGetLocalSize(subA, &nlocal, NULL));
8146: PetscCheck(subA->nblocks >= 0 && subA->nblocks <= nlocal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Number of local blocks %" PetscInt_FMT " is not in [0, %" PetscInt_FMT "]", subA->nblocks, nlocal);
8147: for (PetscInt i = 0; i < subA->nblocks; i++) ncnt += subA->bsizes[i];
8148: 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);
8149: PetscCall(ISRestoreIndices(isrow, &rows));
8150: PetscFunctionReturn(PETSC_SUCCESS);
8151: }
8153: /*@
8154: MatSetBlockSizes - Sets the matrix block row and column sizes.
8156: Logically Collective
8158: Input Parameters:
8159: + mat - the matrix
8160: . rbs - row block size
8161: - cbs - column block size
8163: Level: intermediate
8165: Notes:
8166: Block row formats are `MATBAIJ` and `MATSBAIJ`. These formats ALWAYS have square block storage in the matrix.
8167: If you pass a different block size for the columns than the rows, the row block size determines the square block storage.
8168: This must be called before `MatSetUp()` or MatXXXSetPreallocation() (or will default to 1) and the block size cannot be changed later.
8170: For `MATAIJ` matrix this function can be called at a later stage, provided that the specified block sizes
8171: are compatible with the matrix local sizes.
8173: The row and column block size determine the blocksize of the "row" and "column" vectors returned by `MatCreateVecs()`.
8175: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSize()`, `MatGetBlockSizes()`
8176: @*/
8177: PetscErrorCode MatSetBlockSizes(Mat mat, PetscInt rbs, PetscInt cbs)
8178: {
8179: PetscFunctionBegin;
8183: PetscTryTypeMethod(mat, setblocksizes, rbs, cbs);
8184: if (mat->rmap->refcnt) {
8185: ISLocalToGlobalMapping l2g = NULL;
8186: PetscLayout nmap = NULL;
8188: PetscCall(PetscLayoutDuplicate(mat->rmap, &nmap));
8189: if (mat->rmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->rmap->mapping, &l2g));
8190: PetscCall(PetscLayoutDestroy(&mat->rmap));
8191: mat->rmap = nmap;
8192: mat->rmap->mapping = l2g;
8193: }
8194: if (mat->cmap->refcnt) {
8195: ISLocalToGlobalMapping l2g = NULL;
8196: PetscLayout nmap = NULL;
8198: PetscCall(PetscLayoutDuplicate(mat->cmap, &nmap));
8199: if (mat->cmap->mapping) PetscCall(ISLocalToGlobalMappingDuplicate(mat->cmap->mapping, &l2g));
8200: PetscCall(PetscLayoutDestroy(&mat->cmap));
8201: mat->cmap = nmap;
8202: mat->cmap->mapping = l2g;
8203: }
8204: PetscCall(PetscLayoutSetBlockSize(mat->rmap, rbs));
8205: PetscCall(PetscLayoutSetBlockSize(mat->cmap, cbs));
8206: PetscFunctionReturn(PETSC_SUCCESS);
8207: }
8209: /*@
8210: MatSetBlockSizesFromMats - Sets the matrix block row and column sizes to match a pair of matrices
8212: Logically Collective
8214: Input Parameters:
8215: + mat - the matrix
8216: . fromRow - matrix from which to copy row block size
8217: - fromCol - matrix from which to copy column block size (can be same as `fromRow`)
8219: Level: developer
8221: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqBAIJ()`, `MatCreateBAIJ()`, `MatGetBlockSize()`, `MatSetBlockSizes()`
8222: @*/
8223: PetscErrorCode MatSetBlockSizesFromMats(Mat mat, Mat fromRow, Mat fromCol)
8224: {
8225: PetscFunctionBegin;
8229: PetscTryTypeMethod(mat, setblocksizes, fromRow->rmap->bs, fromCol->cmap->bs);
8230: PetscCall(PetscLayoutSetBlockSize(mat->rmap, fromRow->rmap->bs));
8231: PetscCall(PetscLayoutSetBlockSize(mat->cmap, fromCol->cmap->bs));
8232: PetscFunctionReturn(PETSC_SUCCESS);
8233: }
8235: /*@
8236: MatResidual - Default routine to calculate the residual r = b - Ax
8238: Collective
8240: Input Parameters:
8241: + mat - the matrix
8242: . b - the right-hand-side
8243: - x - the approximate solution
8245: Output Parameter:
8246: . r - location to store the residual
8248: Level: developer
8250: .seealso: [](ch_matrices), `Mat`, `MatMult()`, `MatMultAdd()`, `PCMGSetResidual()`
8251: @*/
8252: PetscErrorCode MatResidual(Mat mat, Vec b, Vec x, Vec r)
8253: {
8254: PetscFunctionBegin;
8260: MatCheckPreallocated(mat, 1);
8261: PetscCall(PetscLogEventBegin(MAT_Residual, mat, 0, 0, 0));
8262: if (!mat->ops->residual) {
8263: PetscCall(MatMult(mat, x, r));
8264: PetscCall(VecAYPX(r, -1.0, b));
8265: } else {
8266: PetscUseTypeMethod(mat, residual, b, x, r);
8267: }
8268: PetscCall(PetscLogEventEnd(MAT_Residual, mat, 0, 0, 0));
8269: PetscFunctionReturn(PETSC_SUCCESS);
8270: }
8272: /*@C
8273: MatGetRowIJ - Returns the compressed row storage i and j indices for the local rows of a sparse matrix
8275: Collective
8277: Input Parameters:
8278: + mat - the matrix
8279: . shift - 0 or 1 indicating we want the indices starting at 0 or 1
8280: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8281: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8282: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8283: always used.
8285: Output Parameters:
8286: + n - number of local rows in the (possibly compressed) matrix, use `NULL` if not needed
8287: . 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
8288: . ja - the column indices, use `NULL` if not needed
8289: - done - indicates if the routine actually worked and returned appropriate ia[] and ja[] arrays; callers
8290: are responsible for handling the case when done == `PETSC_FALSE` and ia and ja are not set
8292: Level: developer
8294: Notes:
8295: You CANNOT change any of the ia[] or ja[] values.
8297: Use `MatRestoreRowIJ()` when you are finished accessing the ia[] and ja[] values.
8299: Fortran Notes:
8300: Use
8301: .vb
8302: PetscInt, pointer :: ia(:),ja(:)
8303: call MatGetRowIJ(mat,shift,symmetric,inodecompressed,n,ia,ja,done,ierr)
8304: ! Access the ith and jth entries via ia(i) and ja(j)
8305: .ve
8307: .seealso: [](ch_matrices), `Mat`, `MATAIJ`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`, `MatSeqAIJGetArray()`
8308: @*/
8309: PetscErrorCode MatGetRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8310: {
8311: PetscFunctionBegin;
8314: if (n) PetscAssertPointer(n, 5);
8315: if (ia) PetscAssertPointer(ia, 6);
8316: if (ja) PetscAssertPointer(ja, 7);
8317: if (done) PetscAssertPointer(done, 8);
8318: MatCheckPreallocated(mat, 1);
8319: if (!mat->ops->getrowij && done) *done = PETSC_FALSE;
8320: else {
8321: if (done) *done = PETSC_TRUE;
8322: PetscCall(PetscLogEventBegin(MAT_GetRowIJ, mat, 0, 0, 0));
8323: PetscUseTypeMethod(mat, getrowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8324: PetscCall(PetscLogEventEnd(MAT_GetRowIJ, mat, 0, 0, 0));
8325: }
8326: PetscFunctionReturn(PETSC_SUCCESS);
8327: }
8329: /*@C
8330: MatGetColumnIJ - Returns the compressed column storage i and j indices for sequential matrices.
8332: Collective
8334: Input Parameters:
8335: + mat - the matrix
8336: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8337: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be
8338: symmetrized
8339: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8340: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8341: always used.
8343: Output Parameters:
8344: + n - number of columns in the (possibly compressed) matrix
8345: . ia - the column pointers; that is ia[0] = 0, ia[col] = i[col-1] + number of elements in that col of the matrix
8346: . ja - the row indices
8347: - done - `PETSC_TRUE` or `PETSC_FALSE`, indicating whether the values have been returned
8349: Level: developer
8351: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8352: @*/
8353: PetscErrorCode MatGetColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8354: {
8355: PetscFunctionBegin;
8358: PetscAssertPointer(n, 5);
8359: if (ia) PetscAssertPointer(ia, 6);
8360: if (ja) PetscAssertPointer(ja, 7);
8361: PetscAssertPointer(done, 8);
8362: MatCheckPreallocated(mat, 1);
8363: if (!mat->ops->getcolumnij) *done = PETSC_FALSE;
8364: else {
8365: *done = PETSC_TRUE;
8366: PetscUseTypeMethod(mat, getcolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8367: }
8368: PetscFunctionReturn(PETSC_SUCCESS);
8369: }
8371: /*@C
8372: MatRestoreRowIJ - Call after you are completed with the ia,ja indices obtained with `MatGetRowIJ()`.
8374: Collective
8376: Input Parameters:
8377: + mat - the matrix
8378: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8379: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8380: . inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8381: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8382: always used.
8383: . n - size of (possibly compressed) matrix
8384: . ia - the row pointers
8385: - ja - the column indices
8387: Output Parameter:
8388: . done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8390: Level: developer
8392: Note:
8393: This routine zeros out `n`, `ia`, and `ja`. This is to prevent accidental
8394: us of the array after it has been restored. If you pass `NULL`, it will
8395: not zero the pointers. Use of ia or ja after `MatRestoreRowIJ()` is invalid.
8397: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatRestoreColumnIJ()`
8398: @*/
8399: PetscErrorCode MatRestoreRowIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8400: {
8401: PetscFunctionBegin;
8404: if (ia) PetscAssertPointer(ia, 6);
8405: if (ja) PetscAssertPointer(ja, 7);
8406: if (done) PetscAssertPointer(done, 8);
8407: MatCheckPreallocated(mat, 1);
8409: if (!mat->ops->restorerowij && done) *done = PETSC_FALSE;
8410: else {
8411: if (done) *done = PETSC_TRUE;
8412: PetscUseTypeMethod(mat, restorerowij, shift, symmetric, inodecompressed, n, ia, ja, done);
8413: if (n) *n = 0;
8414: if (ia) *ia = NULL;
8415: if (ja) *ja = NULL;
8416: }
8417: PetscFunctionReturn(PETSC_SUCCESS);
8418: }
8420: /*@C
8421: MatRestoreColumnIJ - Call after you are completed with the ia,ja indices obtained with `MatGetColumnIJ()`.
8423: Collective
8425: Input Parameters:
8426: + mat - the matrix
8427: . shift - 1 or zero indicating we want the indices starting at 0 or 1
8428: . symmetric - `PETSC_TRUE` or `PETSC_FALSE` indicating the matrix data structure should be symmetrized
8429: - inodecompressed - `PETSC_TRUE` or `PETSC_FALSE` indicating if the nonzero structure of the
8430: inodes or the nonzero elements is wanted. For `MATBAIJ` matrices the compressed version is
8431: always used.
8433: Output Parameters:
8434: + n - size of (possibly compressed) matrix
8435: . ia - the column pointers
8436: . ja - the row indices
8437: - done - `PETSC_TRUE` or `PETSC_FALSE` indicated that the values have been returned
8439: Level: developer
8441: .seealso: [](ch_matrices), `Mat`, `MatGetColumnIJ()`, `MatRestoreRowIJ()`
8442: @*/
8443: PetscErrorCode MatRestoreColumnIJ(Mat mat, PetscInt shift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
8444: {
8445: PetscFunctionBegin;
8448: if (ia) PetscAssertPointer(ia, 6);
8449: if (ja) PetscAssertPointer(ja, 7);
8450: PetscAssertPointer(done, 8);
8451: MatCheckPreallocated(mat, 1);
8453: if (!mat->ops->restorecolumnij) *done = PETSC_FALSE;
8454: else {
8455: *done = PETSC_TRUE;
8456: PetscUseTypeMethod(mat, restorecolumnij, shift, symmetric, inodecompressed, n, ia, ja, done);
8457: if (n) *n = 0;
8458: if (ia) *ia = NULL;
8459: if (ja) *ja = NULL;
8460: }
8461: PetscFunctionReturn(PETSC_SUCCESS);
8462: }
8464: /*@
8465: MatColoringPatch - Used inside matrix coloring routines that use `MatGetRowIJ()` and/or
8466: `MatGetColumnIJ()`.
8468: Collective
8470: Input Parameters:
8471: + mat - the matrix
8472: . ncolors - maximum color value
8473: . n - number of entries in colorarray
8474: - colorarray - array indicating color for each column
8476: Output Parameter:
8477: . iscoloring - coloring generated using colorarray information
8479: Level: developer
8481: .seealso: [](ch_matrices), `Mat`, `MatGetRowIJ()`, `MatGetColumnIJ()`
8482: @*/
8483: PetscErrorCode MatColoringPatch(Mat mat, PetscInt ncolors, PetscInt n, ISColoringValue colorarray[], ISColoring *iscoloring)
8484: {
8485: PetscFunctionBegin;
8488: PetscAssertPointer(colorarray, 4);
8489: PetscAssertPointer(iscoloring, 5);
8490: MatCheckPreallocated(mat, 1);
8492: if (!mat->ops->coloringpatch) {
8493: PetscCall(ISColoringCreate(PetscObjectComm((PetscObject)mat), ncolors, n, colorarray, PETSC_OWN_POINTER, iscoloring));
8494: } else {
8495: PetscUseTypeMethod(mat, coloringpatch, ncolors, n, colorarray, iscoloring);
8496: }
8497: PetscFunctionReturn(PETSC_SUCCESS);
8498: }
8500: /*@
8501: MatSetUnfactored - Resets a factored matrix to be treated as unfactored.
8503: Logically Collective
8505: Input Parameter:
8506: . mat - the factored matrix to be reset
8508: Level: developer
8510: Notes:
8511: This routine should be used only with factored matrices formed by in-place
8512: factorization via ILU(0) (or by in-place LU factorization for the `MATSEQDENSE`
8513: format). This option can save memory, for example, when solving nonlinear
8514: systems with a matrix-free Newton-Krylov method and a matrix-based, in-place
8515: ILU(0) preconditioner.
8517: One can specify in-place ILU(0) factorization by calling
8518: .vb
8519: PCType(pc,PCILU);
8520: PCFactorSeUseInPlace(pc);
8521: .ve
8522: or by using the options -pc_type ilu -pc_factor_in_place
8524: In-place factorization ILU(0) can also be used as a local
8525: solver for the blocks within the block Jacobi or additive Schwarz
8526: methods (runtime option: -sub_pc_factor_in_place). See Users-Manual: ch_pc
8527: for details on setting local solver options.
8529: Most users should employ the `KSP` interface for linear solvers
8530: instead of working directly with matrix algebra routines such as this.
8531: See, e.g., `KSPCreate()`.
8533: .seealso: [](ch_matrices), `Mat`, `PCFactorSetUseInPlace()`, `PCFactorGetUseInPlace()`
8534: @*/
8535: PetscErrorCode MatSetUnfactored(Mat mat)
8536: {
8537: PetscFunctionBegin;
8540: MatCheckPreallocated(mat, 1);
8541: mat->factortype = MAT_FACTOR_NONE;
8542: if (!mat->ops->setunfactored) PetscFunctionReturn(PETSC_SUCCESS);
8543: PetscUseTypeMethod(mat, setunfactored);
8544: PetscFunctionReturn(PETSC_SUCCESS);
8545: }
8547: /*@
8548: MatCreateSubMatrix - Gets a single submatrix on the same number of processors
8549: as the original matrix.
8551: Collective
8553: Input Parameters:
8554: + mat - the original matrix
8555: . isrow - parallel `IS` containing the rows this processor should obtain
8556: . 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.
8557: - cll - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
8559: Output Parameter:
8560: . newmat - the new submatrix, of the same type as the original matrix
8562: Level: advanced
8564: Notes:
8565: The submatrix will be able to be multiplied with vectors using the same layout as `iscol`.
8567: Some matrix types place restrictions on the row and column indices, such
8568: 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;
8569: for example, if the block size is 3 one cannot select the 0 and 2 rows without selecting the 1 row.
8571: The index sets may not have duplicate entries.
8573: The first time this is called you should use a cll of `MAT_INITIAL_MATRIX`,
8574: the `MatCreateSubMatrix()` routine will create the newmat for you. Any additional calls
8575: to this routine with a mat of the same nonzero structure and with a call of `MAT_REUSE_MATRIX`
8576: will reuse the matrix generated the first time. You should call `MatDestroy()` on `newmat` when
8577: you are finished using it.
8579: The communicator of the newly obtained matrix is ALWAYS the same as the communicator of
8580: the input matrix.
8582: If `iscol` is `NULL` then all columns are obtained (not supported in Fortran).
8584: If `isrow` and `iscol` have a nontrivial block-size, then the resulting matrix has this block-size as well. This feature
8585: is used by `PCFIELDSPLIT` to allow easy nesting of its use.
8587: Example usage:
8588: Consider the following 8x8 matrix with 34 non-zero values, that is
8589: assembled across 3 processors. Let's assume that proc0 owns 3 rows,
8590: proc1 owns 3 rows, proc2 owns 2 rows. This division can be shown
8591: as follows
8592: .vb
8593: 1 2 0 | 0 3 0 | 0 4
8594: Proc0 0 5 6 | 7 0 0 | 8 0
8595: 9 0 10 | 11 0 0 | 12 0
8596: -------------------------------------
8597: 13 0 14 | 15 16 17 | 0 0
8598: Proc1 0 18 0 | 19 20 21 | 0 0
8599: 0 0 0 | 22 23 0 | 24 0
8600: -------------------------------------
8601: Proc2 25 26 27 | 0 0 28 | 29 0
8602: 30 0 0 | 31 32 33 | 0 34
8603: .ve
8605: Suppose `isrow` = [0 1 | 4 | 6 7] and `iscol` = [1 2 | 3 4 5 | 6]. The resulting submatrix is
8607: .vb
8608: 2 0 | 0 3 0 | 0
8609: Proc0 5 6 | 7 0 0 | 8
8610: -------------------------------
8611: Proc1 18 0 | 19 20 21 | 0
8612: -------------------------------
8613: Proc2 26 27 | 0 0 28 | 29
8614: 0 0 | 31 32 33 | 0
8615: .ve
8617: .seealso: [](ch_matrices), `Mat`, `MatCreateSubMatrices()`, `MatCreateSubMatricesMPI()`, `MatCreateSubMatrixVirtual()`, `MatSubMatrixVirtualUpdate()`
8618: @*/
8619: PetscErrorCode MatCreateSubMatrix(Mat mat, IS isrow, IS iscol, MatReuse cll, Mat *newmat)
8620: {
8621: PetscMPIInt size;
8622: Mat *local;
8623: IS iscoltmp;
8624: PetscBool flg;
8626: PetscFunctionBegin;
8630: PetscAssertPointer(newmat, 5);
8633: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
8634: PetscCheck(cll != MAT_IGNORE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_IGNORE_MATRIX");
8635: PetscCheck(cll != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Cannot use MAT_INPLACE_MATRIX");
8637: MatCheckPreallocated(mat, 1);
8638: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
8640: if (!iscol || isrow == iscol) {
8641: PetscBool stride;
8642: PetscMPIInt grabentirematrix = 0, grab;
8643: PetscCall(PetscObjectTypeCompare((PetscObject)isrow, ISSTRIDE, &stride));
8644: if (stride) {
8645: PetscInt first, step, n, rstart, rend;
8646: PetscCall(ISStrideGetInfo(isrow, &first, &step));
8647: if (step == 1) {
8648: PetscCall(MatGetOwnershipRange(mat, &rstart, &rend));
8649: if (rstart == first) {
8650: PetscCall(ISGetLocalSize(isrow, &n));
8651: if (n == rend - rstart) grabentirematrix = 1;
8652: }
8653: }
8654: }
8655: PetscCallMPI(MPIU_Allreduce(&grabentirematrix, &grab, 1, MPI_INT, MPI_MIN, PetscObjectComm((PetscObject)mat)));
8656: if (grab) {
8657: PetscCall(PetscInfo(mat, "Getting entire matrix as submatrix\n"));
8658: if (cll == MAT_INITIAL_MATRIX) {
8659: *newmat = mat;
8660: PetscCall(PetscObjectReference((PetscObject)mat));
8661: }
8662: PetscFunctionReturn(PETSC_SUCCESS);
8663: }
8664: }
8666: if (!iscol) {
8667: PetscCall(ISCreateStride(PetscObjectComm((PetscObject)mat), mat->cmap->n, mat->cmap->rstart, 1, &iscoltmp));
8668: } else {
8669: iscoltmp = iscol;
8670: }
8672: /* if original matrix is on just one processor then use submatrix generated */
8673: if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1 && cll == MAT_REUSE_MATRIX) {
8674: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_REUSE_MATRIX, &newmat));
8675: goto setproperties;
8676: } else if (mat->ops->createsubmatrices && !mat->ops->createsubmatrix && size == 1) {
8677: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscoltmp, MAT_INITIAL_MATRIX, &local));
8678: *newmat = *local;
8679: PetscCall(PetscFree(local));
8680: goto setproperties;
8681: } else if (!mat->ops->createsubmatrix) {
8682: /* Create a new matrix type that implements the operation using the full matrix */
8683: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8684: switch (cll) {
8685: case MAT_INITIAL_MATRIX:
8686: PetscCall(MatCreateSubMatrixVirtual(mat, isrow, iscoltmp, newmat));
8687: break;
8688: case MAT_REUSE_MATRIX:
8689: PetscCall(MatSubMatrixVirtualUpdate(*newmat, mat, isrow, iscoltmp));
8690: break;
8691: default:
8692: SETERRQ(PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "Invalid MatReuse, must be either MAT_INITIAL_MATRIX or MAT_REUSE_MATRIX");
8693: }
8694: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8695: goto setproperties;
8696: }
8698: PetscCall(PetscLogEventBegin(MAT_CreateSubMat, mat, 0, 0, 0));
8699: PetscUseTypeMethod(mat, createsubmatrix, isrow, iscoltmp, cll, newmat);
8700: PetscCall(PetscLogEventEnd(MAT_CreateSubMat, mat, 0, 0, 0));
8702: setproperties:
8703: if ((*newmat)->symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->structurally_symmetric == PETSC_BOOL3_UNKNOWN && (*newmat)->spd == PETSC_BOOL3_UNKNOWN && (*newmat)->hermitian == PETSC_BOOL3_UNKNOWN) {
8704: PetscCall(ISEqualUnsorted(isrow, iscoltmp, &flg));
8705: if (flg) PetscCall(MatPropagateSymmetryOptions(mat, *newmat));
8706: }
8707: if (!iscol) PetscCall(ISDestroy(&iscoltmp));
8708: if (*newmat && cll == MAT_INITIAL_MATRIX) PetscCall(PetscObjectStateIncrease((PetscObject)*newmat));
8709: if (!iscol || isrow == iscol) PetscCall(MatSelectVariableBlockSizes(*newmat, mat, isrow));
8710: PetscFunctionReturn(PETSC_SUCCESS);
8711: }
8713: /*@
8714: MatPropagateSymmetryOptions - Propagates symmetry options set on a matrix to another matrix
8716: Not Collective
8718: Input Parameters:
8719: + A - the matrix we wish to propagate options from
8720: - B - the matrix we wish to propagate options to
8722: Level: beginner
8724: Note:
8725: Propagates the options associated to `MAT_SYMMETRY_ETERNAL`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_HERMITIAN`, `MAT_SPD`, `MAT_SYMMETRIC`, and `MAT_STRUCTURAL_SYMMETRY_ETERNAL`
8727: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MatIsSymmetricKnown()`, `MatIsSPDKnown()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetricKnown()`
8728: @*/
8729: PetscErrorCode MatPropagateSymmetryOptions(Mat A, Mat B)
8730: {
8731: PetscFunctionBegin;
8734: B->symmetry_eternal = A->symmetry_eternal;
8735: B->structural_symmetry_eternal = A->structural_symmetry_eternal;
8736: B->symmetric = A->symmetric;
8737: B->structurally_symmetric = A->structurally_symmetric;
8738: B->spd = A->spd;
8739: B->hermitian = A->hermitian;
8740: PetscFunctionReturn(PETSC_SUCCESS);
8741: }
8743: /*@
8744: MatStashSetInitialSize - sets the sizes of the matrix stash, that is
8745: used during the assembly process to store values that belong to
8746: other processors.
8748: Not Collective
8750: Input Parameters:
8751: + mat - the matrix
8752: . size - the initial size of the stash.
8753: - bsize - the initial size of the block-stash(if used).
8755: Options Database Keys:
8756: + -matstash_initial_size size or size0,size1,...,sizep-1 - set initial size
8757: - -matstash_block_initial_size bsize or bsize0,bsize1,...,bsizep-1 - set initial block size
8759: Level: intermediate
8761: Notes:
8762: The block-stash is used for values set with `MatSetValuesBlocked()` while
8763: the stash is used for values set with `MatSetValues()`
8765: Run with the option -info and look for output of the form
8766: MatAssemblyBegin_MPIXXX:Stash has MM entries, uses nn mallocs.
8767: to determine the appropriate value, MM, to use for size and
8768: MatAssemblyBegin_MPIXXX:Block-Stash has BMM entries, uses nn mallocs.
8769: to determine the value, BMM to use for bsize
8771: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashGetInfo()`
8772: @*/
8773: PetscErrorCode MatStashSetInitialSize(Mat mat, PetscInt size, PetscInt bsize)
8774: {
8775: PetscFunctionBegin;
8778: PetscCall(MatStashSetInitialSize_Private(&mat->stash, size));
8779: PetscCall(MatStashSetInitialSize_Private(&mat->bstash, bsize));
8780: PetscFunctionReturn(PETSC_SUCCESS);
8781: }
8783: /*@
8784: MatInterpolateAdd - $w = y + A*x$ or $A^T*x$ depending on the shape of
8785: the matrix
8787: Neighbor-wise Collective
8789: Input Parameters:
8790: + A - the matrix
8791: . x - the vector to be multiplied by the interpolation operator
8792: - y - the vector to be added to the result
8794: Output Parameter:
8795: . w - the resulting vector
8797: Level: intermediate
8799: Notes:
8800: `w` may be the same vector as `y`.
8802: This allows one to use either the restriction or interpolation (its transpose)
8803: matrix to do the interpolation
8805: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8806: @*/
8807: PetscErrorCode MatInterpolateAdd(Mat A, Vec x, Vec y, Vec w)
8808: {
8809: PetscInt M, N, Ny;
8811: PetscFunctionBegin;
8816: PetscCall(MatGetSize(A, &M, &N));
8817: PetscCall(VecGetSize(y, &Ny));
8818: if (M == Ny) PetscCall(MatMultAdd(A, x, y, w));
8819: else PetscCall(MatMultTransposeAdd(A, x, y, w));
8820: PetscFunctionReturn(PETSC_SUCCESS);
8821: }
8823: /*@
8824: MatInterpolate - $y = A*x$ or $A^T*x$ depending on the shape of
8825: the matrix
8827: Neighbor-wise Collective
8829: Input Parameters:
8830: + A - the matrix
8831: - x - the vector to be interpolated
8833: Output Parameter:
8834: . y - the resulting vector
8836: Level: intermediate
8838: Note:
8839: This allows one to use either the restriction or interpolation (its transpose)
8840: matrix to do the interpolation
8842: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatRestrict()`, `PCMG`
8843: @*/
8844: PetscErrorCode MatInterpolate(Mat A, Vec x, Vec y)
8845: {
8846: PetscInt M, N, Ny;
8848: PetscFunctionBegin;
8852: PetscCall(MatGetSize(A, &M, &N));
8853: PetscCall(VecGetSize(y, &Ny));
8854: if (M == Ny) PetscCall(MatMult(A, x, y));
8855: else PetscCall(MatMultTranspose(A, x, y));
8856: PetscFunctionReturn(PETSC_SUCCESS);
8857: }
8859: /*@
8860: MatRestrict - $y = A*x$ or $A^T*x$
8862: Neighbor-wise Collective
8864: Input Parameters:
8865: + A - the matrix
8866: - x - the vector to be restricted
8868: Output Parameter:
8869: . y - the resulting vector
8871: Level: intermediate
8873: Note:
8874: This allows one to use either the restriction or interpolation (its transpose)
8875: matrix to do the restriction
8877: .seealso: [](ch_matrices), `Mat`, `MatMultAdd()`, `MatMultTransposeAdd()`, `MatInterpolate()`, `PCMG`
8878: @*/
8879: PetscErrorCode MatRestrict(Mat A, Vec x, Vec y)
8880: {
8881: PetscInt M, N, Nx;
8883: PetscFunctionBegin;
8887: PetscCall(MatGetSize(A, &M, &N));
8888: PetscCall(VecGetSize(x, &Nx));
8889: if (M == Nx) PetscCall(MatMultTranspose(A, x, y));
8890: else PetscCall(MatMult(A, x, y));
8891: PetscFunctionReturn(PETSC_SUCCESS);
8892: }
8894: /*@
8895: MatMatInterpolateAdd - $Y = W + A*X$ or $W + A^T*X$ depending on the shape of `A`
8897: Neighbor-wise Collective
8899: Input Parameters:
8900: + A - the matrix
8901: . x - the input dense matrix to be multiplied
8902: - w - the input dense matrix to be added to the result
8904: Output Parameter:
8905: . y - the output dense matrix
8907: Level: intermediate
8909: Note:
8910: This allows one to use either the restriction or interpolation (its transpose)
8911: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8912: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8914: .seealso: [](ch_matrices), `Mat`, `MatInterpolateAdd()`, `MatMatInterpolate()`, `MatMatRestrict()`, `PCMG`
8915: @*/
8916: PetscErrorCode MatMatInterpolateAdd(Mat A, Mat x, Mat w, Mat *y)
8917: {
8918: PetscInt M, N, Mx, Nx, Mo, My = 0, Ny = 0;
8919: PetscBool trans = PETSC_TRUE;
8920: MatReuse reuse = MAT_INITIAL_MATRIX;
8922: PetscFunctionBegin;
8928: PetscCall(MatGetSize(A, &M, &N));
8929: PetscCall(MatGetSize(x, &Mx, &Nx));
8930: if (N == Mx) trans = PETSC_FALSE;
8931: 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);
8932: Mo = trans ? N : M;
8933: if (*y) {
8934: PetscCall(MatGetSize(*y, &My, &Ny));
8935: if (Mo == My && Nx == Ny) reuse = MAT_REUSE_MATRIX;
8936: else {
8937: 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);
8938: PetscCall(MatDestroy(y));
8939: }
8940: }
8942: if (w && *y == w) { /* this is to minimize changes in PCMG */
8943: PetscBool flg;
8945: PetscCall(PetscObjectQuery((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject *)&w));
8946: if (w) {
8947: PetscInt My, Ny, Mw, Nw;
8949: PetscCall(PetscObjectTypeCompare((PetscObject)*y, ((PetscObject)w)->type_name, &flg));
8950: PetscCall(MatGetSize(*y, &My, &Ny));
8951: PetscCall(MatGetSize(w, &Mw, &Nw));
8952: if (!flg || My != Mw || Ny != Nw) w = NULL;
8953: }
8954: if (!w) {
8955: PetscCall(MatDuplicate(*y, MAT_COPY_VALUES, &w));
8956: PetscCall(PetscObjectCompose((PetscObject)*y, "__MatMatIntAdd_w", (PetscObject)w));
8957: PetscCall(PetscObjectDereference((PetscObject)w));
8958: } else PetscCall(MatCopy(*y, w, UNKNOWN_NONZERO_PATTERN));
8959: }
8960: if (!trans) PetscCall(MatMatMult(A, x, reuse, PETSC_DETERMINE, y));
8961: else PetscCall(MatTransposeMatMult(A, x, reuse, PETSC_DETERMINE, y));
8962: if (w) PetscCall(MatAXPY(*y, 1.0, w, UNKNOWN_NONZERO_PATTERN));
8963: PetscFunctionReturn(PETSC_SUCCESS);
8964: }
8966: /*@
8967: MatMatInterpolate - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8969: Neighbor-wise Collective
8971: Input Parameters:
8972: + A - the matrix
8973: - x - the input dense matrix
8975: Output Parameter:
8976: . y - the output dense matrix
8978: Level: intermediate
8980: Note:
8981: This allows one to use either the restriction or interpolation (its transpose)
8982: matrix to do the interpolation. `y` matrix can be reused if already created with the proper sizes,
8983: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
8985: .seealso: [](ch_matrices), `Mat`, `MatInterpolate()`, `MatRestrict()`, `MatMatRestrict()`, `PCMG`
8986: @*/
8987: PetscErrorCode MatMatInterpolate(Mat A, Mat x, Mat *y)
8988: {
8989: PetscFunctionBegin;
8990: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
8991: PetscFunctionReturn(PETSC_SUCCESS);
8992: }
8994: /*@
8995: MatMatRestrict - $Y = A*X$ or $A^T*X$ depending on the shape of `A`
8997: Neighbor-wise Collective
8999: Input Parameters:
9000: + A - the matrix
9001: - x - the input dense matrix
9003: Output Parameter:
9004: . y - the output dense matrix
9006: Level: intermediate
9008: Note:
9009: This allows one to use either the restriction or interpolation (its transpose)
9010: matrix to do the restriction. `y` matrix can be reused if already created with the proper sizes,
9011: otherwise it will be recreated. `y` must be initialized to `NULL` if not supplied.
9013: .seealso: [](ch_matrices), `Mat`, `MatRestrict()`, `MatInterpolate()`, `MatMatInterpolate()`, `PCMG`
9014: @*/
9015: PetscErrorCode MatMatRestrict(Mat A, Mat x, Mat *y)
9016: {
9017: PetscFunctionBegin;
9018: PetscCall(MatMatInterpolateAdd(A, x, NULL, y));
9019: PetscFunctionReturn(PETSC_SUCCESS);
9020: }
9022: /*@
9023: MatGetNullSpace - retrieves the null space of a matrix.
9025: Logically Collective
9027: Input Parameters:
9028: + mat - the matrix
9029: - nullsp - the null space object
9031: Level: developer
9033: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetNullSpace()`, `MatNullSpace`
9034: @*/
9035: PetscErrorCode MatGetNullSpace(Mat mat, MatNullSpace *nullsp)
9036: {
9037: PetscFunctionBegin;
9039: PetscAssertPointer(nullsp, 2);
9040: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->nullsp) ? mat->transnullsp : mat->nullsp;
9041: PetscFunctionReturn(PETSC_SUCCESS);
9042: }
9044: /*@C
9045: MatGetNullSpaces - gets the null spaces, transpose null spaces, and near null spaces from an array of matrices
9047: Logically Collective
9049: Input Parameters:
9050: + n - the number of matrices
9051: - mat - the array of matrices
9053: Output Parameters:
9054: . nullsp - an array of null spaces, `NULL` for each matrix that does not have a null space, length 3 * `n`
9056: Level: developer
9058: Note:
9059: Call `MatRestoreNullspaces()` to provide these to another array of matrices
9061: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9062: `MatNullSpaceRemove()`, `MatRestoreNullSpaces()`
9063: @*/
9064: PetscErrorCode MatGetNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9065: {
9066: PetscFunctionBegin;
9067: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9068: PetscAssertPointer(mat, 2);
9069: PetscAssertPointer(nullsp, 3);
9071: PetscCall(PetscCalloc1(3 * n, nullsp));
9072: for (PetscInt i = 0; i < n; i++) {
9074: (*nullsp)[i] = mat[i]->nullsp;
9075: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[i]));
9076: (*nullsp)[n + i] = mat[i]->nearnullsp;
9077: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[n + i]));
9078: (*nullsp)[2 * n + i] = mat[i]->transnullsp;
9079: PetscCall(PetscObjectReference((PetscObject)(*nullsp)[2 * n + i]));
9080: }
9081: PetscFunctionReturn(PETSC_SUCCESS);
9082: }
9084: /*@C
9085: MatRestoreNullSpaces - sets the null spaces, transpose null spaces, and near null spaces obtained with `MatGetNullSpaces()` for an array of matrices
9087: Logically Collective
9089: Input Parameters:
9090: + n - the number of matrices
9091: . mat - the array of matrices
9092: - nullsp - an array of null spaces
9094: Level: developer
9096: Note:
9097: Call `MatGetNullSpaces()` to create `nullsp`
9099: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`,
9100: `MatNullSpaceRemove()`, `MatGetNullSpaces()`
9101: @*/
9102: PetscErrorCode MatRestoreNullSpaces(PetscInt n, Mat mat[], MatNullSpace *nullsp[])
9103: {
9104: PetscFunctionBegin;
9105: PetscCheck(n >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of matrices %" PetscInt_FMT " must be non-negative", n);
9106: PetscAssertPointer(mat, 2);
9107: PetscAssertPointer(nullsp, 3);
9108: PetscAssertPointer(*nullsp, 3);
9110: for (PetscInt i = 0; i < n; i++) {
9112: PetscCall(MatSetNullSpace(mat[i], (*nullsp)[i]));
9113: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[i]));
9114: PetscCall(MatSetNearNullSpace(mat[i], (*nullsp)[n + i]));
9115: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[n + i]));
9116: PetscCall(MatSetTransposeNullSpace(mat[i], (*nullsp)[2 * n + i]));
9117: PetscCall(PetscObjectDereference((PetscObject)(*nullsp)[2 * n + i]));
9118: }
9119: PetscCall(PetscFree(*nullsp));
9120: PetscFunctionReturn(PETSC_SUCCESS);
9121: }
9123: /*@
9124: MatSetNullSpace - attaches a null space to a matrix.
9126: Logically Collective
9128: Input Parameters:
9129: + mat - the matrix
9130: - nullsp - the null space object
9132: Level: advanced
9134: Notes:
9135: This null space is used by the `KSP` linear solvers to solve singular systems.
9137: 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`
9139: 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
9140: to zero but the linear system will still be solved in a least squares sense.
9142: The fundamental theorem of linear algebra (Gilbert Strang, Introduction to Applied Mathematics, page 72) states that
9143: the domain of a matrix $A$ (from $R^n$ to $R^m$ ($m$ rows, $n$ columns) $R^n$ = the direct sum of the null space of $A$, $n(A)$, plus the range of $A^T$, $R(A^T)$.
9144: 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
9145: $n(A^T))$ 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
9146: 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 $n(A^T)$.
9147: This $\hat{b}$ can be obtained by calling `MatNullSpaceRemove()` with the null space of the transpose of the matrix.
9149: If the matrix is known to be symmetric because it is an `MATSBAIJ` matrix or one has called
9150: `MatSetOption`(mat,`MAT_SYMMETRIC` or possibly `MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`); this
9151: routine also automatically calls `MatSetTransposeNullSpace()`.
9153: The user should call `MatNullSpaceDestroy()`.
9155: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetTransposeNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`,
9156: `KSPSetPCSide()`
9157: @*/
9158: PetscErrorCode MatSetNullSpace(Mat mat, MatNullSpace nullsp)
9159: {
9160: PetscFunctionBegin;
9163: PetscCall(PetscObjectReference((PetscObject)nullsp));
9164: PetscCall(MatNullSpaceDestroy(&mat->nullsp));
9165: mat->nullsp = nullsp;
9166: if (mat->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetTransposeNullSpace(mat, nullsp));
9167: PetscFunctionReturn(PETSC_SUCCESS);
9168: }
9170: /*@
9171: MatGetTransposeNullSpace - retrieves the null space of the transpose of a matrix.
9173: Logically Collective
9175: Input Parameters:
9176: + mat - the matrix
9177: - nullsp - the null space object
9179: Level: developer
9181: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatSetTransposeNullSpace()`, `MatSetNullSpace()`, `MatGetNullSpace()`
9182: @*/
9183: PetscErrorCode MatGetTransposeNullSpace(Mat mat, MatNullSpace *nullsp)
9184: {
9185: PetscFunctionBegin;
9188: PetscAssertPointer(nullsp, 2);
9189: *nullsp = (mat->symmetric == PETSC_BOOL3_TRUE && !mat->transnullsp) ? mat->nullsp : mat->transnullsp;
9190: PetscFunctionReturn(PETSC_SUCCESS);
9191: }
9193: /*@
9194: MatSetTransposeNullSpace - attaches the null space of a transpose of a matrix to the matrix
9196: Logically Collective
9198: Input Parameters:
9199: + mat - the matrix
9200: - nullsp - the null space object
9202: Level: advanced
9204: Notes:
9205: This allows solving singular linear systems defined by the transpose of the matrix using `KSP` solvers with left preconditioning.
9207: See `MatSetNullSpace()`
9209: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatSetNullSpace()`, `MatGetTransposeNullSpace()`, `MatNullSpaceRemove()`, `KSPSetPCSide()`
9210: @*/
9211: PetscErrorCode MatSetTransposeNullSpace(Mat mat, MatNullSpace nullsp)
9212: {
9213: PetscFunctionBegin;
9216: PetscCall(PetscObjectReference((PetscObject)nullsp));
9217: PetscCall(MatNullSpaceDestroy(&mat->transnullsp));
9218: mat->transnullsp = nullsp;
9219: PetscFunctionReturn(PETSC_SUCCESS);
9220: }
9222: /*@
9223: MatSetNearNullSpace - attaches a null space to a matrix, which is often the null space (rigid body modes) of the operator without boundary conditions
9224: This null space will be used to provide near null space vectors to a multigrid preconditioner built from this matrix.
9226: Logically Collective
9228: Input Parameters:
9229: + mat - the matrix
9230: - nullsp - the null space object
9232: Level: advanced
9234: Notes:
9235: Overwrites any previous near null space that may have been attached
9237: You can remove the null space by calling this routine with an `nullsp` of `NULL`
9239: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatCreate()`, `MatNullSpaceCreate()`, `MatSetNullSpace()`, `MatNullSpaceCreateRigidBody()`, `MatGetNearNullSpace()`
9240: @*/
9241: PetscErrorCode MatSetNearNullSpace(Mat mat, MatNullSpace nullsp)
9242: {
9243: PetscFunctionBegin;
9247: MatCheckPreallocated(mat, 1);
9248: PetscCall(PetscObjectReference((PetscObject)nullsp));
9249: PetscCall(MatNullSpaceDestroy(&mat->nearnullsp));
9250: mat->nearnullsp = nullsp;
9251: PetscFunctionReturn(PETSC_SUCCESS);
9252: }
9254: /*@
9255: MatGetNearNullSpace - Get null space attached with `MatSetNearNullSpace()`
9257: Not Collective
9259: Input Parameter:
9260: . mat - the matrix
9262: Output Parameter:
9263: . nullsp - the null space object, `NULL` if not set
9265: Level: advanced
9267: .seealso: [](ch_matrices), `Mat`, `MatNullSpace`, `MatSetNearNullSpace()`, `MatGetNullSpace()`, `MatNullSpaceCreate()`
9268: @*/
9269: PetscErrorCode MatGetNearNullSpace(Mat mat, MatNullSpace *nullsp)
9270: {
9271: PetscFunctionBegin;
9274: PetscAssertPointer(nullsp, 2);
9275: MatCheckPreallocated(mat, 1);
9276: *nullsp = mat->nearnullsp;
9277: PetscFunctionReturn(PETSC_SUCCESS);
9278: }
9280: /*@
9281: MatICCFactor - Performs in-place incomplete Cholesky factorization of matrix.
9283: Collective
9285: Input Parameters:
9286: + mat - the matrix
9287: . row - row/column permutation
9288: - info - information on desired factorization process
9290: Level: developer
9292: Notes:
9293: Probably really in-place only when level of fill is zero, otherwise allocates
9294: new space to store factored matrix and deletes previous memory.
9296: Most users should employ the `KSP` interface for linear solvers
9297: instead of working directly with matrix algebra routines such as this.
9298: See, e.g., `KSPCreate()`.
9300: Fortran Note:
9301: A valid (non-null) `info` argument must be provided
9303: .seealso: [](ch_matrices), `Mat`, `MatFactorInfo`, `MatGetFactor()`, `MatICCFactorSymbolic()`, `MatLUFactorNumeric()`, `MatCholeskyFactor()`
9304: @*/
9305: PetscErrorCode MatICCFactor(Mat mat, IS row, const MatFactorInfo *info)
9306: {
9307: PetscFunctionBegin;
9311: PetscAssertPointer(info, 3);
9312: PetscCheck(mat->rmap->N == mat->cmap->N, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONG, "matrix must be square");
9313: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
9314: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
9315: MatCheckPreallocated(mat, 1);
9316: PetscUseTypeMethod(mat, iccfactor, row, info);
9317: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9318: PetscFunctionReturn(PETSC_SUCCESS);
9319: }
9321: /*@
9322: MatDiagonalScaleLocal - Scales columns of a matrix given the scaling values including the
9323: ghosted ones.
9325: Not Collective
9327: Input Parameters:
9328: + mat - the matrix
9329: - diag - the diagonal values, including ghost ones
9331: Level: developer
9333: Notes:
9334: Works only for `MATMPIAIJ` and `MATMPIBAIJ` matrices
9336: This allows one to avoid during communication to perform the scaling that must be done with `MatDiagonalScale()`
9338: .seealso: [](ch_matrices), `Mat`, `MatDiagonalScale()`
9339: @*/
9340: PetscErrorCode MatDiagonalScaleLocal(Mat mat, Vec diag)
9341: {
9342: PetscMPIInt size;
9344: PetscFunctionBegin;
9349: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must be already assembled");
9350: PetscCall(PetscLogEventBegin(MAT_Scale, mat, 0, 0, 0));
9351: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
9352: if (size == 1) {
9353: PetscInt n, m;
9354: PetscCall(VecGetSize(diag, &n));
9355: PetscCall(MatGetSize(mat, NULL, &m));
9356: PetscCheck(m == n, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only supported for sequential matrices when no ghost points/periodic conditions");
9357: PetscCall(MatDiagonalScale(mat, NULL, diag));
9358: } else PetscUseMethod(mat, "MatDiagonalScaleLocal_C", (Mat, Vec), (mat, diag));
9359: PetscCall(PetscLogEventEnd(MAT_Scale, mat, 0, 0, 0));
9360: PetscCall(PetscObjectStateIncrease((PetscObject)mat));
9361: PetscFunctionReturn(PETSC_SUCCESS);
9362: }
9364: /*@
9365: MatGetInertia - Gets the inertia from a factored matrix
9367: Collective
9369: Input Parameter:
9370: . mat - the matrix
9372: Output Parameters:
9373: + nneg - number of negative eigenvalues
9374: . nzero - number of zero eigenvalues
9375: - npos - number of positive eigenvalues
9377: Level: advanced
9379: Note:
9380: Matrix must have been factored by `MatCholeskyFactor()`
9382: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatCholeskyFactor()`
9383: @*/
9384: PetscErrorCode MatGetInertia(Mat mat, PetscInt *nneg, PetscInt *nzero, PetscInt *npos)
9385: {
9386: PetscFunctionBegin;
9389: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9390: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Numeric factor mat is not assembled");
9391: PetscUseTypeMethod(mat, getinertia, nneg, nzero, npos);
9392: PetscFunctionReturn(PETSC_SUCCESS);
9393: }
9395: /*@C
9396: MatSolves - Solves $A x = b$, given a factored matrix, for a collection of vectors
9398: Neighbor-wise Collective
9400: Input Parameters:
9401: + mat - the factored matrix obtained with `MatGetFactor()`
9402: - b - the right-hand-side vectors
9404: Output Parameter:
9405: . x - the result vectors
9407: Level: developer
9409: Note:
9410: The vectors `b` and `x` cannot be the same. I.e., one cannot
9411: call `MatSolves`(A,x,x).
9413: .seealso: [](ch_matrices), `Mat`, `Vecs`, `MatSolveAdd()`, `MatSolveTranspose()`, `MatSolveTransposeAdd()`, `MatSolve()`
9414: @*/
9415: PetscErrorCode MatSolves(Mat mat, Vecs b, Vecs x)
9416: {
9417: PetscFunctionBegin;
9420: PetscCheck(x != b, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_IDN, "x and b must be different vectors");
9421: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Unfactored matrix");
9422: if (!mat->rmap->N && !mat->cmap->N) PetscFunctionReturn(PETSC_SUCCESS);
9424: MatCheckPreallocated(mat, 1);
9425: PetscCall(PetscLogEventBegin(MAT_Solves, mat, 0, 0, 0));
9426: PetscUseTypeMethod(mat, solves, b, x);
9427: PetscCall(PetscLogEventEnd(MAT_Solves, mat, 0, 0, 0));
9428: PetscFunctionReturn(PETSC_SUCCESS);
9429: }
9431: /*@
9432: MatIsSymmetric - Test whether a matrix is symmetric
9434: Collective
9436: Input Parameters:
9437: + A - the matrix to test
9438: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact transpose)
9440: Output Parameter:
9441: . flg - the result
9443: Level: intermediate
9445: Notes:
9446: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9448: If the matrix does not yet know if it is symmetric or not this can be an expensive operation, also available `MatIsSymmetricKnown()`
9450: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9451: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9453: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetricKnown()`,
9454: `MAT_SYMMETRIC`, `MAT_SYMMETRY_ETERNAL`
9455: @*/
9456: PetscErrorCode MatIsSymmetric(Mat A, PetscReal tol, PetscBool *flg)
9457: {
9458: PetscFunctionBegin;
9460: PetscAssertPointer(flg, 3);
9461: if (A->symmetric != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->symmetric);
9462: else {
9463: if (A->ops->issymmetric) PetscUseTypeMethod(A, issymmetric, tol, flg);
9464: else PetscCall(MatIsTranspose(A, A, tol, flg));
9465: if (!tol) PetscCall(MatSetOption(A, MAT_SYMMETRIC, *flg));
9466: }
9467: PetscFunctionReturn(PETSC_SUCCESS);
9468: }
9470: /*@
9471: MatIsHermitian - Test whether a matrix is Hermitian
9473: Collective
9475: Input Parameters:
9476: + A - the matrix to test
9477: - tol - difference between value and its transpose less than this amount counts as equal (use 0.0 for exact Hermitian)
9479: Output Parameter:
9480: . flg - the result
9482: Level: intermediate
9484: Notes:
9485: For real numbers `MatIsSymmetric()` and `MatIsHermitian()` return identical results
9487: If the matrix does not yet know if it is Hermitian or not this can be an expensive operation, also available `MatIsHermitianKnown()`
9489: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9490: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMEMTRY_ETERNAL`,`PETSC_TRUE`)
9492: .seealso: [](ch_matrices), `Mat`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitianKnown()`, `MatIsStructurallySymmetric()`, `MatSetOption()`,
9493: `MatIsSymmetricKnown()`, `MatIsSymmetric()`, `MAT_HERMITIAN`, `MAT_SYMMETRY_ETERNAL`
9494: @*/
9495: PetscErrorCode MatIsHermitian(Mat A, PetscReal tol, PetscBool *flg)
9496: {
9497: PetscFunctionBegin;
9499: PetscAssertPointer(flg, 3);
9500: if (A->hermitian != PETSC_BOOL3_UNKNOWN && !tol) *flg = PetscBool3ToBool(A->hermitian);
9501: else {
9502: if (A->ops->ishermitian) PetscUseTypeMethod(A, ishermitian, tol, flg);
9503: else PetscCall(MatIsHermitianTranspose(A, A, tol, flg));
9504: if (!tol) PetscCall(MatSetOption(A, MAT_HERMITIAN, *flg));
9505: }
9506: PetscFunctionReturn(PETSC_SUCCESS);
9507: }
9509: /*@
9510: MatIsSymmetricKnown - Checks if a matrix knows if it is symmetric or not and its symmetric state
9512: Not Collective
9514: Input Parameter:
9515: . A - the matrix to check
9517: Output Parameters:
9518: + set - `PETSC_TRUE` if the matrix knows its symmetry state (this tells you if the next flag is valid)
9519: - flg - the result (only valid if set is `PETSC_TRUE`)
9521: Level: advanced
9523: Notes:
9524: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsSymmetric()`
9525: if you want it explicitly checked
9527: One can declare that a matrix is symmetric with `MatSetOption`(mat,`MAT_SYMMETRIC`,`PETSC_TRUE`) and if it is known to remain symmetric
9528: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9530: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9531: @*/
9532: PetscErrorCode MatIsSymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9533: {
9534: PetscFunctionBegin;
9536: PetscAssertPointer(set, 2);
9537: PetscAssertPointer(flg, 3);
9538: if (A->symmetric != PETSC_BOOL3_UNKNOWN) {
9539: *set = PETSC_TRUE;
9540: *flg = PetscBool3ToBool(A->symmetric);
9541: } else *set = PETSC_FALSE;
9542: PetscFunctionReturn(PETSC_SUCCESS);
9543: }
9545: /*@
9546: MatIsSPDKnown - Checks if a matrix knows if it is symmetric positive definite or not and its symmetric positive definite state
9548: Not Collective
9550: Input Parameter:
9551: . A - the matrix to check
9553: Output Parameters:
9554: + set - `PETSC_TRUE` if the matrix knows its symmetric positive definite state (this tells you if the next flag is valid)
9555: - flg - the result (only valid if set is `PETSC_TRUE`)
9557: Level: advanced
9559: Notes:
9560: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`).
9562: One can declare that a matrix is SPD with `MatSetOption`(mat,`MAT_SPD`,`PETSC_TRUE`) and if it is known to remain SPD
9563: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SPD_ETERNAL`,`PETSC_TRUE`)
9565: .seealso: [](ch_matrices), `Mat`, `MAT_SPD_ETERNAL`, `MAT_SPD`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9566: @*/
9567: PetscErrorCode MatIsSPDKnown(Mat A, PetscBool *set, PetscBool *flg)
9568: {
9569: PetscFunctionBegin;
9571: PetscAssertPointer(set, 2);
9572: PetscAssertPointer(flg, 3);
9573: if (A->spd != PETSC_BOOL3_UNKNOWN) {
9574: *set = PETSC_TRUE;
9575: *flg = PetscBool3ToBool(A->spd);
9576: } else *set = PETSC_FALSE;
9577: PetscFunctionReturn(PETSC_SUCCESS);
9578: }
9580: /*@
9581: MatIsHermitianKnown - Checks if a matrix knows if it is Hermitian or not and its Hermitian state
9583: Not Collective
9585: Input Parameter:
9586: . A - the matrix to check
9588: Output Parameters:
9589: + set - `PETSC_TRUE` if the matrix knows its Hermitian state (this tells you if the next flag is valid)
9590: - flg - the result (only valid if set is `PETSC_TRUE`)
9592: Level: advanced
9594: Notes:
9595: Does not check the matrix values directly, so this may return unknown (set = `PETSC_FALSE`). Use `MatIsHermitian()`
9596: if you want it explicitly checked
9598: One can declare that a matrix is Hermitian with `MatSetOption`(mat,`MAT_HERMITIAN`,`PETSC_TRUE`) and if it is known to remain Hermitian
9599: after changes to the matrices values one can call `MatSetOption`(mat,`MAT_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9601: .seealso: [](ch_matrices), `Mat`, `MAT_SYMMETRY_ETERNAL`, `MAT_HERMITIAN`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`
9602: @*/
9603: PetscErrorCode MatIsHermitianKnown(Mat A, PetscBool *set, PetscBool *flg)
9604: {
9605: PetscFunctionBegin;
9607: PetscAssertPointer(set, 2);
9608: PetscAssertPointer(flg, 3);
9609: if (A->hermitian != PETSC_BOOL3_UNKNOWN) {
9610: *set = PETSC_TRUE;
9611: *flg = PetscBool3ToBool(A->hermitian);
9612: } else *set = PETSC_FALSE;
9613: PetscFunctionReturn(PETSC_SUCCESS);
9614: }
9616: /*@
9617: MatIsStructurallySymmetric - Test whether a matrix is structurally symmetric
9619: Collective
9621: Input Parameter:
9622: . A - the matrix to test
9624: Output Parameter:
9625: . flg - the result
9627: Level: intermediate
9629: Notes:
9630: If the matrix does yet know it is structurally symmetric this can be an expensive operation, also available `MatIsStructurallySymmetricKnown()`
9632: 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
9633: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9635: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MAT_STRUCTURAL_SYMMETRY_ETERNAL`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsSymmetric()`, `MatSetOption()`, `MatIsStructurallySymmetricKnown()`
9636: @*/
9637: PetscErrorCode MatIsStructurallySymmetric(Mat A, PetscBool *flg)
9638: {
9639: PetscFunctionBegin;
9641: PetscAssertPointer(flg, 2);
9642: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) *flg = PetscBool3ToBool(A->structurally_symmetric);
9643: else {
9644: PetscUseTypeMethod(A, isstructurallysymmetric, flg);
9645: PetscCall(MatSetOption(A, MAT_STRUCTURALLY_SYMMETRIC, *flg));
9646: }
9647: PetscFunctionReturn(PETSC_SUCCESS);
9648: }
9650: /*@
9651: MatIsStructurallySymmetricKnown - Checks if a matrix knows if it is structurally symmetric or not and its structurally symmetric state
9653: Not Collective
9655: Input Parameter:
9656: . A - the matrix to check
9658: Output Parameters:
9659: + set - PETSC_TRUE if the matrix knows its structurally symmetric state (this tells you if the next flag is valid)
9660: - flg - the result (only valid if set is PETSC_TRUE)
9662: Level: advanced
9664: Notes:
9665: 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
9666: symmetric after changes to the matrices values one can call `MatSetOption`(mat,`MAT_STRUCTURAL_SYMMETRY_ETERNAL`,`PETSC_TRUE`)
9668: Use `MatIsStructurallySymmetric()` to explicitly check if a matrix is structurally symmetric (this is an expensive operation)
9670: .seealso: [](ch_matrices), `Mat`, `MAT_STRUCTURALLY_SYMMETRIC`, `MatTranspose()`, `MatIsTranspose()`, `MatIsHermitian()`, `MatIsStructurallySymmetric()`, `MatSetOption()`, `MatIsSymmetric()`, `MatIsHermitianKnown()`
9671: @*/
9672: PetscErrorCode MatIsStructurallySymmetricKnown(Mat A, PetscBool *set, PetscBool *flg)
9673: {
9674: PetscFunctionBegin;
9676: PetscAssertPointer(set, 2);
9677: PetscAssertPointer(flg, 3);
9678: if (A->structurally_symmetric != PETSC_BOOL3_UNKNOWN) {
9679: *set = PETSC_TRUE;
9680: *flg = PetscBool3ToBool(A->structurally_symmetric);
9681: } else *set = PETSC_FALSE;
9682: PetscFunctionReturn(PETSC_SUCCESS);
9683: }
9685: /*@
9686: MatStashGetInfo - Gets how many values are currently in the matrix stash, i.e. need
9687: to be communicated to other processors during the `MatAssemblyBegin()`/`MatAssemblyEnd()` process
9689: Not Collective
9691: Input Parameter:
9692: . mat - the matrix
9694: Output Parameters:
9695: + nstash - the size of the stash
9696: . reallocs - the number of additional mallocs incurred.
9697: . bnstash - the size of the block stash
9698: - breallocs - the number of additional mallocs incurred.in the block stash
9700: Level: advanced
9702: .seealso: [](ch_matrices), `MatAssemblyBegin()`, `MatAssemblyEnd()`, `Mat`, `MatStashSetInitialSize()`
9703: @*/
9704: PetscErrorCode MatStashGetInfo(Mat mat, PetscInt *nstash, PetscInt *reallocs, PetscInt *bnstash, PetscInt *breallocs)
9705: {
9706: PetscFunctionBegin;
9707: PetscCall(MatStashGetInfo_Private(&mat->stash, nstash, reallocs));
9708: PetscCall(MatStashGetInfo_Private(&mat->bstash, bnstash, breallocs));
9709: PetscFunctionReturn(PETSC_SUCCESS);
9710: }
9712: /*@
9713: MatCreateVecs - Get vector(s) compatible with the matrix, i.e. with the same
9714: parallel layout, `PetscLayout` for rows and columns
9716: Collective
9718: Input Parameter:
9719: . mat - the matrix
9721: Output Parameters:
9722: + right - (optional) vector that the matrix can be multiplied against
9723: - left - (optional) vector that the matrix vector product can be stored in
9725: Options Database Key:
9726: . -mat_vec_type type - set the `VecType` of the created vectors during `MatSetFromOptions()`
9728: Level: advanced
9730: Notes:
9731: 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()`.
9733: The `VecType` of the created vectors is determined by the `MatType` of `mat`. This can be overridden by using `MatSetVecType()` or the option `-mat_vec_type`.
9735: These are new vectors which are not owned by the `mat`, they should be destroyed with `VecDestroy()` when no longer needed.
9737: .seealso: [](ch_matrices), `Mat`, `Vec`, `VecCreate()`, `VecDestroy()`, `DMCreateGlobalVector()`, `MatSetVecType()`
9738: @*/
9739: PetscErrorCode MatCreateVecs(Mat mat, Vec *right, Vec *left)
9740: {
9741: PetscFunctionBegin;
9744: if (mat->ops->getvecs) {
9745: PetscUseTypeMethod(mat, getvecs, right, left);
9746: } else {
9747: if (right) {
9748: PetscCheck(mat->cmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for columns not yet setup");
9749: PetscCall(VecCreateWithLayout_Private(mat->cmap, right));
9750: PetscCall(VecSetType(*right, mat->defaultvectype));
9751: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9752: if (mat->boundtocpu && mat->bindingpropagates) {
9753: PetscCall(VecSetBindingPropagates(*right, PETSC_TRUE));
9754: PetscCall(VecBindToCPU(*right, PETSC_TRUE));
9755: }
9756: #endif
9757: }
9758: if (left) {
9759: PetscCheck(mat->rmap->n >= 0, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "PetscLayout for rows not yet setup");
9760: PetscCall(VecCreateWithLayout_Private(mat->rmap, left));
9761: PetscCall(VecSetType(*left, mat->defaultvectype));
9762: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
9763: if (mat->boundtocpu && mat->bindingpropagates) {
9764: PetscCall(VecSetBindingPropagates(*left, PETSC_TRUE));
9765: PetscCall(VecBindToCPU(*left, PETSC_TRUE));
9766: }
9767: #endif
9768: }
9769: }
9770: PetscFunctionReturn(PETSC_SUCCESS);
9771: }
9773: /*@
9774: MatFactorInfoInitialize - Initializes a `MatFactorInfo` data structure
9775: with default values.
9777: Not Collective
9779: Input Parameter:
9780: . info - the `MatFactorInfo` data structure
9782: Level: developer
9784: Notes:
9785: The solvers are generally used through the `KSP` and `PC` objects, for example
9786: `PCLU`, `PCILU`, `PCCHOLESKY`, `PCICC`
9788: Once the data structure is initialized one may change certain entries as desired for the particular factorization to be performed
9790: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorInfo`
9791: @*/
9792: PetscErrorCode MatFactorInfoInitialize(MatFactorInfo *info)
9793: {
9794: PetscFunctionBegin;
9795: PetscCall(PetscMemzero(info, sizeof(MatFactorInfo)));
9796: PetscFunctionReturn(PETSC_SUCCESS);
9797: }
9799: /*@
9800: MatFactorSetSchurIS - Set indices corresponding to the Schur complement you wish to have computed
9802: Collective
9804: Input Parameters:
9805: + mat - the factored matrix
9806: - is - the index set defining the Schur indices (0-based)
9808: Level: advanced
9810: Notes:
9811: Call `MatFactorSolveSchurComplement()` or `MatFactorSolveSchurComplementTranspose()` after this call to solve a Schur complement system.
9813: You can call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` after this call.
9815: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9817: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorGetSchurComplement()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSolveSchurComplement()`,
9818: `MatFactorSolveSchurComplementTranspose()`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9819: @*/
9820: PetscErrorCode MatFactorSetSchurIS(Mat mat, IS is)
9821: {
9822: PetscErrorCode (*f)(Mat, IS);
9824: PetscFunctionBegin;
9829: PetscCheckSameComm(mat, 1, is, 2);
9830: PetscCheck(mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Only for factored matrix");
9831: PetscCall(PetscObjectQueryFunction((PetscObject)mat, "MatFactorSetSchurIS_C", &f));
9832: PetscCheck(f, PetscObjectComm((PetscObject)mat), PETSC_ERR_SUP, "The selected MatSolverType does not support Schur complement computation. You should use MATSOLVERMUMPS or MATSOLVERMKL_PARDISO");
9833: PetscCall(MatDestroy(&mat->schur));
9834: PetscCall((*f)(mat, is));
9835: PetscCheck(mat->schur, PetscObjectComm((PetscObject)mat), PETSC_ERR_PLIB, "Schur complement has not been created");
9836: PetscFunctionReturn(PETSC_SUCCESS);
9837: }
9839: /*@
9840: MatFactorCreateSchurComplement - Create a Schur complement matrix object using Schur data computed during the factorization step
9842: Logically Collective
9844: Input Parameters:
9845: + F - the factored matrix obtained by calling `MatGetFactor()`
9846: . S - location where to return the Schur complement, can be `NULL`
9847: - status - the status of the Schur complement matrix, can be `NULL`
9849: Level: advanced
9851: Notes:
9852: You must call `MatFactorSetSchurIS()` before calling this routine.
9854: This functionality is only supported for `MATSOLVERMUMPS` and `MATSOLVERMKL_PARDISO`
9856: The routine provides a copy of the Schur matrix stored within the solver data structures.
9857: The caller must destroy the object when it is no longer needed.
9858: If `MatFactorInvertSchurComplement()` has been called, the routine gets back the inverse.
9860: Use `MatFactorGetSchurComplement()` to get access to the Schur complement matrix inside the factored matrix instead of making a copy of it (which this function does)
9862: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9864: Developer Note:
9865: The reason this routine exists is because the representation of the Schur complement within the factor matrix may be different than a standard PETSc
9866: matrix representation and we normally do not want to use the time or memory to make a copy as a regular PETSc matrix.
9868: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorSchurStatus`, `MATSOLVERMUMPS`, `MATSOLVERMKL_PARDISO`
9869: @*/
9870: PetscErrorCode MatFactorCreateSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9871: {
9872: PetscFunctionBegin;
9874: if (S) PetscAssertPointer(S, 2);
9875: if (status) PetscAssertPointer(status, 3);
9876: if (S) {
9877: PetscErrorCode (*f)(Mat, Mat *);
9879: PetscCall(PetscObjectQueryFunction((PetscObject)F, "MatFactorCreateSchurComplement_C", &f));
9880: if (f) PetscCall((*f)(F, S));
9881: else PetscCall(MatDuplicate(F->schur, MAT_COPY_VALUES, S));
9882: }
9883: if (status) *status = F->schur_status;
9884: PetscFunctionReturn(PETSC_SUCCESS);
9885: }
9887: /*@
9888: MatFactorGetSchurComplement - Gets access to a Schur complement matrix using the current Schur data within a factored matrix
9890: Logically Collective
9892: Input Parameters:
9893: + F - the factored matrix obtained by calling `MatGetFactor()`
9894: . S - location where to return the Schur complement, can be `NULL`
9895: - status - the status of the Schur complement matrix, can be `NULL`
9897: Level: advanced
9899: Notes:
9900: You must call `MatFactorSetSchurIS()` before calling this routine.
9902: Schur complement mode is currently implemented for sequential matrices with factor type of `MATSOLVERMUMPS`
9904: The routine returns a the Schur Complement stored within the data structures of the solver.
9906: If `MatFactorInvertSchurComplement()` has previously been called, the returned matrix is actually the inverse of the Schur complement.
9908: The returned matrix should not be destroyed; the caller should call `MatFactorRestoreSchurComplement()` when the object is no longer needed.
9910: Use `MatFactorCreateSchurComplement()` to create a copy of the Schur complement matrix that is within a factored matrix
9912: See `MatCreateSchurComplement()` or `MatGetSchurComplement()` for ways to create virtual or approximate Schur complements.
9914: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorRestoreSchurComplement()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9915: @*/
9916: PetscErrorCode MatFactorGetSchurComplement(Mat F, Mat *S, MatFactorSchurStatus *status)
9917: {
9918: PetscFunctionBegin;
9920: if (S) {
9921: PetscAssertPointer(S, 2);
9922: *S = F->schur;
9923: }
9924: if (status) {
9925: PetscAssertPointer(status, 3);
9926: *status = F->schur_status;
9927: }
9928: PetscFunctionReturn(PETSC_SUCCESS);
9929: }
9931: static PetscErrorCode MatFactorUpdateSchurStatus_Private(Mat F)
9932: {
9933: Mat S = F->schur;
9935: PetscFunctionBegin;
9936: switch (F->schur_status) {
9937: case MAT_FACTOR_SCHUR_UNFACTORED: // fall-through
9938: case MAT_FACTOR_SCHUR_INVERTED:
9939: if (S) {
9940: S->ops->solve = NULL;
9941: S->ops->matsolve = NULL;
9942: S->ops->solvetranspose = NULL;
9943: S->ops->matsolvetranspose = NULL;
9944: S->ops->solveadd = NULL;
9945: S->ops->solvetransposeadd = NULL;
9946: S->factortype = MAT_FACTOR_NONE;
9947: PetscCall(PetscFree(S->solvertype));
9948: }
9949: case MAT_FACTOR_SCHUR_FACTORED: // fall-through
9950: break;
9951: default:
9952: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
9953: }
9954: PetscFunctionReturn(PETSC_SUCCESS);
9955: }
9957: /*@
9958: MatFactorRestoreSchurComplement - Restore the Schur complement matrix object obtained from a call to `MatFactorGetSchurComplement()`
9960: Logically Collective
9962: Input Parameters:
9963: + F - the factored matrix obtained by calling `MatGetFactor()`
9964: . S - location where the Schur complement is stored
9965: - status - the status of the Schur complement matrix (see `MatFactorSchurStatus`)
9967: Level: advanced
9969: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorCreateSchurComplement()`, `MatFactorSchurStatus`
9970: @*/
9971: PetscErrorCode MatFactorRestoreSchurComplement(Mat F, Mat *S, MatFactorSchurStatus status)
9972: {
9973: PetscFunctionBegin;
9975: if (S) {
9977: *S = NULL;
9978: }
9979: F->schur_status = status;
9980: PetscCall(MatFactorUpdateSchurStatus_Private(F));
9981: PetscFunctionReturn(PETSC_SUCCESS);
9982: }
9984: /*@
9985: MatFactorSolveSchurComplementTranspose - Solve the transpose of the Schur complement system computed during the factorization step
9987: Logically Collective
9989: Input Parameters:
9990: + F - the factored matrix obtained by calling `MatGetFactor()`
9991: . rhs - location where the right-hand side of the Schur complement system is stored
9992: - sol - location where the solution of the Schur complement system has to be returned
9994: Level: advanced
9996: Notes:
9997: The sizes of the vectors should match the size of the Schur complement
9999: Must be called after `MatFactorSetSchurIS()`
10001: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplement()`
10002: @*/
10003: PetscErrorCode MatFactorSolveSchurComplementTranspose(Mat F, Vec rhs, Vec sol)
10004: {
10005: PetscFunctionBegin;
10012: PetscCheckSameComm(F, 1, rhs, 2);
10013: PetscCheckSameComm(F, 1, sol, 3);
10014: PetscCall(MatFactorFactorizeSchurComplement(F));
10015: switch (F->schur_status) {
10016: case MAT_FACTOR_SCHUR_FACTORED:
10017: PetscCall(MatSolveTranspose(F->schur, rhs, sol));
10018: break;
10019: case MAT_FACTOR_SCHUR_INVERTED:
10020: PetscCall(MatMultTranspose(F->schur, rhs, sol));
10021: break;
10022: default:
10023: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10024: }
10025: PetscFunctionReturn(PETSC_SUCCESS);
10026: }
10028: /*@
10029: MatFactorSolveSchurComplement - Solve the Schur complement system computed during the factorization step
10031: Logically Collective
10033: Input Parameters:
10034: + F - the factored matrix obtained by calling `MatGetFactor()`
10035: . rhs - location where the right-hand side of the Schur complement system is stored
10036: - sol - location where the solution of the Schur complement system has to be returned
10038: Level: advanced
10040: Notes:
10041: The sizes of the vectors should match the size of the Schur complement
10043: Must be called after `MatFactorSetSchurIS()`
10045: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorSolveSchurComplementTranspose()`
10046: @*/
10047: PetscErrorCode MatFactorSolveSchurComplement(Mat F, Vec rhs, Vec sol)
10048: {
10049: PetscFunctionBegin;
10056: PetscCheckSameComm(F, 1, rhs, 2);
10057: PetscCheckSameComm(F, 1, sol, 3);
10058: PetscCall(MatFactorFactorizeSchurComplement(F));
10059: switch (F->schur_status) {
10060: case MAT_FACTOR_SCHUR_FACTORED:
10061: PetscCall(MatSolve(F->schur, rhs, sol));
10062: break;
10063: case MAT_FACTOR_SCHUR_INVERTED:
10064: PetscCall(MatMult(F->schur, rhs, sol));
10065: break;
10066: default:
10067: SETERRQ(PetscObjectComm((PetscObject)F), PETSC_ERR_SUP, "Unhandled MatFactorSchurStatus %d", F->schur_status);
10068: }
10069: PetscFunctionReturn(PETSC_SUCCESS);
10070: }
10072: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseInvertFactors_Private(Mat);
10073: #if PetscDefined(HAVE_CUDA)
10074: PETSC_SINGLE_LIBRARY_INTERN PetscErrorCode MatSeqDenseCUDAInvertFactors_Internal(Mat);
10075: #endif
10077: /* Schur status updated in the interface */
10078: static PetscErrorCode MatFactorInvertSchurComplement_Private(Mat F)
10079: {
10080: Mat S = F->schur;
10082: PetscFunctionBegin;
10083: if (S) {
10084: PetscMPIInt size;
10085: PetscBool isdense, isdensecuda;
10087: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)S), &size));
10088: PetscCheck(size <= 1, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not yet implemented");
10089: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSE, &isdense));
10090: PetscCall(PetscObjectTypeCompare((PetscObject)S, MATSEQDENSECUDA, &isdensecuda));
10091: PetscCheck(isdense || isdensecuda, PetscObjectComm((PetscObject)S), PETSC_ERR_SUP, "Not implemented for type %s", ((PetscObject)S)->type_name);
10092: PetscCall(PetscLogEventBegin(MAT_FactorInvS, F, 0, 0, 0));
10093: if (isdense) {
10094: PetscCall(MatSeqDenseInvertFactors_Private(S));
10095: } else if (isdensecuda) {
10096: #if defined(PETSC_HAVE_CUDA)
10097: PetscCall(MatSeqDenseCUDAInvertFactors_Internal(S));
10098: #endif
10099: }
10100: // HIP??????????????
10101: PetscCall(PetscLogEventEnd(MAT_FactorInvS, F, 0, 0, 0));
10102: }
10103: PetscFunctionReturn(PETSC_SUCCESS);
10104: }
10106: /*@
10107: MatFactorInvertSchurComplement - Invert the Schur complement matrix computed during the factorization step
10109: Logically Collective
10111: Input Parameter:
10112: . F - the factored matrix obtained by calling `MatGetFactor()`
10114: Level: advanced
10116: Notes:
10117: Must be called after `MatFactorSetSchurIS()`.
10119: Call `MatFactorGetSchurComplement()` or `MatFactorCreateSchurComplement()` AFTER this call to actually compute the inverse and get access to it.
10121: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorGetSchurComplement()`, `MatFactorCreateSchurComplement()`
10122: @*/
10123: PetscErrorCode MatFactorInvertSchurComplement(Mat F)
10124: {
10125: PetscFunctionBegin;
10128: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED) PetscFunctionReturn(PETSC_SUCCESS);
10129: PetscCall(MatFactorFactorizeSchurComplement(F));
10130: PetscCall(MatFactorInvertSchurComplement_Private(F));
10131: F->schur_status = MAT_FACTOR_SCHUR_INVERTED;
10132: PetscFunctionReturn(PETSC_SUCCESS);
10133: }
10135: /*@
10136: MatFactorFactorizeSchurComplement - Factorize the Schur complement matrix computed during the factorization step
10138: Logically Collective
10140: Input Parameter:
10141: . F - the factored matrix obtained by calling `MatGetFactor()`
10143: Level: advanced
10145: Note:
10146: Must be called after `MatFactorSetSchurIS()`
10148: .seealso: [](ch_matrices), `Mat`, `MatGetFactor()`, `MatFactorSetSchurIS()`, `MatFactorInvertSchurComplement()`
10149: @*/
10150: PetscErrorCode MatFactorFactorizeSchurComplement(Mat F)
10151: {
10152: MatFactorInfo info;
10154: PetscFunctionBegin;
10157: if (F->schur_status == MAT_FACTOR_SCHUR_INVERTED || F->schur_status == MAT_FACTOR_SCHUR_FACTORED) PetscFunctionReturn(PETSC_SUCCESS);
10158: PetscCall(PetscLogEventBegin(MAT_FactorFactS, F, 0, 0, 0));
10159: PetscCall(PetscMemzero(&info, sizeof(MatFactorInfo)));
10160: if (F->factortype == MAT_FACTOR_CHOLESKY) { /* LDL^t regarded as Cholesky */
10161: PetscCall(MatCholeskyFactor(F->schur, NULL, &info));
10162: } else {
10163: PetscCall(MatLUFactor(F->schur, NULL, NULL, &info));
10164: }
10165: PetscCall(PetscLogEventEnd(MAT_FactorFactS, F, 0, 0, 0));
10166: F->schur_status = MAT_FACTOR_SCHUR_FACTORED;
10167: PetscFunctionReturn(PETSC_SUCCESS);
10168: }
10170: /*@
10171: MatPtAP - Creates the matrix product $C = P^T * A * P$
10173: Neighbor-wise Collective
10175: Input Parameters:
10176: + A - the matrix
10177: . P - the projection matrix
10178: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10179: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(P)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10180: if the result is a dense matrix this is irrelevant
10182: Output Parameter:
10183: . C - the product matrix
10185: Level: intermediate
10187: Notes:
10188: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10190: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_PtAP`
10191: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10193: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10195: Developer Note:
10196: For matrix types without special implementation the function fallbacks to `MatMatMult()` followed by `MatTransposeMatMult()`.
10198: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatRARt()`
10199: @*/
10200: PetscErrorCode MatPtAP(Mat A, Mat P, MatReuse scall, PetscReal fill, Mat *C)
10201: {
10202: PetscFunctionBegin;
10203: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10204: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10206: if (scall == MAT_INITIAL_MATRIX) {
10207: PetscCall(MatProductCreate(A, P, NULL, C));
10208: PetscCall(MatProductSetType(*C, MATPRODUCT_PtAP));
10209: PetscCall(MatProductSetAlgorithm(*C, "default"));
10210: PetscCall(MatProductSetFill(*C, fill));
10212: (*C)->product->api_user = PETSC_TRUE;
10213: PetscCall(MatProductSetFromOptions(*C));
10214: PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "MatProduct %s not supported for A %s and P %s", MatProductTypes[MATPRODUCT_PtAP], ((PetscObject)A)->type_name, ((PetscObject)P)->type_name);
10215: PetscCall(MatProductSymbolic(*C));
10216: } else { /* scall == MAT_REUSE_MATRIX */
10217: PetscCall(MatProductReplaceMats(A, P, NULL, *C));
10218: }
10220: PetscCall(MatProductNumeric(*C));
10221: if (A->symmetric == PETSC_BOOL3_TRUE) {
10222: PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10223: (*C)->spd = A->spd;
10224: }
10225: PetscFunctionReturn(PETSC_SUCCESS);
10226: }
10228: /*@
10229: MatRARt - Creates the matrix product $C = R * A * R^T$
10231: Neighbor-wise Collective
10233: Input Parameters:
10234: + A - the matrix
10235: . R - the projection matrix
10236: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10237: - fill - expected fill as ratio of nnz(C)/nnz(A), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10238: if the result is a dense matrix this is irrelevant
10240: Output Parameter:
10241: . C - the product matrix
10243: Level: intermediate
10245: Notes:
10246: `C` will be created and must be destroyed by the user with `MatDestroy()`.
10248: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_RARt`
10249: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10251: This routine is currently only implemented for pairs of `MATAIJ` matrices and classes
10252: which inherit from `MATAIJ`. Due to PETSc sparse matrix block row distribution among processes,
10253: the parallel `MatRARt()` is implemented computing the explicit transpose of `R`, which can be very expensive.
10254: We recommend using `MatPtAP()` when possible.
10256: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10258: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MatMatMult()`, `MatPtAP()`
10259: @*/
10260: PetscErrorCode MatRARt(Mat A, Mat R, MatReuse scall, PetscReal fill, Mat *C)
10261: {
10262: PetscFunctionBegin;
10263: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*C, 5);
10264: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10266: if (scall == MAT_INITIAL_MATRIX) {
10267: PetscCall(MatProductCreate(A, R, NULL, C));
10268: PetscCall(MatProductSetType(*C, MATPRODUCT_RARt));
10269: PetscCall(MatProductSetAlgorithm(*C, "default"));
10270: PetscCall(MatProductSetFill(*C, fill));
10272: (*C)->product->api_user = PETSC_TRUE;
10273: PetscCall(MatProductSetFromOptions(*C));
10274: PetscCheck((*C)->ops->productsymbolic, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "MatProduct %s not supported for A %s and R %s", MatProductTypes[MATPRODUCT_RARt], ((PetscObject)A)->type_name, ((PetscObject)R)->type_name);
10275: PetscCall(MatProductSymbolic(*C));
10276: } else { /* scall == MAT_REUSE_MATRIX */
10277: PetscCall(MatProductReplaceMats(A, R, NULL, *C));
10278: }
10280: PetscCall(MatProductNumeric(*C));
10281: if (A->symmetric == PETSC_BOOL3_TRUE) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10282: PetscFunctionReturn(PETSC_SUCCESS);
10283: }
10285: static PetscErrorCode MatProduct_Private(Mat A, Mat B, MatReuse scall, PetscReal fill, MatProductType ptype, Mat *C)
10286: {
10287: PetscBool flg = PETSC_TRUE;
10289: PetscFunctionBegin;
10290: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "MAT_INPLACE_MATRIX product not supported");
10291: if (scall == MAT_INITIAL_MATRIX) {
10292: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_INITIAL_MATRIX and product type %s\n", MatProductTypes[ptype]));
10293: PetscCall(MatProductCreate(A, B, NULL, C));
10294: PetscCall(MatProductSetAlgorithm(*C, MATPRODUCTALGORITHMDEFAULT));
10295: PetscCall(MatProductSetFill(*C, fill));
10296: } else { /* scall == MAT_REUSE_MATRIX */
10297: Mat_Product *product = (*C)->product;
10299: PetscCall(PetscObjectBaseTypeCompareAny((PetscObject)*C, &flg, MATSEQDENSE, MATMPIDENSE, ""));
10300: if (flg && product && product->type != ptype) {
10301: PetscCall(MatProductClear(*C));
10302: product = NULL;
10303: }
10304: PetscCall(PetscInfo(A, "Calling MatProduct API with MAT_REUSE_MATRIX %s product present and product type %s\n", product ? "with" : "without", MatProductTypes[ptype]));
10305: if (!product) { /* user provide the dense matrix *C without calling MatProductCreate() or reusing it from previous calls */
10306: PetscCheck(flg, PetscObjectComm((PetscObject)*C), PETSC_ERR_SUP, "Call MatProductCreate() first");
10307: PetscCall(MatProductCreate_Private(A, B, NULL, *C));
10308: product = (*C)->product;
10309: product->fill = fill;
10310: product->clear = PETSC_TRUE;
10311: } else { /* user may change input matrices A or B when MAT_REUSE_MATRIX */
10312: flg = PETSC_FALSE;
10313: PetscCall(MatProductReplaceMats(A, B, NULL, *C));
10314: }
10315: }
10316: if (flg) {
10317: (*C)->product->api_user = PETSC_TRUE;
10318: PetscCall(MatProductSetType(*C, ptype));
10319: PetscCall(MatProductSetFromOptions(*C));
10320: PetscCall(MatProductSymbolic(*C));
10321: }
10322: PetscCall(MatProductNumeric(*C));
10323: PetscFunctionReturn(PETSC_SUCCESS);
10324: }
10326: /*@
10327: MatMatMult - Performs matrix-matrix multiplication $ C=A*B $.
10329: Neighbor-wise Collective
10331: Input Parameters:
10332: + A - the left matrix
10333: . B - the right matrix
10334: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10335: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10336: if the result is a dense matrix this is irrelevant
10338: Output Parameter:
10339: . C - the product matrix
10341: Notes:
10342: Unless scall is `MAT_REUSE_MATRIX` C will be created.
10344: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call and C was obtained from a previous
10345: call to this function with `MAT_INITIAL_MATRIX`.
10347: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value actually needed.
10349: In the special case where matrix `B` (and hence `C`) are dense you can create the correctly sized matrix `C` yourself and then call this routine with `MAT_REUSE_MATRIX`,
10350: rather than first having `MatMatMult()` create it for you. You can NEVER do this if the matrix `C` is sparse.
10352: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10354: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AB`
10355: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10357: Example of Usage:
10358: .vb
10359: MatProductCreate(A,B,NULL,&C);
10360: MatProductSetType(C,MATPRODUCT_AB);
10361: MatProductSymbolic(C);
10362: MatProductNumeric(C); // compute C=A * B
10363: MatProductReplaceMats(A1,B1,NULL,C); // compute C=A1 * B1
10364: MatProductNumeric(C);
10365: MatProductReplaceMats(A2,NULL,NULL,C); // compute C=A2 * B1
10366: MatProductNumeric(C);
10367: .ve
10369: Level: intermediate
10371: .seealso: [](ch_matrices), `Mat`, `MatProductType`, `MATPRODUCT_AB`, `MatTransposeMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`, `MatProductCreate()`, `MatProductSymbolic()`, `MatProductReplaceMats()`, `MatProductNumeric()`
10372: @*/
10373: PetscErrorCode MatMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10374: {
10375: PetscFunctionBegin;
10376: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AB, C));
10377: PetscFunctionReturn(PETSC_SUCCESS);
10378: }
10380: /*@
10381: MatMatTransposeMult - Performs matrix-matrix multiplication $C = A*B^T$.
10383: Neighbor-wise Collective
10385: Input Parameters:
10386: + A - the left matrix
10387: . B - the right matrix
10388: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10389: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10391: Output Parameter:
10392: . C - the product matrix
10394: Options Database Key:
10395: . -matmattransmult_mpidense_mpidense_via {allgatherv,cyclic} - Choose between algorithms for `MATMPIDENSE` matrices: the
10396: first redundantly copies the transposed `B` matrix on each process and requires O(log P) communication complexity;
10397: the second never stores more than one portion of the `B` matrix at a time but requires O(P) communication complexity.
10399: Level: intermediate
10401: Notes:
10402: C will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10404: `MAT_REUSE_MATRIX` can only be used if the matrices A and B have the same nonzero pattern as in the previous call
10406: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10407: actually needed.
10409: This routine is currently only implemented for pairs of `MATSEQAIJ` matrices, for the `MATSEQDENSE` class,
10410: and for pairs of `MATMPIDENSE` matrices.
10412: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABt`
10413: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10415: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10417: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABt`, `MatMatMult()`, `MatTransposeMatMult()`, `MatPtAP()`, `MatProductAlgorithm`, `MatProductType`
10418: @*/
10419: PetscErrorCode MatMatTransposeMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10420: {
10421: PetscFunctionBegin;
10422: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_ABt, C));
10423: if (A == B) PetscCall(MatSetOption(*C, MAT_SYMMETRIC, PETSC_TRUE));
10424: PetscFunctionReturn(PETSC_SUCCESS);
10425: }
10427: /*@
10428: MatTransposeMatMult - Performs matrix-matrix multiplication $C = A^T*B$.
10430: Neighbor-wise Collective
10432: Input Parameters:
10433: + A - the left matrix
10434: . B - the right matrix
10435: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10436: - fill - expected fill as ratio of nnz(C)/(nnz(A) + nnz(B)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if not known
10438: Output Parameter:
10439: . C - the product matrix
10441: Level: intermediate
10443: Notes:
10444: `C` will be created if `MAT_INITIAL_MATRIX` and must be destroyed by the user with `MatDestroy()`.
10446: `MAT_REUSE_MATRIX` can only be used if `A` and `B` have the same nonzero pattern as in the previous call.
10448: This is a convenience routine that wraps the use of `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_AtB`
10449: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10451: To determine the correct fill value, run with -info and search for the string "Fill ratio" to see the value
10452: actually needed.
10454: This routine is currently implemented for pairs of `MATAIJ` matrices and pairs of `MATSEQDENSE` matrices and classes
10455: which inherit from `MATSEQAIJ`. `C` will be of the same type as the input matrices.
10457: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10459: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_AtB`, `MatMatMult()`, `MatMatTransposeMult()`, `MatPtAP()`
10460: @*/
10461: PetscErrorCode MatTransposeMatMult(Mat A, Mat B, MatReuse scall, PetscReal fill, Mat *C)
10462: {
10463: PetscFunctionBegin;
10464: PetscCall(MatProduct_Private(A, B, scall, fill, MATPRODUCT_AtB, C));
10465: PetscFunctionReturn(PETSC_SUCCESS);
10466: }
10468: /*@
10469: MatMatMatMult - Performs matrix-matrix-matrix multiplication D=A*B*C.
10471: Neighbor-wise Collective
10473: Input Parameters:
10474: + A - the left matrix
10475: . B - the middle matrix
10476: . C - the right matrix
10477: . scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10478: - fill - expected fill as ratio of nnz(D)/(nnz(A) + nnz(B)+nnz(C)), use `PETSC_DETERMINE` or `PETSC_CURRENT` if you do not have a good estimate
10479: if the result is a dense matrix this is irrelevant
10481: Output Parameter:
10482: . D - the product matrix
10484: Level: intermediate
10486: Notes:
10487: Unless `scall` is `MAT_REUSE_MATRIX` `D` will be created.
10489: `MAT_REUSE_MATRIX` can only be used if the matrices `A`, `B`, and `C` have the same nonzero pattern as in the previous call
10491: This is a convenience routine that wraps the use of the `MatProductCreate()` with a `MatProductType` of `MATPRODUCT_ABC`
10492: functionality into a single function call. For more involved matrix-matrix operations see `MatProductCreate()`.
10494: To determine the correct fill value, run with `-info` and search for the string "Fill ratio" to see the value
10495: actually needed.
10497: If you have many matrices with the same non-zero structure to multiply, you
10498: should use `MAT_REUSE_MATRIX` in all calls but the first
10500: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
10502: .seealso: [](ch_matrices), `Mat`, `MatProductCreate()`, `MATPRODUCT_ABC`, `MatMatMult`, `MatPtAP()`, `MatMatTransposeMult()`, `MatTransposeMatMult()`
10503: @*/
10504: PetscErrorCode MatMatMatMult(Mat A, Mat B, Mat C, MatReuse scall, PetscReal fill, Mat *D)
10505: {
10506: PetscFunctionBegin;
10507: if (scall == MAT_REUSE_MATRIX) MatCheckProduct(*D, 6);
10508: PetscCheck(scall != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
10510: if (scall == MAT_INITIAL_MATRIX) {
10511: PetscCall(MatProductCreate(A, B, C, D));
10512: PetscCall(MatProductSetType(*D, MATPRODUCT_ABC));
10513: PetscCall(MatProductSetAlgorithm(*D, "default"));
10514: PetscCall(MatProductSetFill(*D, fill));
10516: (*D)->product->api_user = PETSC_TRUE;
10517: PetscCall(MatProductSetFromOptions(*D));
10518: PetscCheck((*D)->ops->productsymbolic, PetscObjectComm((PetscObject)*D), PETSC_ERR_SUP, "MatProduct %s not supported for A %s, B %s and C %s", MatProductTypes[MATPRODUCT_ABC], ((PetscObject)A)->type_name, ((PetscObject)B)->type_name,
10519: ((PetscObject)C)->type_name);
10520: PetscCall(MatProductSymbolic(*D));
10521: } else { /* user may change input matrices when REUSE */
10522: PetscCall(MatProductReplaceMats(A, B, C, *D));
10523: }
10524: PetscCall(MatProductNumeric(*D));
10525: PetscFunctionReturn(PETSC_SUCCESS);
10526: }
10528: /*@
10529: MatCreateRedundantMatrix - Create redundant matrices and put them into processors of subcommunicators.
10531: Collective
10533: Input Parameters:
10534: + mat - the matrix
10535: . nsubcomm - the number of subcommunicators (= number of redundant parallel or sequential matrices)
10536: . subcomm - MPI communicator split from the communicator where mat resides in (or `MPI_COMM_NULL` if nsubcomm is used)
10537: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10539: Output Parameter:
10540: . matredundant - redundant matrix
10542: Level: advanced
10544: Notes:
10545: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the
10546: original matrix has not changed from that last call to `MatCreateRedundantMatrix()`.
10548: This routine creates the duplicated matrices in the subcommunicators; you should NOT create them before
10549: calling it.
10551: `PetscSubcommCreate()` can be used to manage the creation of the subcomm but need not be.
10553: .seealso: [](ch_matrices), `Mat`, `MatDestroy()`, `PetscSubcommCreate()`, `PetscSubcomm`
10554: @*/
10555: PetscErrorCode MatCreateRedundantMatrix(Mat mat, PetscInt nsubcomm, MPI_Comm subcomm, MatReuse reuse, Mat *matredundant)
10556: {
10557: MPI_Comm comm;
10558: PetscMPIInt size;
10559: PetscInt mloc_sub, nloc_sub, rstart, rend, M = mat->rmap->N, N = mat->cmap->N, bs = mat->rmap->bs;
10560: Mat_Redundant *redund = NULL;
10561: PetscSubcomm psubcomm = NULL;
10562: MPI_Comm subcomm_in = subcomm;
10563: Mat *matseq;
10564: IS isrow, iscol;
10565: PetscBool newsubcomm = PETSC_FALSE;
10567: PetscFunctionBegin;
10569: if (nsubcomm && reuse == MAT_REUSE_MATRIX) {
10570: PetscAssertPointer(*matredundant, 5);
10572: }
10574: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
10575: if (size == 1 || nsubcomm == 1) {
10576: if (reuse == MAT_INITIAL_MATRIX) {
10577: PetscCall(MatDuplicate(mat, MAT_COPY_VALUES, matredundant));
10578: } else {
10579: PetscCheck(*matredundant != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10580: PetscCall(MatCopy(mat, *matredundant, SAME_NONZERO_PATTERN));
10581: }
10582: PetscFunctionReturn(PETSC_SUCCESS);
10583: }
10585: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10586: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10587: MatCheckPreallocated(mat, 1);
10589: PetscCall(PetscLogEventBegin(MAT_RedundantMat, mat, 0, 0, 0));
10590: if (subcomm_in == MPI_COMM_NULL && reuse == MAT_INITIAL_MATRIX) { /* get subcomm if user does not provide subcomm */
10591: /* create psubcomm, then get subcomm */
10592: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
10593: PetscCallMPI(MPI_Comm_size(comm, &size));
10594: PetscCheck(nsubcomm >= 1 && nsubcomm <= size, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "nsubcomm must between 1 and %d", size);
10596: PetscCall(PetscSubcommCreate(comm, &psubcomm));
10597: PetscCall(PetscSubcommSetNumber(psubcomm, nsubcomm));
10598: PetscCall(PetscSubcommSetType(psubcomm, PETSC_SUBCOMM_CONTIGUOUS));
10599: PetscCall(PetscSubcommSetFromOptions(psubcomm));
10600: PetscCall(PetscCommDuplicate(PetscSubcommChild(psubcomm), &subcomm, NULL));
10601: newsubcomm = PETSC_TRUE;
10602: PetscCall(PetscSubcommDestroy(&psubcomm));
10603: }
10605: /* get isrow, iscol and a local sequential matrix matseq[0] */
10606: if (reuse == MAT_INITIAL_MATRIX) {
10607: mloc_sub = PETSC_DECIDE;
10608: nloc_sub = PETSC_DECIDE;
10609: if (bs < 1) {
10610: PetscCall(PetscSplitOwnership(subcomm, &mloc_sub, &M));
10611: PetscCall(PetscSplitOwnership(subcomm, &nloc_sub, &N));
10612: } else {
10613: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &mloc_sub, &M));
10614: PetscCall(PetscSplitOwnershipBlock(subcomm, bs, &nloc_sub, &N));
10615: }
10616: PetscCallMPI(MPI_Scan(&mloc_sub, &rend, 1, MPIU_INT, MPI_SUM, subcomm));
10617: rstart = rend - mloc_sub;
10618: PetscCall(ISCreateStride(PETSC_COMM_SELF, mloc_sub, rstart, 1, &isrow));
10619: PetscCall(ISCreateStride(PETSC_COMM_SELF, N, 0, 1, &iscol));
10620: PetscCall(ISSetIdentity(iscol));
10621: } else { /* reuse == MAT_REUSE_MATRIX */
10622: PetscCheck(*matredundant != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10623: /* retrieve subcomm */
10624: PetscCall(PetscObjectGetComm((PetscObject)*matredundant, &subcomm));
10625: redund = (*matredundant)->redundant;
10626: isrow = redund->isrow;
10627: iscol = redund->iscol;
10628: matseq = redund->matseq;
10629: }
10630: PetscCall(MatCreateSubMatrices(mat, 1, &isrow, &iscol, reuse, &matseq));
10632: /* get matredundant over subcomm */
10633: if (reuse == MAT_INITIAL_MATRIX) {
10634: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], nloc_sub, reuse, matredundant));
10636: /* create a supporting struct and attach it to C for reuse */
10637: PetscCall(PetscNew(&redund));
10638: (*matredundant)->redundant = redund;
10639: redund->isrow = isrow;
10640: redund->iscol = iscol;
10641: redund->matseq = matseq;
10642: if (newsubcomm) {
10643: redund->subcomm = subcomm;
10644: } else {
10645: redund->subcomm = MPI_COMM_NULL;
10646: }
10647: } else {
10648: PetscCall(MatCreateMPIMatConcatenateSeqMat(subcomm, matseq[0], PETSC_DECIDE, reuse, matredundant));
10649: }
10650: #if defined(PETSC_HAVE_VIENNACL) || defined(PETSC_HAVE_CUDA) || defined(PETSC_HAVE_HIP)
10651: if (matseq[0]->boundtocpu && matseq[0]->bindingpropagates) {
10652: PetscCall(MatBindToCPU(*matredundant, PETSC_TRUE));
10653: PetscCall(MatSetBindingPropagates(*matredundant, PETSC_TRUE));
10654: }
10655: #endif
10656: PetscCall(PetscLogEventEnd(MAT_RedundantMat, mat, 0, 0, 0));
10657: PetscFunctionReturn(PETSC_SUCCESS);
10658: }
10660: /*@C
10661: MatGetMultiProcBlock - Create multiple 'parallel submatrices' from
10662: a given `Mat`. Each submatrix can span multiple procs.
10664: Collective
10666: Input Parameters:
10667: + mat - the matrix
10668: . subComm - the sub communicator obtained as if by `MPI_Comm_split(PetscObjectComm((PetscObject)mat))`
10669: - scall - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
10671: Output Parameter:
10672: . subMat - parallel sub-matrices each spanning a given `subcomm`
10674: Level: advanced
10676: Notes:
10677: The submatrix partition across processors is dictated by `subComm` a
10678: communicator obtained by `MPI_comm_split()` or via `PetscSubcommCreate()`. The `subComm`
10679: is not restricted to be grouped with consecutive original MPI processes.
10681: Due the `MPI_Comm_split()` usage, the parallel layout of the submatrices
10682: map directly to the layout of the original matrix [wrt the local
10683: row,col partitioning]. So the original 'DiagonalMat' naturally maps
10684: into the 'DiagonalMat' of the `subMat`, hence it is used directly from
10685: the `subMat`. However the offDiagMat looses some columns - and this is
10686: reconstructed with `MatSetValues()`
10688: This is used by `PCBJACOBI` when a single block spans multiple MPI processes.
10690: .seealso: [](ch_matrices), `Mat`, `MatCreateRedundantMatrix()`, `MatCreateSubMatrices()`, `PCBJACOBI`
10691: @*/
10692: PetscErrorCode MatGetMultiProcBlock(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
10693: {
10694: PetscMPIInt commsize, subCommSize;
10696: PetscFunctionBegin;
10697: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &commsize));
10698: PetscCallMPI(MPI_Comm_size(subComm, &subCommSize));
10699: PetscCheck(subCommSize <= commsize, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_OUTOFRANGE, "CommSize %d < SubCommZize %d", commsize, subCommSize);
10701: PetscCheck(scall != MAT_REUSE_MATRIX || *subMat != mat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
10702: PetscCall(PetscLogEventBegin(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10703: PetscUseTypeMethod(mat, getmultiprocblock, subComm, scall, subMat);
10704: PetscCall(PetscLogEventEnd(MAT_GetMultiProcBlock, mat, 0, 0, 0));
10705: PetscFunctionReturn(PETSC_SUCCESS);
10706: }
10708: /*@
10709: MatGetLocalSubMatrix - Gets a reference to a submatrix specified in local numbering
10711: Not Collective
10713: Input Parameters:
10714: + mat - matrix to extract local submatrix from
10715: . isrow - local row indices for submatrix
10716: - iscol - local column indices for submatrix
10718: Output Parameter:
10719: . submat - the submatrix
10721: Level: intermediate
10723: Notes:
10724: `submat` should be disposed of with `MatRestoreLocalSubMatrix()`.
10726: Depending on the format of `mat`, the returned `submat` may not implement `MatMult()`. Its communicator may be
10727: the same as `mat`, it may be `PETSC_COMM_SELF`, or some other sub-communictor of `mat`'s.
10729: `submat` always implements `MatSetValuesLocal()`. If `isrow` and `iscol` have the same block size, then
10730: `MatSetValuesBlockedLocal()` will also be implemented.
10732: `mat` must have had a `ISLocalToGlobalMapping` provided to it with `MatSetLocalToGlobalMapping()`.
10733: Matrices obtained with `DMCreateMatrix()` generally already have the local to global mapping provided.
10735: .seealso: [](ch_matrices), `Mat`, `MatRestoreLocalSubMatrix()`, `MatCreateLocalRef()`, `MatSetLocalToGlobalMapping()`
10736: @*/
10737: PetscErrorCode MatGetLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10738: {
10739: PetscFunctionBegin;
10743: PetscCheckSameComm(isrow, 2, iscol, 3);
10744: PetscAssertPointer(submat, 4);
10745: PetscCheck(mat->rmap->mapping, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Matrix must have local to global mapping provided before this call");
10747: if (mat->ops->getlocalsubmatrix) {
10748: PetscUseTypeMethod(mat, getlocalsubmatrix, isrow, iscol, submat);
10749: } else {
10750: PetscCall(MatCreateLocalRef(mat, isrow, iscol, submat));
10751: }
10752: (*submat)->assembled = mat->assembled;
10753: PetscFunctionReturn(PETSC_SUCCESS);
10754: }
10756: /*@
10757: MatRestoreLocalSubMatrix - Restores a reference to a submatrix specified in local numbering obtained with `MatGetLocalSubMatrix()`
10759: Not Collective
10761: Input Parameters:
10762: + mat - matrix to extract local submatrix from
10763: . isrow - local row indices for submatrix
10764: . iscol - local column indices for submatrix
10765: - submat - the submatrix
10767: Level: intermediate
10769: .seealso: [](ch_matrices), `Mat`, `MatGetLocalSubMatrix()`
10770: @*/
10771: PetscErrorCode MatRestoreLocalSubMatrix(Mat mat, IS isrow, IS iscol, Mat *submat)
10772: {
10773: PetscFunctionBegin;
10777: PetscCheckSameComm(isrow, 2, iscol, 3);
10778: PetscAssertPointer(submat, 4);
10781: if (mat->ops->restorelocalsubmatrix) {
10782: PetscUseTypeMethod(mat, restorelocalsubmatrix, isrow, iscol, submat);
10783: } else {
10784: PetscCall(MatDestroy(submat));
10785: }
10786: *submat = NULL;
10787: PetscFunctionReturn(PETSC_SUCCESS);
10788: }
10790: /*@
10791: MatFindZeroDiagonals - Finds all the rows of a matrix that have zero or no diagonal entry in the matrix
10793: Collective
10795: Input Parameter:
10796: . mat - the matrix
10798: Output Parameter:
10799: . is - if any rows have zero diagonals this contains the list of them
10801: Level: developer
10803: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10804: @*/
10805: PetscErrorCode MatFindZeroDiagonals(Mat mat, IS *is)
10806: {
10807: PetscFunctionBegin;
10810: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10811: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10813: if (!mat->ops->findzerodiagonals) {
10814: Vec diag;
10815: const PetscScalar *a;
10816: PetscInt *rows;
10817: PetscInt rStart, rEnd, r, nrow = 0;
10819: PetscCall(MatCreateVecs(mat, &diag, NULL));
10820: PetscCall(MatGetDiagonal(mat, diag));
10821: PetscCall(MatGetOwnershipRange(mat, &rStart, &rEnd));
10822: PetscCall(VecGetArrayRead(diag, &a));
10823: for (r = 0; r < rEnd - rStart; ++r)
10824: if (a[r] == 0.0) ++nrow;
10825: PetscCall(PetscMalloc1(nrow, &rows));
10826: nrow = 0;
10827: for (r = 0; r < rEnd - rStart; ++r)
10828: if (a[r] == 0.0) rows[nrow++] = r + rStart;
10829: PetscCall(VecRestoreArrayRead(diag, &a));
10830: PetscCall(VecDestroy(&diag));
10831: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)mat), nrow, rows, PETSC_OWN_POINTER, is));
10832: } else {
10833: PetscUseTypeMethod(mat, findzerodiagonals, is);
10834: }
10835: PetscFunctionReturn(PETSC_SUCCESS);
10836: }
10838: /*@
10839: MatFindOffBlockDiagonalEntries - Finds all the rows of a matrix that have entries outside of the main diagonal block (defined by the matrix block size)
10841: Collective
10843: Input Parameter:
10844: . mat - the matrix
10846: Output Parameter:
10847: . is - contains the list of rows with off block diagonal entries
10849: Level: developer
10851: .seealso: [](ch_matrices), `Mat`, `MatMultTranspose()`, `MatMultAdd()`, `MatMultTransposeAdd()`
10852: @*/
10853: PetscErrorCode MatFindOffBlockDiagonalEntries(Mat mat, IS *is)
10854: {
10855: PetscFunctionBegin;
10858: PetscCheck(mat->assembled, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10859: PetscCheck(!mat->factortype, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10861: PetscUseTypeMethod(mat, findoffblockdiagonalentries, is);
10862: PetscFunctionReturn(PETSC_SUCCESS);
10863: }
10865: /*@C
10866: MatInvertBlockDiagonal - Inverts the block diagonal entries.
10868: Collective; No Fortran Support
10870: Input Parameter:
10871: . mat - the matrix
10873: Output Parameter:
10874: . values - the block inverses in column major order (FORTRAN-like)
10876: Level: advanced
10878: Notes:
10879: The size of the blocks is determined by the block size of the matrix.
10881: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10883: The blocks all have the same size, use `MatInvertVariableBlockDiagonal()` for variable block size
10885: .seealso: [](ch_matrices), `Mat`, `MatInvertVariableBlockEnvelope()`, `MatInvertBlockDiagonalMat()`
10886: @*/
10887: PetscErrorCode MatInvertBlockDiagonal(Mat mat, const PetscScalar *values[])
10888: {
10889: PetscFunctionBegin;
10891: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10892: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10893: PetscUseTypeMethod(mat, invertblockdiagonal, values);
10894: PetscFunctionReturn(PETSC_SUCCESS);
10895: }
10897: /*@
10898: MatInvertVariableBlockDiagonal - Inverts the point block diagonal entries.
10900: Collective; No Fortran Support
10902: Input Parameters:
10903: + mat - the matrix
10904: . nblocks - the number of blocks on the process, set with `MatSetVariableBlockSizes()`
10905: - bsizes - the size of each block on the process, set with `MatSetVariableBlockSizes()`
10907: Output Parameter:
10908: . values - the block inverses in column major order (FORTRAN-like)
10910: Level: advanced
10912: Notes:
10913: Use `MatInvertBlockDiagonal()` if all blocks have the same size
10915: The blocks never overlap between two MPI processes, use `MatInvertVariableBlockEnvelope()` for that case
10917: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`, `MatSetVariableBlockSizes()`, `MatInvertVariableBlockEnvelope()`
10918: @*/
10919: PetscErrorCode MatInvertVariableBlockDiagonal(Mat mat, PetscInt nblocks, const PetscInt bsizes[], PetscScalar values[])
10920: {
10921: PetscFunctionBegin;
10923: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
10924: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
10925: PetscUseTypeMethod(mat, invertvariableblockdiagonal, nblocks, bsizes, values);
10926: PetscFunctionReturn(PETSC_SUCCESS);
10927: }
10929: /*@
10930: MatInvertBlockDiagonalMat - set the values of matrix C to be the inverted block diagonal of matrix A
10932: Collective
10934: Input Parameters:
10935: + A - the matrix
10936: - C - matrix with inverted block diagonal of `A`. This matrix should be created and may have its type set.
10938: Level: advanced
10940: Note:
10941: The blocksize of the matrix is used to determine the blocks on the diagonal of `C`
10943: .seealso: [](ch_matrices), `Mat`, `MatInvertBlockDiagonal()`
10944: @*/
10945: PetscErrorCode MatInvertBlockDiagonalMat(Mat A, Mat C)
10946: {
10947: const PetscScalar *vals;
10948: PetscInt *dnnz;
10949: PetscInt m, rstart, rend, bs, i, j;
10951: PetscFunctionBegin;
10952: PetscCall(MatInvertBlockDiagonal(A, &vals));
10953: PetscCall(MatGetBlockSize(A, &bs));
10954: PetscCall(MatGetLocalSize(A, &m, NULL));
10955: PetscCall(MatSetLayouts(C, A->rmap, A->cmap));
10956: PetscCall(MatSetBlockSizes(C, A->rmap->bs, A->cmap->bs));
10957: PetscCall(PetscMalloc1(m / bs, &dnnz));
10958: for (j = 0; j < m / bs; j++) dnnz[j] = 1;
10959: PetscCall(MatXAIJSetPreallocation(C, bs, dnnz, NULL, NULL, NULL));
10960: PetscCall(PetscFree(dnnz));
10961: PetscCall(MatGetOwnershipRange(C, &rstart, &rend));
10962: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_FALSE));
10963: for (i = rstart / bs; i < rend / bs; i++) PetscCall(MatSetValuesBlocked(C, 1, &i, 1, &i, &vals[(i - rstart / bs) * bs * bs], INSERT_VALUES));
10964: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_TRUE));
10965: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
10966: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
10967: PetscCall(MatSetOption(C, MAT_NO_OFF_PROC_ENTRIES, PETSC_FALSE));
10968: PetscCall(MatSetOption(C, MAT_ROW_ORIENTED, PETSC_TRUE));
10969: PetscFunctionReturn(PETSC_SUCCESS);
10970: }
10972: /*@
10973: MatTransposeColoringDestroy - Destroys a coloring context for matrix product $C = A*B^T$ that was created
10974: via `MatTransposeColoringCreate()`.
10976: Collective
10978: Input Parameter:
10979: . c - coloring context
10981: Level: intermediate
10983: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`
10984: @*/
10985: PetscErrorCode MatTransposeColoringDestroy(MatTransposeColoring *c)
10986: {
10987: MatTransposeColoring matcolor = *c;
10989: PetscFunctionBegin;
10990: if (!matcolor) PetscFunctionReturn(PETSC_SUCCESS);
10991: if (--((PetscObject)matcolor)->refct > 0) {
10992: matcolor = NULL;
10993: PetscFunctionReturn(PETSC_SUCCESS);
10994: }
10996: PetscCall(PetscFree3(matcolor->ncolumns, matcolor->nrows, matcolor->colorforrow));
10997: PetscCall(PetscFree(matcolor->rows));
10998: PetscCall(PetscFree(matcolor->den2sp));
10999: PetscCall(PetscFree(matcolor->colorforcol));
11000: PetscCall(PetscFree(matcolor->columns));
11001: if (matcolor->brows > 0) PetscCall(PetscFree(matcolor->lstart));
11002: PetscCall(PetscHeaderDestroy(c));
11003: PetscFunctionReturn(PETSC_SUCCESS);
11004: }
11006: /*@
11007: MatTransColoringApplySpToDen - Given a symbolic matrix product $C = A*B^T$ for which
11008: a `MatTransposeColoring` context has been created, computes a dense $B^T$ by applying
11009: `MatTransposeColoring` to sparse `B`.
11011: Collective
11013: Input Parameters:
11014: + coloring - coloring context created with `MatTransposeColoringCreate()`
11015: - B - sparse matrix
11017: Output Parameter:
11018: . Btdense - dense matrix $B^T$
11020: Level: developer
11022: Note:
11023: These are used internally for some implementations of `MatRARt()`
11025: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplyDenToSp()`
11026: @*/
11027: PetscErrorCode MatTransColoringApplySpToDen(MatTransposeColoring coloring, Mat B, Mat Btdense)
11028: {
11029: PetscFunctionBegin;
11034: PetscCall((*B->ops->transcoloringapplysptoden)(coloring, B, Btdense));
11035: PetscFunctionReturn(PETSC_SUCCESS);
11036: }
11038: /*@
11039: MatTransColoringApplyDenToSp - Given a symbolic matrix product $C_{sp} = A*B^T$ for which
11040: a `MatTransposeColoring` context has been created and a dense matrix $C_{den} = A*B^T_{dense}$
11041: in which `B^T_{dens}` is obtained from `MatTransColoringApplySpToDen()`, recover sparse matrix
11042: $C_{sp}$ from $C_{den}$.
11044: Collective
11046: Input Parameters:
11047: + matcoloring - coloring context created with `MatTransposeColoringCreate()`
11048: - Cden - matrix product of a sparse matrix and a dense matrix Btdense
11050: Output Parameter:
11051: . Csp - sparse matrix
11053: Level: developer
11055: Note:
11056: These are used internally for some implementations of `MatRARt()`
11058: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringCreate()`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`
11059: @*/
11060: PetscErrorCode MatTransColoringApplyDenToSp(MatTransposeColoring matcoloring, Mat Cden, Mat Csp)
11061: {
11062: PetscFunctionBegin;
11067: PetscCall((*Csp->ops->transcoloringapplydentosp)(matcoloring, Cden, Csp));
11068: PetscCall(MatAssemblyBegin(Csp, MAT_FINAL_ASSEMBLY));
11069: PetscCall(MatAssemblyEnd(Csp, MAT_FINAL_ASSEMBLY));
11070: PetscFunctionReturn(PETSC_SUCCESS);
11071: }
11073: /*@
11074: MatTransposeColoringCreate - Creates a matrix coloring context for the matrix product $C = A*B^T$.
11076: Collective
11078: Input Parameters:
11079: + mat - the matrix product C
11080: - iscoloring - the coloring of the matrix; usually obtained with `MatColoringCreate()` or `DMCreateColoring()`
11082: Output Parameter:
11083: . color - the new coloring context
11085: Level: intermediate
11087: .seealso: [](ch_matrices), `Mat`, `MatTransposeColoringDestroy()`, `MatTransColoringApplySpToDen()`,
11088: `MatTransColoringApplyDenToSp()`
11089: @*/
11090: PetscErrorCode MatTransposeColoringCreate(Mat mat, ISColoring iscoloring, MatTransposeColoring *color)
11091: {
11092: MatTransposeColoring c;
11093: MPI_Comm comm;
11095: PetscFunctionBegin;
11096: PetscAssertPointer(color, 3);
11098: PetscCall(PetscLogEventBegin(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11099: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
11100: PetscCall(PetscHeaderCreate(c, MAT_TRANSPOSECOLORING_CLASSID, "MatTransposeColoring", "Matrix product C=A*B^T via coloring", "Mat", comm, MatTransposeColoringDestroy, NULL));
11101: c->ctype = iscoloring->ctype;
11102: PetscUseTypeMethod(mat, transposecoloringcreate, iscoloring, c);
11103: *color = c;
11104: PetscCall(PetscLogEventEnd(MAT_TransposeColoringCreate, mat, 0, 0, 0));
11105: PetscFunctionReturn(PETSC_SUCCESS);
11106: }
11108: /*@
11109: MatGetNonzeroState - Returns a 64-bit integer representing the current state of nonzeros in the matrix. If the
11110: matrix has had new nonzero locations added to (or removed from) the matrix since the previous call, the value will be larger.
11112: Not Collective
11114: Input Parameter:
11115: . mat - the matrix
11117: Output Parameter:
11118: . state - the current state
11120: Level: intermediate
11122: Notes:
11123: You can only compare states from two different calls to the SAME matrix, you cannot compare calls between
11124: different matrices
11126: Use `PetscObjectStateGet()` to check for changes to the numerical values in a matrix
11128: Use the result of `PetscObjectGetId()` to compare if a previously checked matrix is the same as the current matrix, do not compare object pointers.
11130: .seealso: [](ch_matrices), `Mat`, `PetscObjectStateGet()`, `PetscObjectGetId()`
11131: @*/
11132: PetscErrorCode MatGetNonzeroState(Mat mat, PetscObjectState *state)
11133: {
11134: PetscFunctionBegin;
11136: *state = mat->nonzerostate;
11137: PetscFunctionReturn(PETSC_SUCCESS);
11138: }
11140: /*@
11141: MatCreateMPIMatConcatenateSeqMat - Creates a single large PETSc matrix by concatenating sequential
11142: matrices from each processor
11144: Collective
11146: Input Parameters:
11147: + comm - the communicators the parallel matrix will live on
11148: . seqmat - the input sequential matrices
11149: . n - number of local columns (or `PETSC_DECIDE`)
11150: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11152: Output Parameter:
11153: . mpimat - the parallel matrix generated
11155: Level: developer
11157: Note:
11158: The number of columns of the matrix in EACH processor MUST be the same.
11160: .seealso: [](ch_matrices), `Mat`
11161: @*/
11162: PetscErrorCode MatCreateMPIMatConcatenateSeqMat(MPI_Comm comm, Mat seqmat, PetscInt n, MatReuse reuse, Mat *mpimat)
11163: {
11164: PetscMPIInt size;
11166: PetscFunctionBegin;
11167: PetscCallMPI(MPI_Comm_size(comm, &size));
11168: if (size == 1) {
11169: if (reuse == MAT_INITIAL_MATRIX) {
11170: PetscCall(MatDuplicate(seqmat, MAT_COPY_VALUES, mpimat));
11171: } else {
11172: PetscCall(MatCopy(seqmat, *mpimat, SAME_NONZERO_PATTERN));
11173: }
11174: PetscFunctionReturn(PETSC_SUCCESS);
11175: }
11177: PetscCheck(reuse != MAT_REUSE_MATRIX || seqmat != *mpimat, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MAT_REUSE_MATRIX means reuse the matrix passed in as the final argument, not the original matrix");
11179: PetscCall(PetscLogEventBegin(MAT_Merge, seqmat, 0, 0, 0));
11180: PetscCall((*seqmat->ops->creatempimatconcatenateseqmat)(comm, seqmat, n, reuse, mpimat));
11181: PetscCall(PetscLogEventEnd(MAT_Merge, seqmat, 0, 0, 0));
11182: PetscFunctionReturn(PETSC_SUCCESS);
11183: }
11185: /*@
11186: MatSubdomainsCreateCoalesce - Creates index subdomains by coalescing adjacent MPI processes' ownership ranges.
11188: Collective
11190: Input Parameters:
11191: + A - the matrix to create subdomains from
11192: - N - requested number of subdomains
11194: Output Parameters:
11195: + n - number of subdomains resulting on this MPI process
11196: - iss - `IS` list with indices of subdomains on this MPI process
11198: Level: advanced
11200: Note:
11201: The number of subdomains must be smaller than the communicator size
11203: .seealso: [](ch_matrices), `Mat`, `IS`
11204: @*/
11205: PetscErrorCode MatSubdomainsCreateCoalesce(Mat A, PetscInt N, PetscInt *n, IS *iss[])
11206: {
11207: MPI_Comm comm, subcomm;
11208: PetscMPIInt size, rank, color;
11209: PetscInt rstart, rend, k;
11211: PetscFunctionBegin;
11212: PetscCall(PetscObjectGetComm((PetscObject)A, &comm));
11213: PetscCallMPI(MPI_Comm_size(comm, &size));
11214: PetscCallMPI(MPI_Comm_rank(comm, &rank));
11215: PetscCheck(N >= 1 && N < size, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "number of subdomains must be > 0 and < %d, got N = %" PetscInt_FMT, size, N);
11216: *n = 1;
11217: k = size / N + (size % N > 0); /* There are up to k ranks to a color */
11218: color = rank / k;
11219: PetscCallMPI(MPI_Comm_split(comm, color, rank, &subcomm));
11220: PetscCall(PetscMalloc1(1, iss));
11221: PetscCall(MatGetOwnershipRange(A, &rstart, &rend));
11222: PetscCall(ISCreateStride(subcomm, rend - rstart, rstart, 1, iss[0]));
11223: PetscCallMPI(MPI_Comm_free(&subcomm));
11224: PetscFunctionReturn(PETSC_SUCCESS);
11225: }
11227: /*@
11228: MatGalerkin - Constructs the coarse grid problem matrix via Galerkin projection.
11230: If the interpolation and restriction operators are the same, uses `MatPtAP()`.
11231: If they are not the same, uses `MatMatMatMult()`.
11233: Once the coarse grid problem is constructed, correct for interpolation operators
11234: that are not of full rank, which can legitimately happen in the case of non-nested
11235: geometric multigrid.
11237: Input Parameters:
11238: + restrct - restriction operator
11239: . dA - fine grid matrix
11240: . interpolate - interpolation operator
11241: . reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
11242: - fill - expected fill, use `PETSC_DETERMINE` or `PETSC_DETERMINE` if you do not have a good estimate
11244: Output Parameter:
11245: . A - the Galerkin coarse matrix
11247: Options Database Key:
11248: . -pc_mg_galerkin (both|pmat|mat|none) - for what matrices the Galerkin process should be used
11250: Level: developer
11252: Note:
11253: The deprecated `PETSC_DEFAULT` in `fill` also means use the current value
11255: .seealso: [](ch_matrices), `Mat`, `MatPtAP()`, `MatMatMatMult()`
11256: @*/
11257: PetscErrorCode MatGalerkin(Mat restrct, Mat dA, Mat interpolate, MatReuse reuse, PetscReal fill, Mat *A)
11258: {
11259: IS zerorows;
11260: Vec diag;
11262: PetscFunctionBegin;
11263: PetscCheck(reuse != MAT_INPLACE_MATRIX, PetscObjectComm((PetscObject)A), PETSC_ERR_SUP, "Inplace product not supported");
11264: /* Construct the coarse grid matrix */
11265: if (interpolate == restrct) {
11266: PetscCall(MatPtAP(dA, interpolate, reuse, fill, A));
11267: } else {
11268: PetscCall(MatMatMatMult(restrct, dA, interpolate, reuse, fill, A));
11269: }
11271: /* If the interpolation matrix is not of full rank, A will have zero rows.
11272: This can legitimately happen in the case of non-nested geometric multigrid.
11273: In that event, we set the rows of the matrix to the rows of the identity,
11274: ignoring the equations (as the RHS will also be zero). */
11276: PetscCall(MatFindZeroRows(*A, &zerorows));
11278: if (zerorows != NULL) { /* if there are any zero rows */
11279: PetscCall(MatCreateVecs(*A, &diag, NULL));
11280: PetscCall(MatGetDiagonal(*A, diag));
11281: PetscCall(VecISSet(diag, zerorows, 1.0));
11282: PetscCall(MatDiagonalSet(*A, diag, INSERT_VALUES));
11283: PetscCall(VecDestroy(&diag));
11284: PetscCall(ISDestroy(&zerorows));
11285: }
11286: PetscFunctionReturn(PETSC_SUCCESS);
11287: }
11289: /*@C
11290: MatSetOperation - Allows user to set a matrix operation for any matrix type
11292: Logically Collective
11294: Input Parameters:
11295: + mat - the matrix
11296: . op - the name of the operation
11297: - f - the function that provides the operation
11299: Level: developer
11301: Example Usage:
11302: .vb
11303: extern PetscErrorCode usermult(Mat, Vec, Vec);
11305: PetscCall(MatCreateXXX(comm, ..., &A));
11306: PetscCall(MatSetOperation(A, MATOP_MULT, (PetscErrorCodeFn *)usermult));
11307: .ve
11309: Notes:
11310: See the file `include/petscmat.h` for a complete list of matrix
11311: operations, which all have the form MATOP_<OPERATION>, where
11312: <OPERATION> is the name (in all capital letters) of the
11313: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11315: All user-provided functions (except for `MATOP_DESTROY`) should have the same calling
11316: sequence as the usual matrix interface routines, since they
11317: are intended to be accessed via the usual matrix interface
11318: routines, e.g.,
11319: .vb
11320: MatMult(Mat, Vec, Vec) -> usermult(Mat, Vec, Vec)
11321: .ve
11323: In particular each function MUST return `PETSC_SUCCESS` on success and
11324: nonzero on failure.
11326: This routine is distinct from `MatShellSetOperation()` in that it can be called on any matrix type.
11328: .seealso: [](ch_matrices), `Mat`, `MatGetOperation()`, `MatCreateShell()`, `MatShellSetContext()`, `MatShellSetOperation()`
11329: @*/
11330: PetscErrorCode MatSetOperation(Mat mat, MatOperation op, PetscErrorCodeFn *f)
11331: {
11332: PetscFunctionBegin;
11334: if (op == MATOP_VIEW && !mat->ops->viewnative && f != (PetscErrorCodeFn *)mat->ops->view) mat->ops->viewnative = mat->ops->view;
11335: (((PetscErrorCodeFn **)mat->ops)[op]) = f;
11336: PetscFunctionReturn(PETSC_SUCCESS);
11337: }
11339: /*@C
11340: MatGetOperation - Gets a matrix operation for any matrix type.
11342: Not Collective
11344: Input Parameters:
11345: + mat - the matrix
11346: - op - the name of the operation
11348: Output Parameter:
11349: . f - the function that provides the operation
11351: Level: developer
11353: Example Usage:
11354: .vb
11355: PetscErrorCode (*usermult)(Mat, Vec, Vec);
11357: MatGetOperation(A, MATOP_MULT, (PetscErrorCodeFn **)&usermult);
11358: .ve
11360: Notes:
11361: See the file `include/petscmat.h` for a complete list of matrix
11362: operations, which all have the form MATOP_<OPERATION>, where
11363: <OPERATION> is the name (in all capital letters) of the
11364: user interface routine (e.g., `MatMult()` -> `MATOP_MULT`).
11366: This routine is distinct from `MatShellGetOperation()` in that it can be called on any matrix type.
11368: .seealso: [](ch_matrices), `Mat`, `MatSetOperation()`, `MatCreateShell()`, `MatShellGetContext()`, `MatShellGetOperation()`
11369: @*/
11370: PetscErrorCode MatGetOperation(Mat mat, MatOperation op, PetscErrorCodeFn **f)
11371: {
11372: PetscFunctionBegin;
11374: *f = (((PetscErrorCodeFn **)mat->ops)[op]);
11375: PetscFunctionReturn(PETSC_SUCCESS);
11376: }
11378: /*@
11379: MatHasOperation - Determines whether the given matrix supports the particular operation.
11381: Not Collective
11383: Input Parameters:
11384: + mat - the matrix
11385: - op - the operation, for example, `MATOP_GET_DIAGONAL`
11387: Output Parameter:
11388: . has - either `PETSC_TRUE` or `PETSC_FALSE`
11390: Level: advanced
11392: Note:
11393: See `MatSetOperation()` for additional discussion on naming convention and usage of `op`.
11395: .seealso: [](ch_matrices), `Mat`, `MatCreateShell()`, `MatGetOperation()`, `MatSetOperation()`
11396: @*/
11397: PetscErrorCode MatHasOperation(Mat mat, MatOperation op, PetscBool *has)
11398: {
11399: PetscFunctionBegin;
11401: PetscAssertPointer(has, 3);
11402: if (mat->ops->hasoperation) {
11403: PetscUseTypeMethod(mat, hasoperation, op, has);
11404: } else {
11405: if (((void **)mat->ops)[op]) *has = PETSC_TRUE;
11406: else {
11407: *has = PETSC_FALSE;
11408: if (op == MATOP_CREATE_SUBMATRIX) {
11409: PetscMPIInt size;
11411: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)mat), &size));
11412: if (size == 1) PetscCall(MatHasOperation(mat, MATOP_CREATE_SUBMATRICES, has));
11413: }
11414: }
11415: }
11416: PetscFunctionReturn(PETSC_SUCCESS);
11417: }
11419: /*@
11420: MatHasCongruentLayouts - Determines whether the rows and columns layouts of the matrix are congruent
11422: Collective
11424: Input Parameter:
11425: . mat - the matrix
11427: Output Parameter:
11428: . cong - either `PETSC_TRUE` or `PETSC_FALSE`
11430: Level: beginner
11432: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatSetSizes()`, `PetscLayout`
11433: @*/
11434: PetscErrorCode MatHasCongruentLayouts(Mat mat, PetscBool *cong)
11435: {
11436: PetscFunctionBegin;
11439: PetscAssertPointer(cong, 2);
11440: if (!mat->rmap || !mat->cmap) {
11441: *cong = mat->rmap == mat->cmap ? PETSC_TRUE : PETSC_FALSE;
11442: PetscFunctionReturn(PETSC_SUCCESS);
11443: }
11444: if (mat->congruentlayouts == PETSC_DECIDE) { /* first time we compare rows and cols layouts */
11445: PetscCall(PetscLayoutSetUp(mat->rmap));
11446: PetscCall(PetscLayoutSetUp(mat->cmap));
11447: PetscCall(PetscLayoutCompare(mat->rmap, mat->cmap, cong));
11448: if (*cong) mat->congruentlayouts = 1;
11449: else mat->congruentlayouts = 0;
11450: } else *cong = mat->congruentlayouts ? PETSC_TRUE : PETSC_FALSE;
11451: PetscFunctionReturn(PETSC_SUCCESS);
11452: }
11454: PetscErrorCode MatSetInf(Mat A)
11455: {
11456: PetscFunctionBegin;
11457: PetscUseTypeMethod(A, setinf);
11458: PetscFunctionReturn(PETSC_SUCCESS);
11459: }
11461: /*@
11462: MatCreateGraph - create a scalar matrix (that is a matrix with one vertex for each block vertex in the original matrix), for use in graph algorithms
11463: and possibly removes small values from the graph structure.
11465: Collective
11467: Input Parameters:
11468: + A - the matrix
11469: . sym - `PETSC_TRUE` indicates that the graph should be symmetrized
11470: . scale - `PETSC_TRUE` indicates that the graph edge weights should be symmetrically scaled with the diagonal entry
11471: . filter - filter value - < 0: does nothing; == 0: removes only 0.0 entries; otherwise: removes entries with abs(entries) <= value
11472: . num_idx - size of 'index' array
11473: - index - array of block indices to use for graph strength of connection weight
11475: Output Parameter:
11476: . graph - the resulting graph
11478: Level: advanced
11480: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `PCGAMG`
11481: @*/
11482: PetscErrorCode MatCreateGraph(Mat A, PetscBool sym, PetscBool scale, PetscReal filter, PetscInt num_idx, PetscInt index[], Mat *graph)
11483: {
11484: PetscFunctionBegin;
11488: PetscAssertPointer(graph, 7);
11489: PetscCall(PetscLogEventBegin(MAT_CreateGraph, A, 0, 0, 0));
11490: PetscUseTypeMethod(A, creategraph, sym, scale, filter, num_idx, index, graph);
11491: PetscCall(PetscLogEventEnd(MAT_CreateGraph, A, 0, 0, 0));
11492: PetscFunctionReturn(PETSC_SUCCESS);
11493: }
11495: /*@
11496: MatEliminateZeros - eliminate the nondiagonal zero entries in place from the nonzero structure of a sparse `Mat` in place,
11497: meaning the same memory is used for the matrix, and no new memory is allocated.
11499: Collective
11501: Input Parameters:
11502: + A - the matrix
11503: - keep - if for a given row of `A`, the diagonal coefficient is zero, indicates whether it should be left in the structure or eliminated as well
11505: Level: intermediate
11507: Developer Note:
11508: The entries in the sparse matrix data structure are shifted to fill in the unneeded locations in the data. Thus the end
11509: of the arrays in the data structure are unneeded.
11511: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateGraph()`, `MatFilter()`
11512: @*/
11513: PetscErrorCode MatEliminateZeros(Mat A, PetscBool keep)
11514: {
11515: PetscFunctionBegin;
11517: PetscUseTypeMethod(A, eliminatezeros, keep);
11518: PetscFunctionReturn(PETSC_SUCCESS);
11519: }
11521: /*@C
11522: MatGetCurrentMemType - Get the memory location of the matrix
11524: Not Collective, but the result will be the same on all MPI processes
11526: Input Parameter:
11527: . A - the matrix whose memory type we are checking
11529: Output Parameter:
11530: . m - the memory type
11532: Level: intermediate
11534: .seealso: [](ch_matrices), `Mat`, `MatBoundToCPU()`, `PetscMemType`
11535: @*/
11536: PetscErrorCode MatGetCurrentMemType(Mat A, PetscMemType *m)
11537: {
11538: PetscFunctionBegin;
11540: PetscAssertPointer(m, 2);
11541: if (A->ops->getcurrentmemtype) PetscUseTypeMethod(A, getcurrentmemtype, m);
11542: else *m = PETSC_MEMTYPE_HOST;
11543: PetscFunctionReturn(PETSC_SUCCESS);
11544: }